Search

Scent marks play a crucial role in both territorial and sexual communication in many species. We investigated how free-ranging dogs respond to scent marks from individuals of different identities in terms of sex and group, across varying strategic locations within their territory. Both male and female dogs showed heightened interest in scent marks compared to control, exhibiting stronger territorial responses,. with males being more territorial than females. Overmarking behaviour was predominantly observed in males, particularly in response to male scent marks and those from neighbouring groups. Behavioural cluster analysis revealed distinct responses to different scent marks, with neighbouring group male scents eliciting the most distinct reactions. Our findings highlight the multifaceted role of scent marks in free-ranging dog communication, mediating both territorial defence and intrasexual competition. The differential responses based on the identity and gender of the scent-marker emphasize the complexity of olfactory signalling in this species. This study contributes to understanding the social behaviour of dogs in their natural habitat, and opens up possibilities for future explorations in the role of olfactory cues in the social dynamics of the species.

Quantum machine learning (QML) is a rapidly emerging area of research, driven by the capabilities of Noisy Intermediate-Scale Quantum (NISQ) devices. With the progress in the research of QML models, there is a rise in third-party quantum cloud services to cater to the increasing demand for resources. New security concerns surface, specifically regarding the protection of intellectual property (IP) from untrustworthy service providers. One of the most pressing risks is the potential for reverse engineering (RE) by malicious actors who may steal proprietary quantum IPs such as trained parameters and QML architecture, modify them to remove additional watermarks or signatures and re-transpile them for other quantum hardware. Prior work presents a brute force approach to RE the QML parameters which takes exponential time overhead. In this paper, we introduce an autoencoder-based approach to extract the parameters from transpiled QML models deployed on untrusted third-party vendors. We experiment on multi-qubit classifiers and note that they can be reverse-engineered under restricted conditions with a mean error of order 10^-1. The amount of time taken to prepare the dataset and train the model to reverse engineer the QML circuit being of the order 10^3 seconds (which is 10^2x better than the previously reported value for 4-layered 4-qubit classifiers) makes the threat of RE highly potent, underscoring the need for continued development of effective defenses., Comment: 7 pages, 4 figures

Side channel attacks (SCAs) remain a significant threat to the security of cryptographic systems in modern embedded devices. Even mathematically secure cryptographic algorithms, when implemented in hardware, inadvertently leak information through physical side channel signatures such as power consumption, electromagnetic (EM) radiation, light emissions, and acoustic emanations. Exploiting these side channels significantly reduces the search space of the attacker. In recent years, physical countermeasures have significantly increased the minimum traces to disclosure (MTD) to 1 billion. Among them, signature attenuation is the first method to achieve this mark. Signature attenuation often relies on analog techniques, and digital signature attenuation reduces MTD to 20 million, requiring additional methods for high resilience. We focus on improving the digital signature attenuation by an order of magnitude (MTD 200M). Additionally, we explore possible attacks against signature attenuation countermeasure. We introduce a Voltage drop Linear region Biasing (VLB) attack technique that reduces the MTD to over 2000 times less than the previous threshold. This is the first known attack against a physical side-channel attack (SCA) countermeasure. We have implemented an attack detector with a response time of 0.8 milliseconds to detect such attacks, limiting SCA leakage window to sub-ms, which is insufficient for a successful attack., Comment: Extended from CICC. Now under revision at Journal of Solid-State Circuits

We present results from a search for X-ray/gamma-ray counterparts of gravitational-wave (GW) candidates from the third observing run (O3) of the LIGO-Virgo-KAGRA (LVK) network using the Swift Burst Alert Telescope (Swift-BAT). The search includes 636 GW candidates received in low latency, 86 of which have been confirmed by the offline analysis and included in the third cumulative Gravitational-Wave Transient Catalogs (GWTC-3). Targeted searches were carried out on the entire GW sample using the maximum--likelihood NITRATES pipeline on the BAT data made available via the GUANO infrastructure. We do not detect any significant electromagnetic emission that is temporally and spatially coincident with any of the GW candidates. We report flux upper limits in the 15-350 keV band as a function of sky position for all the catalog candidates. For GW candidates where the Swift-BAT false alarm rate is less than 10$^{-3}$ Hz, we compute the GW--BAT joint false alarm rate. Finally, the derived Swift-BAT upper limits are used to infer constraints on the putative electromagnetic emission associated with binary black hole mergers., Comment: 50 pages, 10 figures, 4 tables

Quantum Machine Learning (QML) amalgamates quantum computing paradigms with machine learning models, providing significant prospects for solving complex problems. However, with the expansion of numerous third-party vendors in the Noisy Intermediate-Scale Quantum (NISQ) era of quantum computing, the security of QML models is of prime importance, particularly against reverse engineering, which could expose trained parameters and algorithms of the models. We assume the untrusted quantum cloud provider is an adversary having white-box access to the transpiled user-designed trained QML model during inference. Reverse engineering (RE) to extract the pre-transpiled QML circuit will enable re-transpilation and usage of the model for various hardware with completely different native gate sets and even different qubit technology. Such flexibility may not be obtained from the transpiled circuit which is tied to a particular hardware and qubit technology. The information about the number of parameters, and optimized values can allow further training of the QML model to alter the QML model, tamper with the watermark, and/or embed their own watermark or refine the model for other purposes. In this first effort to investigate the RE of QML circuits, we perform RE and compare the training accuracy of original and reverse-engineered Quantum Neural Networks (QNNs) of various sizes. We note that multi-qubit classifiers can be reverse-engineered under specific conditions with a mean error of order 1e-2 in a reasonable time. We also propose adding dummy fixed parametric gates in the QML models to increase the RE overhead for defense. For instance, adding 2 dummy qubits and 2 layers increases the overhead by ~1.76 times for a classifier with 2 qubits and 3 layers with a performance overhead of less than 9%. We note that RE is a very powerful attack model which warrants further efforts on defenses., Comment: 11 pages, 12 figures

