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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., Comment: Prepared for submission to JINST

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 meta-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 meta-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 can be potentially exploited by malicious user applications. We show that this new way to access main memory opens opportunities for high-throughput timing attack vectors 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. To achieve this, IMPACT (i) eliminates expensive cache bypassing steps required by processor-centric main memory and cache-based timing attacks and (ii) leverages the intrinsic parallelism of PiM operations. First, we showcase two covert-channel attack variants that run on the host CPU and leverage PiM architectures to gain direct and fast access to main memory and establish high-throughput communication covert channels. Second, we showcase a side-channel attack on a DNA sequence analysis application that leaks the private characteristics of a user's sample genome by leveraging PiM operations. Our results demonstrate that (i) our covert channels achieve up to 14.16 Mb/s communication throughput, which is 6.38x faster than the state-of-the-art main memory-based covert channels, and (ii) our side-channel attack allows the attacker to determine the properties of a sample genome at a throughput of 7.5 Mb/s with 96% accuracy. We discuss and evaluate several countermeasures for IMPACT to enable secure and robust PiM architectures.

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.

We propose a new RowHammer mitigation mechanism, CoMeT, that prevents RowHammer bitflips with low area, performance, and energy costs in DRAM-based systems at very low RowHammer thresholds. The key idea of CoMeT is to use low-cost and scalable hash-based counters to track DRAM row activations. CoMeT uses the Count-Min Sketch technique that maps each DRAM row to a group of counters, as uniquely as possible, using multiple hash functions. When a DRAM row is activated, CoMeT increments the counters mapped to that DRAM row. Because the mapping from DRAM rows to counters is not completely unique, activating one row can increment one or more counters mapped to another row. Thus, CoMeT may overestimate, but never underestimates, a DRAM row's activation count. This property of CoMeT allows it to securely prevent RowHammer bitflips while properly configuring its hash functions reduces overestimations. As a result, CoMeT 1) implements substantially fewer counters than the number of DRAM rows in a DRAM bank and 2) does not significantly overestimate a DRAM row's activation count. Our comprehensive evaluations show that CoMeT prevents RowHammer bitflips with an average performance overhead of only 4.01% across 61 benign single-core workloads for a very low RowHammer threshold of 125, normalized to a system with no RowHammer mitigation. CoMeT achieves a good trade-off between performance, energy, and area overheads. Compared to the best-performing state-of-the-art mitigation, CoMeT requires 74.2x less area overhead at the RowHammer threshold 125 and incurs a small performance overhead on average for all RowHammer thresholds. Compared to the best-performing low-area-cost mechanism, at a very low RowHammer threshold of 125, CoMeT improves performance by up to 39.1% while incurring a similar area overhead. CoMeT is openly and freely available at https://github.com/CMU-SAFARI/CoMeT., Comment: To appear at HPCA 2024

Processing-using-DRAM (PuD) is an emerging paradigm that leverages the analog operational properties of DRAM circuitry to enable massively parallel in-DRAM computation. PuD has the potential to reduce or eliminate costly data movement between processing elements and main memory. Prior works experimentally demonstrate three-input MAJ (MAJ3) and two-input AND and OR operations in commercial off-the-shelf (COTS) DRAM chips. Yet, demonstrations on COTS DRAM chips do not provide a functionally complete set of operations. We experimentally demonstrate that COTS DRAM chips are capable of performing 1) functionally-complete Boolean operations: NOT, NAND, and NOR and 2) many-input (i.e., more than two-input) AND and OR operations. We present an extensive characterization of new bulk bitwise operations in 256 off-the-shelf modern DDR4 DRAM chips. We evaluate the reliability of these operations using a metric called success rate: the fraction of correctly performed bitwise operations. Among our 19 new observations, we highlight four major results. First, we can perform the NOT operation on COTS DRAM chips with a 98.37% success rate on average. Second, we can perform up to 16-input NAND, NOR, AND, and OR operations on COTS DRAM chips with high reliability (e.g., 16-input NAND, NOR, AND, and OR with an average success rate of 94.94%, 95.87%, 94.94%, and 95.85%, respectively). Third, data pattern only slightly affects bitwise operations. Our results show that executing NAND, NOR, AND, and OR operations with random data patterns decreases the success rate compared to all logic-1/logic-0 patterns by 1.39%, 1.97%, 1.43%, and 1.98%, respectively. Fourth, bitwise operations are highly resilient to temperature changes, with small success rate fluctuations of at most 1.66% when the temperature is increased from 50C to 95C. We open-source our infrastructure at https://github.com/CMU-SAFARI/FCDRAM, Comment: A shorter version of this work is to appear at the 30th IEEE International Symposium on High-Performance Computer Architecture (HPCA-30), 2024

