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Dense retrievers have demonstrated significant potential for neural information retrieval; however, they lack robustness to domain shifts, limiting their efficacy in zero-shot settings across diverse domains. In this paper, we set out to analyze the possible factors that lead to successful domain adaptation of dense retrievers. We include domain similarity proxies between generated queries to test and source domains. Furthermore, we conduct a case study comparing two powerful domain adaptation techniques. We find that generated query type distribution is an important factor, and generating queries that share a similar domain to the test documents improves the performance of domain adaptation methods. This study further emphasizes the importance of domain-tailored generated queries.

Dense retrievers have demonstrated significant potential for neural information retrieval; however, they exhibit a lack of robustness to domain shifts, thereby limiting their efficacy in zero-shot settings across diverse domains. Previous research has investigated unsupervised domain adaptation techniques to adapt dense retrievers to target domains. However, these studies have not focused on explainability analysis to understand how such adaptations alter the model's behavior. In this paper, we propose utilizing the integrated gradients framework to develop an interpretability method that provides both instance-based and ranking-based explanations for dense retrievers. To generate these explanations, we introduce a novel baseline that reveals both query and document attributions. This method is used to analyze the effects of domain adaptation on input attributions for query and document tokens across two datasets: the financial question answering dataset (FIQA) and the biomedical information retrieval dataset (TREC-COVID). Our visualizations reveal that domain-adapted models focus more on in-domain terminology compared to non-adapted models, exemplified by terms such as "hedge," "gold," "corona," and "disease." This research addresses how unsupervised domain adaptation techniques influence the behavior of dense retrievers when adapted to new domains. Additionally, we demonstrate that integrated gradients are a viable choice for explaining and analyzing the internal mechanisms of these opaque neural models.

Dense retrievers have demonstrated significant potential for neural information retrieval; however, they exhibit a lack of robustness to domain shifts, thereby limiting their efficacy in zero-shot settings across diverse domains. A state-of-the-art domain adaptation technique is Generative Pseudo Labeling (GPL). GPL uses synthetic query generation and initially mined hard negatives to distill knowledge from cross-encoder to dense retrievers in the target domain. In this paper, we analyze the documents retrieved by the domain-adapted model and discover that these are more relevant to the target queries than those of the non-domain-adapted model. We then propose refreshing the hard-negative index during the knowledge distillation phase to mine better hard negatives. Our remining R-GPL approach boosts ranking performance in 13/14 BEIR datasets and 9/12 LoTTe datasets. Our contributions are (i) analyzing hard negatives returned by domain-adapted and non-domain-adapted models and (ii) applying the GPL training with and without hard-negative re-mining in LoTTE and BEIR datasets.

Nanomechanical resonators promise diverse applications ranging from mass spectrometry to quantum information processing, requiring long phonon lifetimes and frequency stability. Although two-level system (TLS) defects govern dissipation at millikelvin temperatures, the nature of frequency fluctuations remains poorly understood. In nanoscale devices, where acoustic fields are confined to sub-wavelength volumes, strong coupling to individual TLS should dominate over weak coupling to defect ensembles. In this work, we monitor fast frequency fluctuations of phononic crystal nanomechanical resonators, while varying temperature ($10$ mK$-1$ K), drive power ($10^2-10^5$ phonons), and the phononic band structure. We consistently observe random telegraph signals (RTS) which we attribute to state transitions of individual TLS. The frequency noise is well-explained by mechanical coupling to individual far off-resonant TLS, which are either thermally excited or strongly coupled to thermal fluctuators. Understanding this fundamental decoherence process, particularly its RTS structure, opens a clear path towards noise suppression for quantum and sensing applications., Comment: 18 pages, 5 main figures, 2 supplementary figures

