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Bose-Fermi mixtures naturally appear in various physical systems. In semiconductor heterostructures, such mixtures can be realized, with bosons as excitons and fermions as dopant charges. However, the complexity of these hybrid systems challenges the comprehension of the mechanisms that determine physical properties such as mobility. In this study, we investigate interlayer exciton diffusion in an H-stacked WSe$_2$/WS$_2$ heterobilayer. Our measurements are performed in the dilute exciton density limit at low temperatures to examine how the presence of charges affects exciton mobility. Remarkably, for charge doping near the Mott insulator phase, we observe a giant enhancement of exciton diffusion of three orders of magnitude compared to charge neutrality. We attribute this observation to mobile valence holes, which experience a suppressed moir\'e potential due to the electronic charge order in the conduction band, and recombine with any conduction electron in a non-monogamous manner. This new mechanism emerges for sufficiently large fillings in the vicinity of correlated generalized Wigner crystal and Mott insulating states. Our results demonstrate the potential to characterize correlated electron states through exciton diffusion and provide insights into the rich interplay of bosons and fermions in semiconductor heterostructures., Comment: Main text: 19 pages, 4 figures. Supplementary Material: 9 pages, 6 figures

A geometrically nonlinear theory for field dislocation thermomechanics based entirely on measurable state variables is proposed. Instead of starting from an ordering-dependent multiplicative decomposition of the total deformation gradient tensor, the additive decomposition of the velocity gradient into elastic, plastic and thermal distortion rates is obtained as a natural consequence of the conservation of the Burgers vector. Based on this equation, the theory consistently captures the contribution of transient heterogeneous temperature fields on the evolution of the (polar) dislocation density. The governing equations of the model are obtained from the conservation of Burgers vector, mass, linear and angular momenta, and the First Law. The Second Law is used to deduce the thermodynamical driving forces for dislocation velocity. An evolution equation for temperature is obtained from the First Law and the Helmholtz free energy density, which is taken as a function of the following measurable quantities: elastic distortion, temperature and the dislocation density (the theory allows prescribing additional measurable quantities as internal state variables if needed). Furthermore, the theory allows one to compute the Taylor-Quinney factor, which is material and strain rate dependent. Accounting for the polar dislocation density as a state variable in the Helmholtz free energy of the system allows for temperature solutions in the form of dispersive waves with finite propagation speed, despite using Fourier's law of heat conduction as the constitutive assumption for the heat flux vector., Comment: 34 pages, 3 figures, preprint submitted to journal

This paper explores the properties of multiplicative Lie algebra structures on a nilpotent group of class $2$. We also present a method for determining a multiplicative Lie algebra structure on a group that serves as an extension of one Lie ring by another Lie ring such as a metacyclic group and a nilpotent group of class $2$.

Superconducting circuits provide a versatile and controllable platform for studies of fundamental quantum phenomena as well as for quantum technology applications. A conventional technique to read out the state of a quantum circuit or to characterize its properties is based on rf measurement schemes involving costly and complex instrumentation. Here we demonstrate a simple dc measurement of a thermal spectrometer to investigate properties of a superconducting circuit, in this proof-of-concept experiment a coplanar waveguide resonator. A fraction of the microwave photons in the resonator is absorbed by an on-chip bolometer, resulting in a measurable temperature rise. By monitoring the dc signal of the thermometer due to this process, we are able to determine the resonance frequency and the lineshape (quality factor) of the resonator. The demonstrated scheme, which is a simple dc measurement, has a wide band up to 200 GHz, well exceeding that of the typical rf spectrometer. Moreover, the thermal measurement yields a highly frequency independent reference level of the Lorentzian absorption signal, unlike the conventional rf measurement. In the low power regime, the measurement is fully calibration-free. Our technique thus offers an alternative spectrometer for quantum circuits, which is in many ways superior with respect to conventional methods., Comment: 13 pages and 10 figures

Large Language Models (LLMs) have demonstrated significant performance improvements across various cognitive tasks. An emerging application is using LLMs to enhance retrieval-augmented generation (RAG) capabilities. These systems require LLMs to understand user queries, retrieve relevant information, and synthesize coherent and accurate responses. Given the increasing real-world deployment of such systems, comprehensive evaluation becomes crucial. To this end, we propose FRAMES (Factuality, Retrieval, And reasoning MEasurement Set), a high-quality evaluation dataset designed to test LLMs' ability to provide factual responses, assess retrieval capabilities, and evaluate the reasoning required to generate final answers. While previous work has provided datasets and benchmarks to evaluate these abilities in isolation, FRAMES offers a unified framework that provides a clearer picture of LLM performance in end-to-end RAG scenarios. Our dataset comprises challenging multi-hop questions that require the integration of information from multiple sources. We present baseline results demonstrating that even state-of-the-art LLMs struggle with this task, achieving 0.40 accuracy with no retrieval. The accuracy is significantly improved with our proposed multi-step retrieval pipeline, achieving an accuracy of 0.66 (>50% improvement). We hope our work will help bridge evaluation gaps and assist in developing more robust and capable RAG systems., Comment: Arxiv Preprint

We investigated the electromagnetic quadrupole transition of baryon decuplet ($J^P= \frac{3}{2}^+$) to octet ($J^P= \frac{1}{2}^+$) using the statistical framework together with the principle of detailed balance. The statistical approach assumed the expansion of hadrons in terms of various quark-gluon Fock states. By specifying the appropriate multiplicity in spin, color $\&$ flavor space, the relative probabilities of strange and non-strange quark-gluon Fock state are calculated. These probabilities further accumulated in the form of statistical parameters, highlighting the importance of sea quarks and gluons in the electromagnetic transition. Our calculations includes the individual contribution of valence and sea (scalar, vector and tensor ) to the transition moment of baryons. The effect of flavor SU(3) symmetry and its breaking in both valence and sea quarks is studied by incorporating the strange quark mass. The strangeness in the sea is constrained by a suppression factor $(1-C_l)^{n-1}$, which depends upon the free energy of gluons. The computed results get affected upto 60 $\%$ and exhibit the dominance of octet sea. The present work has been compared with updated experimental data and various theoretical predictions. The results obtained may offer important insights for future experimental studies.