In this paper, we compute the WQFT partition function, specifically the eikonal phase in a black hole scattering event in the dynamical Chern-Simons theory, using the techniques of spinning worldline quantum field theory. We consider the scattering of spinning black holes and highlight the necessary details for the calculation of the partition function. We present the $\epsilon$-expansion of the essential two-loop integrals using Integration-by-Parts (IBP) reduction and differential equation techniques, which we then utilize to compute the linear-in-order spin eikonal phase up to 3PM. Additionally, we discuss the dependence of the phase on the spin orientations of the black holes., Comment: 57 Pages

Automatic summarization of legal case judgements, which are known to be long and complex, has traditionally been tried via extractive summarization models. In recent years, generative models including abstractive summarization models and Large language models (LLMs) have gained huge popularity. In this paper, we explore the applicability of such models for legal case judgement summarization. We applied various domain specific abstractive summarization models and general domain LLMs as well as extractive summarization models over two sets of legal case judgements from the United Kingdom (UK) Supreme Court and the Indian (IN) Supreme Court and evaluated the quality of the generated summaries. We also perform experiments on a third dataset of legal documents of a different type, Government reports from the United States (US). Results show that abstractive summarization models and LLMs generally perform better than the extractive methods as per traditional metrics for evaluating summary quality. However, detailed investigation shows the presence of inconsistencies and hallucinations in the outputs of the generative models, and we explore ways to reduce the hallucinations and inconsistencies in the summaries. Overall, the investigation suggests that further improvements are needed to enhance the reliability of abstractive models and LLMs for legal case judgement summarization. At present, a human-in-the-loop technique is more suitable for performing manual checks to identify inconsistencies in the generated summaries., Comment: Accepted at Artificial Intelligence and Law, Springer, 2024

Nearest neighbor classifier is arguably the most simple and popular nonparametric classifier available in the literature. However, due to the concentration of pairwise distances and the violation of the neighborhood structure, this classifier often suffers in high-dimension, low-sample size (HDLSS) situations, especially when the scale difference between the competing classes dominates their location difference. Several attempts have been made in the literature to take care of this problem. In this article, we discuss some of these existing methods and propose some new ones. We carry out some theoretical investigations in this regard and analyze several simulated and benchmark datasets to compare the empirical performances of proposed methods with some of the existing ones.

GRAMS (Gamma-Ray and AntiMatter Survey) is a next-generation balloon/satellite experiment utilizing a LArTPC (Liquid Argon Time Projection Chamber), to simultaneously target astrophysical observations of cosmic MeV gamma-rays and conduct an indirect dark matter search using antimatter. While LArTPCs are widely used in particle physics experiments, they have never been operated at balloon altitudes. An engineering balloon flight with a small-scale LArTPC (eGRAMS) was conducted on July 27th, 2023, to establish a system for safely operating a LArTPC at balloon altitudes and to obtain cosmic-ray data from the LArTPC. The flight was launched from the Japan Aerospace Exploration Agency's (JAXA) Taiki Aerospace Research Field in Hokkaido, Japan. The total flight duration was 3 hours and 12 minutes, including a level flight of 44 minutes at a maximum altitude of 28.9~km. The flight system was landed on the sea and successfully recovered. The LArTPC was successfully operated throughout the flight, and about 0.5 million events of the cosmic-ray data including muons, protons, and Compton scattering gamma-ray candidates, were collected. This pioneering flight demonstrates the feasibility of operating a LArTPC in high-altitude environments, paving the way for future GRAMS missions and advancing our capabilities in MeV gamma-ray astronomy and dark matter research.

The increasing prevalence of large language models (LLMs) such as GPT-4 in various applications has led to a surge in the size of prompts required for optimal performance, leading to challenges in computational efficiency. Prompt compression aims to reduce the inference cost by minimizing input tokens without compromising on the task performance. However, existing prompt compression techniques either rely on sub-optimal metrics such as information entropy or model it as a task-agnostic token classification problem that fails to capture task-specific information. To address these issues, we propose a novel and efficient reinforcement learning (RL) based task-aware prompt compression method. To ensure low latency requirements, we leverage existing Transformer encoder-based token classification model while guiding the learning process with task-specific reward signals using lightweight REINFORCE algorithm. We evaluate the performance of our method on three diverse and challenging tasks including text summarization, question answering and code summarization. We demonstrate that our RL-guided compression method improves the task performance by 8% - 260% across these three scenarios over state-of-the-art compression techniques while satisfying the same compression rate and latency requirements., Comment: Submitted to COLING 2025

The quantum speedup in solving optimization problems via adiabatic quantum annealing is often hindered by the closing of the energy gap during the anneal, especially when this gap scales exponentially with system size. In this work, we address this by demonstrating that for the Maximum Weighted Independent Set (MWIS) problem, an informed choice of $n-$local catalysts (operators involving $n$ qubits) can re-open the gap or prevent it from closing during the anneal process. By analyzing first-order phase transitions in toy instances of the MWIS problem, we identify effective forms of catalysts and also show that non-stoquasticity is not essential to avoid such phase transitions. While some of the toy problems studied might not be classically NP-hard, they reveal that $n-$local catalysts exponentially improve gap scaling and need to be connected across unfrustrated loops in the problem graph to be effective. Our analysis suggests that non-local quantum fluctuations entangling multiple qubits are key to achieving the desired quantum advantage., Comment: 13 pages, 13 figures

