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Racial disparities in violence exposure and criminal justice contact are a subject of growing policy and public concern. We conduct a large-scale, randomized controlled trial of a six-month behavioral health intervention combining intensive mentoring and group therapy designed to reduce criminal justice and violence involvement among Black and Latinx youth in Chicago. Over 24 months, youth offered the program experienced an 18 percent reduction in the probability of any arrest and a 23 percent reduction in the probability of a violent-crime arrest. These statistically significant impacts, with smaller magnitudes, continue to persist up to 3 years post randomization. To better understand the behavior change we observe given an arrest is a proxy for criminal behavior, we create a supervised machine learning algorithm from arrest narratives that determines if an arrest was initiated more or less at the discretion of police. We find that the program's impacts are concentrated in arrests where officers have less discretion in initiating contact, while having little impact on more discretionary contact arrests (e.g. a young person exhibiting "suspicious" behavior). This analysis suggests the effects of the program are being driven by a reduction in youth offending behavior rather than by avoiding police contact.

Demand for bike sharing is impacted by various factors, such as weather conditions, events, and the availability of other transportation modes. This impact remains elusive due to the complex interdependence of these factors or locationrelated user behavior variations. It is also not clear which factor is additional information which are not already contained in the historical demand. Intermodal dependencies between bike-sharing and other modes are also underexplored, and the value of this information has not been studied in degraded situations. The proposed study analyzes the impact of adding contextual data, such as weather, time embedding, and road traffic flow, to predict bike-sharing Origin-Destination (OD) flows in atypical weather situations Our study highlights a mild relationship between prediction quality of bike-sharing demand and road traffic flow, while the introduced time embedding allows outperforming state-of-the-art results, particularly in the case of degraded weather conditions. Including weather data as an additional input further improves our model with respect to the basic ST-ED-RMGC prediction model by reducing of more than 20% the prediction error in degraded weather condition.

Text-to-SQL enables users to interact with databases through natural language, simplifying access to structured data. Although highly capable large language models (LLMs) achieve strong accuracy for complex queries, they incur unnecessary latency and dollar cost for simpler ones. In this paper, we introduce the first LLM routing approach for Text-to-SQL, which dynamically selects the most cost-effective LLM capable of generating accurate SQL for each query. We present two routing strategies (score- and classification-based) that achieve accuracy comparable to the most capable LLM while reducing costs. We design the routers for ease of training and efficient inference. In our experiments, we highlight a practical and explainable accuracy-cost trade-off on the BIRD dataset., Comment: Table Representation Learning Workshop at NeurIPS 2024

We demonstrate a method for radionuclide assay that is spectroscopic with 100 % counting efficiency for alpha decay. Advancing both cryogenic decay energy spectrometry (DES) and drop-on-demand inkjet metrology, a solution of Am-241 was assayed for massic activity (Bq/g) with a relative combined standard uncertainty less than 1 %. We implement live-timed counting, spectroscopic analysis, validation by liquid scintillation (LS) counting, and confirmation of quantitative solution transfer. Experimental DES spectra are well modeled with a Monte Carlo simulation. The model was further used to simulate Pu-238 and Pu-240 impurities, calculate detection limits, and demonstrate the potential for tracer-free multi-nuclide analysis, which will be valuable for new cancer therapeutics based on decay chains, Standard Reference Materials (SRMs) containing impurities, and more widely in nuclear energy, environmental monitoring, security, and forensics.

Building effective dense retrieval systems remains difficult when relevance supervision is not available. Recent work has looked to overcome this challenge by using a Large Language Model (LLM) to generate hypothetical documents that can be used to find the closest real document. However, this approach relies solely on the LLM to have domain-specific knowledge relevant to the query, which may not be practical. Furthermore, generating hypothetical documents can be inefficient as it requires the LLM to generate a large number of tokens for each query. To address these challenges, we introduce Real Document Embeddings from Relevance Feedback (ReDE-RF). Inspired by relevance feedback, ReDE-RF proposes to re-frame hypothetical document generation as a relevance estimation task, using an LLM to select which documents should be used for nearest neighbor search. Through this re-framing, the LLM no longer needs domain-specific knowledge but only needs to judge what is relevant. Additionally, relevance estimation only requires the LLM to output a single token, thereby improving search latency. Our experiments show that ReDE-RF consistently surpasses state-of-the-art zero-shot dense retrieval methods across a wide range of low-resource retrieval datasets while also making significant improvements in latency per-query.

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.