Memory-centric computing aims to enable computation capability in and near all places where data is generated and stored. As such, it can greatly reduce the large negative performance and energy impact of data access and data movement, by 1) fundamentally avoiding data movement, 2) reducing data access latency & energy, and 3) exploiting large parallelism of memory arrays. Many recent studies show that memory-centric computing can largely improve system performance & energy efficiency. Major industrial vendors and startup companies have recently introduced memory chips with sophisticated computation capabilities. Going forward, both hardware and software stack should be revisited and designed carefully to take advantage of memory-centric computing. This work describes several major recent advances in memory-centric computing, specifically in Processing-in-DRAM, a paradigm where the operational characteristics of a DRAM chip are exploited and enhanced to perform computation on data stored in DRAM. Specifically, we describe 1) new techniques that slightly modify DRAM chips to enable both enhanced computation capability and easier programmability, 2) new experimental studies that demonstrate the functionally-complete bulk-bitwise computational capability of real commercial off-the-shelf DRAM chips, without any modifications to the DRAM chip or the interface, and 3) new DRAM designs that improve access granularity & efficiency, unleashing the true potential of Processing-in-DRAM., Comment: This paper is an extended version of an IEDM 2024 Invited Paper in the AI Memory focus session

Agentic AI systems use specialized agents to handle tasks within complex workflows, enabling automation and efficiency. However, optimizing these systems often requires labor-intensive, manual adjustments to refine roles, tasks, and interactions. This paper introduces a framework for autonomously optimizing Agentic AI solutions across industries, such as NLP-driven enterprise applications. The system employs agents for Refinement, Execution, Evaluation, Modification, and Documentation, leveraging iterative feedback loops powered by an LLM (Llama 3.2-3B). The framework achieves optimal performance without human input by autonomously generating and testing hypotheses to improve system configurations. This approach enhances scalability and adaptability, offering a robust solution for real-world applications in dynamic environments. Case studies across diverse domains illustrate the transformative impact of this framework, showcasing significant improvements in output quality, relevance, and actionability. All data for these case studies, including original and evolved agent codes, along with their outputs, are here: https://anonymous.4open.science/r/evolver-1D11/

Parity-violating electron scattering experiments on $\rm ^{48}Ca$ (CREX) and $\rm ^{208}Pb$ (PREX-II) offer valuable insight into the isovector properties of finite nuclei, providing constraints for the density dependence of the nuclear equation of state, which is crucial for understanding astrophysical phenomena. In this work, we establish functional dependencies between the properties of finite nuclei - such as weak charge form factors and neutron skin thickness - and the bulk properties of neutron stars, including tidal deformability from binary neutron star mergers and neutron star radii. The dependencies are formulated by introducing a family of $\beta$-equilibrated equations of state based on relativistic energy density functionals with point coupling interactions. The charge minus the weak form factors derived from CREX and PREX-II measurements, combined with the observational constraints on tidal deformability from the GW170817 event, are used to constrain the symmetry energy and neutron star radii. Notably, the energy density expanded up to the fourth order in symmetry energy yields larger radii compared to calculations limited to the second order term. However, the results reveal a discrepancy between the constraints provided by the CREX and PREX-II experiments. For a more quantitative assessment, higher precision parity-violating electron scattering data and neutron star observations are required., Comment: 10 pages, 5 figures, 2 tables

Faster-than-Nyquist (FTN) signaling is a non-orthogonal transmission technique offering a promising solution for future generations of communications. This paper studies the capacity of FTN signaling in multiple-input multiple-output (MIMO) channels for high acceleration factors. In our previous study [1], we found the capacity for MIMO FTN channels if the acceleration factor is larger than a certain threshold, which depends on the bandwidth of the pulse shape used. In this paper, we extend the capacity analysis to acceleration factors smaller than this mentioned threshold. In addition to capacity, we conduct peak-to-average power ratio (PAPR) analysis and simulation for MIMO FTN for varying acceleration factors for both Gaussian and QPSK symbol sets. Our analysis reveals important insights about transmission power and received signal-to-noise ratio (SNR) variation in FTN. As the acceleration factor approaches 0, if the transmission power is fixed, the received SNR diminishes, or if the received SNR is fixed, PAPR at the transmitter explodes.