We investigate the quantum phases of a frustrated antiferromagnetic Heisenberg spin-1/2 model Hamiltonian on a Kagome-strip chain (KSC), a one-dimensional analogue of the Kagome lattice, and construct its phase diagram in an extended exchange parameter space. The isolated unit cell of this lattice comprises of five spin-1/2 particles, giving rise to several types of magnetic ground states in a unit cell: a spin-$3/2$ state as well as spin-$1/2$ states with and without additional degeneracies. We explore the ground state properties of the fully connected thermodynamic system using exact diagonalization and density matrix renormalization group methods, identifying several distinct quantum phases. All but one of the phases exhibit gapless spin excitations. The exception is a dimerized spin-gapped phase that covers a large part of the phase diagram and includes the uniformly exchange coupled system. We argue that this phase can be understood by perturbing around the limit of decoupled unit cells where each unit cell has six degenerate ground states. We use degenerate perturbation theory to obtain an effective Hamiltonian, a Kugel-Khomskii model with an anisotropic spin-one orbital degree of freedom, which helps explain the origin of dimerization.

One of the most intriguing 6G vertical markets is precision agriculture, where communications, sensing, control, and robotics technologies are used to improve agricultural outputs and decrease environmental impact. Ambient IoT (A-IoT), which uses a network of devices that harvest ambient energy to enable communications, is expected to play an important role in agricultural use cases due to its low costs, simplicity, and battery-free (or battery-assisted) operation. In this paper, we review the use cases of precision agriculture and discuss the challenges. We discuss how A-IoT can be used for precision agriculture and compare it with other ambient energy source technologies. We also discuss research directions related to both A-IoT and precision agriculture., Comment: 7 pages, 4 figures and 2 tables

Cancer drug response prediction (DRP) models present a promising approach towards precision oncology, tailoring treatments to individual patient profiles. While deep learning (DL) methods have shown great potential in this area, models that can be successfully translated into clinical practice and shed light on the molecular mechanisms underlying treatment response will likely emerge from collaborative research efforts. This highlights the need for reusable and adaptable models that can be improved and tested by the wider scientific community. In this study, we present a scoring system for assessing the reusability of prediction DRP models, and apply it to 17 peer-reviewed DL-based DRP models. As part of the IMPROVE (Innovative Methodologies and New Data for Predictive Oncology Model Evaluation) project, which aims to develop methods for systematic evaluation and comparison DL models across scientific domains, we analyzed these 17 DRP models focusing on three key categories: software environment, code modularity, and data availability and preprocessing. While not the primary focus, we also attempted to reproduce key performance metrics to verify model behavior and adaptability. Our assessment of 17 DRP models reveals both strengths and shortcomings in model reusability. To promote rigorous practices and open-source sharing, we offer recommendations for developing and sharing prediction models. Following these recommendations can address many of the issues identified in this study, improving model reusability without adding significant burdens on researchers. This work offers the first comprehensive assessment of reusability and reproducibility across diverse DRP models, providing insights into current model sharing practices and promoting standards within the DRP and broader AI-enabled scientific research community., Comment: 12 pages, 2 figures

Large language models (LLMs) have significantly advanced audio processing through audio codecs that convert audio into discrete tokens, enabling the application of language modeling techniques to audio data. However, audio codecs often operate at high frame rates, resulting in slow training and inference, especially for autoregressive models. To address this challenge, we present the Low Frame-rate Speech Codec (LFSC): a neural audio codec that leverages finite scalar quantization and adversarial training with large speech language models to achieve high-quality audio compression with a 1.89 kbps bitrate and 21.5 frames per second. We demonstrate that our novel codec can make the inference of LLM-based text-to-speech models around three times faster while improving intelligibility and producing quality comparable to previous models., Comment: Submitted to ICASSP 2025

The ubiquity of top-rich final states in the context of beyond the Standard Model (BSM) searches has led to their status as extensively studied signatures at the LHC. Over the past decade, numerous endeavours have been undertaken in the literature to develop methods for efficiently distinguishing boosted top quark jets from QCD jets. Although cut-based strategies for boosted top tagging, which rely on substructure information from fat jets resulting from the hadronic decay of boosted top quarks, were introduced in the literature as early as 2008, recent years have witnessed a surge in the utilization of machine learning-based approaches for the classification of top-jets from QCD jets. The review focuses on the present status of boosted top tagging and its application for BSM searchers., Comment: Accepted for publication in "The European Physical Journal Special Topics", 28 pages, 20 figures

Let the complex reflection group $G(m,p,n)$ act on the unit polydisc $\mathbb D^n$ in $\mathbb C^n.$ A $\boldsymbol\Theta_n$-contraction is a commuting tuple of operators on a Hilbert space having $$\overline{\boldsymbol\Theta}_n:=\{\boldsymbol\theta(z)=(\theta_1(z),\ldots,\theta_n(z)):z\in\overline{\mathbb D}^n\}$$ as a spectral set, where $\{\theta_i\}_{i=1}^n$ is a homogeneous system of parameters associated to $G(m,p,n).$ A plethora of examples of $\boldsymbol\Theta_n$-contractions is exhibited. Under a mild hypothesis, it is shown that these $\boldsymbol\Theta_n$-contractions are mutually unitarily inequivalent. These inequivalence results are obtained concretely for the weighted Bergman modules under the action of the permutation groups and the dihedral groups. The division problem is shown to have negative answers for the Hardy module and the Bergman module on the bidisc. A Beurling-Lax-Halmos type representation for the invariant subspaces of $\boldsymbol\Theta_n$-isometries is obtained., Comment: 23 pages. arXiv admin note: text overlap with arXiv:1301.2837