We analyze near-infrared integral field spectropolarimetry of the AB Aurigae protoplanetary disk and protoplanet (AB Aur b), obtained with SCExAO/CHARIS in 22 wavelength channels covering the J, H, and K passbands ($\lambda_{\rm o}$ = 1.1--2.4 $\mu m$) over angular separations of $\rho$ $\approx$ 0.13" to 1.1" ($\sim$20--175 au). Our images resolve spiral structures in the disk in each CHARIS channel. At the longest wavelengths, the data may reveal an extension of the western spiral seen in previous polarimetric data at $\rho$ $<$ 0.3" out to larger distances clockwise from the protoplanet AB Aur b, coincident with the ALMA-detected $CO$ gas spiral. While AB Aur b is detectable in complementary total intensity data, it is a non-detection in polarized light at $\lambda$ $>$ 1.3 $\mu $m. While the observed disk color is extremely red across $JHK$, the disk has a blue intrinsic scattering color consistent with small dust grains. The disk's polarization spectrum is redder than AB Aur b's total intensity spectrum. The polarization fraction peaks at $\sim$ 0.6 along the major disk axis. Radiative transfer modeling of the CHARIS data shows that small, porous dust grains with a porosity of $p$ = 0.6--0.8 better reproduce the scattered-light appearance of the disk than more compact spheres ($p$ = 0.3), especially the polarization fraction. This work demonstrates the utility of integral field spectropolarimetry to characterize structures in protoplanetary disks and elucidate the properties of the disks' dust., Comment: 23 pages, 17 Figures, accepted to AAS Journals

This paper investigates how semantic communication can effectively influence and potentially redefine the limitations imposed by physical layer settings. Reconfigurable Intelligent Surfaces (RIS) enable the intelligent configuration of the physical layer of communication systems. However, its practical implementation is hampered by several limitations. The Semantic Communication (SemCom) paradigm introduces additional degrees of freedom that can be exploited to improve the robustness of communication against physical layer impairments. In essence, SemCom ensures that the data representation remains robust even under adverse physical conditions by emphasizing the transmission of meaningful information in a manner that is less susceptible to degradation. Through the use of SemCom, potential RIS gains are demonstrated in terms of RIS area size and the phase shift precision of its active elements., Comment: This paper has been accepted for publication in IEEE Globecom Workshop on Generative Horizons: Exploring Goals through Semantic Communication

Structured internal representations (cognitive maps) shape cognition, from imagining the future and counterfactual past, to transferring knowledge to new settings. Our understanding of how such representations are formed and maintained in biological and artificial neural networks has grown enormously. The cognitive mapping hypothesis of schizophrenia extends this enquiry to psychiatry, proposing that diverse symptoms - from delusions to conceptual disorganisation - stem from abnormalities in how the brain forms structured representations. These abnormalities may arise from a confluence of neurophysiological perturbations (excitation-inhibition imbalance, resulting in attractor instability and impaired representational capacity), and/or environmental factors such as early life psychosocial stressors (which impinge on representation learning). This proposal thus links knowledge of neural circuit abnormalities, environmental risk factors, and symptoms., Comment: 25 pages, 2 figures, 1 table, 3 boxes. Final accepted version for publication at "Trends in Cognitive Science" 2024

As the demand for healthcare professionals increases, Latinos are increasing their representation in healthcare positions. Despite their increased representation, Latinos are more likely to be in healthcare support occupations rather than in healthcare management or professional occupation that offer higher salaries. "Excelencia" has analyzed national datasets to connect healthcare employers with institutions that are graduating and preparing Latinos for the workforce. This brief identifies the top institutions graduating Latinos (2019-2020) from certificates to the doctoral level in healthcare. These top 25 institutions are meeting the nation's economic needs by intentionally serving Latino students and producing Latino talent. This brief also highlights institutions and their intentional efforts to support post-completion success by providing educational credentials needed to support the current and future healthcare workforce.

Although more Latinos are entering the STEM workforce, they continue to be underrepresented in high-salary STEM occupations. To ensure America's future, institutions and STEM employers must both play an active role in preparing, selecting, and sourcing Latino talent for a global economy. Excelencia's research demonstrates that select institutions are investing in the recruitment, development, and completion of Latino talent and STEM employers have opportunities to strengthen their workforce. To bridge this gap, Excelencia has analyzed national datasets to connect STEM employers with institutions that are graduating and preparing Latinos for the workforce. This brief identifies the top institutions graduating Latinos (2019-20) in STEM from certificates to the doctorate level. These top 25 institutions are meeting the nation's economic needs by intentionally serving Latino students and producing Latino talent. This brief also highlights institutions and their intentional efforts to support post-completion success by providing educational credentials needed to support the current and future STEM workforce.