In this review/tutorial article, we present recent progress on optimal control of partially observed Markov Decision Processes (POMDPs). We first present regularity and continuity conditions for POMDPs and their belief-MDP reductions, where these constitute weak Feller and Wasserstein regularity and controlled filter stability. These are then utilized to arrive at existence results on optimal policies for both discounted and average cost problems, and regularity of value functions. Then, we study rigorous approximation results involving quantization based finite model approximations as well as finite window approximations under controlled filter stability. Finally, we present several recent reinforcement learning theoretic results which rigorously establish convergence to near optimality under both criteria.

Establishing the existence of exact or near Markov or stationary perfect Nash equilibria in nonzero-sum Markov games over Borel spaces remains a challenging problem, with few positive results to date. In this paper, we establish the existence of approximate Markov and stationary Nash equilibria for nonzero-sum stochastic games over Borel spaces, assuming only mild regularity conditions on the model. Our approach involves analyzing a quantized version of the game, for which we provide an explicit construction under both finite-horizon and discounted cost criteria. This work has significant implications for emerging applications such as multi-agent learning. Our results apply to both compact and non-compact state spaces. For the compact state space case, we first approximate the standard Borel model with a finite state-action model. Using the existence of Markov and stationary perfect Nash equilibria for these finite models under finite-horizon and discounted cost criteria, we demonstrate that these joint policies constitute approximate Markov and stationary perfect equilibria under mild continuity conditions on the one-stage costs and transition probabilities. For the non-compact state space case, we achieve similar results by first approximating the model with a compact-state model. Compared with previous results in the literature, which we comprehensively review, we provide more general and complementary conditions, along with explicit approximation models whose equilibria are $\epsilon$-equilibria for the original model.

The real time analysis and secure transmission of electrocardiogram (ECG) signals are critical for ensuring both effective medical diagnosis and patient data privacy. In this study, we developed a real time ECG monitoring system that integrates chaotic encryption to protect the integrity and confidentiality of ECG signals during acquisition, transmission, and storage. By leveraging the logistic map as the chaotic function for encryption, our system offers a highly secure framework that dynamically encrypts ECG signals without adding significant latency. To validate the system's reliability, we applied a series of security tests. The results demonstrate that chaotic encryption is effective in enhancing data security, as evidenced by high entropy values and strong key sensitivity, ensuring protection against common cryptographic attacks. Additionally, the system's real time disease detection model, based on deep learning, operates seamlessly with encrypted data, providing accurate diagnosis without compromising security. Our findings indicate that chaotic encryption, paired with real time analysis, is a powerful method for protecting sensitive medical data, making this approach particularly relevant for telemedicine and remote patient monitoring applications. The success of this system highlights its potential for broader application to other biomedical signals, providing a secure infrastructure for the future of digital health.

This study introduces the development of a state of the art, real time ECG monitoring and analysis system, incorporating cutting edge medical technology and innovative data security measures. Our system performs three distinct functions thaat real time ECG monitoring and disease detection, encrypted storage and synchronized visualization, and statistical analysis on encrypted data. At its core, the system uses a three lead ECG preamplifier connected through a serial port to capture, display, and record real time ECG data. These signals are securely stored in the cloud using robust encryption methods. Authorized medical personnel can access and decrypt this data on their computers, with AES encryption ensuring synchronized real time data tracking and visualization. Furthermore, the system performs statistical operations on the ECG data stored in the cloud without decrypting it, using Fully Homomorphic Encryption (FHE). This enables privacy preserving data analysis while ensuring the security and confidentiality of patient information. By integrating these independent functions, our system significantly enhances the security and efficiency of health monitoring. It supports critical tasks such as disease detection, patient monitoring, and preliminary intervention, all while upholding stringent data privacy standards. We provided detailed discussions on the system's architecture, hardware configuration, software implementation, and clinical performance. The results highlight the potential of this system to improve patient care through secure and efficient ECG monitoring and analysis. This work represents a significant leap forward in medical technology. By incorporating FHE into both data transmission and storage processes, we ensure continuous encryption of data throughout its lifecycle while enabling real time disease diagnosis.