Label-free characterization of biological specimens seeks to supplement existing imaging techniques and avoid the need for contrast agents that can disturb the native state of living samples. Conventional label-free optical imaging techniques are compatible with living samples but face challenges such as poor sectioning capability, fragmentary morphology, and lack chemical specific information. Here, we combined simultaneous label-free autofluorescence multi-harmonic (SLAM) microscopy and gradient light interference microscopy (GLIM) to extract both chemical specific and morphological tomography of 3D cultured kidney mesangial cells. Imaging 3D in vitro kidney models is essential to understand kidney function and pathology. Our correlative approach enables imaging and quantification of these cells to extract both morphology and chemical-specific signals that is crucial for understanding kidney function. In our approach, SLAM offers a nonlinear imaging platform with a single-excitation source to simultaneously acquire autofluorescence (FAD and NAD(P)H), second, and third harmonic signal from the 3D cultured cells. Complementarily, GLIM acquires high-contrast quantitative phase information to quantify structural changes in samples with thickness of up to 250 micron. Our correlative imaging results demonstrate a versatile and hassle-free platform for morpho-chemical cellular tomography to investigate functions such as metabolism and matrix deposition of kidney mesangial cells in 3D under controlled physiological conditions.

Tiny Machine Learning (TinyML) enables efficient, lowcost, and privacy preserving machine learning inference directly on microcontroller units (MCUs) connected to sensors. Optimizing models for these constrained environments is crucial. This paper investigates how reducing data acquisition rates affects TinyML models for time series classification, focusing on resource-constrained, battery operated IoT devices. By lowering data sampling frequency, we aim to reduce computational demands RAM usage, energy consumption, latency, and MAC operations by approximately fourfold while maintaining similar classification accuracies. Our experiments with six benchmark datasets (UCIHAR, WISDM, PAMAP2, MHEALTH, MITBIH, and PTB) showed that reducing data acquisition rates significantly cut energy consumption and computational load, with minimal accuracy loss. For example, a 75\% reduction in acquisition rate for MITBIH and PTB datasets led to a 60\% decrease in RAM usage, 75\% reduction in MAC operations, 74\% decrease in latency, and 70\% reduction in energy consumption, without accuracy loss. These results offer valuable insights for deploying efficient TinyML models in constrained environments.

The assembly of galaxy clusters is understood to be a hierarchical process with a continuous accretion of galaxies over time, which increases the cluster size and mass. Late-type galaxies that fall into clusters can undergo ram-pressure stripping, forming extended gas tails within which star formation can happen. The number, location, and tail orientations of such galaxies provide clues about the galaxy infall process, the assembly of the cluster over time, and the consequences of infall for galaxy evolution. Here, we utilise the $\sim$ 0.5 degree diameter circular field of the Ultraviolet Imaging Telescope to image six galaxy clusters at z < 0.06 that are known to contain jellyfish galaxies. We searched for stripping candidates in the ultraviolet images of these clusters, which revealed 54 candidates showing signs of unilateral extra-planar emission, due to ram-pressure stripping. Seven candidates had already been identified as likely stripping based on optical B-band imaging. We identified 47 new candidates through UV imaging. Spectroscopic redshift information is available for 39 of these candidate galaxies, of which 19 are associated with six clusters. The galaxies with spectroscopic redshifts that are not part of the clusters appear to be within structures at different redshifts identified as additional peaks in the redshift distribution of galaxies, indicating that they might be ram-pressure stripped or disturbed galaxies in other structures along the line of sight. We examine the orbital history of these galaxies based on their location in the position-velocity phase-space diagram and explore a possible connection to the orientation of the tail direction among cluster member candidates. The tails of confirmed cluster member galaxies are found to be oriented away from the cluster centre., Comment: Accepted for publication in A&A

Dialog systems, such as voice assistants, are expected to engage with users in complex, evolving conversations. Unfortunately, traditional automatic speech recognition (ASR) systems deployed in such applications are usually trained to recognize each turn independently and lack the ability to adapt to the conversational context or incorporate user feedback. In this work, we introduce a general framework for ASR in dialog systems that can go beyond learning from single-turn utterances and learn over time how to adapt to both explicit supervision and implicit user feedback present in multi-turn conversations. We accomplish that by leveraging advances in student-teacher learning and context-aware dialog processing, and designing contrastive self-supervision approaches with Ohm, a new online hard-negative mining approach. We show that leveraging our new framework compared to traditional training leads to relative WER reductions of close to 10% in real-world dialog systems, and up to 26% on public synthetic data., Comment: Presented at ICML 2024

Active-matter systems are inherently out-of-equilibrium and perform mechanical work by utilizing their internal energy sources. Breakdown of time-reversal symmetry (BTRS) is a hallmark of such dissipative non-equilibrium dynamics. We introduce a robust, experimentally accessible, noninvasive, quantitative measure of BTRS in terms of the Kullback-Leibler divergence in collision events, demonstrated in our novel artificial active matter, comprised of battery-powered spherical rolling robots whose energetics in different modes of motion can be measured with high precision. Our dimensionless measure characterizes how dissipation and internal energetics are influenced by kinematic constraints from interactions with the environment. We propose this measure of BTRS as an empirical estimate of the distance from equilibrium. An energetic insight into this departure of active matter from equilibrium comes from our demonstration of a non-trivial fluctuation symmetry, which reveals a potentially universal thermodynamic characteristic of active energetics. As a many-body consequence of BTRS in our experimental active system, we demonstrate the emergence of activity-induced herding, which has no equilibrium analogue., Comment: 9 pages, 7 figures, supplemental information attached

