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This paper explores the integration of active machine learning (ML) for 6G networks, an area that remains under-explored yet holds potential. Unlike passive ML systems, active ML can be made to interact with the network environment. It actively selects informative and representative data points for training, thereby reducing the volume of data needed while accelerating the learning process. While active learning research mainly focuses on data annotation, we call for a network-centric active learning framework that considers both annotation (i.e., what is the label) and data acquisition (i.e., which and how many samples to collect). Moreover, we explore the synergy between generative artificial intelligence (AI) and active learning to overcome existing limitations in both active learning and generative AI. This paper also features a case study on a mmWave throughput prediction problem to demonstrate the practical benefits and improved performance of active learning for 6G networks. Furthermore, we discuss how the implications of active learning extend to numerous 6G network use cases. We highlight the potential of active learning based 6G networks to enhance computational efficiency, data annotation and acquisition efficiency, adaptability, and overall network intelligence. We conclude with a discussion on challenges and future research directions for active learning in 6G networks, including development of novel query strategies, distributed learning integration, and inclusion of human- and machine-in-the-loop learning., Comment: Submitted to IEEE Network Magazine

Cyberwar strategy and tactics today are primitive and ad-hoc, resulting in an ineffective and reactive cyber fighting force. A Cyberwar Playbook is an encoding of knowledge on how to effectively handle a variety of cyberwar situations. It takes a troubleshooting approach and defines the cyber tactics, techniques and procedures one may employ to counter or avert cyber-based attacks. It provides focus and clarity in time of chaos allowing a clear path of response to be chosen.

Relying on a teacher-level randomized experiment with a sample of 87 middle and high school teachers, this study was designed to examine the implementation and impact of MyTeachingPartner-Secondary delivered by local coaches who were trained and supported by the program provider. Due to disruptions caused by the COVID-19 pandemic, implementation of the 2-year program during the first year (2021-22) was weak, and only four of six study districts continued to participate in the study in the second year. Implementation during the second year was stronger but still did not achieve fidelity based on pre-specified fidelity thresholds. Analyses of teacher survey data revealed that the program had a statistically significant positive impact on teachers' enthusiasm about teaching and a marginally significant positive impact on teachers' self-efficacy at the end of the 2-year program. Nevertheless, the study did not find any significant impact on the quality of classroom interactions or student engagement based on observations at the end of Year 2 or on students' math or English language arts achievement at the end of Year 1. (Year 2 student achievement data are not yet available.) Results from this study need to be interpreted with caution given study limitations resulting largely from the influence of the pandemic. [American Institutes for Research (AIR) collaborated with Teachstone to implement this study.]

This study introduces an innovative framework that employs large language models (LLMs) to automate the design and generation of curricula for reinforcement learning (RL). As mobile networks evolve towards the 6G era, managing their increasing complexity and dynamic nature poses significant challenges. Conventional RL approaches often suffer from slow convergence and poor generalization due to conflicting objectives and the large state and action spaces associated with mobile networks. To address these shortcomings, we introduce curriculum learning, a method that systematically exposes the RL agent to progressively challenging tasks, improving convergence and generalization. However, curriculum design typically requires extensive domain knowledge and manual human effort. Our framework mitigates this by utilizing the generative capabilities of LLMs to automate the curriculum design process, significantly reducing human effort while improving the RL agent's convergence and performance. We deploy our approach within a simulated mobile network environment and demonstrate improved RL convergence rates, generalization to unseen scenarios, and overall performance enhancements. As a case study, we consider autonomous coordination and user association in mobile networks. Our obtained results highlight the potential of combining LLM-based curriculum generation with RL for managing next-generation wireless networks, marking a significant step towards fully autonomous network operations., Comment: To appear in IEEE/CIC ICCC

Push-Relabel is one of the most celebrated network flow algorithms. Maintaining a pre-flow that saturates a cut, it enjoys better theoretical and empirical running time than other flow algorithms, such as Ford-Fulkerson. In practice, Push-Relabel is even faster than what theoretical guarantees can promise, in part because of the use of good heuristics for seeding and updating the iterative algorithm. However, it remains unclear how to run Push-Relabel on an arbitrary initialization that is not necessarily a pre-flow or cut-saturating. We provide the first theoretical guarantees for warm-starting Push-Relabel with a predicted flow, where our learning-augmented version benefits from fast running time when the predicted flow is close to an optimal flow, while maintaining robust worst-case guarantees. Interestingly, our algorithm uses the gap relabeling heuristic, which has long been employed in practice, even though prior to our work there was no rigorous theoretical justification for why it can lead to run-time improvements. We then provide experiments that show our warm-started Push-Relabel also works well in practice.

