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In today's road infrastructure projects, the idea of creating “added value” by integrating infrastructure and spatial development often remains all talk and no action. The challenge generally lies in public resistance, which is fueled by a lack of understanding of what matters to citizens and the trade-offs involved in road infrastructure projects. Responding to this challenge, this thesis aims to develop concepts and tools that better support context-sensitive road infrastructure designs by considering citizens’ values and their perspectives on necessary trade-off choices. Two new tools are developed: the Place Value Identifier (PVI) and the Conjoint Analysis (CA) survey. The PVI is an online tool that uses Public Participation Geographic Information System (PPGIS) to map both the 'soft' value of places and 'hard' land use data. By systematically identifying the values of landscapes from large groups of citizens, PVI helps planners to identify potential areas of public resistance and develop more context-sensitive project designs. The CA survey was found to be a valuable method to better understand citizens' trade-off choices in road infrastructure projects and has helped to identify discrepancies between citizens and policy preferences for project alternatives. Results show the substantial value placed by a large group of citizens on green spaces and the prevention of fragmentation of agricultural and natural areas. Contrarily, travel time gain – often a focal point for project initiation – emerges as the least important factor for these citizens. Understanding these priorities of citizens offers enriched intelligence for planners. This intelligence can be used as input for more context-sensitive designs and planning choices, in order to enhance public approval and their awareness of the decision-making task a planner is faced with.

Trade-offs involving multiple criteria that cannot be satisfied at the same time are ubiquitous in engineering design activities. Navigating trade-off decisions can be challenging, especially when it comes to sustainability-related decisions in early-stage projects. Through a systematic literature review, we unravel the challenges related to sustainability trade-offs in technology development, concept design, and other front-end of innovation activities. The challenges, which were evaluated by experts from industry and academia, range from technical and organisational to psychological aspects.

According to the standard Humean theory of the laws of nature, Lewis' Best System Analysis (BSA), laws of nature have their status at least partly as the result of an optimal trade-off between scientific values such as simplicity and descriptive strength. This idea has recently come under pressure. Authors like Roberts and Woodward have pointed out that there might, pace what proponents of the BSAs like to suggest, be no such trade-off in the way laws of nature are identified in the natural sciences. Roberts has it that considerations of strength in theory choice are never compromised by simplicity considerations; and Woodward argues that choices between scientific theories and the associated laws often rather involve a threshold of descriptive strength which has to be met before simplicity can even begin to matter. In this paper, we make three points: first, we employ recent results from automated scientific discovery (or rather a subfield of it known as `symbolic regression') to provide concrete support for Roberts and Woodward's point that scientific theories are chosen based on strength-thresholds. Second, drawing on the same example, we argue that discussions of the BSA involve an overly simplistic view of what a trade-off between theoretical virtues amounts to; taking into account how trade-offs are treated in scientific practice, the idea of there being a single optimal theory has to make way for the view that a properly operationalized trade-off allows for a whole Pareto front of equally optimal theories. Third, we argue that a threshold behaviour with respect to a virtue like strength is not only compatible with this more systematic yet more complex view of trade-offs but also helps to bring it in line with the BSA's original preference for a single, or given the possibility of multiple optimal theories, a small number of best systems.

Identifying the genetic basis of local adaptation and fitness trade-offs across environments is a central goal of evolutionary biology. Cold acclimation is an adaptive plastic response for surviving seasonal freezing, and costs of acclimation may be a general mechanism for fitness trade-offs across environments in temperate zone species. Starting with locally adapted ecotypes of Arabidopsis thaliana from Italy and Sweden, we examined the fitness consequences of a naturally occurring functional polymorphism in CBF2. This gene encodes a transcription factor that is a major regulator of cold-acclimated freezing tolerance and resides within a locus responsible for a genetic trade-off for long-term mean fitness. We estimated the consequences of alternate genotypes of CBF2 on 5-y mean fitness and fitness components at the native field sites by comparing near-isogenic lines with alternate genotypes of CBF2 to their genetic background ecotypes. The effects of CBF2 were validated at the nucleotide level using gene-edited lines in the native genetic backgrounds grown in simulated parental environments. The foreign CBF2 genotype in the local genetic background reduced long-term mean fitness in Sweden by more than 10%, primarily via effects on survival. In Italy, fitness was reduced by more than 20%, primarily via effects on fecundity. At both sites, the effects were temporally variable and much stronger in some years. The gene-edited lines confirmed that CBF2 encodes the causal variant underlying this genetic trade-off. Additionally, we demonstrated a substantial fitness cost of cold acclimation, which has broad implications for potential maladaptive responses to climate change.

Identifying the genetic basis of local adaptation and fitness trade-offs across environments is a central goal of evolutionary biology. Cold acclimation is an adaptive plastic response for surviving seasonal freezing, and costs of acclimation may be a general mechanism for fitness trade-offs across environments in temperate zone species. Starting with locally adapted ecotypes of Arabidopsis thaliana from Italy and Sweden, we examined the fitness consequences of a naturally occurring functional polymorphism in CBF2. This gene encodes a transcription factor that is a major regulator of cold-acclimated freezing tolerance and resides within a locus responsible for a genetic trade-off for long-term mean fitness. We estimated the consequences of alternate genotypes of CBF2 on 5-y mean fitness and fitness components at the native field sites by comparing near-isogenic lines with alternate genotypes of CBF2 to their genetic background ecotypes. The effects of CBF2 were validated at the nucleotide level using gene-edited lines in the native genetic backgrounds grown in simulated parental environments. The foreign CBF2 genotype in the local genetic background reduced long-term mean fitness in Sweden by more than 10%, primarily via effects on survival. In Italy, fitness was reduced by more than 20%, primarily via effects on fecundity. At both sites, the effects were temporally variable and much stronger in some years. The gene-edited lines confirmed that CBF2 encodes the causal variant underlying this genetic trade-off. Additionally, we demonstrated a substantial fitness cost of cold acclimation, which has broad implications for potential maladaptive responses to climate change.

Identifying the genetic basis of local adaptation and fitness trade-offs across environments is a central goal of evolutionary biology. Cold acclimation is an adaptive plastic response for surviving seasonal freezing, and costs of acclimation may be a general mechanism for fitness trade-offs across environments in temperate zone species. Starting with locally adapted ecotypes of Arabidopsis thaliana from Italy and Sweden, we examined the fitness consequences of a naturally occurring functional polymorphism in CBF2. This gene encodes a transcription factor that is a major regulator of cold-acclimated freezing tolerance and resides within a locus responsible for a genetic trade-off for long-term mean fitness. We estimated the consequences of alternate genotypes of CBF2 on 5-y mean fitness and fitness components at the native field sites by comparing near-isogenic lines with alternate genotypes of CBF2 to their genetic background ecotypes. The effects of CBF2 were validated at the nucleotide level using gene-edited lines in the native genetic backgrounds grown in simulated parental environments. The foreign CBF2 genotype in the local genetic background reduced long-term mean fitness in Sweden by more than 10%, primarily via effects on survival. In Italy, fitness was reduced by more than 20%, primarily via effects on fecundity. At both sites, the effects were temporally variable and much stronger in some years. The gene-edited lines confirmed that CBF2 encodes the causal variant underlying this genetic trade-off. Additionally, we demonstrated a substantial fitness cost of cold acclimation, which has broad implications for potential maladaptive responses to climate change.

