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In blockchain systems, fair transaction ordering is crucial for a trusted and regulation-compliant economic ecosystem. Unlike traditional State Machine Replication (SMR) systems, which focus solely on liveness and safety, blockchain systems also require a fairness property. This paper examines these properties and aims to eliminate algorithmic bias in transaction ordering services. We build on the notion of equal opportunity. We characterize transactions in terms of relevant and irrelevant features, requiring that the order be determined solely by the relevant ones. Specifically, transactions with identical relevant features should have an equal chance of being ordered before one another. We extend this framework to define a property where the greater the distance in relevant features between transactions, the higher the probability of prioritizing one over the other. We reveal a surprising link between equal opportunity in SMR and Differential Privacy (DP), showing that any DP mechanism can be used to ensure fairness in SMR. This connection not only enhances our understanding of the interplay between privacy and fairness in distributed computing but also opens up new opportunities for designing fair distributed protocols using well-established DP techniques.

This work presents an approach to synthesize a Lyapunov-like function to ensure incrementally input-to-state stability ($\delta$-ISS) property for an unknown discrete-time system. To deal with challenges posed by unknown system dynamics, we parameterize the Lyapunov-like function as a neural network, which we train using the data samples collected from the unknown system along with appropriately designed loss functions. We propose a validity condition to test the obtained function and incorporate it into the training framework to ensure provable correctness at the end of the training. Finally, the usefulness of the proposed technique is proved using two case studies: a scalar non-linear dynamical system and a permanent magnet DC motor.

The Anderson localization (AL) and the non-Hermitian skin effect (NHSE) are two distinct wavefunction-localization phenomena arising out of disorder and non-reciprocity, respectively. An integration of both in a single framework will provide a platform to study the interplay between the two. In this connection, we consider the one-dimensional Aubry-Andr\'e (AA) model, which has garnered significant attention among the disordered models due to its self-dual properties. Hence, we investigate a non-reciprocal AA model with complex phase modulation and implement it in suitably designed topolectrical circuits featuring an interface, segregating two non-equivalent circuit networks. In the circuit, the voltage profile localizes at the interface due to the NHSE, while the AL limits localization phenomena within the vicinity of the excitation node. This competing phenomenon leads to a controllable, node-dependent localization or even partial delocalization of the output voltage. Our results not only provide a practical platform for experimentally studying and controlling the wave localization phenomenon but also highlight the potential of circuit architectures in designing highly sensitive sensors and information transfer in communication devices., Comment: 11 pages, (5+2) figures

The superheated emulsion detector consisting of the droplets of tetra-fluoroethane (C2HC$_2$H$_2$F$_4$2F4) has been fabricated at the laboratory and installed at the 555m deep underground laboratory, JUSL during July to Dec 2022. The 500ml detector ran for an effective period of 48.6 days at a threshold of 5.87 keV with an exposure of 2.47 kg-days. The acoustic signals produced due to the bubble nucleation were collected by the acoustic sensor and FPGAbased data acquisition system. The data shows a minimum sensitivity of SI-nucleon for carbon at WIMP mass of 22.81 GeV/c$^2$ and SD (p) for fluorine at 30.67 GeV/c$^2$. The threshold of WIMP mass is 5.16 GeV/c$^2$ for F and 4.44 GeV/c$^2$ for C at the operating threshold of 5.87 keV. The first result of the dark matter direct search experiment named InDEx with tetra-fluoro-ethane active liquid from JUSL underground laboratory is reported in this article.

Continuous gravitational waves (CWs) emission from neutron stars carries information about their internal structure and equation of state, and it can provide tests of General Relativity. We present a search for CWs from a set of 45 known pulsars in the first part of the fourth LIGO--Virgo--KAGRA observing run, known as O4a. We conducted a targeted search for each pulsar using three independent analysis methods considering the single-harmonic and the dual-harmonic emission models. We find no evidence of a CW signal in O4a data for both models and set upper limits on the signal amplitude and on the ellipticity, which quantifies the asymmetry in the neutron star mass distribution. For the single-harmonic emission model, 29 targets have the upper limit on the amplitude below the theoretical spin-down limit. The lowest upper limit on the amplitude is $6.4\!\times\!10^{-27}$ for the young energetic pulsar J0537-6910, while the lowest constraint on the ellipticity is $8.8\!\times\!10^{-9}$ for the bright nearby millisecond pulsar J0437-4715. Additionally, for a subset of 16 targets we performed a narrowband search that is more robust regarding the emission model, with no evidence of a signal. We also found no evidence of non-standard polarizations as predicted by the Brans-Dicke theory., Comment: main paper: 12 pages, 6 figures, 4 tables

In Reinforcement Learning (RL) theory, we impose restrictive assumptions to design an algorithm with provably sublinear regret. Common assumptions, like linear or RKHS models, and Gaussian or log-concave posteriors over the models, do not explain practical success of RL across a wider range of distributions and models. Thus, we study how to design RL algorithms with sublinear regret for isoperimetric distributions, specifically the ones satisfying the Log-Sobolev Inequality (LSI). LSI distributions include the standard setups of RL and others, such as many non-log-concave and perturbed distributions. First, we show that the Posterior Sampling-based RL (PSRL) yields sublinear regret if the data distributions satisfy LSI under some mild additional assumptions. Also, when we cannot compute or sample from an exact posterior, we propose a Langevin sampling-based algorithm design: LaPSRL. We show that LaPSRL achieves order optimal regret and subquadratic complexity per episode. Finally, we deploy LaPSRL with a Langevin sampler -- SARAH-LD, and test it for different bandit and MDP environments. Experimental results validate the generality of LaPSRL across environments and its competitive performance with respect to the baselines.