Aerial surveillance demands rapid and precise detection of moving objects in dynamic environments. Event cameras, which draw inspiration from biological vision systems, present a promising alternative to frame-based sensors due to their exceptional temporal resolution, superior dynamic range, and minimal power requirements. Unlike traditional frame-based sensors that capture redundant information at fixed intervals, event cameras asynchronously record pixel-level brightness changes, providing a continuous and efficient data stream ideal for fast motion segmentation. While these sensors are ideal for fast motion segmentation, existing event-based motion segmentation methods often suffer from limitations such as the need for per-scene parameter tuning or reliance on manual labelling, hindering their scalability and practical deployment. In this paper, we address these challenges by introducing a novel motion segmentation method that leverages self-supervised vision transformers on both event data and optical flow information. Our approach eliminates the need for human annotations and reduces dependency on scene-specific parameters. In this paper, we used the EVK4-HD Prophesee event camera onboard a highly dynamic aerial platform in urban settings. We conduct extensive evaluations of our framework across multiple datasets, demonstrating state-of-the-art performance compared to existing benchmarks. Our method can effectively handle various types of motion and an arbitrary number of moving objects. Code and dataset are available at: \url{https://samiarja.github.io/evairborne/}, Comment: 17 pages, 11 figures, 8 tables

An imbalance between bodily heat production and heat dissipation leads to heat stress in organisms. In addition to diminished animal well-being, heat stress is detrimental to the poultry industry as poultry entails fast growth and high yield, resulting in greater metabolic activity and higher body heat production. When stressed, cells overexpress heat shock proteins (such as HSP70, a well-established intracellular stress indicator) and may undergo changes in their mechanical properties. Liquid crystals (LCs, fluids with orientational order) have been recently employed to rapidly characterize changes in mechanical properties of cells enabling a means of optically reporting the presence of disease in organisms. In this work, we explore the difference in the expression of HSP70 to a change in the LC response pattern via the use of convolutional neural networks (CNNs). The machine-learning (ML) models were trained on hundreds of such LC-response micrographs of chicken red blood cells with and without heat stress. Trained models exhibited remarkable accuracy of up to 99% on detecting the presence of heat stress in unseen microscope samples. We also show that crosslinking the chicken and human RBCs using glutaraldehyde in order to simulate a diseased cell was an efficient strategy for planning, building, training, and evaluating ML models. Overall, our efforts build towards the rapid detection of disease in organisms, which is accompanied by a distinct change in the mechanical properties of cells. We aim to eventuate CNN-enabled LC-sensors can rapidly report the presence of disease in scenarios where human judgment could be prohibitively difficult or slow.

We present a next-to-leading order perturbative QCD calculation of dimuon production in neutrino-nucleus collisions. This process is typically calculated by assuming it to be proportional to inclusive charm production, which requires an effective acceptance correction to take the experimental cuts on the decay-muon kinematics into account. Here, we instead compute the dimuon production cross section directly as a convolution of semi-inclusive deep inelastic scattering to produce charmed hadrons, and a decay function fitted to $e^+e^-$ data to produce a muon from the charmed hadrons. The presented approach is in a good agreement with available experimental data and will serve as a starting point for higher-order QCD calculations without an external acceptance correction. The uncertainties arising from the decay function and scale dependence are sizeably smaller than those from the nuclear parton distribution functions. We also calculate the effective acceptances within our approach and compare them to those usually used in global fits of parton distribution functions, finding differences of the order of $10\,\%$, depending on the kinematics, perturbative order, and applied parton distributions., Comment: v2: Version accepted for publication in JHEP

The evolving landscape of explainable artificial intelligence (XAI) aims to improve the interpretability of intricate machine learning (ML) models, yet faces challenges in formalisation and empirical validation, being an inherently unsupervised process. In this paper, we bring together various benchmark datasets and novel performance metrics in an initial benchmarking platform, the Explainable AI Comparison Toolkit (EXACT), providing a standardised foundation for evaluating XAI methods. Our datasets incorporate ground truth explanations for class-conditional features, and leveraging novel quantitative metrics, this platform assesses the performance of post-hoc XAI methods in the quality of the explanations they produce. Our recent findings have highlighted the limitations of popular XAI methods, as they often struggle to surpass random baselines, attributing significance to irrelevant features. Moreover, we show the variability in explanations derived from different equally performing model architectures. This initial benchmarking platform therefore aims to allow XAI researchers to test and assure the high quality of their newly developed methods.