Identifying the genetic basis of local adaptation and fitness trade-offs across environments is a central goal of evolutionary biology. Cold acclimation is an adaptive plastic response for surviving seasonal freezing, and costs of acclimation may be a general mechanism for fitness trade-offs across environments in temperate zone species. Starting with locally adapted ecotypes of Arabidopsis thaliana from Italy and Sweden, we examined the fitness consequences of a naturally occurring functional polymorphism in CBF2. This gene encodes a transcription factor that is a major regulator of cold-acclimated freezing tolerance and resides within a locus responsible for a genetic trade-off for long-term mean fitness. We estimated the consequences of alternate genotypes of CBF2 on 5-y mean fitness and fitness components at the native field sites by comparing near-isogenic lines with alternate genotypes of CBF2 to their genetic background ecotypes. The effects of CBF2 were validated at the nucleotide level using gene-edited lines in the native genetic backgrounds grown in simulated parental environments. The foreign CBF2 genotype in the local genetic background reduced long-term mean fitness in Sweden by more than 10%, primarily via effects on survival. In Italy, fitness was reduced by more than 20%, primarily via effects on fecundity. At both sites, the effects were temporally variable and much stronger in some years. The gene-edited lines confirmed that CBF2 encodes the causal variant underlying this genetic trade-off. Additionally, we demonstrated a substantial fitness cost of cold acclimation, which has broad implications for potential maladaptive responses to climate change.

Humans seem to arbitrate between automatic and controlled processing by optimizing a trade-off between cognitive effort and performance. Previous research has described ways of how these costs and benefits can be quantified and how the trade-off between them can be performed. However, it remains unclear how the costs should be weighed relative to the benefits and how the cost of the arbitration mechanism itself factors in. Here, we derive measures for these separate factors from a single objective: the variational free energy. We demonstrate that by minimizing this objective, the trade-off between automatic and controlled processing as well as meta-control is optimized implicitly. As a proof of concept, we show that the congruency and proportion congruency effects in the Stroop task directly result from this optimization, given an environment with specific statistical regularities.

Proyek adalah inisiatif sementara dengan waktu, sumber daya, dan tujuan terbatas. Karena keterbatasan waktu dan keuangan, perencanaan proyek sangat penting, dengan manajemen proyek menjadi faktor kunci dalam keberhasilannya. Konsistensi dalam kualitas, biaya, dan waktu proyek konstruksi membutuhkan pemilihan metode konstruksi yang efektif. Tujuan dari penelitian ini adalah mengetahui biaya pembangunan dengan penambahan jam kerja menerapkan metode TCTO, mengetahui waktu percepatan pembangunan dengan penambahan jam kerja denagan metode TCTO. Metode ini berdampak pada durasi konstruksi, penggunaan material, peralatan, dan tenaga kerja. Dalam kasus Gedung Workshop Teknik Mesin UPN Veteran Jawa Timur, masalah muncul selama pekerjaan tiang bor karena tanah berawa, membatasi pemasangan hanya 2 titik per hari, mengakibatkan penundaan 9 minggu dari minggu 11 hingga minggu 20. Selain itu, tantangan termasuk pembuangan tanah tumpukan bosan dan kerusakan peralatan yang memakan waktu, membutuhkan perbaikan mingguan yang berlangsung sekitar 4 jam. Oleh karena itu, diperlukan percepatan untuk mengatasi keterlambatan tersebut, yang dapat dicapai melalui pendekatan Project Crashing dengan menggunakan metode Time Cost Trade Off (TCTO), Projects are temporary initiatives with limited time, resources, and goals. Due to time and financial constraints, project planning is crucial, with project management being a key factor in its success. Consistency in the quality, cost, and time of a construction project requires the selection of effective construction methods. The purpose of this study is find out the construction costs by adding working hours using the TCTO method, knowing the acceleration of construction time by adding working hours using the TCTO method. These methods impact the construction duration, material usage, equipment, and labor. In the case of the Workshop Building for Mechanical Engineering at UPN Veteran East Java, issues arose during boredpile work due to marshy soil, limiting the installation to only 2 points per day, resulting in a 9-week delay from week 11 to week 20. Additionally, challenges included the time-consuming disposal of boredpile soil and equipment damage, requiring weekly repairs lasting approximately 4 hours. Therefore, acceleration is needed to address these delays, which can be achieved through the Project Crashing approach using the Time Cost Trade Off (TCTO) method.

Proyek adalah inisiatif sementara dengan waktu, sumber daya, dan tujuan terbatas. Karena keterbatasan waktu dan keuangan, perencanaan proyek sangat penting, dengan manajemen proyek menjadi faktor kunci dalam keberhasilannya. Konsistensi dalam kualitas, biaya, dan waktu proyek konstruksi membutuhkan pemilihan metode konstruksi yang efektif. Tujuan dari penelitian ini adalah mengetahui biaya pembangunan dengan penambahan jam kerja menerapkan metode TCTO, mengetahui waktu percepatan pembangunan dengan penambahan jam kerja denagan metode TCTO. Metode ini berdampak pada durasi konstruksi, penggunaan material, peralatan, dan tenaga kerja. Dalam kasus Gedung Workshop Teknik Mesin UPN Veteran Jawa Timur, masalah muncul selama pekerjaan tiang bor karena tanah berawa, membatasi pemasangan hanya 2 titik per hari, mengakibatkan penundaan 9 minggu dari minggu 11 hingga minggu 20. Selain itu, tantangan termasuk pembuangan tanah tumpukan bosan dan kerusakan peralatan yang memakan waktu, membutuhkan perbaikan mingguan yang berlangsung sekitar 4 jam. Oleh karena itu, diperlukan percepatan untuk mengatasi keterlambatan tersebut, yang dapat dicapai melalui pendekatan Project Crashing dengan menggunakan metode Time Cost Trade Off (TCTO), Projects are temporary initiatives with limited time, resources, and goals. Due to time and financial constraints, project planning is crucial, with project management being a key factor in its success. Consistency in the quality, cost, and time of a construction project requires the selection of effective construction methods. The purpose of this study is find out the construction costs by adding working hours using the TCTO method, knowing the acceleration of construction time by adding working hours using the TCTO method. These methods impact the construction duration, material usage, equipment, and labor. In the case of the Workshop Building for Mechanical Engineering at UPN Veteran East Java, issues arose during boredpile work due to marshy soil, limiting the installation to only 2 points per day, resulting in a 9-week delay from week 11 to week 20. Additionally, challenges included the time-consuming disposal of boredpile soil and equipment damage, requiring weekly repairs lasting approximately 4 hours. Therefore, acceleration is needed to address these delays, which can be achieved through the Project Crashing approach using the Time Cost Trade Off (TCTO) method.

Objectives: Discrete choice experiments including a duration attribute (DCEd) represent a promising candidate method for valuing health-related quality-of-life instruments. However, it has not been established that DCEd can produce similar results as composite time trade-off (cTTO) or EuroQol Valuation Technology (EQ-VT) valuations of the EQ-5D-5L instrument. This study provides a direct comparison between cTTO and EQ-VT, and DCEd valuation methods. Methods: An EQ-VT study was conducted in Trinidad and Tobago to value the EQ-5D-5L. 1079 respondents each completed 10 cTTO tasks and 12 discrete choice experiments tasks without a duration attribute. A separate sample of 970 respondents each completed 18 split-triplet DCEd tasks. Several regression models were applied to the EQ-VT data, and the DCEd data were analyzed using mixed logit models with an exponential discount rate. The estimated values were compared using scatterplots and Bland-Altman plots. Results: The ordering of dimensions was identical in level 5 for cTTO/EQ-VT and DCEd models, with pain/discomfort being the most important dimension and usual activities being least important. cTTO/EQ-VT models produced a value for state 55555 ranging between −0.52 and −0.69, whereas this was −0.543 for the nonlinear mixed logit model for the DCEd data. Scatterplots and Bland-Altman plots suggested excellent agreement between cTTO/EQ-VT and DCEd-based estimates. Conclusions: CTTO/EQ-VT and DCEd valuations produce similar results when correcting DCEd for nonlinear time preferences. The ordering of importance of the dimensions and scale are identical, suggesting that the 2 methods measure the same construct and produce similar results.