The nation is facing a shortage of educators prepared to meet the needs of an increasingly diverse student population. Latinos are the nation's fastest growing population but are underrepresented in higher-skilled occupations in the classroom and overrepresented in supportive roles with lower wages. This shows the need for employers and institutions to create pathways into the field of education as Latinos see the value to educate others and the community. To ensure America's future, institutions and employers in education must both play an active role in preparing, selecting, and sourcing Latino talent for a global economy. To bridge this gap, "Excelencia" has analyzed national datasets to connect employers in education with institutions that are graduating and preparing Latinos for the workforce. This brief identifies the top institutions graduating Latinos (2019-20) from certificates to the doctoral level in education. These top 25 institutions are meeting the nation's economic needs by intentionally serving Latino students and producing Latino talent. This brief also highlights institutions and their intentional efforts to support post-completion success by providing education credentials needed to support the current and future education workforce.

Real-time control (RTC) is pivotal for any Adaptive Optics (AO) system, including high-contrast imaging of exoplanets and circumstellar environments. It is the brain of the AO system, and what wavefront sensing and control (WFS\&C) techniques need to work with to achieve unprecedented image quality and contrast, ultimately advancing our understanding of exoplanetary systems in the context of high contrast imaging (HCI). Developing WFS\&C algorithms first happens in simulation or a lab before deployment on-sky. The transition to on-sky testing is often challenging due to the different RTCs used. Sharing common RTC standards across labs and telescope instruments would considerably simplify this process. A data architecture based on the interprocess communication method known as shared memory is ideally suited for this purpose. The CACAO package, an example of RTC based on shared memory, was initially developed for the Subaru-SCExAO instrument and now deployed on several benches and instruments. This proceeding discusses the challenges, requirements, implementation strategies, and performance evaluations associated with integrating a shared memory-based RTC. The Santa Cruz Extreme AO Laboratory (SEAL) bench is a platform for WFS\&C development for large ground-based segmented telescopes. Currently, SEAL offers the user a non-real-time version of CACAO, a shared-memory based RTC package initially developed for the Subaru-SCExAO instrument, and now deployed on several benches and instruments. We show here the example of the SEAL RTC upgrade as a precursor to both RTC upgrade at the 3-m Shane telescopes at Lick Observatory (Shane-AO) and a future development platform for the Keck II AO. This paper is aimed at specialists in AO, astronomers, and WFS\&C scientists seeking a deeper introduction to the world of RTCs., Comment: SPIE Astronomical Telescope and Intrumentation 2024

With the ever-growing complexity of models in the field of remote sensing (RS), there is an increasing demand for solutions that balance model accuracy with computational efficiency. Knowledge distillation (KD) has emerged as a powerful tool to meet this need, enabling the transfer of knowledge from large, complex models to smaller, more efficient ones without significant loss in performance. This review article provides an extensive examination of KD and its innovative applications in RS. KD, a technique developed to transfer knowledge from a complex, often cumbersome model (teacher) to a more compact and efficient model (student), has seen significant evolution and application across various domains. Initially, we introduce the fundamental concepts and historical progression of KD methods. The advantages of employing KD are highlighted, particularly in terms of model compression, enhanced computational efficiency, and improved performance, which are pivotal for practical deployments in RS scenarios. The article provides a comprehensive taxonomy of KD techniques, where each category is critically analyzed to demonstrate the breadth and depth of the alternative options, and illustrates specific case studies that showcase the practical implementation of KD methods in RS tasks, such as instance segmentation and object detection. Further, the review discusses the challenges and limitations of KD in RS, including practical constraints and prospective future directions, providing a comprehensive overview for researchers and practitioners in the field of RS. Through this organization, the paper not only elucidates the current state of research in KD but also sets the stage for future research opportunities, thereby contributing significantly to both academic research and real-world applications., Comment: 50 pages, 11 figures and 9 tables

We describe the motivation, design, and early results for our 42-night, 125 star Subaru/SCExAO direct imaging survey for planets around accelerating stars. Unlike prior large surveys, ours focuses only on stars showing evidence for an astrometric acceleration plausibly due to the dynamical pull of an unseen planet or brown dwarf. Our program is motivated by results from a recent pilot program that found the first planet jointly discovered from direct imaging and astrometry and resulted in a planet and brown dwarf discovery rate substantially higher than previous unbiased surveys like GPIES. The first preliminary results from our program reveal multiple new companions; discovered planets and brown dwarfs can be further characterized with follow-up data, including higher-resolution spectra. Finally, we describe the critical role this program plays in supporting the Roman Space Telescope Coronagraphic Instrument, providing a currently-missing list of targets suitable for the CGI technological demonstration without which the CGI tech demo risks failure., Comment: 13 pages, 7 figures; Proc. SPIE in press