We show how Carrollian symmetries become important in the construction of one-dimensional fermionic systems with all flat-band spectra from first principles. The key ingredient of this construction is the identification of Compact Localised States (CLSs), which appear naturally by demanding $\textit{supertranslation}$ invariance of the system. We use CLS basis states, with inherent $\textit{ultra-local}$ correlations, to write down an interacting theory which shows a non-trivial phase structure and an emergent Carroll conformal symmetry at the gapless points. We analyze this theory in detail for both zero and finite chemical potential.

The Gromov-Wasserstein (GW) distance is an effective measure of alignment between distributions supported on distinct ambient spaces. Calculating essentially the mutual departure from isometry, it has found vast usage in domain translation and network analysis. It has long been shown to be vulnerable to contamination in the underlying measures. All efforts to introduce robustness in GW have been inspired by similar techniques in optimal transport (OT), which predominantly advocate partial mass transport or unbalancing. In contrast, the cross-domain alignment problem being fundamentally different from OT, demands specific solutions to tackle diverse applications and contamination regimes. Deriving from robust statistics, we discuss three contextually novel techniques to robustify GW and its variants. For each method, we explore metric properties and robustness guarantees along with their co-dependencies and individual relations with the GW distance. For a comprehensive view, we empirically validate their superior resilience to contamination under real machine learning tasks against state-of-the-art methods.

We show that heterogeneity in self-propulsion speed leads to the emergence of effective short-range repulsion among active particles coupled via strong attractive potentials. Taking the example of two harmonically coupled active Brownian particles, we analytically compute the stationary distribution of the distance between them in the strong coupling regime, i.e., where the coupling strength is much larger than the rotational diffusivity of the particles. The effective repulsion in this regime is manifest in the emergence of a minimum distance between the particles, proportional to the difference in their self-propulsion speeds. Physically, this distance of the closest approach is associated to the orientations of the particles being parallel to each other. We show that the physical scenario remains qualitatively similar for any long-range coupling potential, which is attractive everywhere. Moreover, we show that, for a collection of $N$ particles interacting via pairwise attractive potentials, a short-range repulsion emerges for each pair of particles with different self-propulsion speeds. Finally, we show that our results are robust and hold irrespective of the specific active dynamics of the particles.

As quantum computing rapidly advances, its near-term applications are becoming increasingly evident. However, the high cost and under-utilization of quantum resources are prompting a shift from single-user to multi-user access models. In a multi-tenant environment, where multiple users share one quantum computer, protecting user confidentiality becomes crucial. The varied uses of quantum computers increase the risk that sensitive data encoded by one user could be compromised by others, rendering the protection of data integrity and confidentiality essential. In the evolving quantum computing landscape, it is imperative to study these security challenges within the scope of realistic threat model assumptions, wherein an adversarial user can mount practical attacks without relying on any heightened privileges afforded by physical access to a quantum computer or rogue cloud services. In this paper, we demonstrate the potential of crosstalk as an attack vector for the first time on a Noisy Intermediate Scale Quantum (NISQ) machine, that an adversarial user can exploit within a multi-tenant quantum computing model. The proposed side-channel attack is conducted with minimal and realistic adversarial privileges, with the overarching aim of uncovering the quantum algorithm being executed by a victim. Crosstalk signatures are used to estimate the presence of CNOT gates in the victim circuit, and subsequently, this information is encoded and classified by a graph-based learning model to identify the victim quantum algorithm. When evaluated on up to 336 benchmark circuits, our attack framework is found to be able to unveil the victim's quantum algorithm with up to 85.7\% accuracy.

This paper introduces a novel approach to create a high-resolution "map" for physics learning: an "atomic" learning objectives (LOs) system designed to capture detailed cognitive processes and concepts required for problem solving in a college-level introductory physics course. Our method leverages Large Language Models (LLMs) for automated labeling of physics questions and introduces a comprehensive set of metrics to evaluate the quality of the labeling outcomes. The atomic LO system, covering nine chapters of an introductory physics course, uses a "subject-verb-object'' structure to represent specific cognitive processes. We apply this system to 131 questions from expert-curated question banks and the OpenStax University Physics textbook. Each question is labeled with 1-8 atomic LOs across three chapters. Through extensive experiments using various prompting strategies and LLMs, we compare automated LOs labeling results against human expert labeling. Our analysis reveals both the strengths and limitations of LLMs, providing insight into LLMs reasoning processes for labeling LOs and identifying areas for improvement in LOs system design. Our work contributes to the field of learning analytics by proposing a more granular approach to mapping learning objectives with questions. Our findings have significant implications for the development of intelligent tutoring systems and personalized learning pathways in STEM education, paving the way for more effective "learning GPS'' systems., Comment: The paper is accepted to LAK'2025

Enhancing the external validity of trial results is essential for their applicability to real-world populations. However, violations of the positivity assumption can limit both the generalizability and transportability of findings. To address positivity violations in estimating the average treatment effect for a target population, we propose a framework that integrates characterizing the underrepresented group and performing sensitivity analysis for inference in the original target population. Our approach helps identify limitations in trial sampling and improves the robustness of trial findings for real-world populations. We apply this approach to extend findings from phase IV trials of treatments for opioid use disorder to a real-world population based on the 2021 Treatment Episode Data Set.