We describe micro-fabricated rubidium vapor cells with integrated temperature-control functionality and demonstrate their suitability for use in miniaturized ultra-sensitive magnetometers. These functionalized vapor cells (FVCs) embody a dual-chamber design in low-conductivity silicon with anti-permeation coatings and micro-structured thin-film platinum surface traces as resistive heaters and temperature sensors. Thermal tests show our ability to control alkali metal distribution within the FVCs, ensuring a clean sensing chamber for optical measurements. Optical absorption spectroscopy is used to correlate the temperature readings with vapor density and to measure buffer gas pressure, of interest for optimizing sensitivity. Finally, we demonstrate zero-field resonance magnetometry with 18 fT/Hz$^{1/2}$ sensitivity in the 10 Hz to 100 Hz band, limited by laser noise and magnetic shield noise, which indicates that the functionalization does not introduce significant magnetic noise., Comment: Authors HR and MCDT contributed equally

Locating an object in a sequence of frames, given its appearance in the first frame of the sequence, is a hard problem that involves many stages. Usually, state-of-the-art methods focus on bringing novel ideas in the visual encoding or relational modelling phases. However, in this work, we show that bounding box regression from learned joint search and template features is of high importance as well. While previous methods relied heavily on well-learned features representing interactions between search and template, we hypothesize that the receptive field of the input convolutional bounding box network plays an important role in accurately determining the object location. To this end, we introduce two novel bounding box regression networks: inception and deformable. Experiments and ablation studies show that our inception module installed on the recent ODTrack outperforms the latter on three benchmarks: the GOT-10k, the UAV123 and the OTB2015.

In Federated Learning (FL), the limited accessibility of data from diverse locations and user types poses a significant challenge due to restricted user participation. Expanding client access and diversifying data enhance models by incorporating diverse perspectives, thereby enhancing adaptability. However, challenges arise in dynamic and mobile environments where certain devices may become inaccessible as FL clients, impacting data availability and client selection methods. To address this, we propose an On-Demand solution, deploying new clients using Docker Containers on-the-fly. Our On-Demand solution, employing Deep Reinforcement Learning (DRL), targets client availability and selection, while considering data shifts, and container deployment complexities. It employs an autonomous end-to-end solution for handling model deployment and client selection. The DRL strategy uses a Markov Decision Process (MDP) framework, with a Master Learner and a Joiner Learner. The designed cost functions represent the complexity of the dynamic client deployment and selection. Simulated tests show that our architecture can easily adjust to changes in the environment and respond to On-Demand requests. This underscores its ability to improve client availability, capability, accuracy, and learning efficiency, surpassing heuristic and tabular reinforcement learning solutions.

In the ever-changing world of technology, continuous authentication and comprehensive access management are essential during user interactions with a device. Split Learning (SL) and Federated Learning (FL) have recently emerged as promising technologies for training a decentralized Machine Learning (ML) model. With the increasing use of smartphones and Internet of Things (IoT) devices, these distributed technologies enable users with limited resources to complete neural network model training with server assistance and collaboratively combine knowledge between different nodes. In this study, we propose combining these technologies to address the continuous authentication challenge while protecting user privacy and limiting device resource usage. However, the model's training is slowed due to SL sequential training and resource differences between IoT devices with different specifications. Therefore, we use a cluster-based approach to group devices with similar capabilities to mitigate the impact of slow devices while filtering out the devices incapable of training the model. In addition, we address the efficiency and robustness of training ML models by using SL and FL techniques to train the clients simultaneously while analyzing the overhead burden of the process. Following clustering, we select the best set of clients to participate in training through a Genetic Algorithm (GA) optimized on a carefully designed list of objectives. The performance of our proposed framework is compared to baseline methods, and the advantages are demonstrated using a real-life UMDAA-02-FD face detection dataset. The results show that CRSFL, our proposed approach, maintains high accuracy and reduces the overhead burden in continuous authentication scenarios while preserving user privacy.

In the past three years, working with the Pacific Salmon Foundation and various First Nations groups, we have established Starlink-empowered wild salmon monitoring sites in remote Northern British Columbia, Canada. We report our experiences with the network services in these challenging environments, including deep woods and deep valleys, that lack infrastructural support with some close to Starlink's service boundary at the far north. We assess the portability and mobility of the satellite dishes and the quality of existing network access in underdeveloped countries that Starlink expects to cover. Our experiences suggest that network access based on LEO satellite constellations holds promise but faces hurdles such as energy supply constraints and environmental factors like temperature, precipitation, and solar storms. The presence of wildlife and respecting local residents' culture and heritage pose further complications. We envision several technical solutions addressing the challenges and believe that further regulations will be necessary., Comment: 8 pages, 6 figures