In scientific research, charts are usually the primary method for visually representing data. However, the accessibility of charts remains a significant concern. In an effort to improve chart understanding pipelines, we focus on optimizing the chart classification component. We leverage curriculum learning, which is inspired by the human learning process. In this paper, we introduce a novel training approach for chart classification that utilizes coarse-to-fine curriculum learning. Our approach, which we name C2F-CHART (for coarse-to-fine) exploits inter-class similarities to create learning tasks of varying difficulty levels. We benchmark our method on the ICPR 2022 CHART-Infographics UB UNITEC PMC dataset, outperforming the state-of-the-art results., Comment: This paper has been accepted for publication in the proceedings of the 2024 International Conference on Pattern Recognition (ICPR)

Given the rapidly expanding capabilities of generative AI models for radiology, there is a need for robust metrics that can accurately measure the quality of AI-generated radiology reports across diverse hospitals. We develop ReXamine-Global, a LLM-powered, multi-site framework that tests metrics across different writing styles and patient populations, exposing gaps in their generalization. First, our method tests whether a metric is undesirably sensitive to reporting style, providing different scores depending on whether AI-generated reports are stylistically similar to ground-truth reports or not. Second, our method measures whether a metric reliably agrees with experts, or whether metric and expert scores of AI-generated report quality diverge for some sites. Using 240 reports from 6 hospitals around the world, we apply ReXamine-Global to 7 established report evaluation metrics and uncover serious gaps in their generalizability. Developers can apply ReXamine-Global when designing new report evaluation metrics, ensuring their robustness across sites. Additionally, our analysis of existing metrics can guide users of those metrics towards evaluation procedures that work reliably at their sites of interest.

In recent years, numerous large-scale cyberattacks have exploited Internet of Things (IoT) devices, a phenomenon that is expected to escalate with the continuing proliferation of IoT technology. Despite considerable efforts in attack detection, intrusion detection systems remain mostly reactive, responding to specific patterns or observed anomalies. This work proposes a proactive approach to anticipate and mitigate malicious activities before they cause damage. This paper proposes a novel network intrusion prediction framework that combines Large Language Models (LLMs) with Long Short Term Memory (LSTM) networks. The framework incorporates two LLMs in a feedback loop: a fine-tuned Generative Pre-trained Transformer (GPT) model for predicting network traffic and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) for evaluating the predicted traffic. The LSTM classifier model then identifies malicious packets among these predictions. Our framework, evaluated on the CICIoT2023 IoT attack dataset, demonstrates a significant improvement in predictive capabilities, achieving an overall accuracy of 98%, offering a robust solution to IoT cybersecurity challenges.

Context: IoT systems, networks of connected devices powered by software, require studying software quality for maintenance. Despite extensive studies on non-IoT software quality, research on IoT software quality is lacking. It is uncertain if IoT and non-IoT systems software are comparable, hindering the confident application of results and best practices gained on non-IoT systems. Objective: Therefore, we compare the code quality of two equivalent sets of IoT and non-IoT systems to determine whether there are similarities and differences. We also collect and revisit software-engineering best practices in non-IoT contexts to apply them to IoT. Method: We design and apply a systematic method to select two sets of 94 non-IoT and IoT systems software from GitHub with comparable characteristics. We compute quality metrics on the systems in these two sets and then analyse and compare the metric values. We analyse in depth and provide specific examples of IoT system's complexity and how it manifests in the codebases. After the comparison, We systematically select and present a list of best practices to address the observed difference between IoT and non-IoT code. Results: Through a comparison of metrics, we conclude that software for IoT systems is more complex, coupled, larger, less maintainable, and cohesive than non-IoT systems. Several factors, such as integrating multiple hardware and software components and managing data communication between them, contribute to these differences. Considering these differences, we present a revisited best practices list with approaches, tools, or techniques for developing IoT systems. As example, applying modularity, and refactoring are best practices for lowering the complexity. Conclusion: Based on our work, researchers can now make an informed decision using existing studies on the quality of non-IoT systems for IoT systems.