In recent years, the transition to clean bus fleets has accelerated. Although this transition might bring environmental and economic benefits, it requires a long-term strategic plan due to the large investment costs involved. This paper proposes a multi-stage stochastic program to optimize strategic plans for the clean bus fleet transition that explicitly considers the uncertainty scenarios in the cost and efficiency improvements of clean buses. Our optimization model minimizes the total expected cost subject to emission targets, budget restrictions and several other operational considerations. We propose a new forecasting approach that captures the correlation between these improvements to obtain realistic future pathways for Battery Electric Buses (BEBs) and Hydrogen Fuel Cell Buses (HFCBs), which are then given to the multi-stage stochastic program as scenarios. We also utilize a physics-based model for BEBs to accurately capture their energy consumption and recharging needs. As a case study, we focus on the complex public bus network of Istanbul, which aims to transition to a clean bus fleet by 2050. Utilizing real datasets, we solve a five-stage stochastic program spanning a 25-year planning horizon that involves 256 scenarios to obtain dynamic strategic plans that can be used by the policy makers. Our results suggest that BEBs are more advantageous than HFCBs, even in slow BEB but fast HFCB development scenarios. We also conduct several sensitivity analyses to understand the effects of the intermediate emission targets, budget limitations and energy prices.

We study an approximation method for partially observed Markov decision processes (POMDPs) with continuous spaces. Belief MDP reduction, which has been the standard approach to study POMDPs requires rigorous approximation methods for practical applications, due to the state space being lifted to the space of probability measures. Generalizing recent work, in this paper we present rigorous approximation methods via discretizing the observation space and constructing a fully observed finite MDP model using a finite length history of the discrete observations and control actions. We show that the resulting policy is near-optimal under some regularity assumptions on the channel, and under certain controlled filter stability requirements for the hidden state process. Furthermore, by quantizing the measurements, we are able to utilize refined filter stability conditions. We also provide a Q learning algorithm that uses a finite memory of discretized information variables, and prove its convergence to the optimality equation of the finite fully observed MDP constructed using the approximation method.

This paper introduces a fast and robust method for simplifying surface triangle meshes in the wild while maintaining high visual quality. While previous methods achieve excellent results on manifold meshes by using the quadric error metric, they suffer from producing high-quality outputs for user-created meshes, which often contain non-manifold elements and multiple connected components. In this work, we begin by outlining the pitfalls of existing mesh simplification techniques and highlighting the discrepancy in their formulations with existing mesh data. We then propose a method for simplifying these (non-manifold) triangle meshes, while maintaining quality comparable to the existing methods for manifold inputs. Our key idea is to reformulate mesh simplification as a problem of decimating simplicial 2-complexes. This involves a novel construction to turn a triangle soup into a simplicial 2-complex, followed by iteratively collapsing 1-simplices (vertex pairs) with our modified quadric error metric tailored for topology changes. Besides, we also tackle textured mesh simplification. Instead of following existing strategies to preserve mesh UVs, we propose a novel perspective that only focuses on preserving texture colors defined on the surface, regardless of the layout in the texture UV space. This leads to a more robust method for textured mesh simplification that is free from the texture bleeding artifact. Our mesh simplification enables level-of-detail algorithms to operate on arbitrary triangle meshes in the wild. We demonstrate improvements over prior techniques through extensive qualitative and quantitative evaluations, along with user studies.

