Search

Localizing more sources than sensors with a sparse linear array (SLA) has long relied on minimizing a distance between two covariance matrices and recent algorithms often utilize semidefinite programming (SDP). Although deep neural network (DNN)-based methods offer new alternatives, they still depend on covariance matrix fitting. In this paper, we develop a novel methodology that estimates the co-array subspaces from a sample covariance for SLAs. Our methodology trains a DNN to learn signal and noise subspace representations that are invariant to the selection of bases. To learn such representations, we propose loss functions that gauge the separation between the desired and the estimated subspace. In particular, we propose losses that measure the length of the shortest path between subspaces viewed on a union of Grassmannians, and prove that it is possible for a DNN to approximate signal subspaces. The computation of learning subspaces of different dimensions is accelerated by a new batch sampling strategy called consistent rank sampling. The methodology is robust to array imperfections due to its geometry-agnostic and data-driven nature. In addition, we propose a fully end-to-end gridless approach that directly learns angles to study the possibility of bypassing subspace methods. Numerical results show that learning such subspace representations is more beneficial than learning covariances or angles. It outperforms conventional SDP-based methods such as the sparse and parametric approach (SPA) and existing DNN-based covariance reconstruction methods for a wide range of signal-to-noise ratios (SNRs), snapshots, and source numbers for both perfect and imperfect arrays., Comment: 13 pages. Submitted to the IEEE Transactions on Signal Processing

Large-scale test collections play a crucial role in Information Retrieval (IR) research. However, according to the Cranfield paradigm and the research into publicly available datasets, the existing information retrieval research studies are commonly developed on small-scale datasets that rely on human assessors for relevance judgments - a time-intensive and expensive process. Recent studies have shown the strong capability of Large Language Models (LLMs) in producing reliable relevance judgments with human accuracy but at a greatly reduced cost. In this paper, to address the missing large-scale ad-hoc document retrieval dataset, we extend the TREC Deep Learning Track (DL) test collection via additional language model synthetic labels to enable researchers to test and evaluate their search systems at a large scale. Specifically, such a test collection includes more than 1,900 test queries from the previous years of tracks. We compare system evaluation with past human labels from past years and find that our synthetically created large-scale test collection can lead to highly correlated system rankings., Comment: 9 pages, resource paper

In this work, we investigate the impact of the possibility of a small, subsolar mass compact star, such as the recently reported central compact object of HESS J1731-347, on the equation of state (EOS) of neutron stars. We have used a hybrid approach to the nuclear EOS developed recently where the matter around nuclear saturation density is described by a parametric expansion in terms of nuclear empirical parameters and represented in an agnostic way at higher density using piecewise polytropes. We have incorporated the inputs provided by the latest neutron skin measurement experiments from PREX-II and CREX, simultaneous mass-radius measurements of pulsars PSR J0030+0451 and PSR J0740+6620, and the gravitational wave events GW170817 and GW190425. The main results of the study show the effect of HESS J1731-347 on the nuclear parameters and neutron star observables. Our analysis yields the slope of symmetry energy $L=45.71^{+38.18}_{-22.11}$ MeV, the radius of a $1.4 M_\odot$ star, $R_{1.4}=12.18^{+0.71}_{-0.88}$ km, and the maximum mass of a static star, $M_{\rm max}= 2.14^{+0.26}_{-0.17} M_\odot$ within $90\%$ confidence interval, respectively.

With ongoing advancements in nuclear theory and experimentation, together with a growing body of neutron star (NS) observations, a wealth of information on the equation of state (EOS) for matter at extreme densities has become accessible. Here, we utilize a hybrid EOS formulation that combines an empirical parameterization centered around the nuclear saturation density with a generic three-segment piecewise polytrope model at higher densities. We incorporate data derived from chiral effective field theory ($\chi$EFT), perturbative quantum chromodynamics (pQCD), and from experiments such as PREX-II and CREX. Furthermore, we examine the influence of a total of 129 NS mass measurements up to April 2023, as well as simultaneous mass and radius measurements derived from the X-ray emission from surface hot spots on NSs. Additionally, we consider constraints on tidal properties inferred from the gravitational waves emitted by coalescing NS binaries. To integrate this extensive and varied array of constraints, we utilize a hierarchical Bayesian statistical framework to simultaneously deduce the EOS and the distribution of NS masses. We find that incorporating data from $\chi$EFT significantly tightens the constraints on the EOS of NSs near or below the nuclear saturation density. However, constraints derived from pQCD computations and nuclear experiments such as PREX-II and CREX have minimal impact., Comment: 19 pages, 8 figures