In the interplanetary space solar wind plasma, whistler waves are observed in a wide range of heliocentric distance (from ~20 solar radii (RS) to Jupiter's orbit). They are known to interact with solar wind suprathermal electrons (strahl and halo) and to regulate the solar wind heat flux through scattering the strahl electrons. We present the results of applying the technique to determine the whistler wave propagation direction to the spectral data continuously collected by the FIELDS instruments aboard Parker Solar Probe (PSP). The technique was validated based on the results obtained from burst mode magnetic and electric field waveform data collected during Encounter 1. We estimated the effective length of the PSP electric field antennas (EFI) for a variety of solar wind conditions in the whistler wave frequency range and utilized these estimates for determining the whistler wave properties during PSP Encounters 1-11. Our findings show that (1) the enhancement of the whistler wave occurrence rate and wave amplitudes observed between 25-35 RS is predominantly due to the sunward propagating whistler waves population associated with the switchback-related magnetic dips; (2) the anti-sunward or counter-propagating cases are observed at 30-40 RS; (3) between 40-50 RS, sunward and anti-sunward whistlers are observed with comparable occurrence rates; and (4) almost no sunward or counter-propagating whistlers were observed at heliocentric distances above 50 RS., Comment: Accepted for publication in ApJ

We provide a novel analytical proof of an improved version of [10, Theorem 3.1], showing that the complement of a closed set satisfying the extended exterior sphere condition is nothing but the union of closed balls with lower semicontinuous radius function. The improvement lies in the radius function, which is now larger than the one used in [10, Theorem 3.1].

We present follow-up SCExAO/CHARIS $H$ and $K$-band (R $\sim$ 70) high-contrast integral field spectroscopy and Keck/NIRC2 photometry of directly-imaged brown dwarf companion HD 33632 Ab and new radial-velocity data for the system from the SOPHIE spectrograph, complemented by Hipparcos and Gaia astrometry. These data enable more robust spectral characterization compared to lower-resolution spectra from the discovery paper and more than double the available astrometric and radial-velocity baseline. HD 33632 Ab's spectrum is well reproduced by a field L8.5--L9.5 dwarf. Using the Exo-REM atmosphere models, we derive a best-fit temperature, surface gravity and radius of $T_{\rm eff}$ = 1250 $K$, log(g) = 5, and $R$ = 0.97 $R_{\rm J}$ and a solar C/O ratio. Adding the SOPHIE radial-velocity data enables far tighter constraints on the companion's orbital properties (e.g. $i$=${46.6}_{-5.7}^{+2.9}$$^{o}$) and dynamical mass (${51.7}_{-2.5}^{+2.6}$$M_{\rm J}$) than derived from imaging data and \textit{Gaia} eDR3 astrometry data alone. HD 33632 Ab should be a prime target for multi-band imaging and spectroscopy with the James Webb Space Telescope and the Roman Space Telescope's Coronagraphic Instrument, shedding detailed light on HD 33632 Ab's clouds and chemistry and providing a key reference point for understanding young exoplanet atmospheres., Comment: 20 pages, 11 figures, 7 tables; submitted to AAS Journals

This paper investigates the advantages of representing and processing semantic knowledge extracted into graphs within the emerging paradigm of semantic communications. The proposed approach leverages semantic and pragmatic aspects, incorporating recent advances on large language models (LLMs) to achieve compact representations of knowledge to be processed and exchanged between intelligent agents. This is accomplished by using the cascade of LLMs and graph neural networks (GNNs) as semantic encoders, where information to be shared is selected to be meaningful at the receiver. The embedding vectors produced by the proposed semantic encoder represent information in the form of triplets: nodes (semantic concepts entities), edges(relations between concepts), nodes. Thus, semantic information is associated with the representation of relationships among elements in the space of semantic concept abstractions. In this paper, we investigate the potential of achieving high compression rates in communication by incorporating relations that link elements within graph embeddings. We propose sending semantic symbols solely equivalent to node embeddings through the wireless channel and inferring the complete knowledge graph at the receiver. Numerical simulations illustrate the effectiveness of leveraging knowledge graphs to semantically compress and transmit information., Comment: Accepted for publication at the 25th IEEE International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)

We formulate three generalized Bayesian models for analyzing interrater and intrarater reliability in the presence of multilevel data. Stan implementations of these models provide new estimates of interrater and intrarater reliability. We also derive formulas for calculating marginal correlations under each of the three models. Comparisons of the kappa estimates and marginal correlations across the different models are presented from two real-world datasets. Simulations demonstrate properties of the different measures of agreement under different model assumptions.