Identifying the genetic basis of local adaptation and fitness trade-offs across environments is a central goal of evolutionary biology. Cold acclimation is an adaptive plastic response for surviving seasonal freezing, and costs of acclimation may be a general mechanism for fitness trade-offs across environments in temperate zone species. Starting with locally adapted ecotypes of Arabidopsis thaliana from Italy and Sweden, we examined the fitness consequences of a naturally occurring functional polymorphism in CBF2. This gene encodes a transcription factor that is a major regulator of cold-acclimated freezing tolerance and resides within a locus responsible for a genetic trade-off for long-term mean fitness. We estimated the consequences of alternate genotypes of CBF2 on 5-y mean fitness and fitness components at the native field sites by comparing near-isogenic lines with alternate genotypes of CBF2 to their genetic background ecotypes. The effects of CBF2 were validated at the nucleotide level using gene-edited lines in the native genetic backgrounds grown in simulated parental environments. The foreign CBF2 genotype in the local genetic background reduced long-term mean fitness in Sweden by more than 10%, primarily via effects on survival. In Italy, fitness was reduced by more than 20%, primarily via effects on fecundity. At both sites, the effects were temporally variable and much stronger in some years. The gene-edited lines confirmed that CBF2 encodes the causal variant underlying this genetic trade-off. Additionally, we demonstrated a substantial fitness cost of cold acclimation, which has broad implications for potential maladaptive responses to climate change.

Although adversarial training has been the state-of-the-art approach to defend against adversarial examples (AEs), it suffers from a robustness-accuracy trade-off, where high robustness is achieved at the cost of clean accuracy. In this work, we leverage invariance regularization on latent representations to learn discriminative yet adversarially invariant representations, aiming to mitigate this trade-off. We analyze two key issues in representation learning with invariance regularization: (1) a "gradient conflict" between invariance loss and classification objectives, leading to suboptimal convergence, and (2) the mixture distribution problem arising from diverged distributions of clean and adversarial inputs. To address these issues, we propose Asymmetrically Representation-regularized Adversarial Training (AR-AT), which incorporates asymmetric invariance loss with stop-gradient operation and a predictor to improve the convergence, and a split-BatchNorm (BN) structure to resolve the mixture distribution problem. Our method significantly improves the robustness-accuracy trade-off by learning adversarially invariant representations without sacrificing discriminative ability. Furthermore, we discuss the relevance of our findings to knowledge-distillation-based defense methods, contributing to a deeper understanding of their relative successes., Comment: Under review

To defend against privacy leakage of user data, differential privacy is widely used in federated learning, but it is not free. The addition of noise randomly disrupts the semantic integrity of the model and this disturbance accumulates with increased communication rounds. In this paper, we introduce a novel federated learning framework with rigorous privacy guarantees, named FedCEO, designed to strike a trade-off between model utility and user privacy by letting clients ''Collaborate with Each Other''. Specifically, we perform efficient tensor low-rank proximal optimization on stacked local model parameters at the server, demonstrating its capability to flexibly truncate high-frequency components in spectral space. This implies that our FedCEO can effectively recover the disrupted semantic information by smoothing the global semantic space for different privacy settings and continuous training processes. Moreover, we improve the SOTA utility-privacy trade-off bound by an order of $\sqrt{d}$, where $d$ is the input dimension. We illustrate our theoretical results with experiments on representative image datasets. It observes significant performance improvements and strict privacy guarantees under different privacy settings., Comment: 22 pages, 8 figures

The theory of two-sided matching has been extensively developed and applied to many real-life application domains. As the theory has been applied to increasingly diverse types of environments, researchers and practitioners have encountered various forms of distributional constraints. As a mechanism can handle a more general class of constraints, we can assign students more flexibly to colleges to increase students' welfare. However, it turns out that there exists a trade-off between students' welfare (efficiency) and fairness (which means no student has justified envy). Furthermore, this trade-off becomes sharper as the class of constraints becomes more general. The first contribution of this paper is to clarify the boundary on whether a strategyproof and fair mechanism can satisfy certain efficiency properties for each class of constraints. Our second contribution is to establish a weaker fairness requirement called envy-freeness up to $k$ peers (EF-$k$), which is inspired by a similar concept used in the fair division of indivisible items. EF-$k$ guarantees that each student has justified envy towards at most $k$ students. By varying $k$, EF-$k$ can represent different levels of fairness. We investigate theoretical properties associated with EF-$k$. Furthermore, we develop two contrasting strategyproof mechanisms that work for general hereditary constraints, i.e., one mechanism can guarantee a strong efficiency requirement, while the other can guarantee EF-$k$ for any fixed $k$. We evaluate the performance of these mechanisms through computer simulation., Comment: 11 pages

Adversarial training improves the robustness of neural networks against adversarial attacks, albeit at the expense of the trade-off between standard and robust generalization. To unveil the underlying factors driving this phenomenon, we examine the layer-wise learning capabilities of neural networks during the transition from a standard to an adversarial setting. Our empirical findings demonstrate that selectively updating specific layers while preserving others can substantially enhance the network's learning capacity. We therefore propose CURE, a novel training framework that leverages a gradient prominence criterion to perform selective conservation, updating, and revision of weights. Importantly, CURE is designed to be dataset- and architecture-agnostic, ensuring its applicability across various scenarios. It effectively tackles both memorization and overfitting issues, thus enhancing the trade-off between robustness and generalization and additionally, this training approach also aids in mitigating "robust overfitting". Furthermore, our study provides valuable insights into the mechanisms of selective adversarial training and offers a promising avenue for future research., Comment: Accepted as a conference paper at ICLR 2024

There has recently been an explosion of interest in how "higher-order" structures emerge in complex systems. This "emergent" organization has been found in a variety of natural and artificial systems, although at present the field lacks a unified understanding of what the consequences of higher-order synergies and redundancies are for systems. Typical research treat the presence (or absence) of synergistic information as a dependent variable and report changes in the level of synergy in response to some change in the system. Here, we attempt to flip the script: rather than treating higher-order information as a dependent variable, we use evolutionary optimization to evolve boolean networks with significant higher-order redundancies, synergies, or statistical complexity. We then analyse these evolved populations of networks using established tools for characterizing discrete dynamics: the number of attractors, average transient length, and Derrida coefficient. We also assess the capacity of the systems to integrate information. We find that high-synergy systems are unstable and chaotic, but with a high capacity to integrate information. In contrast, evolved redundant systems are extremely stable, but have negligible capacity to integrate information. Finally, the complex systems that balance integration and segregation (known as Tononi-Sporns-Edelman complexity) show features of both chaosticity and stability, with a greater capacity to integrate information than the redundant systems while being more stable than the random and synergistic systems. We conclude that there may be a fundamental trade-off between the robustness of a systems dynamics and its capacity to integrate information (which inherently requires flexibility and sensitivity), and that certain kinds of complexity naturally balance this trade-off.