We present AutoMode-ASR, a novel framework that effectively integrates multiple ASR systems to enhance the overall transcription quality while optimizing cost. The idea is to train a decision model to select the optimal ASR system for each segment based solely on the audio input before running the systems. We achieve this by ensembling binary classifiers determining the preference between two systems. These classifiers are equipped with various features, such as audio embeddings, quality estimation, and signal properties. Additionally, we demonstrate how using a quality estimator can further improve performance with minimal cost increase. Experimental results show a relative reduction in WER of 16.2%, a cost saving of 65%, and a speed improvement of 75%, compared to using a single-best model for all segments. Our framework is compatible with commercial and open-source black-box ASR systems as it does not require changes in model codes., Comment: SPECOM 2024 Conference

This study introduces a straightforward electrode design featuring sharp edges with a curvature of a few hundred nanometers in radius, with which both ion accumulation and nanoparticle deposition can be observed under an alternating electrical potential. The electrodes, termed 'shark-teeth electrodes', are fabricated using a laser ablation technique optimized for facile nanostructure creation. This method involves successive, overlapping ablated discs in a thin film of gold, producing sharp tips that generate strong electric fields. When electrically polarized in an electrolyte solution, these sharp tips form a screening layer, facilitating the observation of ion and nanoparticle behavior. A total-internal reflection microscope is employed to monitor ion accumulation on these electrodes, demonstrating their capability in iontronic microscopy. Additionally, the same electrodes are used to track nanoparticle trapping under high-frequency alternating potentials. This dual functionality allows for the investigation of electrochemical and physical interactions between ions and colloidal nanoparticles, contributing valuable insights to the field of soft matter., Comment: 19 pages, 4 figure, includes additional supplementary information

Exoskeletons can boost human strength and provide assistance to individuals with physical disabilities. However, ensuring safety and optimal performance in their design poses substantial challenges. This study presents the design process for an underactuated hand exoskeleton (U-HEx), first including a single objective (maximizing force transmission), then expanding into multi objective (also minimizing torque variance and actuator displacement). The optimization relies on a Genetic Algorithm, the Big Bang-Big Crunch Algorithm, and their versions for multi-objective optimization. Analyses revealed that using Big Bang-Big Crunch provides high and more consistent results in terms of optimality with lower convergence time. In addition, adding more objectives offers a variety of trade-off solutions to the designers, who might later set priorities for the objectives without repeating the process - at the cost of complicating the optimization algorithm and computational burden. These findings underline the importance of performing proper optimization while designing exoskeletons, as well as providing a significant improvement to this specific robotic design., Comment: 12 pages, 7 figures, 8 talbes, submitted to IEEE Transactions on Robotics

This paper presents a unified differentiable boolean operator for implicit solid shape modeling using Constructive Solid Geometry (CSG). Traditional CSG relies on min, max operators to perform boolean operations on implicit shapes. But because these boolean operators are discontinuous and discrete in the choice of operations, this makes optimization over the CSG representation challenging. Drawing inspiration from fuzzy logic, we present a unified boolean operator that outputs a continuous function and is differentiable with respect to operator types. This enables optimization of both the primitives and the boolean operations employed in CSG with continuous optimization techniques, such as gradient descent. We further demonstrate that such a continuous boolean operator allows modeling of both sharp mechanical objects and smooth organic shapes with the same framework. Our proposed boolean operator opens up new possibilities for future research toward fully continuous CSG optimization., Comment: SIGGRAPH'24

Magnetic skyrmions are promising candidates for future information storing and processing devices. There are different routes for stabilizing the skyrmions. Understanding the interplay mechanism between different scenarios of skyrmion formation is one key factor that can reveal new paths for controlling skyrmion phases. Inspired by the flexibility of two-dimensional materials that offer an exciting playground for manipulating spin textures, we conducted \textit{ab initio} simulations and utilized four-state spin framework to determine magnetic parameters of lateral heterostructure formed by NiBr2 and NiCl2 Monolayers. The obtained spin interaction parameters are utilized to determine the skyrmionic phases via Monte Carlo simulation. Monte Carlo simulation results suggest three distinct phase transition mechanisms exist in the present system. Namely, examination of heat capacity versus temperature curve obtained from average anisotropic exchange energy yields a phase transition between paramagnetic and spin-spiral states at $5$K for pristine NiBr2, a paramagnetic-mixed skyrmion state transition occurs at 17 K for pristine NiCl2, and a high-temperature ferromagnetic-paramagnetic transition at T=80 K is observed for the heterostructure region, indicating that some kind of intrinsic ferromagnetism may originate at the interface of pristine Janus structures.

