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Due to the absorption of high energetic ultraviolet (UV) photons by the surface layers of the cold molecular clouds, only low energetic photons are able to penetrate into the inner regions of these clouds. This leads to lower photo-ionization yield of molecules of higher ionization potential in these environments. However, here we have experimentally shown the ionization of Benzonitrile molecule using 266nm (4.66eV) photons. The low intensity and unfocused laser irradiation of benzonitrile molecules results extensive fragmentation. Moreover, the ion-neutral reactions among the cationic fragments and neutral fragments shows promising molecular mass growth.

Despite the mounting anticipation for the quantum revolution, the success of Quantum Machine Learning (QML) in the Noisy Intermediate-Scale Quantum (NISQ) era hinges on a largely unexplored factor: the generalization error bound, a cornerstone of robust and reliable machine learning models. Current QML research, while exploring novel algorithms and applications extensively, is predominantly situated in the context of noise-free, ideal quantum computers. However, Quantum Circuit (QC) operations in NISQ-era devices are susceptible to various noise sources and errors. In this article, we conduct a Systematic Mapping Study (SMS) to explore the state-of-the-art generalization bound for supervised QML in NISQ-era and analyze the latest practices in the field. Our study systematically summarizes the existing computational platforms with quantum hardware, datasets, optimization techniques, and the common properties of the bounds found in the literature. We further present the performance accuracy of various approaches in classical benchmark datasets like the MNIST and IRIS datasets. The SMS also highlights the limitations and challenges in QML in the NISQ era and discusses future research directions to advance the field. Using a detailed Boolean operators query in five reliable indexers, we collected 544 papers and filtered them to a small set of 37 relevant articles. This filtration was done following the best practice of SMS with well-defined research questions and inclusion and exclusion criteria.

We develop a theory of derived rigid spaces and quasi-coherent sheaves and analytic crystals on them. Amongst other things, we obtain a six-functor formalism for these quasi-coherent sheaves and analytic crystals. We provide evidence that the category of analytic crystals is related to the theory of D-cap-modules introduced by Ardakov--Wadsley., Comment: 74 pages

Given a set $S \subseteq \mathbb{R}^d$, an empty polytope has vertices in $S$ but contains no other point of $S$. Empty polytopes are closely related to so-called Helly numbers, which extend Helly's theorem to more general point sets in $\mathbb{R}^d$. We improve bounds on the number of vertices in empty polytopes in exponential lattices, arithmetic congruence sets, and 2-syndetic sets. We also study hollow polytopes, which have vertices in $S$ and no points of $S$ in their interior. We obtain bounds on the number of vertices in hollow polytopes under certain conditions, such as the vertices being in general position. Finally, we obtain relatively tight asymptotic bounds for polytopes which do not contain lattice segments of large length., Comment: 13 pages, 2 figures

We experimentally demonstrate a cavity-based detection scheme for a cold atomic ensemble with a radiatively open transition. Our method exploits the collective strong coupling of atoms to the cavity mode, which results in off-resonant probing of the atomic ensemble, leading to a dramatic reduction in losses from the detection cycle. We then show the viability of this frequency measurement for detecting a small number of atoms and molecules by theoretical modelling. Compared with the most commonly used fluorescence method, we show that the cavity-based scheme allows rapid and prolonged detection of the system's evolution with minimal destruction.

The use of Social media to share content is on a constant rise. One of the capsize effect of information sharing on Social media includes the spread of sensitive information on the public domain. With the digital gadget market becoming highly competitive and ever-evolving, the trend of an increasing number of sensitive posts leaking information on devices in social media is observed. Many web-blogs on digital gadget market have mushroomed recently, making the problem of information leak all pervasive. Credible leaks on specifics of an upcoming device can cause a lot of financial damage to the respective organization. Hence, it is crucial to assess the credibility of the platforms that continuously post about a smartphone or digital gadget leaks. In this work, we analyze the headlines of leak web-blog posts and their corresponding official press-release. We first collect 54, 495 leak and press-release headlines for different smartphones. We train our custom NER model to capture the evolving smartphone names with an accuracy of 82.14% on manually annotated results. We further propose a credibility score metric for the web-blog, based on the number of falsified and authentic smartphone leak posts.