The charm ($D$) and charm-strange ($D_s$) mesons are investigated in a variational scheme using Gaussian trial wave functions. The Hamiltonian contains Song and Lin potential with a constant term dependent on radial and orbital quantum numbers. The Gaussian wave function used has a dependence on radial distance $r$, radial quantum number $n$, orbital quantum number $l$ and a trial parameter $\mu$. These wave functions are used to compute the expectation of Song and Lin potential dependent on $r$. The total energy (expectation of Hamiltonian) for each state is minimized with a parameter $\mu$ from the wavefunctions. The obtained spectra of $D$ and $D_s$ mesons are in good agreement with other theoretical models and available experimental masses. The mass spectra of $D$ and $D_s$ mesons are also used to plot Regge trajectories in the ($J$, $M^2$) and ($n_r$, $M^2$) planes. In ($J$, $M^2$) plane, both natural and unnatural parity states of $D$ and $D_s$ mesons are plotted. The trajectories are parallel and equidistant from each other. The two-body strong decays of $D$ and $D_s$ are analyzed in the framework of heavy quark effective theory using computed masses. The strong decay widths are given in terms of strong coupling constants. These couplings are also estimated by comparing them with available experimental values for observed states. Also, the partial decay width ratios of different states are analyzed and used to suggest assignments to the observed states. We have assigned the spin-parity to newly observed $D^*_{s2}(2573)$ as the strange partner of $D^*_2(2460)$ identified as $1^3P_2$, $D_1^*(2760)$ and $D^*_{s1}(2860)$ as $1^3D_1$, $D^*_3(2750)$ and $D^*_{s3}(2860)$ as $1^3D_3$, $D_2(2740)$ as $1D_2$, $D_0(2550)$ as $2^1S_0$, $D^*_1(2660)$ and $D^*_{s1}(2700)$ as $2^3S_1$, $D^*_J(3000)$ as $2^3P_0$ $D_J(3000)$ as $2P_1$, $D^*_2(3000)$ as $1^3F_2$ states., Comment: 23 pages, 6 figures

Quantum resonances in collisions and reactions are a sensitive probe of the intermolecular forces. They may dominate the final quantum state distribution, as recently observed for Feshbach resonances in a cold collision experiment (Science 380, 77 (2023)). This raises the question whether the sensitivity of such measurements is sufficient to assess the quality of theoretical models for the interaction. We here compare measured collision cross sections to those obtained with exact quantum coupled-channels scattering calculations for three different ab initio potential energy surfaces. We find that the ability to test the correct prediction of energy redistribution over molecular degrees of freedom is within reach, requiring only a modest improvement in energy resolution of current experiments. Such improvement will enable the separation of individual resonances and allow for an unambiguous experimental test of different theory approaches.

We analyze the ground state pentaquark structures with a single heavy quark ($qqqq\Bar{Q}$) using various phenomenological models. The recent observations of singly heavy tetraquark structures at LHCb serve as a significant motivation for this investigation. We studied the symmetric 15-plet configuration of SU(3) flavor representation with the spin-parity assignment of $5/2^-$, representing the symmetric spin state for the pentaquark systems. We employed an extended Gursey-Radicati mass formula and an effective mass scheme to compute the mass spectra of pentaquark states. Additionally, the methodology of the screened charge scheme is introduced to calculate the magnetic moment assignments, specifically for configurations involving both charm and bottom quarks. We also proposed the potential production modes originating from the weak decay of heavy baryons. We identified the strong decay channels where pentaquark transitions into a light baryon and a heavy meson. Our analysis of mass spectra, magnetic moments, and possible strong decay channels helps us to explore the inner structure of pentaquarks and their underlying quark dynamics. This work not only augments the theoretical frameworks used to describe such systems but is also helpful for future experimental pursuits at facilities like LHCb, fostering further experimental validations and discoveries in heavy quark spectroscopy.