A novel method to reconstruct the energy of hadronic showers in the CMS High Granularity Calorimeter (HGCAL) is presented. The HGCAL is a sampling calorimeter with very fine transverse and longitudinal granularity. The active media are silicon sensors and scintillator tiles readout by SiPMs and the absorbers are a combination of lead and Cu/CuW in the electromagnetic section, and steel in the hadronic section. The shower reconstruction method is based on graph neural networks and it makes use of a dynamic reduction network architecture. It is shown that the algorithm is able to capture and mitigate the main effects that normally hinder the reconstruction of hadronic showers using classical reconstruction methods, by compensating for fluctuations in the multiplicity, energy, and spatial distributions of the shower's constituents. The performance of the algorithm is evaluated using test beam data collected in 2018 prototype of the CMS HGCAL accompanied by a section of the CALICE AHCAL prototype. The capability of the method to mitigate the impact of energy leakage from the calorimeter is also demonstrated.

Large Language Models (LLMs) like ChatGPT demonstrate significant potential in the medical field, often evaluated using multiple-choice questions (MCQs) similar to those found on the USMLE. Despite their prevalence in medical education, MCQs have limitations that might be exacerbated when assessing LLMs. To evaluate the effectiveness of MCQs in assessing the performance of LLMs, we developed a fictional medical benchmark focused on a non-existent gland, the Glianorex. This approach allowed us to isolate the knowledge of the LLM from its test-taking abilities. We used GPT-4 to generate a comprehensive textbook on the Glianorex in both English and French and developed corresponding multiple-choice questions in both languages. We evaluated various open-source, proprietary, and domain-specific LLMs using these questions in a zero-shot setting. The models achieved average scores around 67%, with minor performance differences between larger and smaller models. Performance was slightly higher in English than in French. Fine-tuned medical models showed some improvement over their base versions in English but not in French. The uniformly high performance across models suggests that traditional MCQ-based benchmarks may not accurately measure LLMs' clinical knowledge and reasoning abilities, instead highlighting their pattern recognition skills. This study underscores the need for more robust evaluation methods to better assess the true capabilities of LLMs in medical contexts.

For optimal control of diffusions under several criteria, due to computational or analytical reasons, many studies have a apriori assumed control policies to be Lipschitz or smooth, often with no rigorous analysis on whether this restriction entails loss. While optimality of Markov/stationary Markov policies for expected finite horizon/infinite horizon (discounted/ergodic) cost and cost-up-to-exit time optimal control problems can be established under certain technical conditions, an optimal solution is typically only measurable in the state (and time, if the horizon is finite) with no apriori additional structural properties. In this paper, building on our recent work [S. Pradhan and S. Y\"uksel, Continuity of cost in Borkar control topology and implications on discrete space and time approximations for controlled diffusions under several criteria, Electronic Journal of Probability (2024)] establishing the regularity of optimal cost on the space of control policies under the Borkar control topology for a general class of diffusions, we establish near optimality of smooth/Lipschitz continuous policies for optimal control under expected finite horizon, infinite horizon discounted/average, and up-to-exit time cost criteria. Under mild assumptions, we first show that smooth/Lipschitz continuous policies are dense in the space of Markov/stationary Markov policies under the Borkar topology. Then utilizing the continuity of optimal costs as a function of policies on the space of Markov/stationary policies under the Borkar topology, we establish that optimal policies can be approximated by smooth/Lipschitz continuous policies with arbitrary precision. While our results are extensions of our recent work, the practical significance of an explicit statement and accessible presentation dedicated to Lipschitz and smooth policies, given their prominence in the literature, motivates our current paper., Comment: 10 pages. arXiv admin note: substantial text overlap with arXiv:2209.14982