We tackle the ill-posed inverse rendering problem in 3D reconstruction with a Neural Radiance Field (NeRF) approach informed by Physics-Based Rendering (PBR) theory, named PBR-NeRF. Our method addresses a key limitation in most NeRF and 3D Gaussian Splatting approaches: they estimate view-dependent appearance without modeling scene materials and illumination. To address this limitation, we present an inverse rendering (IR) model capable of jointly estimating scene geometry, materials, and illumination. Our model builds upon recent NeRF-based IR approaches, but crucially introduces two novel physics-based priors that better constrain the IR estimation. Our priors are rigorously formulated as intuitive loss terms and achieve state-of-the-art material estimation without compromising novel view synthesis quality. Our method is easily adaptable to other inverse rendering and 3D reconstruction frameworks that require material estimation. We demonstrate the importance of extending current neural rendering approaches to fully model scene properties beyond geometry and view-dependent appearance. Code is publicly available at https://github.com/s3anwu/pbrnerf, Comment: 16 pages, 7 figures. Code is publicly available at https://github.com/s3anwu/pbrnerf

Quadrature Hybrid Couplers (QHDC) are critical components in RF, mm-wave, and sub-mm wave astronomical instrumentation, where wideband performance with minimal passband ripple is essential. Traditional designs have been limited to 5-sections at most due to computational limitations. In this work, we introduce a new analytical technique to design couplers with larger sections and improved performance. We do this by employing a Markov Chain Monte Carlo (MCMC) based solver. By defining a likelihood function based on S-parameter equations and incorporating physical priors, we derive optimized impedance values that enhance bandwidth beyond what is reported in the literature. Our flexible pipeline allows efficient tuning of the coupler design. The results demonstrate fractional bandwidths that reach 1.0 for a 9-section coupler, substantially outperforming previous designs. Statistical analysis and convergence tests confirm the robustness of our approach., Comment: 9 pages, 13 figures

We study evaporation and precipitate formation mechanics of bacteria-laden liquid bridge using experimental and theoretical analysis. Aqueous suspension of motile and non-motile Salmonella Typhimurium and Pseudomonas aeruginosa typically found in contaminated food and water were used in liquid bridge configuration between hydrophilic substrates. Using inverse logarithmic evaporation flux model, we study volume regression for cylindrical/catenoid volume models with confinement distance as a parameter. For all confinement distances, the regression is linear on normalizing both volume and time as in the case of pure sessile drop. However, in normalized volume and dimensional time space, we observe non-linearities as the evaporation time scales non linearly with the confinement distance. The non-linearities were captured using the catenoid model. The catenoid model conforms to the experimental volume regression data at all confinement distances, and the transient liquid bridge interface evolution profile at high confinement distance. We also study the precipitate pattern and bacterial distribution using micro/nano characterization techniques. We show the average precipitate pattern for both sessile and higher confinement distance resembles coffee ring type deposits although the underlying bacterial distribution differs. For lower confinement, we observe pattern resulting from a combination of coffee ring effect, stick-slick motion, and thin film instability. The reduction in confinement distance causes an altered bacterial agglomeration, resulting in a multi-pattern network instead of a single circumferential edge deposition. We show the aerial size of motile bacteria increases with decreasing confinement, whereas the size for non-motile bacteria remains constant in the precipitate.

We investigate the problem of selecting features for datasets that can be naturally partitioned into subgroups (e.g., according to socio-demographic groups and age), each with its own dominant set of features. Within this subgroup-oriented framework, we address the challenge of systematic missing data, a scenario in which some feature values are missing for all tuples of a subgroup, due to flawed data integration, regulatory constraints, or privacy concerns. Feature selection is governed by finding mutual Information, a popular quantification of correlation, between features and a target variable. Our goal is to identify top-K feature subsets of some fixed size with the highest joint mutual information with a target variable. In the presence of systematic missing data, the closed form of mutual information could not simply be applied. We argue that in such a setting, leveraging relationships between available feature mutual information within a subgroup or across subgroups can assist inferring missing mutual information values. We propose a generalizable model based on heterogeneous graph neural network to identify interdependencies between feature-subgroup-target variable connections by modeling it as a multiplex graph, and employing information propagation between its nodes. We address two distinct scalability challenges related to training and propose principled solutions to tackle them. Through an extensive empirical evaluation, we demonstrate the efficacy of the proposed solutions both qualitatively and running time wise.

This review article provides an overview of random matrix theory (RMT) with a focus on its growing impact on the formulation and inference of statistical models and methodologies. Emphasizing applications within high-dimensional statistics, we explore key theoretical results from RMT and their role in addressing challenges associated with high-dimensional data. The discussion highlights how advances in RMT have significantly influenced the development of statistical methods, particularly in areas such as covariance matrix inference, principal component analysis (PCA), signal processing, and changepoint detection, demonstrating the close interplay between theory and practice in modern high-dimensional statistical inference., Comment: 56 pages, 7 figures

Asteroseismic inferences of main-sequence solar-like oscillators often rely on best-fit models. However, these models cannot fully reproduce the observed mode frequencies, suggesting that the internal structure of the model does not fully match that of the star. Asteroseismic structure inversions provide a way to test the interior of our stellar models. Recently, structure inversion techniques were used to study 12 stars with radiative cores. In this work, we extend that analysis to 43 main-sequence stars with convective cores observed by Kepler to look for differences in the sound speed profiles in the inner 30% of the star by radius. For around half of our stars, the structure inversions show that our models reproduce the internal structure of the star, where the inversions are sensitive, within the observational uncertainties. For the stars where our inversions reveal significant differences, we find cases where our model sound speed is too high and cases where our model sound speed is too low. We use the star with the most significant differences to explore several changes to the physics of our model in an attempt to resolve the inferred differences. These changes include using a different overshoot prescription and including the effects of diffusion, gravitational settling, and radiative levitation. We find that the resulting changes to the model structure are too small to resolve the differences shown in our inversions., Comment: 18 pages, 8 figures, Resubmitted to ApJ after favorable referee report

We analyzed the 7.92$\times 10^{11}$ cosmic-ray-induced muon events collected by the IceCube Neutrino Observatory from May 13, 2011, when the fully constructed experiment started to take data, to May 12, 2023. This dataset provides an up-to-date cosmic-ray arrival direction distribution in the Southern Hemisphere with unprecedented statistical accuracy covering more than a full period length of a solar cycle. Improvements in Monte Carlo event simulation and better handling of year-to-year differences in data processing significantly reduce systematic uncertainties below the level of statistical fluctuations compared to the previously published results. We confirm the observation of a change in the angular structure of the cosmic-ray anisotropy between 10 TeV and 1 PeV, more specifically in the 100-300 TeV energy range.