Smart grids are a valuable data source to study consumer behavior and guide energy policy decisions. In particular, time-series of power consumption over geographical areas are essential in deciding the optimal placement of expensive resources (e.g., transformers, storage elements) and their activation schedules. However, publication of such data raises significant privacy issues, as it may reveal sensitive details about personal habits and lifestyles. Differential privacy (DP) is well-suited for sanitization of individual data, but current DP techniques for time series lead to significant loss in utility, due to the existence of temporal correlation between data readings. We introduce {\em STPT (Spatio-Temporal Private Timeseries)}, a novel method for DP-compliant publication of electricity consumption data that analyzes spatio-temporal attributes and captures both micro and macro patterns by leveraging RNNs. Additionally, it employs a partitioning method for releasing electricity consumption time series based on identified patterns. We demonstrate through extensive experiments, on both real-world and synthetic datasets, that STPT significantly outperforms existing benchmarks, providing a well-balanced trade-off between data utility and user privacy.

Leptoquarks (LQs) can contribute to the magnetic and electric dipole moments of charged leptons, which the current experiments have measured with good accuracy. We revisit the parameter spaces of TeV-scale vector LQs that contribute to these observables and study how these models fare against the LHC bounds. We find that only $U_1$ and $V_2$ can explain the observed positive shift in $(a_\mu^{\rm exp} - a_\mu^{\rm SM})$ through a lepton chirality-flipping contribution with $\mathcal O(1)$ LQ-quark-lepton coupling. We also see how these two LQs can fit the electron dipole moment measurements., Comment: 12 pages, 3 figures, 7 tables

Understanding the mechanical properties of metals at extreme conditions is essential for the advancement of miniaturized technologies. As dimensions decrease, materials will experience higher strain rates at the same applied velocities. Moreover, the interplay effects of strain rates and temperatures are often overlooked and could have critical effects in applications. In this study, for the first time, the rate-dependent and temperature-dependent mechanical response of nickel microparticles have been investigated. The microparticles were obtained by solid-state dewetting of nickel thin films deposited on c-sapphire. They exhibit self-similar shapes with identical sets of planes, facilitating straightforward comparison between particles. This research represents the first in-depth analysis of the mechanical properties of nickel single crystal dewetted microparticles across six orders of magnitude at room temperature and three orders of magnitude at 128 K. Molecular dynamic simulations (MD) were conducted in parallel on particles with the same faceting. In this work, the gap between experiments and simulations has been reduced to over one order of magnitude in size and 3 orders of magnitude in the strain rates. The thermal activation parameter analysis and MD simulations were employed to ascertain whether homogeneous or heterogeneous dislocation nucleation was the dominant mechanism controlling deformation in the particles., Comment: 18 pages (29 in total with supplementary information), 4 figures, 21 Supplementary figures

This work presents the design and development of IIT Bombay Racing's Formula Student style autonomous racecar algorithm capable of running at the racing events of Formula Student-AI, held in the UK. The car employs a cutting-edge sensor suite of the compute unit NVIDIA Jetson Orin AGX, 2 ZED2i stereo cameras, 1 Velodyne Puck VLP16 LiDAR and SBG Systems Ellipse N GNSS/INS IMU. It features deep learning algorithms and control systems to navigate complex tracks and execute maneuvers without any human intervention. The design process involved extensive simulations and testing to optimize the vehicle's performance and ensure its safety. The algorithms have been tested on a small scale, in-house manufactured 4-wheeled robot and on simulation software. The results obtained for testing various algorithms in perception, simultaneous localization and mapping, path planning and controls have been detailed., Comment: 8 pages, 19 figures

The LLMJudge challenge is organized as part of the LLM4Eval workshop at SIGIR 2024. Test collections are essential for evaluating information retrieval (IR) systems. The evaluation and tuning of a search system is largely based on relevance labels, which indicate whether a document is useful for a specific search and user. However, collecting relevance judgments on a large scale is costly and resource-intensive. Consequently, typical experiments rely on third-party labelers who may not always produce accurate annotations. The LLMJudge challenge aims to explore an alternative approach by using LLMs to generate relevance judgments. Recent studies have shown that LLMs can generate reliable relevance judgments for search systems. However, it remains unclear which LLMs can match the accuracy of human labelers, which prompts are most effective, how fine-tuned open-source LLMs compare to closed-source LLMs like GPT-4, whether there are biases in synthetically generated data, and if data leakage affects the quality of generated labels. This challenge will investigate these questions, and the collected data will be released as a package to support automatic relevance judgment research in information retrieval and search., Comment: LLMJudge Challenge Overview, 3 pages