This paper presents volumetric homogenization, a spatially varying homogenization scheme for knitwear simulation. We are motivated by the observation that macro-scale fabric dynamics is strongly correlated with its underlying knitting patterns. Therefore, homogenization towards a single material is less effective when the knitting is complex and non-repetitive. Our method tackles this challenge by homogenizing the yarn-level material locally at volumetric elements. Assigning a virtual volume of a knitting structure enables us to model bending and twisting effects via a simple volume-preserving penalty and thus effectively alleviates the material nonlinearity. We employ an adjoint Gauss-Newton formulation to battle the dimensionality challenge of such per-element material optimization. This intuitive material model makes the forward simulation GPU-friendly. To this end, our pipeline also equips a novel domain-decomposed subspace solver crafted for GPU projective dynamics, which makes our simulator hundreds of times faster than the yarn-level simulator. Experiments validate the capability and effectiveness of volumetric homogenization. Our method produces realistic animations of knitwear matching the quality of full-scale yarn-level simulations. It is also orders of magnitude faster than existing homogenization techniques in both the training and simulation stages.

We experimentally analyze the computational capability of commercial off-the-shelf (COTS) DRAM chips and the robustness of these capabilities under various timing delays between DRAM commands, data patterns, temperature, and voltage levels. We extensively characterize 120 COTS DDR4 chips from two major manufacturers. We highlight four key results of our study. First, COTS DRAM chips are capable of 1) simultaneously activating up to 32 rows (i.e., simultaneous many-row activation), 2) executing a majority of X (MAJX) operation where X>3 (i.e., MAJ5, MAJ7, and MAJ9 operations), and 3) copying a DRAM row (concurrently) to up to 31 other DRAM rows, which we call Multi-RowCopy. Second, storing multiple copies of MAJX's input operands on all simultaneously activated rows drastically increases the success rate (i.e., the percentage of DRAM cells that correctly perform the computation) of the MAJX operation. For example, MAJ3 with 32-row activation (i.e., replicating each MAJ3's input operands 10 times) has a 30.81% higher average success rate than MAJ3 with 4-row activation (i.e., no replication). Third, data pattern affects the success rate of MAJX and Multi-RowCopy operations by 11.52% and 0.07% on average. Fourth, simultaneous many-row activation, MAJX, and Multi-RowCopy operations are highly resilient to temperature and voltage changes, with small success rate variations of at most 2.13% among all tested operations. We believe these empirical results demonstrate the promising potential of using DRAM as a computation substrate. To aid future research and development, we open-source our infrastructure at https://github.com/CMU-SAFARI/SiMRA-DRAM., Comment: To appear in DSN 2024

The adoption of processing-in-memory (PiM) architectures has been gaining momentum because they provide high performance and low energy consumption by alleviating the data movement bottleneck. Yet, the security of such architectures has not been thoroughly explored. The adoption of PiM solutions provides a new way to directly access main memory, which malicious user applications can exploit. We show that this new way to access main memory opens opportunities for high-throughput timing attacks that are hard-to-mitigate without significant performance overhead. We introduce IMPACT, a set of high-throughput main memory-based timing attacks that leverage characteristics of PiM architectures to establish covert and side channels. IMPACT enables high-throughput communication and private information leakage by exploiting the shared DRAM row buffer. To achieve high throughput, IMPACT (i) eliminates cache bypassing steps required by processor-centric main memory and cache-based timing attacks and (ii) leverages the intrinsic parallelism of PiM operations. We showcase two applications of IMPACT. First, we build two covert-channel attacks that run on the host CPU and leverage different PiM approaches to gain direct and fast access to main memory and establish high-throughput communication covert channels. Second, we showcase a side-channel attack that leaks private information of concurrently running victim applications that are accelerated with PiM. Our results demonstrate that (i) our covert channels achieve 12.87 Mb/s and 14.16 Mb/s communication throughput, respectively, which is up to 4.91x and 5.41x faster than the state-of-the-art main memory-based covert channels, and (ii) our side-channel attack allows the attacker to leak secrets with a low error rate. To avoid such covert and side channels in emerging PiM systems, we propose and evaluate three defenses.