We continue the study of the geometry of infinite geodesics in first passage percolation (FPP) on Gromov-hyperbolic groups G, initiated by Benjamini-Tessera and developed further by the authors. It was shown earlier by the authors that, given any fixed direction $\xi\in \partial G$, and under mild conditions on the passage time distribution, there exists almost surely a unique semi-infinite FPP geodesic from each $v\in G$ to $\xi$. Also, these geodesics coalesce to form a tree. Our main topic of study is the set of (random) exceptional directions for which uniqueness or coalescence fails. We study these directions in the context of two random geodesics trees: one formed by the union of all geodesics starting at a given base point, and the other formed by the union of all semi-infinite geodesics in a given direction $\xi\in \partial G$. We show that, under mild conditions, the set of exceptional directions almost surely has a strictly smaller Hausdorff dimension than the boundary, and hence has measure zero with respect to the Patterson-Sullivan measure. We also establish an upper bound on the maximum number of disjoint geodesics in the same direction. For groups that are not virtually free, we show that almost surely exceptional directions exist and are dense in $\partial G$. When the topological dimension of $\partial G$ is greater than one, we establish the existence of uncountably many exceptional directions. When the topological dimension of $\partial G$ is $n$, we prove the existence of directions $\xi$ with at least $(n+1)$ disjoint geodesics. Our results hinge on deep facts about hyperbolic groups. En route, we also establish facts about the structure of random bigeodesics that substantially strengthen prior results., Comment: v2: 43 pages, 2 figures. References added. Section 5.2 added

We present numerous aspects of the evolution of the LP 890-9 (SPECULOOS-2/TOI-4306) planetary system, focusing on the likelihood that planet c can support life. We find that the host star reaches the main sequence in 1 Gyr and that planet c lies close to the inner boundary of the habitable zone. We find the magma ocean stage can last up to 50 Myr, remove 8 Earth-oceans of water, and leave up to 2000 bars of oxygen in the atmosphere. However, if the planet forms with a hydrogen envelope as small as 0.1 Earth-masses, no water will be lost during the star's pre-main sequence phase from thermal escape processes. We find that the planets are unlikely to be in a 3:1 mean motion resonance and that both planets tidally circularize within 0.5 Gyr when tidal dissipation is held constant. However, if tidal dissipation is a function of mantle temperature and rheology, then we find that planet c's orbit may require more than 7 Gyr to circularize, during which time tidal heating may reach hundreds of terawatts. We thus conclude that the habitability of planet c depends most strongly on the initial volatile content and internal properties, but no data yet preclude the viability of an active biosphere on the planet., Comment: 16 pages, 9 figures, accepted to PSJ

We study the preference-based pure exploration problem for bandits with vector-valued rewards. The rewards are ordered using a (given) preference cone $\mathcal{C}$ and our the goal is to identify the set of Pareto optimal arms. First, to quantify the impact of preferences, we derive a novel lower bound on the sample complexity for identifying the most preferred policy with confidence level $1-\delta$. Our lower bound elicits the role played by the geometry of the preference cone and punctuates the difference in hardness compared to existing best-arm identification variants of the problem. We further explicate this geometry when rewards follow Gaussian distributions. We then provide a convex relaxation of the lower bound. and leverage it to design Preference-based Track and Stop (PreTS) algorithm that identifies the most preferred policy. Finally, we show that sample complexity of PreTS is asymptotically tight by deriving a new concentration inequality for vector-valued rewards.

We focus on the problem of Gallbladder Cancer (GBC) detection from Ultrasound (US) images. The problem presents unique challenges to modern Deep Neural Network (DNN) techniques due to low image quality arising from noise, textures, and viewpoint variations. Tackling such challenges would necessitate precise localization performance by the DNN to identify the discerning features for the downstream malignancy prediction. While several techniques have been proposed in the recent years for the problem, all of these methods employ complex custom architectures. Inspired by the success of foundational models for natural image tasks, along with the use of adapters to fine-tune such models for the custom tasks, we investigate the merit of one such design, ViT-Adapter, for the GBC detection problem. We observe that ViT-Adapter relies predominantly on a primitive CNN-based spatial prior module to inject the localization information via cross-attention, which is inefficient for our problem due to the small pathology sizes, and variability in their appearances due to non-regular structure of the malignancy. In response, we propose, LQ-Adapter, a modified Adapter design for ViT, which improves localization information by leveraging learnable content queries over the basic spatial prior module. Our method surpasses existing approaches, enhancing the mean IoU (mIoU) scores by 5.4%, 5.8%, and 2.7% over ViT-Adapters, DINO, and FocalNet-DINO, respectively on the US image-based GBC detection dataset, and establishing a new state-of-the-art (SOTA). Additionally, we validate the applicability and effectiveness of LQ-Adapter on the Kvasir-Seg dataset for polyp detection from colonoscopy images. Superior performance of our design on this problem as well showcases its capability to handle diverse medical imaging tasks across different datasets. Code is released at https://github.com/ChetanMadan/LQ-Adapter, Comment: Accepted at WACV 2025

A lot of claims are made in social media posts, which may contain misinformation or fake news. Hence, it is crucial to identify claims as a first step towards claim verification. Given the huge number of social media posts, the task of identifying claims needs to be automated. This competition deals with the task of 'Claim Span Identification' in which, given a text, parts / spans that correspond to claims are to be identified. This task is more challenging than the traditional binary classification of text into claim or not-claim, and requires state-of-the-art methods in Pattern Recognition, Natural Language Processing and Machine Learning. For this competition, we used a newly developed dataset called HECSI containing about 8K posts in English and about 8K posts in Hindi with claim-spans marked by human annotators. This paper gives an overview of the competition, and the solutions developed by the participating teams., Comment: To appear at ICPR 2024