The development of the sixth generation (6G) of wireless networks is bound to streamline the transition of computation and learning towards the edge of the network. Hierarchical federated learning (HFL) becomes, therefore, a key paradigm to distribute learning across edge devices to reach global intelligence. In HFL, each edge device trains a local model using its respective data and transmits the updated model parameters to an edge server for local aggregation. The edge server, then, transmits the locally aggregated parameters to a central server for global model aggregation. The unreliability of communication channels at the edge and backhaul links, however, remains a bottleneck in assessing the true benefit of HFL-empowered systems. To this end, this paper proposes an unbiased HFL algorithm for unmanned aerial vehicle (UAV)-assisted wireless networks that counteracts the impact of unreliable channels by adjusting the update weights during local and global aggregations at UAVs and terrestrial base stations (BS), respectively. To best characterize the unreliability of the channels involved in HFL, we adopt tools from stochastic geometry to determine the success probabilities of the local and global model parameter transmissions. Accounting for such metrics in the proposed HFL algorithm aims at removing the bias towards devices with better channel conditions in the context of the considered UAV-assisted network.. The paper further examines the theoretical convergence guarantee of the proposed unbiased UAV-assisted HFL algorithm under adverse channel conditions. One of the developed approach's additional benefits is that it allows for optimizing and designing the system parameters, e.g., the number of UAVs and their corresponding heights. The paper results particularly highlight the effectiveness of the proposed unbiased HFL scheme as compared to conventional FL and HFL algorithms.

The Advanced Metering Infrastructure (AMI) is one of the key components of the smart grid. It provides interactive services for managing billing and electricity consumption, but it also introduces new vectors for cyberattacks. Although, the devastating and severe impact of power overloading cyberattacks on smart grid AMI, few researches in the literature have addressed them. In the present paper, we propose a two-level anomaly detection framework based on regression decision trees. The introduced detection approach leverages the regularity and predictability of energy consumption to build reference consumption patterns for the whole neighborhood and each household within it. Using a reference consumption pattern enables detecting power overloading cyberattacks regardless of the attacker's strategy as they cause a drastic change in the consumption pattern. The continuous two-level monitoring of energy consumption load allows efficient and early detection of cyberattacks. We carried out an extensive experiment on a real-world publicly available energy consumption dataset of 500 customers in Ireland. We extracted, from the raw data, the relevant attributes for training the energy consumption patterns. The evaluation shows that our approach achieves a high detection rate, a low false alarm rate, and superior performances compared to existing solutions.

Sensing based on Nitrogen-Vacancy (NV) centers in nanodiamonds (NDs) offers significant potential across various applications. However, optimizing their quantum-optical properties remains challenging. This study focuses on enhancing and controlling the optical properties of NV centers in NDs through surface chemistry tuning and proton beam irradiation. Systematic thermal oxidations were performed to investigate the evolution of surface chemical groups using IR spectroscopy and their influence on optical properties using photoluminescence spectroscopy and PL decay measurements. Proton irradiation was explored over a wide range of fluences (10^14 to 10^17 cm^-2) to precisely control the NV center concentration, identifying conditions that maximize creation and emission intensity. Furthermore, NV center charge state control was achieved by analyzing the NV-/NV0 ratio with varying surface terminations and NV center concentrations. A novel predictive mathematical model was developed to evaluate the efficiencies of forming NV- and NV0. Although tested specifically with proton-irradiated NDs, this model has broad applicability, representing a significant advancement in predicting the outcomes of ion-beam-based color center generation in diamond.

The integration of thermal imaging data with Multimodal Large Language Models (MLLMs) constitutes an exciting opportunity for improving the safety and functionality of autonomous driving systems and many Intelligent Transportation Systems (ITS) applications. This study investigates whether MLLMs can understand complex images from RGB and thermal cameras and detect objects directly. Our goals were to 1) assess the ability of the MLLM to learn from information from various sets, 2) detect objects and identify elements in thermal cameras, 3) determine whether two independent modality images show the same scene, and 4) learn all objects using different modalities. The findings showed that both GPT-4 and Gemini were effective in detecting and classifying objects in thermal images. Similarly, the Mean Absolute Percentage Error (MAPE) for pedestrian classification was 70.39% and 81.48%, respectively. Moreover, the MAPE for bike, car, and motorcycle detection were 78.4%, 55.81%, and 96.15%, respectively. Gemini produced MAPE of 66.53%, 59.35% and 78.18% respectively. This finding further demonstrates that MLLM can identify thermal images and can be employed in advanced imaging automation technologies for ITS applications.