The first edition of the workshop on Large Language Model for Evaluation in Information Retrieval (LLM4Eval 2024) took place in July 2024, co-located with the ACM SIGIR Conference 2024 in the USA (SIGIR 2024). The aim was to bring information retrieval researchers together around the topic of LLMs for evaluation in information retrieval that gathered attention with the advancement of large language models and generative AI. Given the novelty of the topic, the workshop was focused around multi-sided discussions, namely panels and poster sessions of the accepted proceedings papers., Comment: LLM4Eval Workshop Report

Reinforcement Learning (RL) has shown remarkable progress in simulation environments, yet its application to real-world robotic tasks remains limited due to challenges in exploration and generalisation. To address these issues, we introduce NAVINACT, a framework that chooses when the robot should use classical motion planning-based navigation and when it should learn a policy. To further improve the efficiency in exploration, we use imitation data to bootstrap the exploration. NAVINACT dynamically switches between two modes of operation: navigating to a waypoint using classical techniques when away from the objects and reinforcement learning for fine-grained manipulation control when about to interact with objects. NAVINACT consists of a multi-head architecture composed of ModeNet for mode classification, NavNet for waypoint prediction, and InteractNet for precise manipulation. By combining the strengths of RL and Imitation Learning (IL), NAVINACT improves sample efficiency and mitigates distribution shift, ensuring robust task execution. We evaluate our approach across multiple challenging simulation environments and real-world tasks, demonstrating superior performance in terms of adaptability, efficiency, and generalization compared to existing methods. In both simulated and real-world settings, NAVINACT demonstrates robust performance. In simulations, NAVINACT surpasses baseline methods by 10-15\% in training success rates at 30k samples and by 30-40\% during evaluation phases. In real-world scenarios, it demonstrates a 30-40\% higher success rate on simpler tasks compared to baselines and uniquely succeeds in complex, two-stage manipulation tasks. Datasets and supplementary materials can be found on our website: {https://raaslab.org/projects/NAVINACT/}., Comment: 16 pages, 10 figures

We study the kinetics of vapor-liquid phase separation in a quasi one-dimensional confined active matter system using molecular dynamics simulations. Activity is invoked via the Vicsek rule, while passive interaction follows the Lennard-Jones potential. With the system density near the vapor branch, the evolution morphology features disconnected liquid clusters. In the passive limit, coarsening begins with nucleation, followed by an evaporation-condensation growth mechanism, leading to a metastable state without complete phase separation. We aim to understand the impact of Vicsek-like self-propulsion on the structure and growth of these clusters. Our key finding is that Vicsek activity results in a distinct growth mechanism, notably rapid cluster growth and the breakdown of the metastable state through ballistic aggregation. Relevant growth laws are analyzed and explained using appropriate theoretical models.

Quantum Computing is making significant advancements toward creating machines capable of implementing quantum algorithms in various fields, such as quantum cryptography, quantum image processing, and optimization. The development of quantum arithmetic circuits for modulo addition is vital for implementing these quantum algorithms. While it is ideal to use quantum circuits based on fault-tolerant gates to overcome noise and decoherence errors, the current Noisy Intermediate Scale Quantum (NISQ) era quantum computers cannot handle the additional computational cost associated with fault-tolerant designs. Our research aims to minimize circuit depth, which can reduce noise and facilitate the implementation of quantum modulo addition circuits on NISQ machines. This work presents quantum carry-lookahead modulo $(2^n - 1)$ adder (QCLMA), which is designed to receive two n-bit numbers and perform their addition with an O(log n) depth. Compared to existing work of O(n) depth, our proposed QCLMA reduces the depth and helps increase the noise fidelity. In order to increase error resilience, we also focus on creating a tree structure based Carry path, unlike the chain based Carry path of the current work. We run experiments on Quantum Computer IBM Cairo to evaluate the performance of the proposed QCLMA against the existing work and define Quantum State Fidelity Ratio (QSFR) to quantify the closeness of the correct output to the top output. When compared against existing work, the proposed QCLMA achieves a 47.21% increase in QSFR for 4-qubit modulo addition showcasing its superior noise fidelity., Comment: 6 pages, 5 figures, 1 table