We study the dynamics of a single inertial run-and-tumble particle on a straight line. The motion of this particle is characterized by two intrinsic time-scales, namely, an inertial and an active time-scale. We show that interplay of these two time-scales leads to the emergence of four distinct regimes, characterized by different dynamical behaviour of mean-squared displacement and survival probability. We analytically compute the position distributions in these regimes when the two time-scales are well separated. We show that in the large-time limit, the distribution has a large deviation form and compute the corresponding large deviation function analytically. We also find the persistence exponents in the different regimes theoretically. All our results are supported with numerical simulations., Comment: 14 pages, 12 figures; suggestions will be appreciated

We present a detailed analysis of an extragalactic slow classical nova in M31 exhibiting multiple peaks in its light curve. Spectroscopic and photometric observations were used to investigate the underlying physical processes. Shock-induced heating events resulting in the expansion and contraction of the photosphere are likely responsible for the observed multiple peaks. Deviation of the observed spectrum at the peak from the models also suggests the presence of shocks. The successive peaks occurring at increasing intervals could be due to the series of internal shocks generated near or within the photosphere. Spectral modeling suggests a low-mass white dwarf accreting slowly from a companion star. The ejecta mass, estimated from spectral analysis, is $\sim 10^{-4}\mathrm{M_{\odot}}$, which is typical for a slow nova. We estimate the binary, by comparing the archival HST data and eruption properties with stellar and novae models, to comprise a 0.65 $\mathrm{M_{\odot}}$ primary white dwarf and a K III cool evolved secondary star., Comment: 16 pages, 5 figures, 2 tables. Accepted in ApJ

Analog In-memory Computing (IMC) has demonstrated energy-efficient and low latency implementation of convolution and fully-connected layers in deep neural networks (DNN) by using physics for computing in parallel resistive memory arrays. However, recurrent neural networks (RNN) that are widely used for speech-recognition and natural language processing have tasted limited success with this approach. This can be attributed to the significant time and energy penalties incurred in implementing nonlinear activation functions that are abundant in such models. In this work, we experimentally demonstrate the implementation of a non-linear activation function integrated with a ramp analog-to-digital conversion (ADC) at the periphery of the memory to improve in-memory implementation of RNNs. Our approach uses an extra column of memristors to produce an appropriately pre-distorted ramp voltage such that the comparator output directly approximates the desired nonlinear function. We experimentally demonstrate programming different nonlinear functions using a memristive array and simulate its incorporation in RNNs to solve keyword spotting and language modelling tasks. Compared to other approaches, we demonstrate manifold increase in area-efficiency, energy-efficiency and throughput due to the in-memory, programmable ramp generator that removes digital processing overhead.

The reliability of clinical trial outcomes is crucial, especially in guiding medical decisions. In this paper, we introduce the Fragility Index (FI) for time-to-event endpoints in single-arm clinical trials - a novel metric designed to quantify the robustness of study conclusions. The FI represents the smallest number of censored observations that, when reclassified as uncensored events, causes the posterior probability of the median survival time exceeding a specified threshold to fall below a predefined confidence level. While drug effectiveness is typically assessed by determining whether the posterior probability exceeds a specified confidence level, the FI offers a complementary measure, indicating how robust these conclusions are to potential shifts in the data. Using a Bayesian approach, we develop a practical framework for computing the FI based on the exponential survival model. To facilitate the application of our method, we developed an R package fi, which provides a tool to compute the Fragility Index. Through real world case studies involving time to event data from single arms clinical trials, we demonstrate the utility of this index. Our findings highlight how the FI can be a valuable tool for assessing the robustness of survival analyses in single-arm studies, aiding researchers and clinicians in making more informed decisions., Comment: 17 pages, 3 figures

Many ternary mixtures composed of saturated and unsaturated lipids with cholesterol (Chol) exhibit a region of coexistence between liquid-disordered $(L_d)$ and liquid-ordered $(L_o)$ domains, bearing some similarities to lipid rafts in biological membranes. However, biological rafts also contain many proteins that interact with the lipids and modify the distribution of lipids. Here, we extend a previously published lattice model of ternary DPPC/DOPC/Chol mixtures by introducing a small amount of small proteins (peptides). We use Monte Carlo simulations to explore the mixing phase behavior of the components as a function of the interaction parameter representing the affinity between the proteins and the saturated DPPC chains, and for different mixture compositions. At moderate fractions of DPPC, the system is in a two-phase $L_d+L_o$ coexistence, and the proteins exhibit a simple partition behavior between the phases that depends on the protein-lipid affinity parameter. At low DPPC compositions, the mixture is in $L_d$ phase with local nanoscopic ordered domains. Addition of proteins with sufficiently strong attraction to the saturated lipids can induce the separation of a distinct $L_o$ large domain with tightly-packed gel-like clusters of proteins and saturated lipids. Consistent with the theory of phase transitions, we observe that the domain sizes grow when the mixture composition is in the vicinity of the critical point. Our simulations show that the addition of a small amount of proteins to such mixtures can cause their size to grow even further, and lead to the formation of metastable dynamic $L_o$ domains with sizes comparable to biological rafts., Comment: 12 pages, 8 figures. Accepted for publication in J. Chem. Phys

We study a generalized Kardar-Parisi-Zhang (KPZ) equation [D. Jana et al, Phys. Rev. E 109, L032104 (2024)], that sets the paradigm for universality in roughening of growing nonequilibrium surfaces without any conservation laws, but with competing local and nonlocal nonlinear effects. We show that such a generalized KPZ equation in two dimensions can describe a strong coupling rough or a crumpled surface, in addition to a weak coupling phase. The conformation fluctuations of such a rough surface are given by nonuniversal exponents, with orientational long-ranged order and positional short-ranged order, whereas the crumpled phase has positional and orientational short range order. Experimental and theoretical implications of these results are discussed.