Data availability is crucial for advancing artificial intelligence applications, including voice-based technologies. As content creation, particularly in social media, experiences increasing demand, translation and text-to-speech (TTS) technologies have become essential tools. Notably, the performance of these TTS technologies is highly dependent on the quality of the training data, emphasizing the mutual dependence of data availability and technological progress. This paper introduces an end-to-end tool to generate high-quality datasets for text-to-speech (TTS) models to address this critical need for high-quality data. The contributions of this work are manifold and include: the integration of language-specific phoneme distribution into sample selection, automation of the recording process, automated and human-in-the-loop quality assurance of recordings, and processing of recordings to meet specified formats. The proposed application aims to streamline the dataset creation process for TTS models through these features, thereby facilitating advancements in voice-based technologies., Comment: 9 Pages, 6 Figures, 4 Tables, LREC-COLING 2024

This two-part article aims to introduce a quantum analogue to classical Markov decision processes (MDPs). In Part II, building on the formulation of q-MDPs presented in Part I, our focus shifts to the development of algorithms for computing optimal policies and value functions of both open-loop and closed-loop policies. First, by using the duality between the dynamic programming and the semi-definite programming formulations of any q-MDP with open-loop policies, we establish an algorithm that enables us to efficiently compute optimal open-loop quantum policies and value functions. Then, dynamic programming and semi-definite programming formulations for closed-loop policies is established, where duality of these two formulations similarly enables the efficient computation of optimal closed-loop policies and value functions. Finally, given that any q-MDP can be approximated by q-MDPs with classical policies--potentially with higher-dimensional underlying Hilbert spaces than the original model--and since any classical policy is an element of the set of closed-loop policies, we conclude that any q-MDP can be approximated by q-MDPs with closed-loop policies having higher-dimensional Hilbert spaces., Comment: 24 pages

In this paper, the aim is to develop a quantum counterpart to classical Markov decision processes (MDPs). Firstly, we provide a very general formulation of quantum MDPs with state and action spaces in the quantum domain, quantum transitions, and cost functions. Once we formulate the quantum MDP (q-MDP), our focus shifts to establishing the verification theorem that proves the sufficiency of Markovian quantum control policies and provides a dynamic programming principle. Subsequently, a comparison is drawn between our q-MDP model and previously established quantum MDP models (referred to as QOMDPs) found in the literature. Furthermore, approximations of q-MDPs are obtained via finite-action models, which can be formulated as QOMDPs. Finally, classes of open-loop and classical-state-preserving closed-loop policies for q-MDPs are introduced, along with structural results for these policies. In summary, we present a novel quantum MDP model aiming to introduce a new framework, algorithms, and future research avenues. We hope that our approach will pave the way for a new research direction in discrete-time quantum control., Comment: 30 pages

We present a facile desktop fabrication method for origami-based nano-gap indium tin oxide (ITO) electrokinetic particle traps, providing a simplified approach compared to traditional lithographic techniques and effectively trapping of nanoparticles. Our approach involves bending ITO thin films on optically transparent polyethylene terephthalate (PET), creating an array of parallel nano-gaps. By strategically introducing weak points through cut-sharp edges, we successfully controlled the spread of nano-cracks. A single crack spanning the constriction width and splitting the conductive layers forms a nano-gap that can effectively trap small nanoparticles after applying an alternating electric potential across the nanogap. We analyze the conditions for reversible trapping and optimal performance of the nano-gap ITO electrodes with optical microscopy and electrokinetic impedance spectroscopy. Our findings highlight the potential of this facile fabrication method for the use of ITO at active electro-actuated traps in microfluidic systems.

This paper presents a new variant of fictitious play (FP) called team-fictitious-play (Team-FP) that can reach equilibrium in multi-team competition, different from the other variants of FP. We specifically focus on zero-sum potential team games with network separable interactions (ZSPTGs), unifying potential games (if there is a single team) and zero-sum polymatrix games (if each team has a single member) due to their wide range of applications from robotics to financial markets beyond two-team games. Similar to the FP dynamics, in Team-FP, agents follow a simple behavioral rule where they respond (with some inertia and exploration in the update of actions) to the last actions of the neighboring team members and the beliefs formed about the other neighbors' strategies as if the opponents are playing according to some stationary strategy. We show the almost sure convergence of the empirical averages of teams' action profiles to near team-Nash equilibrium in ZSPTGs under standard assumptions on the step sizes used. We formulate a bound on the approximation error, decaying with the exploration in the agents' responses. We further examine the performance of the Team-FP dynamics numerically.