The "Noisy intermediate-scale quantum" NISQ machine era primarily focuses on mitigating noise, controlling errors, and executing high-fidelity operations, hence requiring shallow circuit depth and noise robustness. Approximate computing is a novel computing paradigm that produces imprecise results by relaxing the need for fully precise output for error-tolerant applications including multimedia, data mining, and image processing. We investigate how approximate computing can improve the noise resilience of quantum adder circuits in NISQ quantum computing. We propose five designs of approximate quantum adders to reduce depth while making them noise-resilient, in which three designs are with carryout, while two are without carryout. We have used novel design approaches that include approximating the Sum only from the inputs (pass-through designs) and having zero depth, as they need no quantum gates. The second design style uses a single CNOT gate to approximate the SUM with a constant depth of O(1). We performed our experimentation on IBM Qiskit on noise models including thermal, depolarizing, amplitude damping, phase damping, and bitflip: (i) Compared to exact quantum ripple carry adder without carryout the proposed approximate adders without carryout have improved fidelity ranging from 8.34% to 219.22%, and (ii) Compared to exact quantum ripple carry adder with carryout the proposed approximate adders with carryout have improved fidelity ranging from 8.23% to 371%. Further, the proposed approximate quantum adders are evaluated in terms of various error metrics., Comment: 5 pages, 6 figures, 5 tables

Zero-knowledge proofs (ZKPs) represent a revolutionary advance in computational integrity and privacy technology, enabling the secure and private exchange of information without revealing underlying private data. ZKPs have unique advantages in terms of universality and minimal security assumptions when compared to other privacy-sensitive computational methods for distributed systems, such as homomorphic encryption and secure multiparty computation. Their application spans multiple domains, from enhancing privacy in blockchain to facilitating confidential verification of computational tasks. This survey starts with a high-level overview of the technical workings of ZKPs with a focus on an increasingly relevant subset of ZKPs called zk-SNARKS. While there have been prior surveys on the algorithmic and theoretical aspects of ZKPs, our work is distinguished by providing a broader view of practical aspects and describing many recently-developed use cases of ZKPs across various domains. These application domains span blockchain privacy, scaling, storage, and interoperability, as well as non-blockchain applications like voting, authentication, timelocks, and machine learning. Aimed at both practitioners and researchers, the survey also covers foundational components and infrastructure such as zero-knowledge virtual machines (zkVM), domain-specific languages (DSLs), supporting libraries, frameworks, and protocols. We conclude with a discussion on future directions, positioning ZKPs as pivotal in the advancement of cryptographic practices and digital privacy across many applications.

The modern power grid has seen a rise in the integration of non-linear loads, presenting a significant concern for operators. These loads introduce unwanted harmonics, leading to potential issues such as overheating and improper functioning of circuit breakers. In pursuing a more sustainable grid, the adoption of electric vehicles (EVs) and photovoltaic (PV) systems in residential networks has increased. Understanding and examining the effects of high-order harmonic frequencies beyond $1.5$ kHz is crucial to understanding their impact on the operation and planning of electrical distribution systems under varying nonlinear loading conditions. This study investigates a diverse set of critical power electronic loads within a household modeled using PSCAD/EMTdc, analyzing their unique harmonic spectra. This information is utilized to run the time-series harmonic analysis program in OpenDSS on a modified IEEE 34 bus test system model. The impact of high-order harmonics is quantified using metrics that evaluate total harmonic distortion (THD), transformer harmonic-driven eddy current loss component, and propagation of harmonics from the source to the substation transformer., Comment: accepted for presentation at the 2024 IEEE Energy Conversion Conference and Expo (ECCE)

Recent Odessa disturbance events have brought attention to the challenges associated with the interaction between Inverter-Based Resources (IBRs) and the transmission and distribution system. The NERC event diagnosis report has highlighted several issues, emphasizing the need for continuous performance monitoring of these IBRs by system operators. Key areas of concern include the mismatch of control and protection performance of IBRs between the original equipment manufacturer (OEM)-provided models and field measurements. The inability to replicate the realistic response can result in incorrect reliability and resilience studies. In this paper, we developed an approach on how to emulate the behavior of an IBR using measurement data obtained for system operators to utilize in real-time and long-term planning. Two experiments are conducted in the phasor domain and electromagnetic transients (EMT) domain to emulate the behavior for grid forming and grid following inverters under various operating conditions and the effectiveness of the proposed model is demonstrated in terms of accuracy and ease of utilizing user-defined models (UDMs)., Comment: accepted for presentation at The 2024 Annual Conference of the IEEE Industrial Electronics Society (IECON)

This paper introduces a novel reinforcement learning (RL) strategy designed to facilitate rapid autonomy transfer by utilizing pre-trained critic value functions from multiple environments. Unlike traditional methods that require extensive retraining or fine-tuning, our approach integrates existing knowledge, enabling an RL agent to adapt swiftly to new settings without requiring extensive computational resources. Our contributions include development of the Multi-Critic Actor-Critic (MCAC) algorithm, establishing its convergence, and empirical evidence demonstrating its efficacy. Our experimental results show that MCAC significantly outperforms the baseline actor-critic algorithm, achieving up to 22.76x faster autonomy transfer and higher reward accumulation. This advancement underscores the potential of leveraging accumulated knowledge for efficient adaptation in RL applications.