The large number of parameters in Pretrained Language Models enhance their performance, but also make them resource-intensive, making it challenging to deploy them on commodity hardware like a single GPU. Due to the memory and power limitations of these devices, model compression techniques are often used to decrease both the model's size and its inference latency. This usually results in a trade-off between model accuracy and efficiency. Therefore, optimizing this balance is essential for effectively deploying LLMs on commodity hardware. A significant portion of the efficiency challenge is the Feed-forward network (FFN) component, which accounts for roughly $\frac{2}{3}$ total parameters and inference latency. In this paper, we first observe that only a few neurons of FFN module have large output norm for any input tokens, a.k.a. heavy hitters, while the others are sparsely triggered by different tokens. Based on this observation, we explicitly split the FFN into two parts according to the heavy hitters. We improve the efficiency-accuracy trade-off of existing compression methods by allocating more resource to FFN parts with heavy hitters. In practice, our method can reduce model size by 43.1\% and bring $1.25\sim1.56\times$ wall clock time speedup on different hardware with negligible accuracy drop.

Suatu proyek dikatakan berhasil jika pembangunan diselesaikan tepat waktu, sesuai anggaran dan memiliki kualitas yang baik. Akan tetapi, dalam pelaksanaan proyek konstruksi sejatinya selalu ada permasalahan-permasalahan yang terjadi, diantaranya yaitu bertambahnya waktu pelaksanaan sehingga penyelesaian proyek menjadi lebih lama/terlambat. Pada tugas akhir ini penulis menerapkan metode Time Cost Trade Off pada percepatan waktu “Pembangunan Gedung FKIK Universitas Jambi”. Penelitian ini bertujuan untuk menghitung dan membandingkan biaya setelah dilakukan percepatan dengan metode Time Cost Trade Off. Untuk penerapan metode ini, ditentukan lintasan kritis dengan microsoft project 2016, kemudian menghitung biaya percepatan dari setiap item pekerjaan dan biaya yang dikeluarkan dengan menambah jam kerja (lembur) dan tenaga kerja, lalu membandingkan hasil keduanya dengan microsoft excel. Dalam penerapan metode Time Cost Trade Off pada Pembangunan Gedung FKIK Universitas Jambi, didapat alternatif terbaik dengan penambahan tenaga kerja, karena menghasilkan kenaikan biaya 42%, sedangkan penambahan jam kerja menghasilkan 47%.ini dikarenakan percepatan dengan penambahan tenaga kerja tidak ada kenaikan upah pekerja dan terjadi penurunan produktifitas pekerja seperti pada percepatan dengan penambahan jam kerja (lembur). Akan tetapi ada pertimbangan lain yaitu perhitungkan biaya operasional dan mobilisasi dari pekerja tambahan yang kita datangkan.

Suatu proyek dikatakan berhasil jika pembangunan diselesaikan tepat waktu, sesuai anggaran dan memiliki kualitas yang baik. Akan tetapi, dalam pelaksanaan proyek konstruksi sejatinya selalu ada permasalahan-permasalahan yang terjadi, diantaranya yaitu bertambahnya waktu pelaksanaan sehingga penyelesaian proyek menjadi lebih lama/terlambat. Pada tugas akhir ini penulis menerapkan metode Time Cost Trade Off pada percepatan waktu “Pembangunan Gedung FKIK Universitas Jambi”. Penelitian ini bertujuan untuk menghitung dan membandingkan biaya setelah dilakukan percepatan dengan metode Time Cost Trade Off. Untuk penerapan metode ini, ditentukan lintasan kritis dengan microsoft project 2016, kemudian menghitung biaya percepatan dari setiap item pekerjaan dan biaya yang dikeluarkan dengan menambah jam kerja (lembur) dan tenaga kerja, lalu membandingkan hasil keduanya dengan microsoft excel. Dalam penerapan metode Time Cost Trade Off pada Pembangunan Gedung FKIK Universitas Jambi, didapat alternatif terbaik dengan penambahan tenaga kerja, karena menghasilkan kenaikan biaya 42%, sedangkan penambahan jam kerja menghasilkan 47%.ini dikarenakan percepatan dengan penambahan tenaga kerja tidak ada kenaikan upah pekerja dan terjadi penurunan produktifitas pekerja seperti pada percepatan dengan penambahan jam kerja (lembur). Akan tetapi ada pertimbangan lain yaitu perhitungkan biaya operasional dan mobilisasi dari pekerja tambahan yang kita datangkan.

This paper studies the performance trade-off in a multi-user backscatter communication (BackCom) system for integrated sensing and communications (ISAC), where the multi-antenna ISAC transmitter sends excitation signals to power multiple single-antenna passive backscatter devices (BD), and the multi-antenna ISAC receiver performs joint sensing (localization) and communication tasks based on the backscattered signals from all BDs. Specifically, the localization performance is measured by the Cram\'{e}r-Rao bound (CRB) on the transmission delay and direction of arrival (DoA) of the backscattered signals, whose closed-form expression is obtained by deriving the corresponding Fisher information matrix (FIM), and the communication performance is characterized by the sum transmission rate of all BDs. Then, to characterize the trade-off between the localization and communication performances, the CRB minimization problem with the communication rate constraint is formulated, and is shown to be non-convex in general. By exploiting the hidden convexity, we propose an approach that combines fractional programming (FP) and Schur complement techniques to transform the original problem into an equivalent convex form. Finally, numerical results reveal the trade-off between the CRB and sum transmission rate achieved by our proposed method.

This paper is concerned with the security problem for interconnected systems, where each subsystem is required to detect local attacks using locally available information and the information received from its neighboring subsystems. Moreover, we consider that there exists an additional eavesdropper being able to infer the private information by eavesdropping transmitted data between subsystems. Then, a privacy-preserving method is employed by adding privacy noise to transmitted data, and the privacy level is measured by mutual information. Nevertheless, adding privacy noise to transmitted data may affect the detection performance metrics such as detection probability and false alarm probability. Thus, we theoretically analyze the trade-off between the privacy and the detection performance. An optimization problem with maximizing both the degree of privacy preservation and the detection probability is established to obtain the covariance of the privacy noise. In addition, the attack detector of each subsystem may not obtain all information about the privacy noise. We further theoretically analyze the trade-off between the privacy and the false alarm probability when the attack detector has no knowledge of the privacy noise covariance. An optimization problem with maximizing the degree of privacy preservation with guaranteeing a bound of false alarm distortion level is established to obtain {\color{black}{the covariance of the privacy noise}}. Moreover, to analyze the effect of the privacy noise on the detection probability, we consider that each subsystem can estimate the unknown privacy noise covariance by the secondary data. Based on the estimated covariance, we construct another attack detector and analyze how the privacy noise affects its detection performance. Finally, a numerical example is provided to verify the effectiveness of theoretical results.