The emergence of large language models (LLMs) has sparked enormous interest due to their potential application across a range of educational tasks. For example, recent work in programming education has used LLMs to generate learning resources, improve error messages, and provide feedback on code. However, one factor that limits progress within the field is that much of the research uses bespoke datasets and different evaluation metrics, making direct comparisons between results unreliable. Thus, there is a pressing need for standardization and benchmarks that facilitate the equitable comparison of competing approaches. One task where LLMs show great promise is program repair, which can be used to provide debugging support and next-step hints to students. In this article, we propose a novel educational program repair benchmark. We curate two high-quality publicly available programming datasets, present a unified evaluation procedure introducing a novel evaluation metric rouge@k for approximating the quality of repairs, and evaluate a set of five recent models to establish baseline performance., Comment: 15 pages, 2 figures, 3 tables. Non-archival report presented at the NeurIPS'23 Workshop on Generative AI for Education (GAIED)

Large language models (LLMs) have shown great potential for the automatic generation of feedback in a wide range of computing contexts. However, concerns have been voiced around the privacy and ethical implications of sending student work to proprietary models. This has sparked considerable interest in the use of open source LLMs in education, but the quality of the feedback that such open models can produce remains understudied. This is a concern as providing flawed or misleading generated feedback could be detrimental to student learning. Inspired by recent work that has utilised very powerful LLMs, such as GPT-4, to evaluate the outputs produced by less powerful models, we conduct an automated analysis of the quality of the feedback produced by several open source models using a dataset from an introductory programming course. First, we investigate the viability of employing GPT-4 as an automated evaluator by comparing its evaluations with those of a human expert. We observe that GPT-4 demonstrates a bias toward positively rating feedback while exhibiting moderate agreement with human raters, showcasing its potential as a feedback evaluator. Second, we explore the quality of feedback generated by several leading open-source LLMs by using GPT-4 to evaluate the feedback. We find that some models offer competitive performance with popular proprietary LLMs, such as ChatGPT, indicating opportunities for their responsible use in educational settings., Comment: 7 pages, 4 figures, 2 tables. Accepted for publication at the 29th annual ACM conference on Innovation and Technology in Computer Science Education (ITiCSE 2024)

The last twenty years have seen the development and popularity of network measurement infrastructures. Internet measurement platforms have become common and have demonstrated their relevance in Internet understanding and security observation. However, despite their popularity, those platforms lack of flexibility and reactivity, as they are usually used for longitudinal measurements. As a consequence, they may miss detecting events that are security or Internet-related. During the same period, operating systems have evolved to virtual machines (VMs) as self-contained units for running applications, with the recent rise of unikernels, ultra-lightweight VMs tailored for specific applications, eliminating the need for a host OS. In this paper, we advocate that measurement infrastructures could take advantage of unikernels to become more flexible and efficient. We propose uTNT, a proof-of-concept unikernel-based implementation of TNT, a traceroute extension able to reveal MPLS tunnels. This paper documents the full toolchain for porting TNT into a unikernel and evaluates uTNT performance with respect to more traditional approaches. The paper also discusses a use case in which uTNT could find a suitable usage. uTNT source code is publicly available on Gitlab., Comment: 10 pages, 11 figures, IFIP extended-abstract

As robotics becomes increasingly vital for environmental protection, there is a growing need for effective deployment methods that match the pace of robotics innovation. Current strategies often fall short, leaving a gap between the potential of robotics and their practical application in the field. Addressing this challenge, we introduce a mobile hub concept designed to provide the necessary infrastructure and support for deploying a diverse, multi-domain robot team effectively. This paper presents the development and insights into `SVAN' (Synchronous Team-Robot Van), a prototype of our mobile hub concept. We delve into the mechanical construction and software setup of SVAN, offering a comprehensive overview of its capabilities and design considerations. Further, we discuss the hardware specifications and share valuable lessons learned during the prototype's development and deployment. In addition to this paper, an accepted video complements our exploration by depicting SVAN in its envisioned role as an environmental guardian, highlighting its potential in ecological monitoring and preservation. Furthermore, our discussion is enriched by referencing a previously accepted paper detailing a novel methodology for continuous UAV mission cycling enabled by a mobile hub like SVAN. These accompanying works underscore our contribution towards addressing the existing gaps in robot deployment strategies, presenting a scalable and efficient framework to overcome operational challenges in environmental robotics., Comment: 7 pages, 10 figures, accepted paper at IEEE ICRA Workshop on Field Robotics 2024

In this study, we present a model for the behavior of Dirac particles under the tensor effect in the spherical core/shell regime. We examine the change of energy levels corresponding to the particles localized in a space of approximately 1.0 fm in the core region of the quantum sphere, with the well width. It also occurs from the analytical solutions that the two different levels accompany particle states of the same mass. Additionally, the solutions exhibiting anomalous behavior, giving rise to antiparticle-type states, occur at heavier mass.