Gravitational waves (GWs) from binary neutron stars (BNSs) are expected to be accompanied by electromagnetic (EM) emissions, which help to identify the host galaxy. Since GW events directly measure their luminosity distances, joint GW-EM observations from BNSs help to study cosmology, particularly the Hubble constant, unaffected by cosmic distance ladder systematics. However, detecting EM counterparts from BNS mergers is not always possible. Additionally, the tidal deformations of BNS components offer insights into the neutron star (NS) equation of state (EoS). In such cases, the tidal parameters of NSs, combined with the knowledge of the NS EoS, can break the degeneracy between mass parameters and redshift, allowing for the inference of the Hubble constant. Several efforts have aimed to infer the Hubble constant using the tidal parameters of BNSs, without EM counterparts, termed dark BNSs. Moreover, some studies have focused on the joint estimation of population and NS EoS for unbiased NS EoS estimation. However, none of the works consistently combined the uncertainties of population, cosmology, and NS EoS within a Bayesian framework. In this study, we propose a novel Bayesian analysis to jointly constrain the NS EoS, population, and cosmological parameters using a population of dark BNSs detected through GW observations. This method can well constrain the Hubble constant with as few as $5$ BNS observations using current-generation detectors. This level of precision is unattainable without incorporating the NS EoS, especially when observing BNS mergers without EM counterpart information., Comment: 26 pages, 14 figures, 4 tables

We explore the utility of qutrits and qubits for simulating the flavor dynamics of dense neutrino systems. The evolution of such systems impacts some important astrophysical processes, such as core-collapse supernovae and the nucleosynthesis of heavy nuclei. Many-body simulations require classical resources beyond current computing capabilities for physically relevant system sizes. Quantum computers are therefore a promising candidate to efficiently simulate the many-body dynamics of collective neutrino oscillations. Previous quantum simulation efforts have primarily focused on properties of the two-flavor approximation due to their direct mapping to qubits. Here, we present new quantum circuits for simulating three-flavor neutrino systems on qutrit- and qubit-based platforms, and demonstrate their feasibility by simulating systems of two, four and eight neutrinos on IBM and Quantinuum quantum computers., Comment: 19 pages, 4 tables, 15 figures

The majority of star formation results in binaries or higher multiple systems, and planets in such systems are constrained to a limited range of orbital parameters in order to remain stable against perturbations from stellar companions. Many planets have been discovered in such multiple systems (such as stellar binaries), and understanding their stability is important in exoplanet searches and characterization. In this chapter, we focus on the orbital stability of planets in stellar binaries. We review key results based on semi-analytical secular (long term) methods, as well as results based on N-body simulations and more recent Machine Learning methods. We discuss planets orbiting one of the stellar binary components (S-type) and those orbiting both stars (P-type) separately., Comment: Preprint of a chapter for the 'Encyclopedia of Astrophysics' (Editor-in-Chief Ilya Mandel, Section Editor Dimitri Veras) to be published by Elsevier as a Reference Module. The number of references was capped

Stability is one of the most fundamental aspects regarding planetary systems. It plays an important role in our understanding on the formation channel of the planetary systems, as well as their habitability. Many approaches have been adopted to determine the stability of these systems, including brute-force N-body simulations, semi-analytical calculations, and more recently machine learning methods. This allows significant advances in our understanding of planetary system dynamics, as well as providing tools to constrain unknown parameters of exoplanetary systems (assuming these systems are stable). In the following, we focus on planets around single star hosts, and we provide an overview of the studies of planetary system stability for compact multi-planet systems and hierarchical multi-planet systems., Comment: Preprint of a chapter for the 'Encyclopedia of Astrophysics' (Editor-in-Chief Ilya Mandel, Section Editor Dimitri Veras) to be published by Elsevier as a Reference Module. The number of references was capped