Magnetic skyrmion crystals with zero net skyrmion charge and zero topological Hall response are interesting candidate phases which can occur at a vanishing magnetic field in centrosymmetric systems. We study a minimal bilayer model of skyrmion crystals having opposite chirality and topological charge in the two layers, and show that it can host nearly flat electronic bands with quasi-uniform Berry curvature and quantum metric. Using Hartree-Fock theory, we show that weak to moderate short-range electron interactions induce two distinct types of symmetry breaking patterns depending on the band dispersion: an intra-unit-cell charge density modulation from Chern band mixing or a layer-imbalanced phase with a nonzero ferroelectric polarization. Both phases break inversion symmetry leading to a spontaneous and large net topological Hall effect, with the phase diagram tunable by external electric fields. Our results may be relevant to centrosymmetric skyrmion materials such as Gd$_2$PdSi$_3$ and Gd$_3$Ru$_4$Al$_{12}$ as well as artificially engineered heterostructures. We also discuss its relation to recent work on twisted transition metal dichalcogenide bilayers.

In this paper, we extend the results of Grantcharov and Robitaille in 2021 on mixed tensor products and Capelli determinants to the superalgebra setting. Specifically, we construct a family of superalgebra homomorphisms $\varphi_R : U(\mathfrak{gl}(m+1|n)) \rightarrow \mathcal{D}'(m|n) \otimes U(\mathfrak{gl}(m|n))$ for a certain space of differential operators $\mathcal{D}'(m|n)$ indexed by a central element $R$ of $\mathcal{D}'(m|n) \otimes U(\mathfrak{gl}(m|n))$. We then use this homomorphism to determine the image of Gelfand generators of the center of $U(\mathfrak{gl}(m+1|n))$. We achieve this by first relating $\varphi_R$ to the corresponding Harish-Chandra homomorphisms and then proving a super-analog of Newton's formula for $\mathfrak{gl}(m)$ relating Capelli generators and Gelfand generators. We also use the homomorphism $\varphi_R$ to obtain representations of $U(\mathfrak{gl}(m+1|n))$ from those of $U(\mathfrak{gl}(m|n))$, and find conditions under which these inflations are simple. Finally, we show that for a distinguished central element $R_1$ in $\mathcal{D}'(m|n)\otimes U(\mathfrak{gl}(m|n))$, the kernel of $\varphi_{R_1}$ is the ideal of $U(\mathfrak{gl}(m+1|n))$ generated by the first Gelfand invariant $G_1$.

The majority of language model training builds on imitation learning. It covers pretraining, supervised fine-tuning, and affects the starting conditions for reinforcement learning from human feedback (RLHF). The simplicity and scalability of maximum likelihood estimation (MLE) for next token prediction led to its role as predominant paradigm. However, the broader field of imitation learning can more effectively utilize the sequential structure underlying autoregressive generation. We focus on investigating the inverse reinforcement learning (IRL) perspective to imitation, extracting rewards and directly optimizing sequences instead of individual token likelihoods and evaluate its benefits for fine-tuning large language models. We provide a new angle, reformulating inverse soft-Q-learning as a temporal difference regularized extension of MLE. This creates a principled connection between MLE and IRL and allows trading off added complexity with increased performance and diversity of generations in the supervised fine-tuning (SFT) setting. We find clear advantages for IRL-based imitation, in particular for retaining diversity while maximizing task performance, rendering IRL a strong alternative on fixed SFT datasets even without online data generation. Our analysis of IRL-extracted reward functions further indicates benefits for more robust reward functions via tighter integration of supervised and preference-based LLM post-training.

Polyacetylene, a simple polymer, has long been studied for its unique electronic properties, yet the role of correlation in polyacetylene still is not clear. Here, we employ various density functional theory formulations to explore its effects on magnetic characteristics of polyacetylene by ab initio basis. Our results indicate that subtle correlation effects beyond the generalized gradient approximation lead to the formation of spin-density wave solutions on the $\pi$-conjugated carbon $p$ orbitals that could compete with other solutions, possibly leading to emergence of intertwined orders. Our study suggests that the phase diagram of polyacetylene should be explored further., Comment: 7 pages, 2 figures