In an era where digital threats are increasingly sophisticated, the intersection of Artificial Intelligence and cybersecurity presents both promising defenses and potent dangers. This paper delves into the escalating threat posed by the misuse of AI, specifically through the use of Large Language Models (LLMs). This study details various techniques like the switch method and character play method, which can be exploited by cybercriminals to generate and automate cyber attacks. Through a series of controlled experiments, the paper demonstrates how these models can be manipulated to bypass ethical and privacy safeguards to effectively generate cyber attacks such as social engineering, malicious code, payload generation, and spyware. By testing these AI generated attacks on live systems, the study assesses their effectiveness and the vulnerabilities they exploit, offering a practical perspective on the risks AI poses to critical infrastructure. We also introduce Occupy AI, a customized, finetuned LLM specifically engineered to automate and execute cyberattacks. This specialized AI driven tool is adept at crafting steps and generating executable code for a variety of cyber threats, including phishing, malware injection, and system exploitation. The results underscore the urgency for ethical AI practices, robust cybersecurity measures, and regulatory oversight to mitigate AI related threats. This paper aims to elevate awareness within the cybersecurity community about the evolving digital threat landscape, advocating for proactive defense strategies and responsible AI development to protect against emerging cyber threats., Comment: Journal Paper

Synthetic infrared (IR) scene and target generation is an important computer vision problem as it allows the generation of realistic IR images and targets for training and testing of various applications, such as remote sensing, surveillance, and target recognition. It also helps reduce the cost and risk associated with collecting real-world IR data. This survey paper aims to provide a comprehensive overview of the conventional mathematical modelling-based methods and deep learning-based methods used for generating synthetic IR scenes and targets. The paper discusses the importance of synthetic IR scene and target generation and briefly covers the mathematics of blackbody and grey body radiations, as well as IR image-capturing methods. The potential use cases of synthetic IR scenes and target generation are also described, highlighting the significance of these techniques in various fields. Additionally, the paper explores possible new ways of developing new techniques to enhance the efficiency and effectiveness of synthetic IR scenes and target generation while highlighting the need for further research to advance this field., Comment: Submitted in Journal of Infrared Physics & Technology

We introduce Imagen 3, a latent diffusion model that generates high quality images from text prompts. We describe our quality and responsibility evaluations. Imagen 3 is preferred over other state-of-the-art (SOTA) models at the time of evaluation. In addition, we discuss issues around safety and representation, as well as methods we used to minimize the potential harm of our models.

The fractional AC Josephson effect is a discerning property of topological superconductivity in hybrid Josephson junctions. Recent experimental observations of missing odd Shapiro steps and half Josephson frequency emission in various materials have sparked significant debate regarding their potential origin in the effect. In this study, we present microwave emission measurements on a resistively shunted Josephson junction based on a HgTe quantum well. We demonstrate that, with significant spurious inductance in the shunt wiring, the experiment operates in a nonlinear dynamic regime characterized by period-doubling. This leads to additional microwave emission peaks at half of the Josephson frequency, $f_J/2$, which can mimic the $4\pi$-periodicity of topological Andreev states. The observed current-voltage characteristics and emission spectra are well-described by a simple RCLSJ model. Furthermore, we show that the nonlinear dynamics of the junction can be controlled using gate voltage, magnetic field, and temperature, with our model accurately reproducing these effects without incorporating any topological attributes. Our observations urge caution in interpreting emission at $f_J/2$ as evidence for gapless Andreev bound states in topological junctions and suggest the appropriate parameter range for future experiments., Comment: 13 pages, 8 figures

This paper presents a machine learning approach to estimate the inertial parameters of a spacecraft in cases when those change during operations, e.g. multiple deployments of payloads, unfolding of appendages and booms, propellant consumption as well as during in-orbit servicing and active debris removal operations. The machine learning approach uses time series clustering together with an optimised actuation sequence generated by reinforcement learning to facilitate distinguishing among different inertial parameter sets. The performance of the proposed strategy is assessed against the case of a multi-satellite deployment system showing that the algorithm is resilient towards common disturbances in such kinds of operations., Comment: 6 pages, 3 figures, 1 table. To be presented in ESA - AI for Space (SPAICE)

This workshop will grow and consolidate a community of interdisciplinary CSCW researchers focusing on the topic of contestable AI. As an outcome of the workshop, we will synthesize the most pressing opportunities and challenges for contestability along AI value chains in the form of a research roadmap. This roadmap will help shape and inspire imminent work in this field. Considering the length and depth of AI value chains, it will especially spur discussions around the contestability of AI systems along various sites of such chains. The workshop will serve as a platform for dialogue and demonstrations of concrete, successful, and unsuccessful examples of AI systems that (could or should) have been contested, to identify requirements, obstacles, and opportunities for designing and deploying contestable AI in various contexts. This will be held primarily as an in-person workshop, with some hybrid accommodation. The day will consist of individual presentations and group activities to stimulate ideation and inspire broad reflections on the field of contestable AI. Our aim is to facilitate interdisciplinary dialogue by bringing together researchers, practitioners, and stakeholders to foster the design and deployment of contestable AI., Comment: 5 pages, 0 figure, to be held as a workshop at CSCW'24

We propose a protocol for the preparation of generalized Greenberger-Horne-Zeilinger (GHZ) states of $N$ atoms each with $d=3$ or $4$ internal levels. We generalize the celebrated one-axis twisting (OAT) Hamiltonian for $N$ qubits to qudits by including OAT interactions of equal strengths between every pair of qudit levels, a protocol we call as balanced OAT (BOAT). Analogous to OAT for qubits, we find that starting from a product state of an arbitrary number of atoms $N$, dynamics under BOAT leads to the formation of GHZ states for qutrits ($d=3$) and ququarts ($d=4$). While BOAT could potentially be realized on several platforms where all-to-all coupling is possible, here we propose specific implementations using trapped ion systems. We show that preparing these states with a fidelity above a threshold value rules out lower dimensional entanglement than that of the generalized GHZ states. For qutrits, we also propose a protocol to bound the fidelity that requires only global addressing of the ion crystal and single-shot readout of one of the levels. Our results open a path for the scalable generation and certification of high-dimensional multipartite entanglement on current atom-based quantum hardware., Comment: 12 pages, 1 figure; comments welcome