We prove that for every odd $q\geq 3$, any $q$-query binary, possibly non-linear locally decodable code ($q$-LDC) $E:\{\pm1\}^k \rightarrow \{\pm1\}^n$ must satisfy $k \leq \tilde{O}(n^{1-2/q})$. For even $q$, this bound was established in a sequence of prior works. For $q=3$, the above bound was achieved in a recent work of Alrabiah, Guruswami, Kothari and Manohar using an argument that crucially exploits known exponential lower bounds for $2$-LDCs. Their strategy hits an inherent bottleneck for $q \geq 5$. Our key insight is identifying a general sufficient condition on the hypergraph of local decoding sets called $t$-approximate strong regularity. This condition demands that 1) the number of hyperedges containing any given subset of vertices of size $t$ (i.e., its co-degree) be equal to the same but arbitrary value $d_t$ up to a multiplicative constant slack, and 2) all other co-degrees be upper-bounded relative to $d_t$. This condition significantly generalizes related proposals in prior works that demand absolute upper bounds on all co-degrees. We give an argument based on spectral bounds on Kikuchi Matrices that lower bounds the blocklength of any LDC whose local decoding sets satisfy $t$-approximate strong regularity for any $t \leq q$. Crucially, unlike prior works, our argument works despite having no non-trivial absolute upper bound on the co-degrees of any set of vertices. To apply our argument to arbitrary $q$-LDCs, we give a new, greedy, approximate strong regularity decomposition that shows that arbitrary, dense enough hypergraphs can be partitioned (up to a small error) into approximately strongly regular pieces satisfying the required relative bounds on the co-degrees.

The paper considers the controller synthesis problem for general MIMO systems with unknown dynamics, aiming to fulfill the temporal reach-avoid-stay task, where the unsafe regions are time-dependent, and the target must be reached within a specified time frame. The primary aim of the paper is to construct the spatiotemporal tube (STT) using a sampling-based approach and thereby devise a closed-form approximation-free control strategy to ensure that system trajectory reaches the target set while avoiding time-dependent unsafe sets. The proposed scheme utilizes a novel method involving STTs to provide controllers that guarantee both system safety and reachability. In our sampling-based framework, we translate the requirements of STTs into a Robust optimization program (ROP). To address the infeasibility of ROP caused by infinite constraints, we utilize the sampling-based Scenario optimization program (SOP). Subsequently, we solve the SOP to generate the tube and closed-form controller for an unknown system, ensuring the temporal reach-avoid-stay specification. Finally, the effectiveness of the proposed approach is demonstrated through three case studies: an omnidirectional robot, a SCARA manipulator, and a magnetic levitation system.

Large Language Models (LLMs) have revolutionized natural language processing (NLP), excelling in tasks like text generation and summarization. However, their increasing adoption in mission-critical applications raises concerns about hardware-based threats, particularly bit-flip attacks (BFAs). BFAs, enabled by fault injection methods such as Rowhammer, target model parameters in memory, compromising both integrity and performance. Identifying critical parameters for BFAs in the vast parameter space of LLMs poses significant challenges. While prior research suggests transformer-based architectures are inherently more robust to BFAs compared to traditional deep neural networks, we challenge this assumption. For the first time, we demonstrate that as few as three bit-flips can cause catastrophic performance degradation in an LLM with billions of parameters. Current BFA techniques are inadequate for exploiting this vulnerability due to the difficulty of efficiently identifying critical parameters within the immense parameter space. To address this, we propose AttentionBreaker, a novel framework tailored for LLMs that enables efficient traversal of the parameter space to identify critical parameters. Additionally, we introduce GenBFA, an evolutionary optimization strategy designed to refine the search further, isolating the most critical bits for an efficient and effective attack. Empirical results reveal the profound vulnerability of LLMs to AttentionBreaker. For example, merely three bit-flips (4.129 x 10^-9% of total parameters) in the LLaMA3-8B-Instruct 8-bit quantized (W8) model result in a complete performance collapse: accuracy on MMLU tasks drops from 67.3% to 0%, and Wikitext perplexity skyrockets from 12.6 to 4.72 x 10^5. These findings underscore the effectiveness of AttentionBreaker in uncovering and exploiting critical vulnerabilities within LLM architectures.

We study quantitative aspects of the following fact: For every graph $F$, there exists a graph $G$ with the property that any $2$-coloring of the triangles of $G$ yields an induced copy of $F$, in which all triangles are monochromatic. We define the Ramsey number $R_{\text{ind}}^{\Delta}(F)$ as the smallest size of such a graph $G$. Although this fact has several proofs, all of them provide tower-type bounds. We study the number $R_{\text{ind}}^{\Delta}(F)$ for some particular classes of graphs $F$., Comment: 23 pages, comments are welcome

We explore the influence of interactions between dark matter (DM) and dark energy (DE) on the cosmic evolution of the Universe within a viscous dark energy (VDE) framework. Moving beyond traditional interacting dark energy (IDE) models, we propose a generalized IDE model adaptable to diverse IDE scenarios via IDE coupling parameters. In order to investigate deviations from $\Lambda$CDM across cosmic epochs by highlighting how viscous and the interactions between DM and DE impact cosmic density and expansion rates, we consider a model agnostic form of VDE. Eventually we perform a Bayesian analysis using the Union 2.1 Supernova Ia dataset and Markov Chain Monte Carlo (MCMC) sampling to obtain optimal values of model parameters. This comprehensive analysis provides insights about the interplay between viscous and IDE in shaping the Universe's expansion history., Comment: 13 pages, 7 figures