As the amount and complexity of data grows, retrieving it has become a more difficult task that requires greater knowledge and resources. This is especially true for the logistics industry, where new technologies for data collection provide tremendous amounts of interconnected real-time data. The Intelligent Query and Learning System (IQLS) simplifies the process by allowing natural language use to simplify data retrieval . It maps structured data into a framework based on the available metadata and available data models. This framework creates an environment for an agent powered by a Large Language Model. The agent utilizes the hierarchical nature of the data to filter iteratively by making multiple small context-aware decisions instead of one-shot data retrieval. After the Data filtering, the IQLS enables the agent to fulfill tasks given by the user query through interfaces. These interfaces range from multimodal transportation information retrieval to route planning under multiple constraints. The latter lets the agent define a dynamic object, which is determined based on the query parameters. This object represents a driver capable of navigating a road network. The road network is depicted as a graph with attributes based on the data. Using a modified version of the Dijkstra algorithm, the optimal route under the given constraints can be determined. Throughout the entire process, the user maintains the ability to interact and guide the system. The IQLS is showcased in a case study on the Canadian logistics sector, allowing geospatial, visual, tabular and text data to be easily queried semantically in natural language.

Next basket recommendation (NBR) is a special type of sequential recommendation that is increasingly receiving attention. So far, most NBR studies have focused on optimizing the accuracy of the recommendation, whereas optimizing for beyond-accuracy metrics, e.g., item fairness and diversity remains largely unexplored. Recent studies into NBR have found a substantial performance difference between recommending repeat items and explore items. Repeat items contribute most of the users' perceived accuracy compared with explore items. Informed by these findings, we identify a potential "short-cut" to optimize for beyond-accuracy metrics while maintaining high accuracy. To leverage and verify the existence of such short-cuts, we propose a plug-and-play two-step repetition-exploration (TREx) framework that treats repeat items and explores items separately, where we design a simple yet highly effective repetition module to ensure high accuracy, while two exploration modules target optimizing only beyond-accuracy metrics. Experiments are performed on two widely-used datasets w.r.t. a range of beyond-accuracy metrics, viz. five fairness metrics and three diversity metrics. Our experimental results verify the effectiveness of TREx. Prima facie, this appears to be good news: we can achieve high accuracy and improved beyond-accuracy metrics at the same time. However, we argue that the real-world value of our algorithmic solution, TREx, is likely to be limited and reflect on the reasonableness of the evaluation setup. We end up challenging existing evaluation paradigms, particularly in the context of beyond-accuracy metrics, and provide insights for researchers to navigate potential pitfalls and determine reasonable metrics to consider when optimizing for accuracy and beyond-accuracy metrics., Comment: To appear at SIGIR'24

Star formation estimates based on the counting of YSOs is commonly applied to nearby star-forming regions in the Galaxy. With this method, the SFRs are measured using the counts of YSOs in a particular protostellar Class, a typical protostellar mass, and the lifetime associated with this Class. However, the assumptions underlying the validity of the method such as that of a constant star formation history (SFH) and whether the method is valid for all protostellar Classes has never been fully tested. In this work, we use Monte Carlo models to test the validity of the method. We build synthetic clusters in which stars form at times that are randomly drawn from a specified SFH. The latter is either constant or time-dependent with a burst like behavior. The masses of the protostars are randomly drawn from an IMF which can be either similar to that of the Milky Way field or be variable . For each star in every cluster, the lifetimes associated with the different protostellar classes are also randomly drawn from Gaussian distribution functions centered around their most likely value as suggested by the observations. We find that only the SFR derived using the Class 0 population can reproduce the true SFR at all epochs, and this is true irrespective of the shape of the SFH. For a constant SFH, the SFR derived using the more evolved populations of protostars (Classes I, F, II, and III) reproduce the real SFR only at later epochs which correspond to epochs at which their numbers have reached a steady state. For a time-dependent burst-like SFH, all SFR estimates based on the number counts of the evolved populations fail to reproduce the true SFR. We also show how the offsets between Class I and Class II based SFRs and the true SFR plotted as a function of the number ratios of Class I and Class II versus Class III YSOs can be used in order to constrain the SFH of observed molecular clouds., Comment: Submitted. Comments are welcome

In this paper, we present a novel approach to improving software quality and efficiency through a Large Language Model (LLM)-based model designed to review code and identify potential issues. Our proposed LLM-based AI agent model is trained on large code repositories. This training includes code reviews, bug reports, and documentation of best practices. It aims to detect code smells, identify potential bugs, provide suggestions for improvement, and optimize the code. Unlike traditional static code analysis tools, our LLM-based AI agent has the ability to predict future potential risks in the code. This supports a dual goal of improving code quality and enhancing developer education by encouraging a deeper understanding of best practices and efficient coding techniques. Furthermore, we explore the model's effectiveness in suggesting improvements that significantly reduce post-release bugs and enhance code review processes, as evidenced by an analysis of developer sentiment toward LLM feedback. For future work, we aim to assess the accuracy and efficiency of LLM-generated documentation updates in comparison to manual methods. This will involve an empirical study focusing on manually conducted code reviews to identify code smells and bugs, alongside an evaluation of best practice documentation, augmented by insights from developer discussions and code reviews. Our goal is to not only refine the accuracy of our LLM-based tool but also to underscore its potential in streamlining the software development lifecycle through proactive code improvement and education., Comment: 8 pages