The translocation of biopolymers, such as DNA and proteins, across cellular or nuclear membranes is essential for numerous biological processes. The translocation dynamics are influenced by the properties of the polymers, such as polymer stiffness, and the geometry of the capsid. In our study, we aim to investigate the impact of polymer stiffness, activity, and different capsid geometries on the packing and ejection dynamics of both passive and active polymers. We employ Langevin dynamics simulations for a systematic investigation. We observe that flexible polymers exhibit packing times that are faster than those of their semi-flexible counterparts. Interestingly, for large polymers compared to the capsid size, sphere facilitates faster packing and unpacking compared to ellipsoid, mimicking the cell nucleus and suggesting a geometrical advantage for biopolymer translocation. In summary, we observe that increasing activity accelerates both the packing and ejection processes for both flexible and semi-flexible polymers. However, the effect is significantly more pronounced for semi-flexible polymers, highlighting the crucial role of polymer flexibility in these dynamics. These findings deepen our understanding of the intricate interplay between polymer flexibility, capsid geometry, and activity, providing valuable insight into the dynamics of polymer packing and ejection processes., Comment: 10 pages, 9 figures

This paper presents a modeling-control synthesis to address the quality control challenges in multistage manufacturing systems (MMSs). A new feedforward control scheme is developed to minimize the quality variations caused by process disturbances in MMSs. Notably, the control framework leverages a stochastic deep Koopman (SDK) model to capture the quality propagation mechanism in the MMSs, highlighted by its ability to transform the nonlinear propagation dynamics into a linear one. Two roll-to-roll case studies are presented to validate the proposed method and demonstrate its effectiveness. The overall method is suitable for nonlinear MMSs and does not require extensive expert knowledge., Comment: The paper was in the proceeding of 2024 American Control Conference. This submitted version addresses a minor correction to one equation (Eq. 14), while the results and conclusions remain the same

Quantum computing (QC) has the potential to revolutionize fields like machine learning, security, and healthcare. Quantum machine learning (QML) has emerged as a promising area, enhancing learning algorithms using quantum computers. However, QML models are lucrative targets due to their high training costs and extensive training times. The scarcity of quantum resources and long wait times further exacerbate the challenge. Additionally, QML providers may rely on a third-party quantum cloud for hosting the model, exposing the models and training data. As QML-as-a-Service (QMLaaS) becomes more prevalent, reliance on third party quantum clouds can pose a significant threat. This paper shows that adversaries in quantum clouds can use white-box access of the QML model during training to extract the state preparation circuit (containing training data) along with the labels. The extracted training data can be reused for training a clone model or sold for profit. We propose a suite of techniques to prune and fix the incorrect labels. Results show that $\approx$90\% labels can be extracted correctly. The same model trained on the adversarially extracted data achieves approximately $\approx$90\% accuracy, closely matching the accuracy achieved when trained on the original data. To mitigate this threat, we propose masking labels/classes and modifying the cost function for label obfuscation, reducing adversarial label prediction accuracy by $\approx$70\%., Comment: 7 pages

Monitoring the carrier-envelope phase (CEP) is of paramount importance for experiments involving few cycle intense laser fields. Common measurement techniques include f-2f interferometry or stereo-ATI setups. These approaches are adequate, but are challenging to implement on demand, at different locations as additional metrology tools, in intense few cycle laser-matter interaction experiments, such as those prevalent in sophisticated user beamlines. In addition there are inherent difficulties for CEP measured at non-conventional laser wavelengths (like e.g. mid infrared) and measurements above 10 kHz laser repetition rates, on single shot basis. Here we demonstrate both by simulations and by experiments a machine learning (ML) driven method for CEP estimation in the mid infrared, which is readily generalizable for any laser wavelength and possibly up to MHz repetition rates. The concept relies on the observation of the spectrum of high harmonic generation (HHG) in bulk material and the use of ML techniques to estimate the CEP of the laser. Once the ML model is trained, the method provides a way for cheap and compact real-time CEP tagging. This technique can complement the otherwise sophisticated monitoring of CEP, and is able to capture the complex correlation between the CEP and the observable HHG spectra.

Natural Question Answering (QA) datasets play a crucial role in developing and evaluating the capabilities of large language models (LLMs), ensuring their effective usage in real-world applications. Despite the numerous QA datasets that have been developed, there is a notable lack of region-specific datasets generated by native users in their own languages. This gap hinders the effective benchmarking of LLMs for regional and cultural specificities. In this study, we propose a scalable framework, NativQA, to seamlessly construct culturally and regionally aligned QA datasets in native languages, for LLM evaluation and tuning. Moreover, to demonstrate the efficacy of the proposed framework, we designed a multilingual natural QA dataset, MultiNativQA, consisting of ~72K QA pairs in seven languages, ranging from high to extremely low resource, based on queries from native speakers covering 18 topics. We benchmark the MultiNativQA dataset with open- and closed-source LLMs. We made both the framework NativQA and MultiNativQA dataset publicly available for the community. (https://nativqa.gitlab.io), Comment: LLMs, Native, Multilingual, Language Diversity, Contextual Understanding, Minority Languages, Culturally Informed, Foundation Models, Large Language Models