The chirality-induced spin selectivity (CISS) effect has garnered significant interest in the field of molecular spintronics due to its potential for creating spin-polarized electrons without the need for a magnet. Recent studies devoted to CISS effects in various chiral materials demonstrate exciting prospects for spintronics, chiral recognition, and quantum information applications. Several experimental studies have confirmed the applicability of chiral molecules towards spin-filtering properties, influencing spin-polarized electron transport, and photoemission. Researchers aim to predict CISS phenomena and apply this concept to practical applications by compiling experimental results and enhancing understanding of the CISS effect. To expand the possibilities of spin manipulation and create new opportunities for spin-based technologies, researchers are diligently exploring different chiral organic and inorganic materials for probing the CISS effect. This ongoing research holds promise for developing novel spin-based technologies and advancing the understanding of the intricate relationship between chirality and electron spin. This review showcases the remarkable CISS effect and its impact on spintronics, as well as its relevance in various other scientific areas., Comment: 29 pages, 20 figures

Let $\mathbb{F}$ be a field, and consider the hypercube $\{ 0, 1 \}^{n}$ in $\mathbb{F}^{n}$. Sziklai and Weiner (Journal of Combinatorial Theory, Series A 2022) showed that if a polynomial $P ( X_{1}, \dots, X_{n} ) \in \mathbb{F}[ X_{1}, \dots, X_{n}]$ vanishes on every point of the hypercube $\{0,1\}^{n}$ except those with at most $r$ many ones then the degree of the polynomial will be at least $n-r$. This is a generalization of Alon and F\"uredi's fundamental result (European Journal of Combinatorics 1993) about polynomials vanishing on every point of the hypercube except at the origin (point with all zero coordinates). Sziklai and Weiner proved their interesting result using M\"{o}bius inversion formula and the Zeilberger method for proving binomial equalities. In this short note, we show that a stronger version of Sziklai and Weiner's result can be derived directly from Alon and F\"{u}redi's result., Comment: 2 pages

In recent years, there has been a surge in the publication of clinical trial reports, making it challenging to conduct systematic reviews. Automatically extracting Population, Intervention, Comparator, and Outcome (PICO) from clinical trial studies can alleviate the traditionally time-consuming process of manually scrutinizing systematic reviews. Existing approaches of PICO frame extraction involves supervised approach that relies on the existence of manually annotated data points in the form of BIO label tagging. Recent approaches, such as In-Context Learning (ICL), which has been shown to be effective for a number of downstream NLP tasks, require the use of labeled examples. In this work, we adopt ICL strategy by employing the pretrained knowledge of Large Language Models (LLMs), gathered during the pretraining phase of an LLM, to automatically extract the PICO-related terminologies from clinical trial documents in unsupervised set up to bypass the availability of large number of annotated data instances. Additionally, to showcase the highest effectiveness of LLM in oracle scenario where large number of annotated samples are available, we adopt the instruction tuning strategy by employing Low Rank Adaptation (LORA) to conduct the training of gigantic model in low resource environment for the PICO frame extraction task. Our empirical results show that our proposed ICL-based framework produces comparable results on all the version of EBM-NLP datasets and the proposed instruction tuned version of our framework produces state-of-the-art results on all the different EBM-NLP datasets. Our project is available at \url{https://github.com/shrimonmuke0202/AlpaPICO.git}., Comment: Accepted at Methods

As AI systems become more advanced, ensuring their alignment with a diverse range of individuals and societal values becomes increasingly critical. But how can we capture fundamental human values and assess the degree to which AI systems align with them? We introduce ValueCompass, a framework of fundamental values, grounded in psychological theory and a systematic review, to identify and evaluate human-AI alignment. We apply ValueCompass to measure the value alignment of humans and language models (LMs) across four real-world vignettes: collaborative writing, education, public sectors, and healthcare. Our findings uncover risky misalignment between humans and LMs, such as LMs agreeing with values like "Choose Own Goals", which are largely disagreed by humans. We also observe values vary across vignettes, underscoring the necessity for context-aware AI alignment strategies. This work provides insights into the design space of human-AI alignment, offering foundations for developing AI that responsibly reflects societal values and ethics.

Pre-trained deep learning embeddings have consistently shown superior performance over handcrafted acoustic features in speech emotion recognition (SER). However, unlike acoustic features with clear physical meaning, these embeddings lack clear interpretability. Explaining these embeddings is crucial for building trust in healthcare and security applications and advancing the scientific understanding of the acoustic information that is encoded in them. This paper proposes a modified probing approach to explain deep learning embeddings in the SER space. We predict interpretable acoustic features (e.g., f0, loudness) from (i) the complete set of embeddings and (ii) a subset of the embedding dimensions identified as most important for predicting each emotion. If the subset of the most important dimensions better predicts a given emotion than all dimensions and also predicts specific acoustic features more accurately, we infer those acoustic features are important for the embedding model for the given task. We conducted experiments using the WavLM embeddings and eGeMAPS acoustic features as audio representations, applying our method to the RAVDESS and SAVEE emotional speech datasets. Based on this evaluation, we demonstrate that Energy, Frequency, Spectral, and Temporal categories of acoustic features provide diminishing information to SER in that order, demonstrating the utility of the probing classifier method to relate embeddings to interpretable acoustic features.