Accurate and timely mapping of flood extent from high-resolution satellite imagery plays a crucial role in disaster management such as damage assessment and relief activities. However, current state-of-the-art solutions are based on U-Net, which can-not segment the flood pixels accurately due to the ambiguous pixels (e.g., tree canopies, clouds) that prevent a direct judgement from only the spectral features. Thanks to the digital elevation model (DEM) data readily available from sources such as United States Geological Survey (USGS), this work explores the use of an elevation map to improve flood extent mapping. We propose, EvaNet, an elevation-guided segmentation model based on the encoder-decoder architecture with two novel techniques: (1) a loss function encoding the physical law of gravity that if a location is flooded (resp. dry), then its adjacent locations with a lower (resp. higher) elevation must also be flooded (resp. dry); (2) a new (de)convolution operation that integrates the elevation map by a location sensitive gating mechanism to regulate how much spectral features flow through adjacent layers. Extensive experiments show that EvaNet significantly outperforms the U-Net baselines, and works as a perfect drop-in replacement for U-Net in existing solutions to flood extent mapping., Comment: Published at the International Joint Conference on Artificial Intelligence (IJCAI, 2024)

Whilst holding great promise for low noise, ease of operation and networking, useful photonic quantum computing has been precluded by the need for beyond-state-of-the-art components, manufactured by the millions. Here we introduce a manufacturable platform for quantum computing with photons. We benchmark a set of monolithically-integrated silicon photonics-based modules to generate, manipulate, network, and detect photonic qubits, demonstrating dual-rail photonic qubits with $99.98\% \pm 0.01\%$ state preparation and measurement fidelity, Hong-Ou-Mandel quantum interference between independent photon sources with $99.50\%\pm0.25\%$ visibility, two-qubit fusion with $99.22\%\pm0.12\%$ fidelity, and a chip-to-chip qubit interconnect with $99.72\%\pm0.04\%$ fidelity, not accounting for loss. In addition, we preview a selection of next generation technologies, demonstrating low-loss silicon nitride waveguides and components, fabrication-tolerant photon sources, high-efficiency photon-number-resolving detectors, low-loss chip-to-fiber coupling, and barium titanate electro-optic phase shifters., Comment: 8 pages, 5 figures

We show that for each $n \geq 2$, the systoles of closed hyperbolic $n$-manifolds form a dense subset of $(0, +\infty)$. We also show that for any $n\geq 2$ and any Salem number $\lambda$, there is a closed arithmetic hyperbolic $n$-manifold of systole $\log(\lambda)$. In particular, the Salem conjecture holds if and only if the systoles of closed arithmetic hyperbolic manifolds in some (any) dimension fail to be dense in $(0, +\infty)$., Comment: 5 pages. Comments welcome

Training Deep Neural Networks (DNNs) can be computationally demanding, particularly when dealing with large models. Recent work has aimed to mitigate this computational challenge by introducing 8-bit floating-point (FP8) formats for multiplication. However, accumulations are still done in either half (16-bit) or single (32-bit) precision arithmetic. In this paper, we investigate lowering accumulator word length while maintaining the same model accuracy. We present a multiply-accumulate (MAC) unit with FP8 multiplier inputs and FP12 accumulations, which leverages an optimized stochastic rounding (SR) implementation to mitigate swamping errors that commonly arise during low precision accumulations. We investigate the hardware implications and accuracy impact associated with varying the number of random bits used for rounding operations. We additionally attempt to reduce MAC area and power by proposing a new scheme to support SR in floating-point MAC and by removing support for subnormal values. Our optimized eager SR unit significantly reduces delay and area when compared to a classic lazy SR design. Moreover, when compared to MACs utilizing single-or half-precision adders, our design showcases notable savings in all metrics. Furthermore, our approach consistently maintains near baseline accuracy across a diverse range of computer vision tasks, making it a promising alternative for low-precision DNN training.