In the realm of automatic speech recognition (ASR), the quest for models that not only perform with high accuracy but also offer transparency in their decision-making processes is crucial. The potential of quality estimation (QE) metrics is introduced and evaluated as a novel tool to enhance explainable artificial intelligence (XAI) in ASR systems. Through experiments and analyses, the capabilities of the NoRefER (No Reference Error Rate) metric are explored in identifying word-level errors to aid post-editors in refining ASR hypotheses. The investigation also extends to the utility of NoRefER in the corpus-building process, demonstrating its effectiveness in augmenting datasets with insightful annotations. The diagnostic aspects of NoRefER are examined, revealing its ability to provide valuable insights into model behaviors and decision patterns. This has proven beneficial for prioritizing hypotheses in post-editing workflows and fine-tuning ASR models. The findings suggest that NoRefER is not merely a tool for error detection but also a comprehensive framework for enhancing ASR systems' transparency, efficiency, and effectiveness. To ensure the reproducibility of the results, all source codes of this study are made publicly available.

In this study, we explore the effect of integrated auxiliary rods at varying angles to the primary cavity rod on the dispersion characteristics of the photonic crystal coupled cavity waveguide (PC CCW). Here, it is intended to break the symmetry of the cavity region by introducing auxiliary rods which gives the degree of freedom for tuning effective index of the PC waveguide. Furthermore, rotational angle variations of auxiliary rods reveal slow light operation of the PC CCW where the group index is maximized and group velocity dispersion, as well as the third-order dispersion, are minimized. In addition, auxiliary rods with a broken symmetry increase not only group index but also operating bandwidth and accordingly increase group bandwidth product by 675\%. Leveraging these results, we demonstrate effective rainbow trapping by manipulating the auxiliary rod angles in photonic crystal coupled cavity waveguides. Our results have encouraging implications for optical buffering, multiplexing, demultiplexing, advanced time-domain and spatial signal processing., Comment: To appear in Optical and Quantum Electronics

We present a comprehensive study on the electronic and magnetic properties of the EuCl$_3$ monolayer using first-principles calculations. By taking into account the spin-orbit coupling (SOC) and the Hubbard effects, we elucidate the influence of these interactions on the structural properties of EuCl$_3$ monolayer. Comparing the lattice parameters obtained with the PBE+SOC and PBE+SOC+Hubbard effects reveals an increase in the lattice parameters when including the Hubbard effect. In the absence of the Hubbard interaction, the magnetic ground state of the EuCl$_3$ monolayer exhibits a preference for a ferromagnetic (FM) configuration as determined by the PBE+SOC calculations. However, the introduction of the Hubbard parameter leads to a shift in the magnetic ground state preference towards an antiferromagnetic (AFM) N\'{e}el state. Based on the calculated energy values, Monte Carlo simulations are carried out to determine the N\'{e}el temperature $(T_N)$. Our simulations yield a N\'{e}el temperature of \SI{390}{\kelvin} for the EuCl$_3$ monolayer, indicating the temperature at which the transition from a paramagnetic phase to an antiferromagnetic phase occurs. These results highlight the importance of incorporating the SOC and the Hubbard effect in accurately describing the electronic and magnetic properties of the EuCl$_3$ monolayer. Our results contribute to a deeper understanding of the fundamental physics underlying the behavior of this intriguing two-dimensional material and provide insights into its potential applications in spintronics and magnetic devices.