In this paper, we consider an $\alpha^{\prime}$ corrected Reissner-Nordstr\"{o}m AdS black hole to study thermodynamics. We study the $P-V$ criticality and thermodynamical stability of the black hole. We obtained a first-order phase transition, which may be interpreted as the large/small black hole phase transition. Therefore, we obtained a van der Waals behaviour and obtained critical points. Finally, we calculate quantum work used to resolve the information loss paradox., Comment: 14 pages, published in JHAP

Cross-lingual speech emotion recognition (SER) is important for a wide range of everyday applications. While recent SER research relies heavily on large pretrained models for emotion training, existing studies often concentrate solely on the final transformer layer of these models. However, given the task-specific nature and hierarchical architecture of these models, each transformer layer encapsulates different levels of information. Leveraging this hierarchical structure, our study focuses on the information embedded across different layers. Through an examination of layer feature similarity across different languages, we propose a novel strategy called a layer-anchoring mechanism to facilitate emotion transfer in cross-lingual SER tasks. Our approach is evaluated using two distinct language affective corpora (MSP-Podcast and BIIC-Podcast), achieving a best UAR performance of 60.21% on the BIIC-podcast corpus. The analysis uncovers interesting insights into the behavior of popular pretrained models.

In recent years, we have witnessed the proliferation of large amounts of online content generated directly by users with virtually no form of external control, leading to the possible spread of misinformation. The search for effective solutions to this problem is still ongoing, and covers different areas of application, from opinion spam to fake news detection. A more recently investigated scenario, despite the serious risks that incurring disinformation could entail, is that of the online dissemination of health information. Early approaches in this area focused primarily on user-based studies applied to Web page content. More recently, automated approaches have been developed for both Web pages and social media content, particularly with the advent of the COVID-19 pandemic. These approaches are primarily based on handcrafted features extracted from online content in association with Machine Learning. In this scenario, we focus on Web page content, where there is still room for research to study structural-, content- and context-based features to assess the credibility of Web pages. Therefore, this work aims to study the effectiveness of such features in association with a deep learning model, starting from an embedded representation of Web pages that has been recently proposed in the context of phishing Web page detection, i.e., Web2Vec.

Active Galactic Nuclei (AGN) are prime candidate sources of the high-energy, astrophysical neutrinos detected by IceCube. This is demonstrated by the real-time multi-messenger detection of the blazar TXS 0506+056 and the recent evidence of neutrino emission from NGC 1068 from a separate time-averaged study. However, the production mechanism of the astrophysical neutrinos in AGN is not well established which can be resolved via correlation studies with photon observations. For neutrinos produced due to photohadronic interactions in AGN, in addition to a correlation of neutrinos with high-energy photons, there would also be a correlation of neutrinos with photons emitted at radio wavelengths. In this work, we perform an in-depth stacking study of the correlation between 15 GHz radio observations of AGN reported in the MOJAVE XV catalog, and ten years of neutrino data from IceCube. We also use a time-dependent approach which improves the statistical power of the stacking analysis. No significant correlation was found for both analyses and upper limits are reported. When compared to the IceCube diffuse flux, at 100 TeV and for a spectral index of 2.5, the upper limits derived are $\sim3\%$ and $\sim9\%$ for the time-averaged and time-dependent case, respectively., Comment: 14 Pages 7 Figures

We present a search for an eV-scale sterile neutrino using 7.5 years of data from the IceCube DeepCore detector. The analysis uses a sample of 21,914 events with energies between 5 and 150 GeV to search for sterile neutrinos through atmospheric muon neutrino disappearance. Improvements in event selection and treatment of systematic uncertainties provide greater statistical power compared to previous DeepCore sterile neutrino searches. Our results are compatible with the absence of mixing between active and sterile neutrino states, and we place constraints on the mixing matrix elements $|U_{\mu 4}|^2 < 0.0534$ and $|U_{\tau 4}|^2 < 0.0574$ at 90% CL under the assumption that $\Delta m^2_{41}\geq 1\;\mathrm{eV^2}$. These null results add to the growing tension between anomalous appearance results and constraints from disappearance searches in the 3+1 sterile neutrino landscape., Comment: 11 pages, 5 figures. Version accepted by Physical Review D for publication

This paper presents a novel mid-wave infrared (MWIR) small target detection dataset (MWIRSTD) comprising 14 video sequences containing approximately 1053 images with annotated targets of three distinct classes of small objects. Captured using cooled MWIR imagers, the dataset offers a unique opportunity for researchers to develop and evaluate state-of-the-art methods for small object detection in realistic MWIR scenes. Unlike existing datasets, which primarily consist of uncooled thermal images or synthetic data with targets superimposed onto the background or vice versa, MWIRSTD provides authentic MWIR data with diverse targets and environments. Extensive experiments on various traditional methods and deep learning-based techniques for small target detection are performed on the proposed dataset, providing valuable insights into their efficacy. The dataset and code are available at https://github.com/avinres/MWIRSTD., Comment: Accepted in ICIP2024