We characterize the quantum states dual to entanglement wedges in arbitrary spacetimes, in settings where the matter entropy can be neglected compared to the geometric entropy. In AdS/CFT, such states obey special entropy inequalities known as the holographic entropy cone. In particular, the mutual information of CFT subregions is monogamous (MMI). We extend this result to arbitrary spacetimes, using a recent proposal for the generalized entanglement wedge e(a) of a gravitating region a. Given independent input regions a, b, and c, we prove MMI: Area[e(a)]+Area[e(b)]+Area[e(c)]-Area[e(ab)]-Area[e(bc)]-Area[e(ca)]+Area[e(abc)] $\leq$ 0. We expect that the full holographic entropy cone can be extended to arbitrary spacetimes using similar methods., Comment: 13 pages, 4 figures

Large Language Models (LLMs) are revolutionizing Software Engineering (SE) by introducing innovative methods for tasks such as collecting requirements, designing software, generating code, and creating test cases, among others. This article focuses on requirements engineering, typically seen as the initial phase of software development that involves multiple system stakeholders. Despite its key role, the challenge of identifying requirements and satisfying all stakeholders within time and budget constraints remains significant. To address the challenges in requirements engineering, this study introduces a web-based software tool utilizing AI agents and prompt engineering to automate task prioritization and apply diverse prioritization techniques, aimed at enhancing project management within the agile framework. This approach seeks to transform the prioritization of agile requirements, tackling the substantial challenge of meeting stakeholder needs within set time and budget limits. Furthermore, the source code of our developed prototype is available on GitHub, allowing for further experimentation and prioritization of requirements, facilitating research and practical application.

The use of pointed or edged tools or objects is one of the most challenging aspects of today's application of physical human-robot interaction (pHRI). One reason for this is that the severity of harm caused by such edged or pointed impactors is less well studied than for blunt impactors. Consequently, the standards specify well-reasoned force and pressure thresholds for blunt impactors and advise avoiding any edges and corners in contacts. Nevertheless, pointed or edged impactor geometries cannot be completely ruled out in real pHRI applications. For example, to allow edged or pointed tools such as screwdrivers near human operators, the knowledge of injury severity needs to be extended so that robot integrators can perform well-reasoned, time-efficient risk assessments. In this paper, we provide the initial datasets on injury prevention for the human hand based on drop tests with surrogates for the human hand, namely pig claws and chicken drumsticks. We then demonstrate the ease and efficiency of robot use using the dataset for contact on two examples. Finally, our experiments provide a set of injuries that may also be expected for human subjects under certain robot mass-velocity constellations in collisions. To extend this work, testing on human samples and a collaborative effort from research institutes worldwide is needed to create a comprehensive human injury avoidance database for any pHRI scenario and thus for safe pHRI applications including edged and pointed geometries., Comment: \c{opyright} 2024 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

Technical Debt is a common issue that arises when short-term gains are prioritized over long-term costs, leading to a degradation in the quality of the code. Self-Admitted Technical Debt (SATD) is a specific type of Technical Debt that involves documenting code to remind developers of its debt. Previous research has explored various aspects of SATD, including detection methods, distribution, and its impact on software quality. To better understand SATD, one comprehension technique is to examine its co-occurrence with other activities, such as refactoring and bug fixing. This study investigates the relationship between removing and adding SATD and activities such as refactoring, bug fixing, adding new features, and testing. To do so, we analyzed 77 open-source Java projects using TODO/FIXME/XXX removal or addition in inline comments as indicators of SATD. We examined the co-occurrence of SATD with each activity in each project through chi-square and odds ratio evaluations. Our results show that SATD removal occurs simultaneously with refactoring in 95% of projects, while its addition occurs in 89% of projects. Furthermore, we found that three types of refactoring - "move class", "remove method", and "move attribute" - occur more frequently in the presence of SATD. However, their distribution is similar in projects with and without SATD., Comment: 6 pages, DOI: 10.1109/ICCKE60553.2023.10326279

This paper presents a cohomological study of modified Rota-Baxter associative algebras in the presence of derivations. The Modified Rota-Baxter operator, which is a modified version and closely related to the classical Rota-Baxter operator, has garnered significant attention due to its applications in various mathematical and physical contexts. In this study, we define a cohomology theory and also investigate a one-parameter formal deformation theory and abelian extensions of modified Rota-Baxter associative algebras under the influence of derivations., Comment: 20 pages

The dark-field signal uncovers details beyond conventional X-ray attenuation contrast, which is especially valuable for material sciences. In particular, dark-field techniques are able to reveal structures beyond the spatial resolution of a setup. However, its implementation is yet limited to the micrometer regime. Therefore, we propose a technique to extend full-field transmission X-ray microscopy by the dark-field signal. The proposed method is based on a well-defined illumination of a beam-shaping condenser, which allows to block the bright-field by motorized apertures in the back focal plane of the objective's lens. This method offers a simple implementation and enables rapid modality changes while maintaining short scan times, making dark-field imaging widely available at the nanometer scale., Comment: Updated version; revised according to the comments from the peer review. There were only minor changes. In general we mainly added more detailed explanations. We changed the title, extended sections 2.1., 2.2., 2.3. and 3., extended Fig. 1 to better illustrate the differences between the two imaging modes, and made some slight adjustments in section 4. for clarifications