Dynamic pricing is a promising strategy to address the challenges of smart charging, as traditional time-of-use (ToU) rates and stationary pricing (SP) do not dynamically react to changes in operating conditions, reducing revenue for charging station (CS) vendors and affecting grid stability. Previous studies evaluated single objectives or linear combinations of objectives for EV CS pricing solutions, simplifying trade-offs and preferences among objectives. We develop a novel formulation for the dynamic pricing problem by addressing multiple conflicting objectives efficiently instead of solely focusing on one objective or metric, as in earlier works. We find optimal trade-offs or Pareto solutions efficiently using Non-dominated Sorting Genetic Algorithms (NSGA) II and NSGA III. A dynamic pricing model quantifies the relationship between demand and price while simultaneously solving multiple conflicting objectives, such as revenue, quality of service (QoS), and peak-to-average ratios (PAR). A single method can only address some of the above aspects of dynamic pricing comprehensively. We present a three-part dynamic pricing approach using a Bayesian model, multi-objective optimization, and multi-criteria decision-making (MCDM) using pseudo-weight vectors. To address the research gap in CS pricing, our method selects solutions using revenue, QoS, and PAR metrics simultaneously. Two California charging sites' real-world data validates our approach., Comment: 12 pages

Astrophysical population simulations predict that a subset of dynamically formed binary black holes (BBHs) may possess eccentricity $\gtrsim 0.1$ at a gravitational wave (GW) frequency of $10 \,\text{Hz}$. Presently, the LIGO-Virgo-KAGRA (LVK) Collaboration tests general relativity (GR) assuming that the binary eccentricity has decayed well before it enters the detector's frequency band. Previous works have shown that binary eccentricity can bias GR tests if unaccounted for. Here we develop two methods to extend parametrized tests of GR to eccentric binaries. The first method extends the standard null parametrized test for quasicircular binaries by adding fractional deviations at each post-Newtonian (PN) order in the eccentric part of the GW phasing (assuming the small-eccentricity limit). Simultaneous measurement of the circular and eccentric deviation parameters ($\delta\hat{\varphi}, \delta\hat{\varphi}^e$) allows us to constrain deviations from GR for eccentric binaries. While strong constraints on the deviation parameters are not achievable with LIGO's projected sensitivity, the multibanding of LISA and CE observations can constrain these deviations to $|\delta\hat{\varphi}_2| \lesssim 3 \times 10^{-3}$ and $|\delta\hat{\varphi}^e_2|\lesssim 2\times 10^{-2}$. The second method looks for GR deviations in the rate of periastron advance ($\Delta\alpha$). The parameter $\Delta\alpha$ ($\Delta\alpha^{\rm GR} \to 0$) can be constrained with LIGO to $|\Delta\alpha|\lesssim 4 \times 10^{-2}$ (with $1 \sigma$ confidence). Multiband sources observed by LISA and CE provide an improved constraint of $|\Delta\alpha|\lesssim 3\times 10^{-5}$. The space-based detector DECIGO provides the best constraint on $\Delta\alpha$ with $|\Delta\alpha|\lesssim 8 \times 10^{-6}$., Comment: 32 pages, 7 figures

When using AI to detect signs of depressive disorder, AI models habitually draw preemptive conclusions. We theorize that using chain-of-thought (CoT) prompting to evaluate Patient Health Questionnaire-8 (PHQ-8) scores will improve the accuracy of the scores determined by AI models. In our findings, when the models reasoned with CoT, the estimated PHQ-8 scores were consistently closer on average to the accepted true scores reported by each participant compared to when not using CoT. Our goal is to expand upon AI models' understanding of the intricacies of human conversation, allowing them to more effectively assess a patient's feelings and tone, therefore being able to more accurately discern mental disorder symptoms; ultimately, we hope to augment AI models' abilities, so that they can be widely accessible and used in the medical field.

This paper considers a novel online fair division problem involving multiple agents in which a learner observes an indivisible item that has to be irrevocably allocated to one of the agents while satisfying a fairness and efficiency constraint. Existing algorithms assume a small number of items with a sufficiently large number of copies, which ensures a good utility estimation for all item-agent pairs. However, such an assumption may not hold in many real-life applications, e.g., an online platform that has a large number of users (items) who only use the platform's service providers (agents) a few times (a few copies of items), which makes it difficult to estimate the utility for all item-agent pairs. To overcome this challenge, we model the online fair division problem using contextual bandits, assuming the utility is an unknown function of the item-agent features. We then propose algorithms for online fair division with sub-linear regret guarantees. Our experimental results also verify the different performance aspects of the proposed algorithms., Comment: We study an online fair division problem that has a large number of items with only a few copies of each item and propose contextual bandits-based algorithms with sub-linear regret guarantees