Robotic exoskeletons can enhance human strength and aid people with physical disabilities. However, designing them to ensure safety and optimal performance presents significant challenges. Developing exoskeletons should incorporate specific optimization algorithms to find the best design. This study investigates the potential of Evolutionary Computation (EC) methods in robotic design optimization, with an underactuated hand exoskeleton (U-HEx) used as a case study. We propose improving the performance and usability of the U-HEx design, which was initially optimized using a naive brute-force approach, by integrating EC techniques such as Genetic Algorithm and Big Bang-Big Crunch Algorithm. Comparative analysis revealed that EC methods consistently yield more precise and optimal solutions than brute force in a significantly shorter time. This allowed us to improve the optimization by increasing the number of variables in the design, which was impossible with naive methods. The results show significant improvements in terms of the torque magnitude the device transfers to the user, enhancing its efficiency. These findings underline the importance of performing proper optimization while designing exoskeletons, as well as providing a significant improvement to this specific robotic design., Comment: 6 pages (+ref), 4 figures, IEEE International Conference on Robotics and Automation (ICRA) 2024

The pursuit of higher data rates and efficient spectrum utilization in modern communication technologies necessitates novel solutions. In order to provide insights into improving spectral efficiency and reducing latency, this study investigates the maximum channel coding rate (MCCR) of finite block length (FBL) multiple-input multiple-output (MIMO) faster-than-Nyquist (FTN) channels. By optimizing power allocation, we derive the system's MCCR expression. Simulation results are compared with the existing literature to reveal the benefits of FTN in FBL transmission.

In this paper, we explore the susceptibility of the independent Q-learning algorithms (a classical and widely used multi-agent reinforcement learning method) to strategic manipulation of sophisticated opponents in normal-form games played repeatedly. We quantify how much strategically sophisticated agents can exploit naive Q-learners if they know the opponents' Q-learning algorithm. To this end, we formulate the strategic actors' interactions as a stochastic game (whose state encompasses Q-function estimates of the Q-learners) as if the Q-learning algorithms are the underlying dynamical system. We also present a quantization-based approximation scheme to tackle the continuum state space and analyze its performance for two competing strategic actors and a single strategic actor both analytically and numerically., Comment: The extended arXiv version of the original paper to appear in IEEE L-CSS

We introduce vertex block descent, a block coordinate descent solution for the variational form of implicit Euler through vertex-level Gauss-Seidel iterations. It operates with local vertex position updates that achieve reductions in global variational energy with maximized parallelism. This forms a physics solver that can achieve numerical convergence with unconditional stability and exceptional computation performance. It can also fit in a given computation budget by simply limiting the iteration count while maintaining its stability and superior convergence rate. We present and evaluate our method in the context of elastic body dynamics, providing details of all essential components and showing that it outperforms alternative techniques. In addition, we discuss and show examples of how our method can be used for other simulation systems, including particle-based simulations and rigid bodies.

This study explores the application of the rate-splitting multiple access (RSMA) technique, vital for interference mitigation in modern communication systems. It investigates the use of precoding methods in RSMA, especially in complex multiple-antenna interference channels, employing deep reinforcement learning. The aim is to optimize precoders and power allocation for common and private data streams involving multiple decision-makers. A multi-agent deep deterministic policy gradient (MADDPG) framework is employed to address this complexity, where decentralized agents collectively learn to optimize actions in a continuous policy space. We also explore the challenges posed by imperfect channel side information at the transmitter. Additionally, decoding order estimation is addressed to determine the optimal decoding sequence for common and private data sequences. Simulation results demonstrate the effectiveness of the proposed RSMA method based on MADDPG, achieving the upper bound in single-antenna scenarios and closely approaching theoretical limits in multi-antenna scenarios. Comparative analysis shows superiority over other techniques such as MADDPG without rate-splitting, maximal ratio transmission (MRT), zero-forcing (ZF), and leakage-based precoding methods. These findings highlight the potential of deep reinforcement learning-driven RSMA in reducing interference and enhancing system performance in communication systems.