The recent IceCube detection of TeV neutrino emission from the nearby active galaxy NGC 1068 suggests that active galactic nuclei (AGN) could make a sizable contribution to the diffuse flux of astrophysical neutrinos. The absence of TeV $\gamma$-rays from NGC 1068 indicates neutrino production in the vicinity of the supermassive black hole, where the high radiation density leads to $\gamma$-ray attenuation. Therefore, any potential neutrino emission from similar sources is not expected to correlate with high-energy $\gamma$-rays. Disk-corona models predict neutrino emission from Seyfert galaxies to correlate with keV X-rays, as they are tracers of coronal activity. Using through-going track events from the Northern Sky recorded by IceCube between 2011 and 2021, we report results from a search for individual and aggregated neutrino signals from 27 additional Seyfert galaxies that are contained in the BAT AGN Spectroscopic Survey (BASS). Besides the generic single power-law, we evaluate the spectra predicted by the disk-corona model. Assuming all sources to be intrinsically similar to NGC 1068, our findings constrain the collective neutrino emission from X-ray bright Seyfert galaxies in the Northern Hemisphere, but, at the same time, show excesses of neutrinos that could be associated with the objects NGC 4151 and CGCG 420-015. These excesses result in a 2.7$\sigma$ significance with respect to background expectations., Comment: 17 pages, 9 figures

Active Galactic Nuclei (AGN) are promising candidate sources of high-energy astrophysical neutrinos since they provide environments rich in matter and photon targets where cosmic ray interactions may lead to the production of gamma rays and neutrinos. We searched for high-energy neutrino emission from AGN using the $\textit{Swift}$-BAT Spectroscopic Survey (BASS) catalog of hard X-ray sources and 12 years of IceCube muon track data. First, upon performing a stacked search, no significant emission was found. Second, we searched for neutrinos from a list of 43 candidate sources and found an excess from the direction of two sources, Seyfert galaxies NGC 1068 and NGC 4151. We observed NGC 1068 at flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $4.02_{-1.52}^{+1.58} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV, with power-law spectral index, $\gamma$ = 3.10$^{+0.26}_{-0.22}$, consistent with previous IceCube results. The observation of a neutrino excess from the direction of NGC 4151 is at a post-trial significance of 2.9$\sigma$. If interpreted as an astrophysical signal, the excess observed from NGC 4151 corresponds to a flux $\phi_{\nu_{\mu}+\bar{\nu}_{\mu}}$ = $1.51_{-0.81}^{+0.99} \times 10^{-11}$ TeV$^{-1}$ cm$^{-2}$ s$^{-1}$ normalized at 1 TeV and $\gamma$ = 2.83$^{+0.35}_{-0.28}$.

Copious amounts of relevance judgments are necessary for the effective training and accurate evaluation of retrieval systems. Conventionally, these judgments are made by human assessors, rendering this process expensive and laborious. A recent study by Thomas et al. from Microsoft Bing suggested that large language models (LLMs) can accurately perform the relevance assessment task and provide human-quality judgments, but unfortunately their study did not yield any reusable software artifacts. Our work presents UMBRELA (a recursive acronym that stands for UMbrela is the Bing RELevance Assessor), an open-source toolkit that reproduces the results of Thomas et al. using OpenAI's GPT-4o model and adds more nuance to the original paper. Across Deep Learning Tracks from TREC 2019 to 2023, we find that LLM-derived relevance judgments correlate highly with rankings generated by effective multi-stage retrieval systems. Our toolkit is designed to be easily extensible and can be integrated into existing multi-stage retrieval and evaluation pipelines, offering researchers a valuable resource for studying retrieval evaluation methodologies. UMBRELA will be used in the TREC 2024 RAG Track to aid in relevance assessments, and we envision our toolkit becoming a foundation for further innovation in the field. UMBRELA is available at https://github.com/castorini/umbrela., Comment: 5 pages, 3 figures

The ExoMol database (www.exomol.com) provides molecular data for spectroscopic studies of hot atmospheres. These data are widely used to model atmospheres of exoplanets, cool stars and other astronomical objects, as well as a variety of terrestrial applications. The 2024 data release reports the current status of the database which contains recommended line lists for 91 molecules and 224 isotopologues giving a total of almost 10$^{12}$ individual transitions. New features of the database include extensive "MARVELization" of line lists to allow them to be used for high resolutions studies, extension of several line lists to ultraviolet wavelengths, provision of photodissociation cross sections and extended provision of broadening parameters. Some of the in-house data specifications have been rewritten in JSON and moved to conformity with other international standards. Data products, including specific heats, a database of lifetimes for plasma studies, and the ExoMolHR web app which allows exclusively high resolution data to be extracted, are discussed.

Differential privacy with gradual expiration models the setting where data items arrive in a stream and at a given time $t$ the privacy loss guaranteed for a data item seen at time $(t-d)$ is $\epsilon g(d)$, where $g$ is a monotonically non-decreasing function. We study the fundamental $\textit{continual (binary) counting}$ problem where each data item consists of a bit, and the algorithm needs to output at each time step the sum of all the bits streamed so far. For a stream of length $T$ and privacy $\textit{without}$ expiration continual counting is possible with maximum (over all time steps) additive error $O(\log^2(T)/\varepsilon)$ and the best known lower bound is $\Omega(\log(T)/\varepsilon)$; closing this gap is a challenging open problem. We show that the situation is very different for privacy with gradual expiration by giving upper and lower bounds for a large set of expiration functions $g$. Specifically, our algorithm achieves an additive error of $ O(\log(T)/\epsilon)$ for a large set of privacy expiration functions. We also give a lower bound that shows that if $C$ is the additive error of any $\epsilon$-DP algorithm for this problem, then the product of $C$ and the privacy expiration function after $2C$ steps must be $\Omega(\log(T)/\epsilon)$. Our algorithm matches this lower bound as its additive error is $O(\log(T)/\epsilon)$, even when $g(2C) = O(1)$. Our empirical evaluation shows that we achieve a slowly growing privacy loss with significantly smaller empirical privacy loss for large values of $d$ than a natural baseline algorithm.