Medical abstractive summarization faces the challenge of balancing faithfulness and informativeness. Current methods often sacrifice key information for faithfulness or introduce confabulations when prioritizing informativeness. While recent advancements in techniques like in-context learning (ICL) and fine-tuning have improved medical summarization, they often overlook crucial aspects such as faithfulness and informativeness without considering advanced methods like model reasoning and self-improvement. Moreover, the field lacks a unified benchmark, hindering systematic evaluation due to varied metrics and datasets. This paper addresses these gaps by presenting a comprehensive benchmark of six advanced abstractive summarization methods across three diverse datasets using five standardized metrics. Building on these findings, we propose uMedSum, a modular hybrid summarization framework that introduces novel approaches for sequential confabulation removal followed by key missing information addition, ensuring both faithfulness and informativeness. Our work improves upon previous GPT-4-based state-of-the-art (SOTA) medical summarization methods, significantly outperforming them in both quantitative metrics and qualitative domain expert evaluations. Notably, we achieve an average relative performance improvement of 11.8% in reference-free metrics over the previous SOTA. Doctors prefer uMedSum's summaries 6 times more than previous SOTA in difficult cases where there are chances of confabulations or missing information. These results highlight uMedSum's effectiveness and generalizability across various datasets and metrics, marking a significant advancement in medical summarization., Comment: 12 pages

The use of multiple modalities (e.g., face and fingerprint) or multiple algorithms (e.g., three face comparators) has shown to improve the recognition accuracy of an operational biometric system. Over time a biometric system may evolve to add new modalities, retire old modalities, or be merged with other biometric systems. This can lead to scenarios where there are missing scores corresponding to the input probe set. Previous work on this topic has focused on either the verification or identification tasks, but not both. Further, the proportion of missing data considered has been less than 50%. In this work, we study the impact of missing score data for both the verification and identification tasks. We show that the application of various score imputation methods along with simple sum fusion can improve recognition accuracy, even when the proportion of missing scores increases to 90%. Experiments show that fusion after score imputation outperforms fusion with no imputation. Specifically, iterative imputation with K nearest neighbors consistently surpasses other imputation methods in both the verification and identification tasks, regardless of the amount of scores missing, and provides imputed values that are consistent with the ground truth complete dataset., Comment: 2022 26th International Conference on Pattern Recognition (ICPR)

In [1], we calibrated a one-factor Cheyette SLV model with a local volatility that is linear in the benchmark forward rate and an uncorrelated CIR stochastic variance to 3M caplets of various maturities. While caplet smiles for many maturities could be reasonably well calibrated across the range of strikes, for instance the 1Y maturity could not be calibrated well across that entire range of strikes. Here, we study whether models with alternative local volatility terms and/or alternative stochastic volatility or variance models can calibrate the 1Y caplet smile better across the strike range better than the model studied in [1]. This is made possible and feasible by the generic simulation, pricing, and calibration frameworks introduced in [1] and some new frameworks presented in this paper. We find that some model settings calibrate well to the 1Y smile across the strike range under study. In particular, a model setting with a local volatility that is piece-wise linear in the benchmark forward rate together with an uncorrelated CIR stochastic variance and one with a local volatility that is linear in the benchmark rate together with a correlated lognormal stochastic volatility with quadratic drift (QDLNSV) as in [2] calibrate well. We discuss why the later might be a preferable model. [1] Arun Kumar Polala and Bernhard Hientzsch. Parametric differential machine learning for pricing and calibration. arXiv preprint arXiv:2302.06682 , 2023. [2] Artur Sepp and Parviz Rakhmonov. A Robust Stochastic Volatility Model for Interest Rate Dynamics. Risk Magazine, 2023

We introduce Transfusion, a recipe for training a multi-modal model over discrete and continuous data. Transfusion combines the language modeling loss function (next token prediction) with diffusion to train a single transformer over mixed-modality sequences. We pretrain multiple Transfusion models up to 7B parameters from scratch on a mixture of text and image data, establishing scaling laws with respect to a variety of uni- and cross-modal benchmarks. Our experiments show that Transfusion scales significantly better than quantizing images and training a language model over discrete image tokens. By introducing modality-specific encoding and decoding layers, we can further improve the performance of Transfusion models, and even compress each image to just 16 patches. We further demonstrate that scaling our Transfusion recipe to 7B parameters and 2T multi-modal tokens produces a model that can generate images and text on a par with similar scale diffusion models and language models, reaping the benefits of both worlds., Comment: 23 pages