Transformer model has gained prominence as a popular deep neural network architecture for neural language processing (NLP) and computer vision (CV) applications. However, the extensive use of nonlinear operations, like softmax, poses a performance bottleneck during transformer inference and comprises up to 40% of the total latency. Hence, we propose innovations at the circuit, architecture, and algorithm levels to accelerate the transformer. At the circuit level, we propose topkima-combining top-k activation selection with in-memory ADC (IMA) to implement a low-energy and low-latency softmax without any sorting latency. Only the k largest activations are sent to the softmax calculation block, reducing the huge computational cost of softmax. Using a modified training scheme with top-k only in the forward pass, experimental results demonstrate only a 0.4% to 1.2% reduction in accuracy across ViT, distilBERT, and BERT-base models when evaluated on CIFAR-10, CIFAR-100, and SQuAD datasets with k=5. At the architecture level, an improved scale-free technique is introduced to reduce the computational cost of attention. The combined system, dubbed Topkima-Former, enhances 1.8x-84x speedup and 1.3x-35x energy efficiency (EE) over prior In-memory computing (IMC) accelerators. Compared to a conventional softmax macro and a digital top-k (Dtopk) softmax macro, our proposed tokima softmax macro achieves about 15x and 8x faster speed respectively., Comment: 7 pages

We investigate the reflected entropy for bipartite mixed state configurations in a $T\bar{T}$ deformed boundary conformal field theory in $2$ dimensions (BCFT$_2$). The bulk dual is described by asymptotically AdS$_3$ geometries with the cut off surface pushed deeper into the bulk and truncated by an end of the world brane. We obtain the reflected entropy up to a linear order in the radial cut-off for static and time dependent configurations involving an eternal black hole, from the island and defect extremal surface (DES) prescriptions in the context of the deformed AdS/BCFT. We observe agreement of the leading order correction for all cases between the two prescriptions. We also obtain the analogous of the Page curves for the reflected entropy and investigate the modification due to the $T\bar{T}$ deformation., Comment: 46 pages, 28 figures

Causal effect estimation is a critical task in statistical learning that aims to find the causal effect on subjects by identifying causal links between a number of predictor (or, explanatory) variables and the outcome of a treatment. In a regressional framework, we assign a treatment and outcome model to estimate the average causal effect. Additionally, for high dimensional regression problems, variable selection methods are also used to find a subset of predictor variables that maximises the predictive performance of the underlying model for better estimation of the causal effect. In this paper, we propose a different approach. We focus on the variable selection aspects of high dimensional causal estimation problem. We suggest a cautious Bayesian group LASSO (least absolute shrinkage and selection operator) framework for variable selection using prior sensitivity analysis. We argue that in some cases, abstaining from selecting (or, rejecting) a predictor is beneficial and we should gather more information to obtain a more decisive result. We also show that for problems with very limited information, expert elicited variable selection can give us a more stable causal effect estimation as it avoids overfitting. Lastly, we carry a comparative study with synthetic dataset and show the applicability of our method in real-life situations.

Sequential learning in a multi-agent resource constrained matching market has received significant interest in the past few years. We study decentralized learning in two-sided matching markets where the demand side (aka players or agents) competes for a `large' supply side (aka arms) with potentially time-varying preferences, to obtain a stable match. Despite a long line of work in the recent past, existing learning algorithms such as Explore-Then-Commit or Upper-Confidence-Bound remain inefficient for this problem. In particular, the per-agent regret achieved by these algorithms scales linearly with the number of arms, $K$. Motivated by the linear contextual bandit framework, we assume that for each agent an arm-mean can be represented by a linear function of a known feature vector and an unknown (agent-specific) parameter. Moreover, our setup captures the essence of a dynamic (non-stationary) matching market where the preferences over arms change over time. Our proposed algorithms achieve instance-dependent logarithmic regret, scaling independently of the number of arms, $K$.

We prove upper bounds which are independent of the dimension of the ambient space, on the number of realizable zero-nonzero patterns as well as sign conditions (when the field of coefficients is ordered) of a finite set of polynomials $\mathcal{P}$ restricted to some algebraic subset $V$.Our bounds (which are tight) depend on the number and the degrees of the polynomials in $\mathcal{P}$, as well as the degree (of the embedding) of $V$ and the dimension of $V$, but are independent of the dimension of the space in which $V$ is embedded. This last feature of our bounds is useful in situations where the ambient dimension could be much larger than $\dim V$. We give several applications of our results. We generalize existing results on bounding the speeds of algebraically defined classes of graphs, as well as lower bounds in terms of the number of connected components for testing membership in semi-algebraic sets using algebraic computation trees. Motivated by quantum complexity theory we introduce a notion of relative rank (additive as well as multiplicative) in finite dimensional vector spaces and algebras relative to a fixed algebraic subset in the vector space or algebra -- which generalizes the classical definition of ranks of tensors. We prove a very general lower bound on the maximum relative rank of finite subsets relative to algebraic subsets of bounded degree and dimension which is independent of the dimension of the vector space or algebra. We show how our lower bound implies a quantum analog of a classical lower bound result of Shannon for Boolean circuits -- that almost all Boolean functions require (classical) circuits of size at least $\Omega(2^n/n)$., Comment: 46 pages. Comments welcome. This paper supersedes the paper by the same authors titled "Quantum Analog of Shannon's Lower Bound Theorem'' (arXiv:2308.13091 [quant-ph])

The majority of atomic nuclei have deformed shapes and nearly all these shapes are symmetric with respect to reflection. There are only a few reflection asymmetric pear-shaped nuclei that have been found in actinide and lanthanide regions, which have static octupole deformation. These nuclei possess an intrinsic electric dipole moment due to the shift between the center of charge and the center of mass. This manifests in the enhancement of the electric dipole transition rates. In this article, we report on the measurement of the lifetimes of the high spin levels of the two alternate parity bands in $^{100}$Ru through the Doppler Shift Attenuation Method. The estimated electric dipole transition rates have been compared with the calculated transition rates using the triaxial projected shell model without octupole deformation, and are found to be an order of magnitude enhanced. Thus, the observation of seven inter-leaved electric dipole transitions with enhanced rates establish $^{100}$Ru as possibly the first octupole deformed nucleus reported in the A $\approx$ 100 mass region.