Nonintegrable many-body quantum systems typically thermalize at long times through the mechanism of quantum chaos. However, some exceptional systems, such as those harboring quantum scars, break thermalization, serving as testbeds for foundational problems of quantum statistical physics. Here, we investigate a class of nonintegrable bond-staggered models that is endowed with a large number of zero-energy eigenstates and possesses a non-Abelian internal symmetry. We use character theory to give a lower bound on the zero-energy degeneracy, which matches exact diagonalization results, and is found to grow exponentially with the system size. We also show that few-magnon zero-energy states have an exact analytical description, allowing us to build a basis of low-entangled fixed-separation states, which is stable to most perturbations found in experiments. This remarkable dynamical stability of special states elucidates our understanding of nonequilibrium processes in non-Abelian chaotic quantum models., Comment: 10 pages, 7 figures

Large-scale video conferencing services incur significant network cost while serving surging global demands. Our work systematically explores the opportunity to offload a fraction of this traffic to the Internet, a cheaper routing option offered already by cloud providers, from WAN without drop in application performance. First, with a large-scale latency measurement study with 3.5 million data points per day spanning 241K source cities and 21 data centers across the globe, we demonstrate that Internet paths perform comparable to or better than the private WAN for parts of the world (e.g., Europe and North America). Next, we present Titan, a live (12+ months) production system that carefully moves a fraction of the conferencing traffic to the Internet using the above observation. Finally, we propose Titan-Next, a research prototype that jointly assigns the conferencing server and routing option (Internet or WAN) for individual calls. With 5 weeks of production data, we show Titan-Next reduces the sum of peak bandwidth on WAN links that defines the operational network cost by up to 61% compared to state-of-the-art baselines. We will open-source parts of the measurement data.

Source Code Authorship Attribution (SCAA) is crucial for software classification because it provides insights into the origin and behavior of software. By accurately identifying the author or group behind a piece of code, experts can better understand the motivations and techniques of developers. In the cybersecurity era, this attribution helps trace the source of malicious software, identify patterns in the code that may indicate specific threat actors or groups, and ultimately enhance threat intelligence and mitigation strategies. This paper presents AuthAttLyzer-V2, a new source code feature extractor for SCAA, focusing on lexical, semantic, syntactic, and N-gram features. Our research explores author identification in C++ by examining 24,000 source code samples from 3,000 authors. Our methodology integrates Random Forest, Gradient Boosting, and XGBoost models, enhanced with SHAP for interpretability. The study demonstrates how ensemble models can effectively discern individual coding styles, offering insights into the unique attributes of code authorship. This approach is pivotal in understanding and interpreting complex patterns in authorship attribution, especially for malware classification.

Internet censorship is pervasive, with significant effort dedicated to understanding what is censored, and where. Prior censorship work however have identified significant inconsistencies in their results; experiments show unexplained non-determinism thought to be caused by censor load, end-host geographic diversity, or incomplete censorship -- inconsistencies which impede reliable, repeatable and correct understanding of global censorship. In this work we investigate the extent to which Equal-cost Multi-path (ECMP) routing is the cause for these inconsistencies, developing methods to measure and compensate for them. We find ECMP routing significantly changes observed censorship across protocols, censor mechanisms, and in 17 countries. We identify that previously observed non-determinism or regional variations are attributable to measurements between fixed end-hosts taking different routes based on Flow-ID; i.e., choice of intra-subnet source IP or ephemeral source port leads to differences in observed censorship. To achieve this we develop new route-stable censorship measurement methods that allow consistent measurement of DNS, HTTP, and HTTPS censorship. We find ECMP routing yields censorship changes across 42% of IPs and 51% of ASes, but that impact is not uniform. We identify numerous causes of the behavior, ranging from likely failed infrastructure, to routes to the same end-host taking geographically diverse paths which experience differences in censorship en-route. Finally, we explore our results in the context of prior global measurement studies, exploring first the applicability of our findings to prior observed variations, and then demonstrating how specific experiments from two studies could be impacted by, and specific results are explainable by, ECMP routing. Our work points to methods for improving future studies, reducing inconsistencies and increasing repeatability., Comment: In Proceedings of the 2024 ACM SIGSAC Conference on Computer and Communications Security (CCS 2024)

In this study, we address the challenge of obstacle avoidance for Unmanned Aerial Vehicles (UAVs) through an innovative composite imitation learning approach that combines Proximal Policy Optimization (PPO) with Behavior Cloning (BC) and Generative Adversarial Imitation Learning (GAIL), enriched by the integration of ray-tracing techniques. Our research underscores the significant role of ray-tracing in enhancing obstacle detection and avoidance capabilities. Moreover, we demonstrate the effectiveness of incorporating GAIL in coordinating the flight paths of two UAVs, showcasing improved collision avoidance capabilities. Extending our methodology, we apply our combined PPO, BC, GAIL, and ray-tracing framework to scenarios involving four UAVs, illustrating its scalability and adaptability to more complex scenarios. The findings indicate that our approach not only improves the reliability of basic PPO based obstacle avoidance but also paves the way for advanced autonomous UAV operations in crowded or dynamic environments.