A face morph is created by combining the face images usually pertaining to two distinct identities. The goal is to generate an image that can be matched with two identities thereby undermining the security of a face recognition system. To deal with this problem, several morph attack detection techniques have been developed. But these methods do not extract any information about the underlying bonafides used to create them. Demorphing addresses this limitation. However, current demorphing techniques are mostly reference-based, i.e, they need an image of one of the identities to recover the other. In this work, we treat demorphing as an ill-posed decomposition problem. We propose a novel method that is reference-free and recovers the bonafides with high accuracy. Our method decomposes the morph into several identity-preserving feature components. A merger network then weighs and combines these components to recover the bonafides. Our method is observed to reconstruct high-quality bonafides in terms of definition and fidelity. Experiments on the CASIA-WebFace, SMDD and AMSL datasets demonstrate the effectiveness of our method.

We provide an algorithm that, given an $n$-vertex $m$-edge Eulerian graph with polynomially bounded weights, computes an $\breve{O}(n\log^{2} n \cdot \varepsilon^{-2})$-edge $\varepsilon$-approximate Eulerian sparsifier with high probability in $\breve{O}(m\log^3 n)$ time (where $\breve{O}(\cdot)$ hides $\text{polyloglog}(n)$ factors). Due to a reduction from [Peng-Song, STOC '22], this yields an $\breve{O}(m\log^3 n + n\log^6 n)$-time algorithm for solving $n$-vertex $m$-edge Eulerian Laplacian systems with polynomially-bounded weights with high probability, improving upon the previous state-of-the-art runtime of $\Omega(m\log^8 n + n\log^{23} n)$. We also give a polynomial-time algorithm that computes $O(\min(n\log n \cdot \varepsilon^{-2} + n\log^{5/3} n \cdot \varepsilon^{-4/3}, n\log^{3/2} n \cdot \varepsilon^{-2}))$-edge sparsifiers, improving the best such sparsity bound of $O(n\log^2 n \cdot \varepsilon^{-2} + n\log^{8/3} n \cdot \varepsilon^{-4/3})$ [Sachdeva-Thudi-Zhao, ICALP '24]. Finally, we show that our techniques extend to yield the first $O(m\cdot\text{polylog}(n))$ time algorithm for computing $O(n\varepsilon^{-1}\cdot\text{polylog}(n))$-edge graphical spectral sketches, as well as a natural Eulerian generalization we introduce. In contrast to prior Eulerian graph sparsification algorithms which used either short cycle or expander decompositions, our algorithms use a simple efficient effective resistance decomposition scheme we introduce. Our algorithms apply a natural sampling scheme and electrical routing (to achieve degree balance) to such decompositions. Our analysis leverages new asymmetric variance bounds specialized to Eulerian Laplacians and tools from discrepancy theory.

The interplay of topology, magnetism, and correlations gives rise to intriguing phases of matter. In this study, through state-of-the-art angle-resolved photoemission spectroscopy, density functional theory and dynamical mean-field theory calculations, we visualize a fourfold degenerate Dirac nodal line at the boundary of the bulk Brillouin zone in the antiferromagnet YMn2Ge2. We further demonstrate that this gapless, antiferromagnetic Dirac nodal line is enforced by the combination of magnetism, space-time inversion symmetry and nonsymmorphic lattice symmetry. The corresponding drumhead surface states traverse the whole surface Brillouin zone. YMn2Ge2 thus serves as a platform to exhibit the interplay of multiple degenerate nodal physics and antiferromagnetism. Interestingly, the magnetic nodal line displays a d-orbital dependent renormalization along its trajectory in momentum space, thereby manifesting Hund coupling. Our findings offer insights into the effect of electronic correlations on magnetic Dirac nodal lines, leading to an antiferromagnetic Hund nodal line.