In the Multi-task Learning (MTL) framework, every task demands distinct feature representations, ranging from low-level to high-level attributes. It is vital to address the specific (feature/parameter) needs of each task, especially in computationally constrained environments. This work, therefore, introduces Layer-Optimized Multi-Task (LOMT) models that utilize structured sparsity to refine feature selection for individual tasks and enhance the performance of all tasks in a multi-task scenario. Structured or group sparsity systematically eliminates parameters from trivial channels and, sometimes, eventually, entire layers within a convolution neural network during training. Consequently, the remaining layers provide the most optimal features for a given task. In this two-step approach, we subsequently leverage this sparsity-induced optimal layer information to build the LOMT models by connecting task-specific decoders to these strategically identified layers, deviating from conventional approaches that uniformly connect decoders at the end of the network. This tailored architecture optimizes the network, focusing on essential features while reducing redundancy. We validate the efficacy of the proposed approach on two datasets, i.e., NYU-v2 and CelebAMask-HD datasets, for multiple heterogeneous tasks. A detailed performance analysis of the LOMT models, in contrast to the conventional MTL models, reveals that the LOMT models outperform for most task combinations. The excellent qualitative and quantitative outcomes highlight the effectiveness of employing structured sparsity for optimal layer (or feature) selection., Comment: Accepted at ECCV 2024 workshop - Computational Aspects of Deep Learning

In this paper, we discuss the capable and isoclinic properties of the tensor square in the context of multiplicative Lie algebras. We also developed the concept of isoclinic extensions and proved several results for multiplicative Lie algebras. Consequently, we demonstrate that covers of a multiplicative Lie algebra are mutually isoclinic.

In this paper, we give an almost linear time and space algorithms to sample from an exponential mechanism with an $\ell_1$-score function defined over an exponentially large non-convex set. As a direct result, on input an $n$ vertex $m$ edges graph $G$, we present the \textit{first} $\widetilde{O}(m)$ time and $O(m)$ space algorithms for differentially privately outputting an $n$ vertex $O(m)$ edges synthetic graph that approximates all the cuts and the spectrum of $G$. These are the \emph{first} private algorithms for releasing synthetic graphs that nearly match this task's time and space complexity in the non-private setting while achieving the same (or better) utility as the previous works in the more practical sparse regime. Additionally, our algorithms can be extended to private graph analysis under continual observation.

In this paper, we introduce the $\ell_p^p$-error metric (for $p \geq 2$) when answering linear queries under the constraint of differential privacy. We characterize such an error under $(\epsilon,\delta)$-differential privacy. Before this paper, tight characterization in the hardness of privately answering linear queries was known under $\ell_2^2$-error metric (Edmonds et al., STOC 2020) and $\ell_p^2$-error metric for unbiased mechanisms (Nikolov and Tang, ITCS 2024). As a direct consequence of our results, we give tight bounds on answering prefix sum and parity queries under differential privacy for all constant $p$ in terms of the $\ell_p^p$ error, generalizing the bounds in Henzinger et al. (SODA 2023) for $p=2$.

We present the first three-parameter fit to a 3+1 sterile neutrino model using 7.634 years of data from the IceCube Neutrino Observatory on $\nu_\mu+\overline{\nu}_\mu$ charged-current interactions in the energy range 500-9976 GeV. Our analysis is sensitive to the mass-squared splitting between the heaviest and lightest mass state ($\Delta m_{41}^2$), the mixing matrix element connecting muon flavor to the fourth mass state ($|U_{\mu4}|^2$), and the element connecting tau flavor to the fourth mass state ($|U_{\tau4}|^2$). Predicted propagation effects in matter enhance the signature through a resonance as atmospheric neutrinos from the Northern Hemisphere traverse the Earth to the IceCube detector at the South Pole. The result is consistent with the no-sterile neutrino hypothesis with a probability of 4.3 %. Profiling the likelihood of each parameter yields the 90 % confidence levels: $ 2.4\,\mathrm{eV}^{2} < \Delta m_{41}^2 <9.6\,\mathrm{eV}^{2} $ , $0.0081 < |U_{\mu4}|^2 < 0.10$ , and $|U_{\tau4}|^2< 0.035$, which narrows the allowed parameter-space for $|U_{\tau4}|^2$. However, the primary result of this analysis is the first map of the 3+1 parameter space exploring the interdependence of $\Delta m_{41}^2$, $|U_{\mu4}|^2$, and $|U_{\tau4}|^2$.