In image compression, with recent advances in generative modeling, the existence of a trade-off between the rate and the perceptual quality (realism) has been brought to light, where the realism is measured by the closeness of the output distribution to the source. It has been shown that randomized codes can be strictly better under a number of formulations. In particular, the role of common randomness has been well studied. We elucidate the role of private randomness in the compression of a memoryless source $X^n=(X_1,...,X_n)$ under two kinds of realism constraints. The near-perfect realism constraint requires the joint distribution of output symbols $(Y_1,...,Y_n)$ to be arbitrarily close the distribution of the source in total variation distance (TVD). The per-symbol near-perfect realism constraint requires that the TVD between the distribution of output symbol $Y_t$ and the source distribution be arbitrarily small, uniformly in the index $t.$ We characterize the corresponding asymptotic rate-distortion trade-off and show that encoder private randomness is not useful if the compression rate is lower than the entropy of the source, however limited the resources in terms of common randomness and decoder private randomness may be., Comment: Submitted to IEEE ISIT 2024

Large language models (LLMs) have emerged and presented their general problem-solving capabilities with one model. However, the model size has increased dramatically with billions of parameters to enable such broad problem-solving capabilities. In addition, due to the dominance of matrix-matrix and matrix-vector multiplications in LLMs, the compute-to-model size ratio is significantly lower than that of CNNs. This shift pushes LLMs from a computation-bound regime to a memory-bound regime. Therefore, optimizing the memory footprint and traffic is an important optimization direction for LLMs today. Model compression methods such as quantization and parameter pruning have been actively explored for achieving the memory footprint and traffic optimization. However, the accuracy-efficiency trade-off of rank pruning for LLMs is not well-understood yet. Therefore, we characterize the accuracy-efficiency trade-off of a low-rank decomposition method, specifically Tucker decomposition, on recent language models, including an open-source LLM, Llama 2. We formalize the low-rank decomposition design space and show that the decomposition design space is enormous (e.g., O($2^{37}$) for Llama2-7B). To navigate such a vast design space, we formulate the design space and perform thorough case studies of accuracy-efficiency trade-offs using six widely used LLM benchmarks on BERT and Llama 2 models. Our results show that we can achieve a 9\% model size reduction with minimal accuracy drops, which range from 4\%p to 10\%p, depending on the difficulty of the benchmark, without any retraining to recover accuracy after decomposition. The results show that low-rank decomposition can be a promising direction for LLM-based applications that require real-time service in scale (e.g., AI agent assist and real-time coding assistant), where the latency is as important as the model accuracy.

As children age, the ability to make decisions about perceptual information improves in terms of both speed and accuracy. However, understanding the delicate changes within both the decision-making process and the ability to optimize the trade-off between speed and accuracy with age remains a challenge. This study employed the diffusion decision model to investigate age-related developments in perceptual decision-making. Additionally, the impact of practice and end-of-block feedback on achieving optimal decision-making was investigated. We gathered behavioral data from 299 children aged 6 to 12 and 50 adults while they performed a motion discrimination task. Adults and older children had narrower decision criteria, higher drift rates, and shorter non-decision times compared to younger children. Furthermore, individuals tended to approach the optimal policy as they aged, and for both children and adults, practicing and receiving detailed feedback could speed up the attainment of the optimal policy.

Adaptive responses to climate change, based on heritable variation in stress tolerance, may be important for plant population persistence. It is unclear which populations will mount the strongest future adaptive responses. It may be fruitful to identify populations that have escaped trade-offs among performance traits, which can hinder adaptation. Barring strong genetic constraints, the extent of trade-offs may depend on spatial relationships among climate variables shaping different traits. Here, we test for climate-driven ecotypic variation and trade-offs among drought and freezing sensitivity, and growth, for Lemmon's willow (Salix lemmonii) in a common garden study of 90 genotypes from 38 sites in the Sierra Nevada, USA. Salix lemmonii exhibits ecotypic variation in leaf turgor loss point, a measure of drought sensitivity, from -0.95 to -0.74 MPa along a gradient of spring snowpack. We also find variation in spring freezing sensitivity with minimum May temperature. However, we find no trade-off, as the climatic gradients shaping these traits are spatially uncorrelated in our study region, despite being negatively correlated across the Sierra Nevada. Species may escape adaptive trade-offs in geographic regions where climate variables are spatially decoupled. These regions may represent valuable reservoirs of heritable adaptive phenotypic variation.

CRISPR-Cas systems have widely been adopted as genome editing tools, with two frequently employed Cas nucleases being SpyCas9 and LbCas12a. Although both nucleases use RNA guides to find and cleave target DNA sites, the two enzymes differ in terms of protospacer-adjacent motif (PAM) requirements, guide architecture and cleavage mechanism. In the last years, rational engineering led to the creation of PAM-relaxed variants SpRYCas9 and impLbCas12a to broaden the targetable DNA space. By employing their catalytically inactive variants (dCas9/dCas12a), we quantified how the protein-specific characteristics impact the target search process. To allow quantification, we fused these nucleases to the photoactivatable fluorescent protein PAmCherry2.1 and performed single-particle tracking in cells of Escherichia coli. From our tracking analysis, we derived kinetic parameters for each nuclease with a non-targeting RNA guide, strongly suggesting that interrogation of DNA by LbdCas12a variants proceeds faster than that of SpydCas9. In the presence of a targeting RNA guide, both simulations and imaging of cells confirmed that LbdCas12a variants are faster and more efficient in finding a specific target site. Our work demonstrates the trade-off of relaxing PAM requirements in SpydCas9 and LbdCas12a using a powerful framework, which can be applied to other nucleases to quantify their DNA target search.

Purpose: The effect of thin-layer capping with activated biochar on sediment-to-water flux was investigated. For the first time, the diffusion of both polycyclic aromatic hydrocarbons (PAHs) and arsenic (As) were studied simultaneously. The fate of As was investigated, under successive dysoxic and oxic conditions, in order to assess and discuss potential trade-off effects when using biochar as an active sorbent for capping of multi-contaminated sediments. Methods: Sediments from the Bureå bay (Sweden), contaminated with PAH and metal elements including As and Hg, were capped with activated biochar and/or bentonite in simple microcosm test systems in the laboratory. The contaminant transport from sediment through the capping to water body was studied by sampling metals in the water phase above the cap over time, or PAH in a heptane layer over water, at regular time intervals. Results: Consistently with the limited previous studies, reductions were observed (e.g., 60–65% for 15-PAH, 100% for chrysene) in sediment-to-water PAH fluxes upon biochar-based thin-layer capping. However, the most important novel finding revealed ambiguous effects of biochar capping on redox-sensitive elements, in particular As. Under the microcosm conditions of the experiment, where pH was affected by the capping material, biochar treatments favoured As mobility under oxic conditions, directly and/or via effects on Fe speciation. While capping limited the diffusion of As under dysoxic conditions, this also favoured greater As mobility under oxic water conditions compared to no capping. Conclusion: Design and monitoring of capping of PAH contaminated sediments should account for potential negative effects on co-occurring contaminants., Validerad;2024;Nivå 2;2024-04-02 (hanlid);Funder: Research Council of Norway (GBV20200775, GBV20210140);Full text license: CC BY 4.0

The vector-valued extension of the famous Witsenhausen counterexample setup is studied where the encoder, i.e. the first decision maker, non-causally knows and encodes the i.i.d. state sequence and the decoder, i.e. the second decision maker, causally estimates the interim state. The coding scheme is transferred from the finite alphabet coordination problem, for which it is proved to be optimal. The extension to the Gaussian setup is based on a non-standard weak typicality approach and requires a careful average estimation error analysis since the interim state is estimated by the decoder. We provide a single-letter expression that characterizes the optimal trade-off between the Witsenhausen power cost and estimation cost. The two auxiliary random variables improve the communication with the decoder, while performing the dual role of the channel input, which also controls the state of the system. Interestingly, we show that a pair of discrete and continuous auxiliary random variables, outperforms both Witsenhausen two-point strategy and the best affine policies. The optimal choice of random variables remains unknown., QC 20240513