The path to interpreting a language model often proceeds via analysis of circuits -- sparse computational subgraphs of the model that capture specific aspects of its behavior. Recent work has automated the task of discovering circuits. Yet, these methods have practical limitations, as they rely either on inefficient search algorithms or inaccurate approximations. In this paper, we frame automated circuit discovery as an optimization problem and propose *Edge Pruning* as an effective and scalable solution. Edge Pruning leverages gradient-based pruning techniques, but instead of removing neurons or components, it prunes the \emph{edges} between components. Our method finds circuits in GPT-2 that use less than half the number of edges compared to circuits found by previous methods while being equally faithful to the full model predictions on standard circuit-finding tasks. Edge Pruning is efficient even with as many as 100K examples, outperforming previous methods in speed and producing substantially better circuits. It also perfectly recovers the ground-truth circuits in two models compiled with Tracr. Thanks to its efficiency, we scale Edge Pruning to CodeLlama-13B, a model over 100x the scale that prior methods operate on. We use this setting for a case study comparing the mechanisms behind instruction prompting and in-context learning. We find two circuits with more than 99.96% sparsity that match the performance of the full model and reveal that the mechanisms in the two settings overlap substantially. Our case study shows that Edge Pruning is a practical and scalable tool for interpretability and sheds light on behaviors that only emerge in large models., Comment: We release our code and data publicly at https://github.com/princeton-nlp/Edge-Pruning

The remarkable performance of large language models (LLMs) in generation tasks has enabled practitioners to leverage publicly available models to power custom applications, such as chatbots and virtual assistants. However, the data used to train or fine-tune these LLMs is often undisclosed, allowing an attacker to compromise the data and inject backdoors into the models. In this paper, we develop a novel inference time defense, named CleanGen, to mitigate backdoor attacks for generation tasks in LLMs. CleanGenis a lightweight and effective decoding strategy that is compatible with the state-of-the-art (SOTA) LLMs. Our insight behind CleanGen is that compared to other LLMs, backdoored LLMs assign significantly higher probabilities to tokens representing the attacker-desired contents. These discrepancies in token probabilities enable CleanGen to identify suspicious tokens favored by the attacker and replace them with tokens generated by another LLM that is not compromised by the same attacker, thereby avoiding generation of attacker-desired content. We evaluate CleanGen against five SOTA backdoor attacks. Our results show that CleanGen achieves lower attack success rates (ASR) compared to five SOTA baseline defenses for all five backdoor attacks. Moreover, LLMs deploying CleanGen maintain helpfulness in their responses when serving benign user queries with minimal added computational overhead.

Quantum modular adders are one of the most fundamental yet versatile quantum computation operations. They help implement functions of higher complexity, such as subtraction and multiplication, which are used in applications such as quantum cryptanalysis, quantum image processing, and securing communication. To the best of our knowledge, there is no existing design of quantum modulo $(2n+1)$ adder. In this work, we propose four quantum adders targeted specifically for modulo $(2n+1)$ addition. These adders can provide both regular and modulo $(2n+1)$ sum concurrently, enhancing their application in residue number system based arithmetic. Our first design, QMA1, is a novel quantum modulo $(2n+1)$ adder. The second proposed adder, QMA2, optimizes the utilization of quantum gates within the QMA1, resulting in 37.5% reduced CNOT gate count, 46.15% reduced CNOT depth, and 26.5% decrease in both Toffoli gates and depth. We propose a third adder QMA3 that uses zero resets, a dynamic circuits based feature that reuses qubits, leading to 25% savings in qubit count. Our fourth design, QMA4, demonstrates the benefit of incorporating additional zero resets to achieve a purer zero state, reducing quantum state preparation errors. Notably, we conducted experiments using 5-qubit configurations of the proposed modulo $(2n+1)$ adders on the IBM Washington, a 127-qubit quantum computer based on the Eagle R1 architecture, to demonstrate a 28.8% reduction in QMA1's error of which: (i) 18.63% error reduction happens due to gate and depth reduction in QMA2, and (ii) 2.53% drop in error due to qubit reduction in QMA3, and (iii) 7.64% error decreased due to application of additional zero resets in QMA4., Comment: 5 Figures, 1 Table