Although deep neural networks are well-known for their remarkable performance in tackling complex tasks, their hunger for computational resources remains a significant hurdle, posing energy-consumption issues and restricting their deployment on resource-constrained devices, which stalls their widespread adoption. In this paper, we present an optimal transport method to reduce the depth of over-parametrized deep neural networks, alleviating their computational burden. More specifically, we propose a new regularization strategy based on the Max-Sliced Wasserstein distance to minimize the distance between the intermediate feature distributions in the neural network. We show that minimizing this distance enables the complete removal of intermediate layers in the network, with almost no performance loss and without requiring any finetuning. We assess the effectiveness of our method on traditional image classification setups. We commit to releasing the source code upon acceptance of the article.

Improving interferometric phase sensitivity is crucial for high-precision measurements in rapidly developing quantum technologies. The Mach-Zehnder interferometer (MZI) is a versatile tool for analyzing this phenomenon. By splitting and recombining a light beam using beam splitters, MZIs allow for precise phase sensitivity analysis using tools like the quantum Cram\'er-Rao bound (QCRB) and the quantum Fisher information (QFI). This paper analyzes the phase sensitivity of a MZI in various scenarios using different detection schemes and input states. We compare the single- and two-parameter quantum estimation and their associated QCRB for three phase-shift situations: in both arms, only in the upper arm (asymmetric), and in both arms symmetrically. We then investigate the phase sensitivity under three detection schemes: difference intensity, single-mode intensity, and balanced homodyne. Additionally, we explore the use of Perelomov and Barut-Girardello coherent states, two types of SU(1,1) coherent states, in all scenarios. Notably, we demonstrate that under optimal conditions, all detection schemes can achieve the QCRB by utilizing entangled SU(1,1) coherent states as input states.

In this paper, we define a q-adic factorial and we demonstrate some properties of a generalized p-adic gamma function. Also, some numerical examples have been given

Network Signalling Data (NSD) have the potential to provide continuous spatio-temporal information about the presence, mobility, and usage patterns of cell phone services by individuals. Such information is invaluable for monitoring large urban areas and supporting the implementation of decision-making services. When analyzed in real time, NSD can enable the early detection of critical urban events, including fires, large accidents, stampedes, terrorist attacks, and sports and leisure gatherings, especially if these events significantly impact mobile phone network activity in the affected areas. This paper presents empirical evidence that advanced NSD can detect anomalies in mobile traffic service consumption, attributable to critical urban events, with fine spatial and temporal resolutions. We introduce two methodologies for real-time anomaly detection from multivariate time series extracted from large-scale NSD, utilizing a range of algorithms adapted from the state-of-the-art in unsupervised machine learning techniques for anomaly detection. Our research includes a comprehensive quantitative evaluation of these algorithms on a large-scale dataset of NSD service consumption for the Paris region. The evaluation uses an original dataset of documented critical or unusual urban events. This dataset has been built as a ground truth basis for assessing the algorithms performance. The obtained results demonstrate that our framework can detect unusual events almost instantaneously and locate the affected areas with high precision, largely outperforming random classifiers. This efficiency and effectiveness underline the potential of NSD-based anomaly detection in significantly enhancing emergency response strategies and urban planning., Comment: submitted to PLOS ONE journal

The recent progression of Large Language Models (LLMs) has witnessed great success in the fields of data-centric applications. LLMs trained on massive textual datasets showed ability to encode not only context but also ability to provide powerful comprehension to downstream tasks. Interestingly, Generative Pre-trained Transformers utilised this ability to bring AI a step closer to human being replacement in at least datacentric applications. Such power can be leveraged to identify anomalies of cyber threats, enhance incident response, and automate routine security operations. We provide an overview for the recent activities of LLMs in cyber defence sections, as well as categorization for the cyber defence sections such as threat intelligence, vulnerability assessment, network security, privacy preserving, awareness and training, automation, and ethical guidelines. Fundamental concepts of the progression of LLMs from Transformers, Pre-trained Transformers, and GPT is presented. Next, the recent works of each section is surveyed with the related strengths and weaknesses. A special section about the challenges and directions of LLMs in cyber security is provided. Finally, possible future research directions for benefiting from LLMs in cyber security is discussed.