We study an information theoretic privacy mechanism design problem for two scenarios where the private data is either observable or hidden. In the hidden private data scenario, an agent observes useful data Y that is correlated with private data X , and generate disclosed data U which maximizes the revealed information about Y while satisfying a bounded privacy leakage constraint. Considering the other scenario, the agent has additional access to X . To design the privacy mechanism, we first extend the Functional Representation Lemma and Strong Functional Representation Lemma by relaxing the independence condition and thereby allowing a certain leakage. We then find lower and upper bounds on the privacy-utility trade-offs in both scenarios. In particular, for the case where no leakage is allowed and X is observable, our upper and lower bounds improve previous bounds. Considering bounded mutual information as privacy constraint and the observable private data scenario we show that if the common information and mutual information between X and Y are equal, then the attained upper bound is tight. Finally, the privacy-utility trade-off with prioritized private data is studied where part of X is more private than the remaining part., QC 20240513

Purpose: The effect of thin-layer capping with activated biochar on sediment-to-water flux was investigated. For the first time, the diffusion of both polycyclic aromatic hydrocarbons (PAHs) and arsenic (As) were studied simultaneously. The fate of As was investigated, under successive dysoxic and oxic conditions, in order to assess and discuss potential trade-off effects when using biochar as an active sorbent for capping of multi-contaminated sediments. Methods: Sediments from the Bureå bay (Sweden), contaminated with PAH and metal elements including As and Hg, were capped with activated biochar and/or bentonite in simple microcosm test systems in the laboratory. The contaminant transport from sediment through the capping to water body was studied by sampling metals in the water phase above the cap over time, or PAH in a heptane layer over water, at regular time intervals. Results: Consistently with the limited previous studies, reductions were observed (e.g., 60–65% for 15-PAH, 100% for chrysene) in sediment-to-water PAH fluxes upon biochar-based thin-layer capping. However, the most important novel finding revealed ambiguous effects of biochar capping on redox-sensitive elements, in particular As. Under the microcosm conditions of the experiment, where pH was affected by the capping material, biochar treatments favoured As mobility under oxic conditions, directly and/or via effects on Fe speciation. While capping limited the diffusion of As under dysoxic conditions, this also favoured greater As mobility under oxic water conditions compared to no capping. Conclusion: Design and monitoring of capping of PAH contaminated sediments should account for potential negative effects on co-occurring contaminants., Validerad;2024;Nivå 2;2024-04-02 (hanlid);Funder: Research Council of Norway (GBV20200775, GBV20210140);Full text license: CC BY 4.0

BACKGROUND: Economic evaluation of healthcare typically assumes that an identical health gain to different patients has the same social value. There is some evidence that the public may give greater value to gains for children and young people, although this evidence is not always consistent. We present a mixed methods study protocol where we aim to explore public preferences regarding health gains to children and young people relative to adults, in an Australian setting. METHODS: This study is a Person Trade Off (PTO) choice experiment that incorporates qualitative components. Within the PTO questions, respondents will be asked to choose between treating different groups of patients that may differ in terms of patient characteristics and group size. PTO questions will be included in an online survey to explore respondent views on the relative value of health gains to different age groups in terms of extending life and improving different aspects of quality of life. The survey will also contain attitudinal questions to help understand the impact of question style upon reported preferences. Additionally, the study will test the impact of forcing respondents to express a preference between two groups compared with allowing them to report that the two groups are equivalent. One-to-one 'think aloud', semi-structured interviews will be conducted to explore a sub-sample of respondents' motivations and views in more detail. Focus groups will be conducted with members of the public to discuss the study findings and explore their views on the role of public preferences in health care prioritisation based on patient age. DISCUSSION: Our planned study will provide valuable information to healthcare decision makers in Australia who may need to decide whether to pay more for health gains for children and young people compared with adults. Additionally, the methodological test of forcing respondent choice or allowing them to express equivalence will contribute towards developing

Integrated sensing and communication (ISAC) is regarded as a promising technique for sixth-generation (6G) communication network. In this letter, we investigate the Pareto bound of the ISAC system in terms of a unified Kullback-Leibler divergence (KLD) performance measure. We firstly use KLD to analyze the detection performance of communication and sensing in the ISAC system. Thereafter, we investigate the impact of constellation and beamforming design on the Pareto bound via deep learning and semi-definite relaxation (SDR) techniques. Simulation results show the trade-off between sensing and communication performance in terms of bit error rate (BER) and probability of detection (PD) under different parameter settings.

BACKGROUND: Economic evaluation of healthcare typically assumes that an identical health gain to different patients has the same social value. There is some evidence that the public may give greater value to gains for children and young people, although this evidence is not always consistent. We present a mixed methods study protocol where we aim to explore public preferences regarding health gains to children and young people relative to adults, in an Australian setting. METHODS: This study is a Person Trade Off (PTO) choice experiment that incorporates qualitative components. Within the PTO questions, respondents will be asked to choose between treating different groups of patients that may differ in terms of patient characteristics and group size. PTO questions will be included in an online survey to explore respondent views on the relative value of health gains to different age groups in terms of extending life and improving different aspects of quality of life. The survey will also contain attitudinal questions to help understand the impact of question style upon reported preferences. Additionally, the study will test the impact of forcing respondents to express a preference between two groups compared with allowing them to report that the two groups are equivalent. One-to-one 'think aloud', semi-structured interviews will be conducted to explore a sub-sample of respondents' motivations and views in more detail. Focus groups will be conducted with members of the public to discuss the study findings and explore their views on the role of public preferences in health care prioritisation based on patient age. DISCUSSION: Our planned study will provide valuable information to healthcare decision makers in Australia who may need to decide whether to pay more for health gains for children and young people compared with adults. Additionally, the methodological test of forcing respondent choice or allowing them to express equivalence will contribute towards developing

AA classic trade-off that school districts face when deciding which matching algorithm to use is that it is not possible to always respect both priorities and preferences. The student-proposing deferred acceptance algorithm (DA) respects priorities but can lead to inefficient allocations. We identify a new condition on school choice markets under which DA is efficient. Our condition generalizes earlier conditions by placing restrictions on how preferences and priorities relate to one another only on the parts that are relevant for the assignment. Whenever there is a unique allocation that respects priorities, our condition captures all the environments for which DA is efficient. We show through stylized examples and simulations that our condition significantly expands the range of known environments for which DA is efficient. We also discuss how our condition sheds light on existing empirical findings., info:eu-repo/semantics/published

Shaping of catalytic material by 3D printing allows for greater design freedom, which can be used to optimize reactor operating windows. A promising concept in this regard is the use of structures with porous baffles, which induce a cross-flow regime that offers enhanced heat transfer at relatively low pressure drop. In this work, eighteen novel cylindrical 3D printed baffled logpile structures were designed and their heat transfer — pressure drop trade-off was quantified experimentally. It was found that the performance of these full-scale structures could be estimated from previous pseudo-2D computational fluid dynamics simulations for variations in configuration and baffle gap spacing. Moreover, the structures with 50 µm baffle gap spacing demonstrated superior heat transfer performance over a packed bed of pellets, as hypothesized. The number of baffles per unit length was introduced as a novel design variable. Remarkably, a reduction of this parameter led to comparable heat transfer performance while achieving a significant decrease in pressure drop. Finally, positioning of consecutive baffles under an angle was demonstrated to have a favorable effect. The results were correlated to facilitate reactor design considerations. Overall, this work sheds light on the process intensification potential of 3D printed baffled logpile structures as novel structured catalysts, enabled by offering enhanced heat transfer characteristics at relatively low pressure drop, both of which can be tailored to meet specific process requirements.