Clustering, as an unsupervised technique, plays a pivotal role in various data analysis applications. Among clustering algorithms, Spectral Clustering on Euclidean Spaces has been extensively studied. However, with the rapid evolution of data complexity, Euclidean Space is proving to be inefficient for representing and learning algorithms. Although Deep Neural Networks on hyperbolic spaces have gained recent traction, clustering algorithms or non-deep machine learning models on non-Euclidean Spaces remain underexplored. In this paper, we propose a spectral clustering algorithm on Hyperbolic Spaces to address this gap. Hyperbolic Spaces offer advantages in representing complex data structures like hierarchical and tree-like structures, which cannot be embedded efficiently in Euclidean Spaces. Our proposed algorithm replaces the Euclidean Similarity Matrix with an appropriate Hyperbolic Similarity Matrix, demonstrating improved efficiency compared to clustering in Euclidean Spaces. Our contributions include the development of the spectral clustering algorithm on Hyperbolic Spaces and the proof of its weak consistency. We show that our algorithm converges at least as fast as Spectral Clustering on Euclidean Spaces. To illustrate the efficacy of our approach, we present experimental results on the Wisconsin Breast Cancer Dataset, highlighting the superior performance of Hyperbolic Spectral Clustering over its Euclidean counterpart. This work opens up avenues for utilizing non-Euclidean Spaces in clustering algorithms, offering new perspectives for handling complex data structures and improving clustering efficiency., Comment: Currently under review in IEEE T-PAMI

We investigate the effects of inclusion of charged tachyonic open-string scalars in the perturbative and the non-perturbative K\"ahler moduli stabilizations in a geometry of three intersecting magnetized D7-brane stacks in type-IIB/F theory and also study the overall influence of this process on the inflaton potential, in a hybrid inflation scenario. We find that a tachyon lowers the minimum of the inflaton potential and assists to end the inflation. For simplicity, we have included one tachyon at a time in the present work and observe that this procedure preserves the features of slow-roll plateau of the potential. An interesting observation here is that the tachyonic part of the potential can be fine-tuned to get an almost zero minimum of the potential, thereby conforming to the small experimental value of the cosmological constant, Comment: Accepted for publication in EPJC, 30 pages, 6 figures, 5 tables

Open-vocabulary audio-language models, like CLAP, offer a promising approach for zero-shot audio classification (ZSAC) by enabling classification with any arbitrary set of categories specified with natural language prompts. In this paper, we propose a simple but effective method to improve ZSAC with CLAP. Specifically, we shift from the conventional method of using prompts with abstract category labels (e.g., Sound of an organ) to prompts that describe sounds using their inherent descriptive features in a diverse context (e.g.,The organ's deep and resonant tones filled the cathedral.). To achieve this, we first propose ReCLAP, a CLAP model trained with rewritten audio captions for improved understanding of sounds in the wild. These rewritten captions describe each sound event in the original caption using their unique discriminative characteristics. ReCLAP outperforms all baselines on both multi-modal audio-text retrieval and ZSAC. Next, to improve zero-shot audio classification with ReCLAP, we propose prompt augmentation. In contrast to the traditional method of employing hand-written template prompts, we generate custom prompts for each unique label in the dataset. These custom prompts first describe the sound event in the label and then employ them in diverse scenes. Our proposed method improves ReCLAP's performance on ZSAC by 1%-18% and outperforms all baselines by 1% - 55%., Comment: Code and Checkpoints: https://github.com/Sreyan88/ReCLAP

This Letter reports new results from the HAYSTAC experiment's search for dark matter axions in our galactic halo. It represents the widest search to date that utilizes squeezing to realize sub-quantum limited noise. The new results cover 1.71 $\mu$eV of newly scanned parameter space in the mass ranges 17.28--18.44 $\mu$eV and 18.71--19.46 $\mu$eV. No statistically significant evidence of an axion signal was observed, excluding couplings $|g_\gamma|\geq$ 2.75$\times$$|g_{\gamma}^{\text{KSVZ}}|$ and $|g_\gamma|\geq$ 2.96$\times$$|g_{\gamma}^{\text{KSVZ}}|$ at the 90$\%$ confidence level over the respective region. By combining this data with previously published results using HAYSTAC's squeezed state receiver, a total of 2.27 $\mu$eV of parameter space has now been scanned between 16.96--19.46 $\mu$eV, excluding $|g_\gamma|\geq$ 2.86$\times$$|g_{\gamma}^{\text{KSVZ}}|$ at the 90$\%$ confidence level. These results demonstrate the squeezed state receiver's ability to probe axion models over a significant mass range while achieving a scan rate enhancement relative to a quantum-limited experiment., Comment: 6 pages, 3 figures

The XENONnT experiment, located at the INFN Laboratori Nazionali del Gran Sasso, Italy, features a 5.9 tonne liquid xenon time projection chamber surrounded by an instrumented neutron veto, all of which is housed within a muon veto water tank. Due to extensive shielding and advanced purification to mitigate natural radioactivity, an exceptionally low background level of (15.8 $\pm$ 1.3) events/(tonne$\cdot$year$\cdot$keV) in the (1, 30) keV region is reached in the inner part of the TPC. XENONnT is thus sensitive to a wide range of rare phenomena related to Dark Matter and Neutrino interactions, both within and beyond the Standard Model of particle physics, with a focus on the direct detection of Dark Matter in the form of weakly interacting massive particles (WIMPs). From May 2021 to December 2021, XENONnT accumulated data in rare-event search mode with a total exposure of one tonne $\cdot$ year. This paper provides a detailed description of the signal reconstruction methods, event selection procedure, and detector response calibration, as well as an overview of the detector performance in this time frame. This work establishes the foundational framework for the `blind analysis' methodology we are using when reporting XENONnT physics results., Comment: 27 pages, 23 figures