The Cherenkov Telescope Array Observatory (CTAO) is the next generation of observatories employing the imaging air Cherenkov technique for the study of very high energy gamma rays. The deployment of deep learning methods for the reconstruction of physical attributes of incident particles has evinced promising outcomes when conducted on simulations. However, the transition of this approach to observational data is accompanied by challenges, as deep learning-based models are susceptible to domain shifts. In this paper, we integrate domain adaptation in the physics-based context of the CTAO and shed light on the gain in performance that these techniques bring using LST-1 real acquisitions.

We present a study of the Effective Field Theory (EFT) generalization of stochastic inflation in a model-independent single-field framework and its impact on primordial black hole (PBH) formation. We show how the Langevin equations for the "soft" modes in quasi de Sitter background is described by the Infra-Red (IR) contributions of scalar perturbations, and the subsequent Fokker-Planck equation driving the probability distribution for the stochastic duration ${\cal N}$, significantly modify in the present EFT picture. An explicit perturbative analysis of the distribution function by implementing the stochastic-$\delta N$ formalism is performed up to the next-to-next-to-next-to-leading order (NNNLO) for both the classical-drift and quantum-diffusion dominated regimes. In the drift-dominated limit, we perturbatively analyse the local non-Gaussianity parameters $(f_{\rm NL}, g_{\rm NL}, \tau_{\rm NL})$ with the EFT-induced modifications. In the diffusion-dominated limit, we numerically compute the probability distribution featuring exponential tails at each order of perturbative treatment., Comment: 83 pages, 19 figures, 1 table, Comments are welcome, Reference list updated, New clarifications and discussions added, Updated version

Implicit gender bias in Large Language Models (LLMs) is a well-documented problem, and implications of gender introduced into automatic translations can perpetuate real-world biases. However, some LLMs use heuristics or post-processing to mask such bias, making investigation difficult. Here, we examine bias in LLMss via back-translation, using the DeepL translation API to investigate the bias evinced when repeatedly translating a set of 56 Software Engineering tasks used in a previous study. Each statement starts with 'she', and is translated first into a 'genderless' intermediate language then back into English; we then examine pronoun-choice in the back-translated texts. We expand prior research in the following ways: (1) by comparing results across five intermediate languages, namely Finnish, Indonesian, Estonian, Turkish and Hungarian; (2) by proposing a novel metric for assessing the variation in gender implied in the repeated translations, avoiding the over-interpretation of individual pronouns, apparent in earlier work; (3) by investigating sentence features that drive bias; (4) and by comparing results from three time-lapsed datasets to establish the reproducibility of the approach. We found that some languages display similar patterns of pronoun use, falling into three loose groups, but that patterns vary between groups; this underlines the need to work with multiple languages. We also identify the main verb appearing in a sentence as a likely significant driver of implied gender in the translations. Moreover, we see a good level of replicability in the results, and establish that our variation metric proves robust despite an obvious change in the behaviour of the DeepL translation API during the course of the study. These results show that the back-translation method can provide further insights into bias in language models., Comment: This work has been accepted to SANER 2024; https://conf.researchr.org/home/saner-2024 REVISION: Some minor enhancements have been made to references. Wording in the Acknowledgement section has been clarified, and slightly abridged to allow the paper to fit again onto an even number of pages. In all versions, the body text, sections I - VI, is identical

Grasping complex computing concepts often poses a challenge for students who struggle to anchor these new ideas to familiar experiences and understandings. To help with this, a good analogy can bridge the gap between unfamiliar concepts and familiar ones, providing an engaging way to aid understanding. However, creating effective educational analogies is difficult even for experienced instructors. We investigate to what extent large language models (LLMs), specifically ChatGPT, can provide access to personally relevant analogies on demand. Focusing on recursion, a challenging threshold concept, we conducted an investigation analyzing the analogies generated by more than 350 first-year computing students. They were provided with a code snippet and tasked to generate their own recursion-based analogies using ChatGPT, optionally including personally relevant topics in their prompts. We observed a great deal of diversity in the analogies produced with student-prescribed topics, in contrast to the otherwise generic analogies, highlighting the value of student creativity when working with LLMs. Not only did students enjoy the activity and report an improved understanding of recursion, but they described more easily remembering analogies that were personally and culturally relevant., Comment: 7 pages, 2 figures, ITiCSE 2024 preprint