Ralph HB Benedict,1 Pamela Vo,2 Nicholas Adlard,2 Olwyn Grennan,3 Ashley Enstone,4 Daisy Bridge,4 Robin Wyn,4 Stanley L Cohan5 1Department of Neurology, Jacobs School of Medicine and Biomedical Sciences, Buffalo, NY, USA; 2Novartis Pharma AG, Basel, Switzerland; 3Novartis Ireland, Dublin, Ireland; 4Adelphi Values PROVE, Bollington, UK; 5Providence Brain and Spine Institute, Providence St. Joseph Health, Portland, OR, USACorrespondence: Pamela Vo, Email pamela.vo@novartis.comIntroduction: Cognitive impairment, especially relating to cognitive processing speed, is a major cause of disability in people with multiple sclerosis (MS). Utility values are quantitative estimates of the quality of life experienced in specific health states and are a key component of cost-effectiveness modelling. However, existing health state utility values in MS typically focus on physical ability and are generally derived using generic (not disease-specific) measures of quality of life. The objective of the current study was to generate health state utility values for levels of cognitive impairment. We used a direct utility elicitation approach called the time trade-off (TTO) methodology.Materials and Methods: Health state descriptions were created following interviews with healthcare professionals, patients, and caregivers in the United States (n=35), and with healthcare professionals in the UK (n=5). Three health states (mild, moderate, and severe impairment) were defined based upon a well-established and validated test for cognitive dysfunction called the Symbol Digit Modalities Test (SDMT) and described using qualitative interview findings. Next, interviews with members of the general public in the UK were conducted to estimate utility values for each health state using the TTO methodology. The procedure was based on the established Measurement and Valuation of Health (MVH) protocol, which generates values on a scale from 0.0 to 1.0.Results: Mean health state utility values were 0.77 ±

Changes in the environment can alter the suitability of habitats for organisms. In marine systems, fish species have their own specific requirements in terms of temperature and other habitat features. Behavioral responses such as thermoregulatory behavior in ectothermic species allow mobile organisms to respond to detrimental changes and search for more suitable habitats. However, for many species, limited information exists on the ecological requirements to help explain species abundance in a changing habitat. An example of a quickly changing habitat is the Wadden Sea, where five bearded rockling (Ciliata mustela) abundance has increased, unlike other Wadden Sea species. The increasing abundance of rockling has coincided with increasing average sea water temperatures and the recovery of mussel and Pacific oyster beds. Warming waters and increased structural habitat may have provided rockling with a more desirable habitat. Therefore, to better understand why rockling abundance is increasing within a changing Wadden Sea, a water temperature preference chamber was used to determine rockling's preferred temperature range. In addition, rockling's affinity for structural habitat and the trade-off between preferred temperature was examined by following their response to the systematic removal of artificial physical structures within the preferred temperature conditions. The preferred temperature range of rockling was found to be 10.4–15.7 °C. Following structure removals, rockling repeatedly moved away from their chosen temperatures to adjacent compartments with different temperatures but containing physical structure, indicating that the presence of physical structure was more important than preferred temperature until 18.6 °C. These novel findings provide insight and experimental support for the hypothesis explaining rockling's increase in the Wadden Sea: 1) mean annual temperatures have been steadily increasing towards rockling's preferred thermal range and 2) increasi

Climate change has significantly increased the frequency and intensity of drought events in recent decades, which may affect the decomposition of organic matter such as deadwood. Previous studies have examined the impacts of microclimate and wood traits on deadwood decomposition, but how wood microbes regulate effects of drought intensity on deadwood decomposition remains unclear. In this study, a field drought experiment was conducted with three throughfall exclusion levels (i.e., control, −35% and −70% rainfall treatments) in a subtropical forest to probe relative importance of microclimate, wood traits, and microbial biomass on wood decomposition. Our results showed that the −35% and −70% rainfall treatments significantly decreased wood CO2 efflux by 28.27% and 47.49%, respectively. Drought-induced decreases in wood CO2 efflux were mainly mediated by wood microbial biomass, particularly wood fungi biomass. The structural equation modelling indicated a shift in the dominant wood microbial communities in regulating wood CO2 efflux from bacteria to fungi as drought intensities increased. Our findings highlight the crucial role of wood microbial community with the trade-off between fungi and bacteria on deadwood decomposition under drought, which should be taken into account to decode forest carbon cycle − climate feedback in the future research.

Trade-off analysis (TOA) is central to policy and decision-making aimed at promoting sustainable agricultural landscapes. Yet, a generic methodological framework to assess trade-offs in agriculture is absent, largely due to the wide range of research disciplines and objectives for which TOA is used. In this study, we systematically reviewed 119 studies that have implemented TOAs in landscapes and regions dominated by agricultural systems around the world. Our results highlight that TOAs tend to be unbalanced, with a strong emphasis on productivity rather than environmental and socio-cultural services. TOAs have mostly been performed at farm or regional scales, rarely considering multiple spatial scales simultaneously. Mostly, TOAs fail to include stakeholders at study development stage, disregard recommendation uncertainty due to outcome variability and overlook risks associated with the TOA outcomes. Increased attention to these aspects is critical for TOAs to guide agricultural landscapes towards sustainability.

Species occupancy is often defined as the proportion of areal units (sites) in a landscape that the focal species occupies, but it is usually estimated from the subset of sites that have been sampled. Assuming no measurement error, we show that three quantities–the degree of sampling bias (in terms of site selection), the proportion of sites that have been sampled and the variability of true occupancy across sites–determine the extent to which a sample-based estimate of occupancy differs from its true value across the wider landscape. That these are the only three quantities (measurement error notwithstanding) to affect the accuracy of estimates of species occupancy is the fundamental insight of the “Meng equation”, an algebraic re-expression of statistical error. We use simulations to show how each of the three quantities vary with the spatial resolution of the analysis and that absolute estimation error is lower at coarser resolutions. Absolute error scales similarly with resolution regardless of the size and clustering of the virtual species’ distribution. Finely resolved estimates of species occupancy have the potential to be more useful than coarse ones, but this potential is only realised if the estimates are at least reasonably accurate. Consequently, wherever there is the potential for sampling bias, there is a trade-off between spatial resolution and accuracy, and the Meng equation provides a theoretical framework in which analysts can consider the balance between the two. An obvious next step is to consider the implications of the Meng equation for estimating a time trend in species occupancy, where it is the confounding of error and true change that is of most interest.

Watermarking generative-AI systems, such as LLMs, has gained considerable interest, driven by their enhanced capabilities across a wide range of tasks. Although current approaches have demonstrated that small, context-dependent shifts in the word distributions can be used to apply and detect watermarks, there has been little work in analyzing the impact that these perturbations have on the quality of generated texts. Balancing high detectability with minimal performance degradation is crucial in terms of selecting the appropriate watermarking setting; therefore this paper proposes a simple analysis framework where comparative assessment, a flexible NLG evaluation framework, is used to assess the quality degradation caused by a particular watermark setting. We demonstrate that our framework provides easy visualization of the quality-detection trade-off of watermark settings, enabling a simple solution to find an LLM watermark operating point that provides a well-balanced performance. This approach is applied to two different summarization systems and a translation system, enabling cross-model analysis for a task, and cross-task analysis., Comment: NAACL 2024 (Findings)