We develop a method to transform a collection of higher-dimensional spin systems from the thermal state with a very high temperature of a local spin-s Hamiltonian to a low-lying energy eigenstate of the same. The procedure utilizes an auxiliary system, interactions between all systems, and appropriate projective measurements of arbitrary rank performed on the auxiliary system. We refer to this process as subspace cooling. The performance of the protocol is assessed by determining the fidelity of the target state with the output one and the success probability of achieving the resulting state. For this analysis, spin-s XXZ and bilinear biquadratic models are employed as the evolving Hamiltonian. We demonstrate that in both scenarios, unit fidelity can be attained after a reasonable number of repeated measurements and a finite amount of evolution time when all the systems are aligned in an open chain, but it fails when the interactions between the spin follow the star configuration. We report that the success probability increases with the rank of the projectors in the measurement for a fixed dimension and that for each dimension, there exists a range of interaction strength and evolution period for which the fidelity gets maximized. Even when some subsystems are in contact with the thermal bath, the method proves to be resistant to decoherence., Comment: 12 pages, 10 figures

Many existing fairness metrics measure group-wise demographic disparities in system behavior or model performance. Calculating these metrics requires access to demographic information, which, in industrial settings, is often unavailable. By contrast, economic inequality metrics, such as the Gini coefficient, require no demographic data to measure. However, reductions in economic inequality do not necessarily correspond to reductions in demographic disparities. In this paper, we empirically explore the relationship between demographic-free inequality metrics -- such as the Gini coefficient -- and standard demographic bias metrics that measure group-wise model performance disparities specifically in the case of engagement inequality on Twitter. We analyze tweets from 174K users over the duration of 2021 and find that demographic-free impression inequality metrics are positively correlated with gender, race, and age disparities in the average case, and weakly (but still positively) correlated with demographic bias in the worst case. We therefore recommend inequality metrics as a potentially useful proxy measure of average group-wise disparities, especially in cases where such disparities cannot be measured directly. Based on these results, we believe they can be used as part of broader efforts to improve fairness between demographic groups in scenarios like content recommendation on social media., Comment: Accepted to the FAccTRec Workshop at ACM RecSys 2024

Laser surface remelting of as-cast Al-Ge eutectic alloy is shown to produce ultrafine lamellar eutectic morphology with interlamellar spacing refined up to ~60 nm and composed of FCC Al solid solution and unusual AlxGey intermetallic phases that do not form during near-equilibrium solidification. The microstructures are characterized and analyzed using a combination of selected area electron diffraction, high-resolution scanning transmission electron microscopy, energy dispersive X-ray spectroscopy to obtain high-resolution elemental maps, and atomistic modeling using density functional theory followed by atomic-scale image simulation. Depending on the local solidification conditions, the crystallography of the AlxGey intermetallic phases in the eutectic microstructure is either monoclinic (C 2/c) or monoclinic (P 21), with high densities of defects in both cases. This is in sharp contrast to the as-cast alloys that showed nominally pure Al and Ge phases with significant solute partitioning and equilibrium FCC and diamond cubic crystal structures, respectively. Corresponding kinetic phase diagrams are proposed to interpret the evolution of nano-lamellar eutectic morphologies with equilibrium Al and metastable AlxGey phases, and to explain increased solid solubility in the Al phases manifested by precipitation of ultrafine clusters of Ge. The reasons for the formation of these metastable eutectics under laser rapid solidification are discussed from the perspective of the competitive growth criterion.

With the rapid advancements in multimodal generative technology, Affective Computing research has provoked discussion about the potential consequences of AI systems equipped with emotional intelligence. Affective Computing involves the design, evaluation, and implementation of Emotion AI and related technologies aimed at improving people's lives. Designing a computational model in affective computing requires vast amounts of multimodal data, including RGB images, video, audio, text, and physiological signals. Moreover, Affective Computing research is deeply engaged with ethical considerations at various stages-from training emotionally intelligent models on large-scale human data to deploying these models in specific applications. Fundamentally, the development of any AI system must prioritize its impact on humans, aiming to augment and enhance human abilities rather than replace them, while drawing inspiration from human intelligence in a safe and responsible manner. The MRAC 2024 Track 1 workshop seeks to extend these principles from controlled, small-scale lab environments to real-world, large-scale contexts, emphasizing responsible development. The workshop also aims to highlight the potential implications of generative technology, along with the ethical consequences of its use, to researchers and industry professionals. To the best of our knowledge, this is the first workshop series to comprehensively address the full spectrum of multimodal, generative affective computing from a responsible AI perspective, and this is the second iteration of this workshop. Webpage: https://react-ws.github.io/2024/, Comment: ACM MM Workshop 2024. Workshop webpage: https://react-ws.github.io/2024/

The detection and localization of deepfake content, particularly when small fake segments are seamlessly mixed with real videos, remains a significant challenge in the field of digital media security. Based on the recently released AV-Deepfake1M dataset, which contains more than 1 million manipulated videos across more than 2,000 subjects, we introduce the 1M-Deepfakes Detection Challenge. This challenge is designed to engage the research community in developing advanced methods for detecting and localizing deepfake manipulations within the large-scale high-realistic audio-visual dataset. The participants can access the AV-Deepfake1M dataset and are required to submit their inference results for evaluation across the metrics for detection or localization tasks. The methodologies developed through the challenge will contribute to the development of next-generation deepfake detection and localization systems. Evaluation scripts, baseline models, and accompanying code will be available on https://github.com/ControlNet/AV-Deepfake1M., Comment: ACM MM 2024. Challenge webpage: https://deepfakes1m.github.io/