Data movement between the processor and the main memory is a first-order obstacle against improving performance and energy efficiency in modern systems. To address this obstacle, Processing-using-Memory (PuM) is a promising approach where bulk-bitwise operations are performed leveraging intrinsic analog properties within the DRAM array and massive parallelism across DRAM columns. Unfortunately, 1) modern off-the-shelf DRAM chips do not officially support PuM operations, and 2) existing techniques of performing PuM operations on off-the-shelf DRAM chips suffer from two key limitations. First, these techniques have low success rates, i.e., only a small fraction of DRAM columns can correctly execute PuM operations because they operate beyond manufacturer-recommended timing constraints, causing these operations to be highly susceptible to noise and process variation. Second, these techniques have limited compute primitives, preventing them from fully leveraging parallelism across DRAM columns and thus hindering their performance benefits. We propose PULSAR, a new technique to enable high-success-rate and high-performance PuM operations in off-the-shelf DRAM chips. PULSAR leverages our new observation that a carefully crafted sequence of DRAM commands simultaneously activates up to 32 DRAM rows. PULSAR overcomes the limitations of existing techniques by 1) replicating the input data to improve the success rate and 2) enabling new bulk bitwise operations (e.g., many-input majority, Multi-RowInit, and Bulk-Write) to improve the performance. Our analysis on 120 off-the-shelf DDR4 chips from two major manufacturers shows that PULSAR achieves a 24.18% higher success rate and 121% higher performance over seven arithmetic-logic operations compared to FracDRAM, a state-of-the-art off-the-shelf DRAM-based PuM technique.

In the classical lossy source coding problem, one encodes long blocks of source symbols that enables the distortion to approach the ultimate Shannon limit. Such a block-coding approach introduces large delays, which is undesirable in many delay-sensitive applications. We consider the zero-delay case, where the goal is to encode and decode a finite-alphabet Markov source without any delay. It has been shown that this problem lends itself to stochastic control techniques, which lead to existence, structural, and general structural approximation results. However, these techniques so far have resulted only in computationally prohibitive algorithmic implementations for code design. To address this problem, we present a reinforcement learning design algorithm and rigorously prove its asymptotic optimality. In particular, we show that a quantized Q-learning algorithm can be used to obtain a near-optimal coding policy for this problem. The proof builds on recent results on quantized Q-learning for weakly Feller controlled Markov chains whose application necessitates the development of supporting technical results on regularity and stability properties, and relating the optimal solutions for discounted and average cost infinite horizon criteria problems. These theoretical results are supported by simulations., Comment: 15 pages, 3 figures; accepted for publication in IEEE Transactions on Information Theory

In this study, we present the design of a photonic crystal (PC) structure with a hexagonal lattice, where adjustments to the PC unit cell symmetry reveal an all-angle self-collimation (SC) effect. By optimizing opto-geometric parameters, such as the rotational angle of auxiliary rods and adjacent distances, we analyze the SC property in detail, leveraging group velocity dispersion (GVD) and third-order dispersion (TOD) characteristics. We also investigate the relationship between symmetry properties and their influence on dispersion characteristics. Through symmetry manipulation, we gain a comprehensive understanding of the underlying mechanisms governing light collimation and confinement in the proposed configurations. The PC structure with a $C_1$ symmetry group exhibits all-angle SC effect within the range of $a/\lambda=0.652$ and $a/\lambda=0.668$ normalized frequencies, with a bandwidth of $\Delta\omega/\omega_c = 2.4\%$. Further breaking the symmetry, transforming from $C_1$ to $C_2$ group symmetry, enhances the SC bandwidth to $\Delta\omega/\omega_c = 6.5\%$ and reveals the perfect linear equi-frequency contours (EFC) at two different frequency bands: all-angle SC between $a/\lambda=0.616$ and $a/\lambda=0.656$ normalized frequencies in the \nth{4} transverse magnetic (TM) band and between $a/\lambda=0.712$ and $a/\lambda=0.760$ in the \nth{5} TM band. Additionally, we propose a composite/hybrid PC structure resembling $C_2$ group symmetry, where two auxiliary rods are replaced by rectangular photonic wires with the same refractive index and width equal to the diameter of auxiliary rods. This hybrid structure exhibits an all-angle SC effect with an operating bandwidth of $\Delta\omega/\omega_c = 11.7\%$, displays near-zero GVD and TOD performance, and offers enhanced robustness against potential fabrication precision issues., Comment: Appeared in Physica Scripta

This paper presents new families of algorithms for the repeated play of two-agent (near) zero-sum games and two-agent zero-sum stochastic games. For example, the family includes fictitious play and its variants as members. Commonly, the algorithms in this family are all uncoupled, rational, and convergent even in heterogeneous cases, e.g., where the dynamics may differ in terms of learning rates, full, none or temporal access to opponent actions, and model-based vs model-free learning. The convergence of heterogeneous dynamics is of practical interest especially in competitive environments since agents may have no means or interests in following the same dynamic with the same parameters. We prove that any mixture of such asymmetries does not impact the algorithms' convergence to equilibrium (or near equilibrium if there is experimentation) in zero-sum games with repeated play and in zero-sum (irreducible) stochastic games with sufficiently small discount factors.