As an emerging cloud computing deployment paradigm, serverless computing is gaining traction due to its efficiency and ability to harness on-demand cloud resources. However, a significant hurdle remains in the form of the cold start problem, causing latency when launching new function instances from scratch. Existing solutions tend to use over-simplistic strategies for function pre-loading/unloading without full invocation pattern exploitation, rendering unsatisfactory optimization of the trade-off between cold start latency and resource waste. To bridge this gap, we propose SPES, the first differentiated scheduler for runtime cold start mitigation by optimizing serverless function provision. Our insight is that the common architecture of serverless systems prompts the concentration of certain invocation patterns, leading to predictable invocation behaviors. This allows us to categorize functions and pre-load/unload proper function instances with finer-grained strategies based on accurate invocation prediction. Experiments demonstrate the success of SPES in optimizing serverless function provision on both sides: reducing the 75th-percentile cold start rates by 49.77% and the wasted memory time by 56.43%, compared to the state-of-the-art. By mitigating the cold start issue, SPES is a promising advancement in facilitating cloud services deployed on serverless architectures., Comment: 12 pages, accepted by ICDE 2024 (40th IEEE International Conference on Data Engineering)

Consider the problem of storing data in a distributed manner over $T$ servers. Specifically, the data needs to (i) be recoverable from any $\tau$ servers, and (ii) remain private from any $z$ colluding servers, where privacy is quantified in terms of mutual information between the data and all the information available at any $z$ colluding servers. For this model, our main results are (i) the fundamental trade-off between storage size and the level of desired privacy, and (ii) the optimal amount of local randomness necessary at the encoder. As a byproduct, our results provide an optimal lower bound on the individual share size of ramp secret sharing schemes under a more general leakage symmetry condition than the ones previously considered in the literature., Comment: 11 pages, 3 figures, two-column, accepted to IEEE Transactions on Information Theory, part of the results was presented at the 2020 IEEE International Symposium on Information Theory (ISIT)

We study the generalization capability of nearly-interpolating linear regressors: $\boldsymbol{\beta}$'s whose training error $\tau$ is positive but small, i.e., below the noise floor. Under a random matrix theoretic assumption on the data distribution and an eigendecay assumption on the data covariance matrix $\boldsymbol{\Sigma}$, we demonstrate that any near-interpolator exhibits rapid norm growth: for $\tau$ fixed, $\boldsymbol{\beta}$ has squared $\ell_2$-norm $\mathbb{E}[\|{\boldsymbol{\beta}}\|_{2}^{2}] = \Omega(n^{\alpha})$ where $n$ is the number of samples and $\alpha >1$ is the exponent of the eigendecay, i.e., $\lambda_i(\boldsymbol{\Sigma}) \sim i^{-\alpha}$. This implies that existing data-independent norm-based bounds are necessarily loose. On the other hand, in the same regime we precisely characterize the asymptotic trade-off between interpolation and generalization. Our characterization reveals that larger norm scaling exponents $\alpha$ correspond to worse trade-offs between interpolation and generalization. We verify empirically that a similar phenomenon holds for nearly-interpolating shallow neural networks., Comment: AISTATS 2024

Watermarking is a technical means to dissuade malfeasant usage of Large Language Models. This paper proposes a novel watermarking scheme, so-called WaterMax, that enjoys high detectability while sustaining the quality of the generated text of the original LLM. Its new design leaves the LLM untouched (no modification of the weights, logits, temperature, or sampling technique). WaterMax balances robustness and complexity contrary to the watermarking techniques of the literature inherently provoking a trade-off between quality and robustness. Its performance is both theoretically proven and experimentally validated. It outperforms all the SotA techniques under the most complete benchmark suite.

The electrification of public transport vehicles offers the potential to relieve city centers of pollutant and noise emissions. Furthermore, electric buses have lower life-cycle greenhouse gas (GHG) emissions than diesel buses, particularly when operated with sustainably produced electricity. However, the heating, ventilation, and air-conditioning (HVAC) system can consume a significant amount of energy, thus limiting the achievable driving range. In this paper, we address the HVAC system in an electric city bus by analyzing the trade-off between the energy consumption and the thermal comfort of the passengers. We do this by developing a dynamic thermal model for the bus cabin, which we simplify by considering it to be in steady state. We introduce a method that is able to quickly optimize the steady-state HVAC system inputs for a large number of samples representative of a year-round operation. A comparison between the results from the steady-state optimization approach and a dynamic simulation reveal small deviations in both the HVAC system power demand and achieved thermal comfort. Thus, the approximation of the system performance with a steady-state model is justified. We present two case studies to demonstrate the practical relevance of the approach. First, we show how the method can be used to compare different system designs based on a year-round performance evaluation. Second, we show how the method can be used to generate accurate setpoints for online controllers. In conclusion, this study shows that a steady-state analysis of the HVAC systems of an electric city bus is a valuable approach to evaluate and optimize its performance., Comment: Preprint submitted to Control Engineering Practice

This paper considers the problem of downlink localization and user equipments (UEs) tracking with an adaptive procedure for a range of distances. We provide the base station (BS) with two signaling schemes and the UEs with two localization algorithms, assuming far-field (FF) and near-field (NF) conditions, respectively. The proposed schemes employ different beam-sweep patterns, where their compatibility depends on the UE range. Consequently, the FF-NF distinction transcends the traditional definition. Our proposed NF scheme requires beam-focusing on specific spots and more transmissions are required to sweep the area. Instead, the FF scheme assumes distant UEs, and fewer beams are sufficient. We derive a low-complexity algorithm that exploits the FF channel model and highlight its practical benefits and the limitations. Also, we propose an iterative adaptive procedure, where the signaling scheme is depends on the expected accuracy-complexity trade-off. Multiple iterations introduce a tracking application, where the formed trajectory dictates the validity of our assumptions. Moreover, the range from the BS, where the FF signaling scheme can be successfully employed, is investigated. We show that the conventional Fraunhofer distance is not sufficient for adaptive localization and tracking algorithms in the mixed NF and FF environment., Comment: arXiv admin note: text overlap with arXiv:2402.02473

Ensuring robustness in face recognition systems across various challenging conditions is crucial for their versatility. State-of-the-art methods often incorporate additional information, such as depth, thermal, or angular data, to enhance performance. However, light field-based face recognition approaches that leverage angular information face computational limitations. This paper investigates the fundamental trade-off between spatio-angular resolution in light field representation to achieve improved face recognition performance. By utilizing macro-pixels with varying angular resolutions while maintaining the overall image size, we aim to quantify the impact of angular information at the expense of spatial resolution, while considering computational constraints. Our experimental results demonstrate a notable performance improvement in face recognition systems by increasing the angular resolution, up to a certain extent, at the cost of spatial resolution., Comment: 12 pages,5 figures,International Conference on Emerging Trends and Applications in Artificial Intelligence (ICETAI) [Accepted]

Dual-functional radar-communication (DFRC) has attracted considerable attention. This paper considers the frequency-selective multipath fading environment and proposes DFRC waveform design strategies based on multiple-input and multiple-output (MIMO) and orthogonal frequency division multiplexing (OFDM) techniques. In the proposed waveform design strategies, the Cramer-Rao bound (CRB) of the radar system, the inter-stream interference (ISI) and the achievable rate of the communication system, are respectively considered as the performance metrics. In this paper, we focus on the performance trade-off between the radar system and the communication system, and the optimization problems are formulated. In the ISI minimization based waveform design strategy, the optimization problem is convex and can be easily solved. In the achievable rate maximization based waveform design strategy, we propose a water-filling (WF) and sequential quadratic programming (SQP) based algorithm to derive the covariance matrix and the precoding matrix. Simulation results validate the proposed DFRC waveform designs and show that the achievable rate maximization based strategy has a better performance than the ISI minimization based strategy.