Balance functions have been regarded in the past as a method of investigating the late-stage hadronization found in the presence of a strongly-coupled medium. They are also used to constrain mechanisms of particle production in large and small collision systems. Measurements of charge balance functions for inclusive and identified particle pairs are reported as a function of charged particle multiplicity in proton--proton collisions simulated with the PYTHIA8 and the EPOS4 models. The charge balance functions of inclusive, pion, kaon, and proton pairs exhibit amplitudes and shapes that depend on particle species and differ significantly in the two models due to the different particle production mechanisms implemented in PYTHIA and EPOS. The shapes and amplitudes also evolve with multiplicity in both models. In addition, the evolution of the longitudinal rms width and that of balance functions integrals with multiplicity (and average transverse momentum) feature significant differences in the two models.

Millimeter-wave refracting telescopes targeting the degree-scale structure of the cosmic microwave background (CMB) have recently grown to diffraction-limited apertures of over 0.5 meters. These instruments are entirely housed in vacuum cryostats to support their sub-kelvin bolometric detectors and to minimize radiative loading from thermal emission due to absorption loss in their transmissive optical elements. The large vacuum window is the only optical element in the system at ambient temperature, and therefore minimizing loss in the window is crucial for maximizing detector sensitivity. This motivates the use of low-loss polymer materials and a window as thin as practicable. However, the window must simultaneously meet the requirement to keep sufficient vacuum, and therefore must limit gas permeation and remain mechanically robust against catastrophic failure under pressure. We report on the development of extremely thin composite polyethylene window technology that meets these goals. Two windows have been deployed for two full observing seasons on the BICEP3 and BA150 CMB telescopes at the South Pole. On BICEP3, the window has demonstrated a 6% improvement in detector sensitivity., Comment: 20 pages, 12 figures, 4 tables

The concept of Vapnik-Chervonenkis (VC) density is pivotal across various mathematical fields, including model theory, discrete geometry, and probability theory. In this paper, we introduce a topological generalization of VC-density. Let $Y$ be a topological space and $\mathcal{X},\mathcal{Z}^{(0)},\ldots,\mathcal{Z}^{(q-1)}$ be families of subspaces of $Y$. We define a two parameter family of numbers, $\mathrm{vcd}^{p,q}_{\mathcal{X},\overline{\mathcal{Z}}}$, which we refer to as the degree $p$, order $q$, VC-density of the pair \[ (\mathcal{X},\overline{\mathcal{Z}} = (\mathcal{Z}^{(0)},\ldots,\mathcal{Z}^{(q-1)}). \] The classical notion of VC-density within this topological framework can be recovered by setting $p=0, q=1$. For $p=0, q > 0$, we recover Shelah's notion of higher-order VC-density for $q$-dependent families. Our definition introduces a new notion when $p>0$. Our main result establishes that that in any model of these theories \[ \mathrm{vcd}^{p,q}_{\mathcal{X},\overline{\mathcal{Z}}} \leq (p+q) \dim X. \] This result generalizes known VC-density bounds in these structures, extending them in multiple ways, as well as providing a uniform proof paradigm applicable to all of them. We give examples to show that our bounds are optimal. We present combinatorial applications of our higher-degree VC-density bounds, deriving topological analogs of well-known results such as the existence of $\varepsilon$-nets and the fractional Helly theorem. We show that with certain restrictions, these results extend to our higher-degree topological setting., Comment: 52 pages. Comments welcome

We present here a real-time analysis of diffraction images acquired at high frame-rate (925 Hz) and its application to macromolecular serial crystallography. The software uses a new signal separation algorithm, able to distinguish the amorphous (or powder diffraction) component from the diffraction signal originating from single crystals. It relies on the ability to work efficiently in azimuthal space and derives from the work performed on pyFAI, the fast azimuthal integration library. Two applications are built upon this separation algorithm: a lossy compression algorithm and a peak-picking algorithm; the performances of both is assessed by comparing data quality after reduction with XDS and CrystFEL., Comment: 43 pages, 12 figures

Learning representations of underlying environmental dynamics from partial observations is a critical challenge in machine learning. In the context of Partially Observable Markov Decision Processes (POMDPs), state representations are often inferred from the history of past observations and actions. We demonstrate that incorporating future information is essential to accurately capture causal dynamics and enhance state representations. To address this, we introduce a Dynamical Variational Auto-Encoder (DVAE) designed to learn causal Markovian dynamics from offline trajectories in a POMDP. Our method employs an extended hindsight framework that integrates past, current, and multi-step future information within a factored-POMDP setting. Empirical results reveal that this approach uncovers the causal graph governing hidden state transitions more effectively than history-based and typical hindsight-based models.

Rotation is an important, yet poorly-modelled phenomenon of stellar structure and evolution. Accurate estimates of internal rotation rates are therefore valuable for constraining stellar evolution models. We aim to assess the accuracy of asteroseismic estimates of internal rotation rates and how these depend on the fundamental stellar parameters. We apply the recently-developed method called extended-MOLA inversions to infer localised estimates of internal rotation rates of synthetic observations of red giants. We search for suitable reference stellar models following a grid-based approach, and assess the robustness of the resulting inferences to the choice of reference model. We find that matching the mixed mode pattern between the observation and the reference model is an important criterion to select suitable reference models. We propose to i) select a set of reference models based on the correlation between the observed rotational splittings and the mode-trapping parameter ii) compute rotation rates for all these models iii) use the mean value obtained across the whole set as the estimate of the internal rotation rates. We find that the effect of a near surface perturbation in the synthetic observations on the rotation rates estimated based on the correlation between the observed rotational splittings and the mode-trapping parameter is negligible. We conclude that when using an ensemble of reference models, constructed based on matching the mixed mode pattern, the input rotation rates can be recovered across a range of fundamental stellar parameters like mass, mixing-length parameter and composition. Further, red-giant rotation rates determined in this way are also independent of a near surface perturbation of stellar structure., Comment: 23 pages, 16 figures, Accepted for publication in Astronomy and Astrophysics