We propose a method for depth estimation under different illumination conditions, i.e., day and night time. As photometry is uninformative in regions under low-illumination, we tackle the problem through a multi-sensor fusion approach, where we take as input an additional synchronized sparse point cloud (i.e., from a LiDAR) projected onto the image plane as a sparse depth map, along with a camera image. The crux of our method lies in the use of the abundantly available synthetic data to first approximate the 3D scene structure by learning a mapping from sparse to (coarse) dense depth maps along with their predictive uncertainty - we term this, SpaDe. In poorly illuminated regions where photometric intensities do not afford the inference of local shape, the coarse approximation of scene depth serves as a prior; the uncertainty map is then used with the image to guide refinement through an uncertainty-driven residual learning (URL) scheme. The resulting depth completion network leverages complementary strengths from both modalities - depth is sparse but insensitive to illumination and in metric scale, and image is dense but sensitive with scale ambiguity. SpaDe can be used in a plug-and-play fashion, which allows for 25% improvement when augmented onto existing methods to preprocess sparse depth. We demonstrate URL on the nuScenes dataset where we improve over all baselines by an average 11.65% in all-day scenarios, 11.23% when tested specifically for daytime, and 13.12% for nighttime scenes., Comment: 8 pages, 4 figures

This paper proposes a data-driven framework to learn a finite-dimensional approximation of a Koopman operator for approximating the state evolution of a dynamical system under noisy observations. To this end, our proposed solution has two main advantages. First, the proposed method only requires the measurement noise to be bounded. Second, the proposed method modifies the existing deep Koopman operator formulations by characterizing the effect of the measurement noise on the Koopman operator learning and then mitigating it by updating the tunable parameter of the observable functions of the Koopman operator, making it easy to implement. The performance of the proposed method is demonstrated on several standard benchmarks. We further compare the presented method with similar methods proposed in the latest literature on Koopman learning.

We carried out the systematic analysis of the $s$-wave triply heavy pentaquarks with possible configurations like $q\Bar{q}QQQ$ and $qqQQ\Bar{Q}$, ($q = u, d, s$ and $Q = c, b$ quarks). Special unitary representations are utilized to study the classification scheme for triply heavy configurations like $q\Bar{q}QQQ$ and $qqQQ\Bar{Q}$. We classified the $q\Bar{q}QQQ$-type pentaquarks into an octet and $qqQQ\Bar{Q}$-type pentaquarks into sextet configurations with the help of SU(3) flavor representation. Also, with the help of SU(2) spin representation, we studied the possible spin assignments ($\frac{1}{2}^-$, $\frac{3}{2}^-$, and $\frac{5}{2}^-$) for ground state triply heavy pentaquarks. Furthermore, we used the formalism of the extended Gursey-Radicati mass formula and effective mass scheme to estimate the masses of triply heavy pentaquarks. Additionally, we calculated the magnetic moment assignments using the effective mass and screened charge schemes. The predicted outcomes align well with the existing theoretical data and benefit future studies. Our work provides a comprehensive framework that combines the theoretical aspects of SU(3) and SU(2) symmetries with practical predictions for observables, offering a strong foundation for experimental verification. This integrated approach enhances our understanding of the complex interactions within triply heavy pentaquarks and underscores their potential role in probing deeper into the dynamics of the strong force. These findings are crucial for designing future high-energy experiments that directly observe these exotic states and confirm their properties, paving the way for new insights into quantum chromodynamics.

Recently, Multi-Modal(MM) Large Language Models(LLMs) have unlocked many complex use-cases that require MM understanding (e.g., image captioning or visual question answering) and MM generation (e.g., text-guided image generation or editing) capabilities. To further improve the output fidelity of MM-LLMs we introduce the model-agnostic UniRAG technique that adds relevant retrieved information to prompts as few-shot examples during inference. Unlike the common belief that Retrieval Augmentation (RA) mainly improves generation or understanding of uncommon entities, our evaluation results on the MSCOCO dataset with common entities show that both proprietary models like GPT4 and Gemini-Pro and smaller open-source models like Llava, LaVIT, and Emu2 significantly enhance their generation quality when their input prompts are augmented with relevant information retrieved by MM retrievers like UniIR models., Comment: 11 pages, 7 figures

We provide supporting details for the search for a 3+1 sterile neutrino using data collected over eleven years at the IceCube Neutrino Observatory. The analysis uses atmospheric muon-flavored neutrinos from 0.5 to 100\, TeV that traverse the Earth to reach the IceCube detector, and finds a best-fit point at $\sin^2(2\theta_{24}) = 0.16$ and $\Delta m^{2}_{41} = 3.5$ eV$^2$ with a goodness-of-fit p-value of 12\% and consistency with the null hypothesis of no oscillations to sterile neutrinos with a p-value of 3.1\%. Several improvements were made over past analyses, which are reviewed in this article, including upgrades to the reconstruction and the study of sources of systematic uncertainty. We provide details of the fit quality and discuss stability tests that split the data for separate samples, comparing results. We find that the fits are consistent between split data sets., Comment: 18 pages, 17 figures, 2 tables. This long-form paper is a companion to the letter "A search for an eV-scale sterile neutrino using improved high-energy {\nu}{\mu} event reconstruction in IceCube."

This Letter presents the result of a 3+1 sterile neutrino search using 10.7 years of IceCube data. We analyze atmospheric muon neutrinos that traverse the Earth with energies ranging from 0.5 to 100 TeV, incorporating significant improvements in modeling neutrino flux and detector response compared to earlier studies. Notably, for the first time, we categorize data into starting and through-going events, distinguishing neutrino interactions with vertices inside or outside the instrumented volume, to improve energy resolution. The best-fit point for a 3+1 model is found to be at $\sin^2(2\theta_{24}) = 0.16$ and $\Delta m^{2}_{41} = 3.5$ eV$^2$, which agrees with previous iterations of this study. The result is consistent with the null hypothesis of no sterile neutrinos with a p-value of 3.1\%., Comment: 9 pages, 3 figures. This letter is supported by the long-form paper "Methods and stability tests associated with the sterile neutrino search using improved high-energy $\nu_\mu$ event reconstruction in IceCube," also appearing on arXiv