Foundation models like the segment anything model require high-quality manual prompts for medical image segmentation, which is time-consuming and requires expertise. SAM and its variants often fail to segment structures in ultrasound (US) images due to domain shift. We propose Sam2Rad, a prompt learning approach to adapt SAM and its variants for US bone segmentation without human prompts. It introduces a prompt predictor network (PPN) with a cross-attention module to predict prompt embeddings from image encoder features. PPN outputs bounding box and mask prompts, and 256-dimensional embeddings for regions of interest. The framework allows optional manual prompting and can be trained end-to-end using parameter-efficient fine-tuning (PEFT). Sam2Rad was tested on 3 musculoskeletal US datasets: wrist (3822 images), rotator cuff (1605 images), and hip (4849 images). It improved performance across all datasets without manual prompts, increasing Dice scores by 2-7% for hip/wrist and up to 33% for shoulder data. Sam2Rad can be trained with as few as 10 labeled images and is compatible with any SAM architecture for automatic segmentation.

Estimating the Most Important Person (MIP) in any social event setup is a challenging problem mainly due to contextual complexity and scarcity of labeled data. Moreover, the causality aspects of MIP estimation are quite subjective and diverse. To this end, we aim to address the problem by annotating a large-scale `in-the-wild' dataset for identifying human perceptions about the `Most Important Person (MIP)' in an image. The paper provides a thorough description of our proposed Multimodal Large Language Model (MLLM) based data annotation strategy, and a thorough data quality analysis. Further, we perform a comprehensive benchmarking of the proposed dataset utilizing state-of-the-art MIP localization methods, indicating a significant drop in performance compared to existing datasets. The performance drop shows that the existing MIP localization algorithms must be more robust with respect to `in-the-wild' situations. We believe the proposed dataset will play a vital role in building the next-generation social situation understanding methods. The code and data is available at https://github.com/surbhimadan92/MIP-GAF., Comment: Accepted for publication at WACV 2025

A general covariant Einstein-Gauss-Bonnet Gravity in Four-Dimensional (4D EGB) spacetime is shown to bypass Lovelock's theorem and is free from Ostrogradsky instability. Meanwhile, the bumblebee theory is a vector-tensor theory. It extends the Einstein--Maxwell theory that allows for the spontaneous symmetry breaking that leads to the field acquiring a vacuum expectation value, introducing Lorentz violation into the system. We investigate rotating black holes in the 4D EGB-bumblebee gravity model where Lorentz symmetry is spontaneously broken -- Kerr EGB bumblebee (KEGBB) black holes. The latest observations from the Event Horizon Telescope (EHT) of the supermassive black holes (SMBHs) M87* and Sgr A* have sparked intensified interest in the study of black hole shadows, which present a novel avenue for investigating SMBHs within the strong-field regime. Motivated by this, we model SMBHs M87* and Sgr A* as KEGBB black holes, and using the EHT observation result, for given $l$, to find earlier upper limits on the $\alpha$ and $a$ are altered. The KEGBB and Kerr black holes are indiscernible in some parameter space, and one cannot rule out the possibility that the former may serve as strong candidates for astrophysical black holes. Employing our newly developed parameter estimation technique, we use two EHT observables -- namely, the angular diameter of the shadow, $d_{sh}$, and the axial ratio, $\mathcal{D}_A$ -- to estimate parameters of M87* and Sgr A* taking into account observational errors associated with the EHT results., Comment: 15 pages, 6 figures, 2 tables. Matched to the published version

The ICPR 2024 Competition on Safe Segmentation of Drive Scenes in Unstructured Traffic and Adverse Weather Conditions served as a rigorous platform to evaluate and benchmark state-of-the-art semantic segmentation models under challenging conditions for autonomous driving. Over several months, participants were provided with the IDD-AW dataset, consisting of 5000 high-quality RGB-NIR image pairs, each annotated at the pixel level and captured under adverse weather conditions such as rain, fog, low light, and snow. A key aspect of the competition was the use and improvement of the Safe mean Intersection over Union (Safe mIoU) metric, designed to penalize unsafe incorrect predictions that could be overlooked by traditional mIoU. This innovative metric emphasized the importance of safety in developing autonomous driving systems. The competition showed significant advancements in the field, with participants demonstrating models that excelled in semantic segmentation and prioritized safety and robustness in unstructured and adverse conditions. The results of the competition set new benchmarks in the domain, highlighting the critical role of safety in deploying autonomous vehicles in real-world scenarios. The contributions from this competition are expected to drive further innovation in autonomous driving technology, addressing the critical challenges of operating in diverse and unpredictable environments., Comment: 15 pages, 7 figures, ICPR Competition Paper

We study a simple model of swarmalators subject to periodic forcing and confined to move around a one-dimensional ring. This is a toy model for physical systems with a mix of sync, swarming, and forcing such as colloidal micromotors. We find several emergent macrostates and characterize the phase boundaries between them analytically. The most novel state is a swarmalator chimera, where the population splits into two sync dots, which enclose a `train' of swarmalators that run around a peanut-shaped loop., Comment: 9 pages, 7 figures

We obtain an asymptotic normality result that reveals the precise asymptotic behavior of the maximum likelihood estimators of parameters for a very general class of linear mixed models containing cross random effects. In achieving the result, we overcome theoretical difficulties that arise from random effects being crossed as opposed to the simpler nested random effects case. Our new theory is for a class of Gaussian response linear mixed models which includes crossed random slopes that partner arbitrary multivariate predictor effects and does not require the cell counts to be balanced. Statistical utilities include confidence interval construction, Wald hypothesis test and sample size calculations.