We introduce Graph Kolmogorov-Arnold Networks (GKAN), an innovative neural network architecture that extends the principles of the recently proposed Kolmogorov-Arnold Networks (KAN) to graph-structured data. By adopting the unique characteristics of KANs, notably the use of learnable univariate functions instead of fixed linear weights, we develop a powerful model for graph-based learning tasks. Unlike traditional Graph Convolutional Networks (GCNs) that rely on a fixed convolutional architecture, GKANs implement learnable spline-based functions between layers, transforming the way information is processed across the graph structure. We present two different ways to incorporate KAN layers into GKAN: architecture 1 -- where the learnable functions are applied to input features after aggregation and architecture 2 -- where the learnable functions are applied to input features before aggregation. We evaluate GKAN empirically using a semi-supervised graph learning task on a real-world dataset (Cora). We find that architecture generally performs better. We find that GKANs achieve higher accuracy in semi-supervised learning tasks on graphs compared to the traditional GCN model. For example, when considering 100 features, GCN provides an accuracy of 53.5 while a GKAN with a comparable number of parameters gives an accuracy of 61.76; with 200 features, GCN provides an accuracy of 61.24 while a GKAN with a comparable number of parameters gives an accuracy of 67.66. We also present results on the impact of various parameters such as the number of hidden nodes, grid-size, and the polynomial-degree of the spline on the performance of GKAN.

Quantum Arithmetic faces limitations such as noise and resource constraints in the current Noisy Intermediate Scale Quantum (NISQ) era quantum computers. We propose using Distributed Quantum Computing (DQC) to overcome these limitations by substituting a higher depth quantum addition circuit with Residue Number System (RNS) based quantum modulo adders. The RNS-based distributed quantum addition circuits possess lower depth and are distributed across multiple quantum computers/jobs, resulting in higher noise resilience. We propose the Quantum Superior Modulo Addition based on RNS Tool (QSMART), which can generate RNS sets of quantum adders based on multiple factors such as depth, range, and efficiency. We also propose a novel design of Quantum Diminished-1 Modulo (2n + 1) Adder (QDMA), which forms a crucial part of RNS-based distributed quantum addition and the QSMART tool. We demonstrate the higher noise resilience of the Residue Number System (RNS) based distributed quantum addition by conducting simulations modeling Quantinuum's H1 ion trap-based quantum computer. Our simulations demonstrate that RNS-based distributed quantum addition has 11.36% to 133.15% higher output probability over 6-bit to 10-bit non-distributed quantum full adders, indicating higher noise fidelity. Furthermore, we present a scalable way of achieving distributed quantum addition higher than limited otherwise by the 20-qubit range of Quantinuum H1., Comment: 6 pages, 5 figures, 2 tables

A nodal surface semimetal (NSSM) features symmetry enforced band crossings along a surface within the three-dimensional (3D) Brillouin zone. The topological robustness of the same does not always come with nonzero Berry fluxes around nodal surfaces. Irrespective of that, however, light irradiation on such system can result in interesting dynamic behavior. We find that depending on the state of polarization, one can obtain additional Weyl points/ nodal surfaces in the Floquet Hamiltonian that the time periodic system gives rise to. For simplicity we only consider two band spinless models without any spin orbit coupling, the main emphasis being to understand the low energy behavior close to the band crossings and its evolution in a Floquet system in the high frequency limit. There we find the nodal surfaces to perish or get duplicated or triplicated for different polarization scenario or different NSSM Hamiltonians. One findings open up important directions on what out of equilibrium NSSM systems has to offer in many active fields including quantum computations., Comment: Initial draft

We describe a compact ultra-high vacuum (UHV) scanning tunneling microscope (STM) system that does not need any external supply of cooling liquids. It achieves temperatures down to 1.5 K and a z-noise down to 300 fmRMS for the frequency range of 0.1 Hz - 5 kHz (feedback loop off). It employs a pulse tube cryocooler (PTC) and a Joule-Thomson (JT) stage inducing only small temperature oscillations at the STM with amplitude below 1 mK. The challenge to combine an effective vibrational decoupling from the PTC with sufficient thermal conduction is tackled by a multipartite approach. We realize a minimal stiffness of the UHV bellows that connect the PTC and the STM chamber. Fine Copper wires mechanically decouple the PTC stages from cooling plates that carry the thermal shields, the JT stage and the STM. Soft springs decouple the STM from the JT stage. Finally, the STM body has an optimized conical shape and is made of the light and stiff material Shapal Hi MSoft such that a strong reduction of low frequency vibrations results for the tunnel junction. The voltage noise in the tunnel junction is 0.12 mV and an RF antenna close to the tunnel junction provides radio frequency excitations up to 40 GHz with amplitudes up to 10 mV., Comment: 12 pages, 8 figures