This paper is a contribution towards a solution for the longstanding open problem of classifying linear systems of conics over finite fields initiated by L. E. Dickson in 1908, through his study of the projective equivalence classes of pencils of conics in $\mathrm{PG}(2,q)$, for $q$ odd. In this paper a set of complete invariants is determined for the projective equivalence classes of webs and of squabs of conics in $\mathrm{PG}(2,q)$, both for $q$ odd and even. Our approach is mainly geometric, and involves a comprehensive study of the geometric and combinatorial properties of the Veronese surface in $\mathrm{PG}(5,q)$. The main contribution is the determination of the distribution of the different types of hyperplanes incident with the $K$-orbit representatives of points and lines of $\mathrm{PG}(5,q)$, where $K\cong\mathrm{PGL}(3,q)$, is the subgroup of $\mathrm{PGL}(6,q)$ stabilizing the Veronese surface.

Collisionless shock waves, ubiquitous in the universe, are crucial for particle acceleration in various astrophysical systems. Currently, the heliosphere is the only natural environment available for their in situ study. In this work, we showcase the collective acceleration of electrons and ions by one of the fastest in situ shocks ever recorded, observed by the pioneering Parker Solar Probe at only 34.5 million kilometers from the Sun. Our analysis of this unprecedented, near-parallel shock shows electron acceleration up to 6 MeV amidst intense multi-scale electromagnetic wave emissions. We also present evidence of a variable shock structure capable of injecting and accelerating ions from the solar wind to high energies through a self-consistent process. The exceptional capability of the probe's instruments to measure electromagnetic fields in a shock traveling at 1% the speed of light has enabled us, for the first time, to confirm that the structure of a strong heliospheric shock aligns with theoretical models of strong shocks observed in astrophysical environments. This alignment offers viable avenues for understanding astrophysical shock processes and the acceleration of charged particles., Comment: Main text: 9 pages, 4 figures, 1 table; Supplementary text: 9 pages, 6 figures

Strain tuning is a powerful experimental method in probing correlated electron systems. Here we study the strain response of the lattice dynamics and electronic structure in semiconductor Ta$_2$NiS$_5$ by polarization-resolved Raman spectroscopy. We observe an increase of the size of the direct semiconducting band gap. Although the majority of the optical phonons show only marginal dependence to applied strain, the frequency of the two B$_{2g}$ phonon modes, which have quadrupolar symmetry and already anomalously soften on cooling under zero strain, increases significantly with tensile strain along the $a$ axis. The corresponding Gr\"uneisen parameters are unusually large in magnitude and negative in sign. These effects are well captured by first-principles density functional theory calculations and indicate close proximity of Ta$_2$NiS$_5$ to a structural instability, similar to that encountered in excitonic insulator candidate Ta$_2$NiSe$_5$.

Function-as-a-Service (FaaS) offers a streamlined cloud computing paradigm, but existing centralized systems suffer from vendor lock-in and single points of failure. We propose DeFaaS, a decentralized FaaS system leveraging blockchain technology and decentralized API management. DeFaaS addresses these limitations by establishing a secure, transparent registry of functions on a blockchain and enabling applications to discover and invoke them. This approach fosters scalability, flexibility, enhanced security, and improved reliability. Furthermore, DeFaaS's architecture extends beyond decentralized FaaS, supporting other distributed computing scenarios like dApps, volunteer computing, and multi-cloud service meshes. DeFaaS represents a significant advancement in decentralized computing with the potential to unlock a multitude of novel applications and use cases., Comment: The 39th ACM/SIGAPP Symposium On Applied Computing

In this study, a novel semi-implicit second-order temporal scheme combined with the finite element method for space discretization is proposed to solve the coupled system of infiltration and solute transport in unsaturated porous media. The Richards equation is used to describe unsaturated flow, while the advection-dispersion equation (ADE) is used for modeling solute transport. The proposed approach is used to linearize the system of equations in time, eliminating the need of iterative processes. A free parameter is introduced to ensure the stability of the scheme. Numerical tests are conducted to analyze the accuracy of the proposed method in comparison with a family of second-order iterative schemes. The proposed numerical technique based on the optimal free parameter is accurate and performs better in terms of efficiency since it offers a considerable gain in computational time compared to the other methods. For reliability and effectiveness evaluation of the developed semi-implicit scheme, four showcase scenarios are used. The first two numerical tests focus on modeling water flow in heterogeneous soil and transient flow in variably saturated zones. The last numerical tests are carried out to simulate the salt and nitrate transport through unsaturated soils. The simulation results are compared with reference solutions and laboratory data, and demonstrate the effectiveness of the proposed scheme in simulating infiltration and solute transport through unsaturated soils.