In this paper, we study an asymptotic preserving (AP), energy stable and positivity preserving semi-implicit finite volume scheme for the Euler-Poisson-Boltzmann (EPB) system in the quasineutral limit. The key to energy stability is the addition of appropriate stabilisation terms into the convective fluxes of mass and momenta, and the source term. The space-time fully-discrete scheme admits the positivity of the mass density, and is consistent with the weak formulation of the EPB system upon mesh refinement. In the quasineutral limit, the numerical scheme yields a consistent, semi-implicit discretisation of the isothermal compressible Euler system, thus leading to the AP property. Several benchmark numerical case studies are performed to confirm the robustness and efficacy of the proposed scheme in the dispersive as well as the quasineutral regimes. The numerical results also corroborates scheme's ability to very well resolve plasma sheaths and the related dynamics, which indicates its potential to applications involving low-temperature plasma problems., Comment: 37 pages

Combining match scores from different biometric systems via fusion is a well-established approach to improving recognition accuracy. However, missing scores can degrade performance as well as limit the possible fusion techniques that can be applied. Imputation is a promising technique in multibiometric systems for replacing missing data. In this paper, we evaluate various score imputation approaches on three multimodal biometric score datasets, viz. NIST BSSR1, BIOCOP2008, and MIT LL Trimodal, and investigate the factors which might influence the effectiveness of imputation. Our studies reveal three key observations: (1) Imputation is preferable over not imputing missing scores, even when the fusion rule does not require complete score data. (2) Balancing the classes in the training data is crucial to mitigate negative biases in the imputation technique towards the under-represented class, even if it involves dropping a substantial number of score vectors. (3) Multivariate imputation approaches seem to be beneficial when scores between modalities are correlated, while univariate approaches seem to benefit scenarios where scores between modalities are less correlated., Comment: Proc. of IEEE International Workshop on Information Forensics and Security (WIFS), (Nuremberg, Germany), December 2023

Near-duplicate images are often generated when applying repeated photometric and geometric transformations that produce imperceptible variants of the original image. Consequently, a deluge of near-duplicates can be circulated online posing copyright infringement concerns. The concerns are more severe when biometric data is altered through such nuanced transformations. In this work, we address the challenge of near-duplicate detection in face images by, firstly, identifying the original image from a set of near-duplicates and, secondly, deducing the relationship between the original image and the near-duplicates. We construct a tree-like structure, called an Image Phylogeny Tree (IPT) using a graph-theoretic approach to estimate the relationship, i.e., determine the sequence in which they have been generated. We further extend our method to create an ensemble of IPTs known as Image Phylogeny Forests (IPFs). We rigorously evaluate our method to demonstrate robustness across other modalities, unseen transformations by latest generative models and IPT configurations, thereby significantly advancing the state-of-the-art performance by 42% on IPF reconstruction accuracy., Comment: Under review

As the complexity of modern systems increases, so does the importance of assessing their security posture through effective vulnerability management and threat modeling techniques. One powerful tool in the arsenal of cybersecurity professionals is the attack graph, a representation of all potential attack paths within a system that an adversary might exploit to achieve a certain objective. Traditional methods of generating attack graphs involve expert knowledge, manual curation, and computational algorithms that might not cover the entire threat landscape due to the ever-evolving nature of vulnerabilities and exploits. This paper explores the approach of leveraging large language models (LLMs), such as ChatGPT, to automate the generation of attack graphs by intelligently chaining Common Vulnerabilities and Exposures (CVEs) based on their preconditions and effects. It also shows how to utilize LLMs to create attack graphs from threat reports.

This study utilizes the advanced capabilities of the GPT-4 multimodal Large Language Model (LLM) to explore its potential in iris recognition - a field less common and more specialized than face recognition. By focusing on this niche yet crucial area, we investigate how well AI tools like ChatGPT can understand and analyze iris images. Through a series of meticulously designed experiments employing a zero-shot learning approach, the capabilities of ChatGPT-4 was assessed across various challenging conditions including diverse datasets, presentation attacks, occlusions such as glasses, and other real-world variations. The findings convey ChatGPT-4's remarkable adaptability and precision, revealing its proficiency in identifying distinctive iris features, while also detecting subtle effects like makeup on iris recognition. A comparative analysis with Gemini Advanced - Google's AI model - highlighted ChatGPT-4's better performance and user experience in complex iris analysis tasks. This research not only validates the use of LLMs for specialized biometric applications but also emphasizes the importance of nuanced query framing and interaction design in extracting significant insights from biometric data. Our findings suggest a promising path for future research and the development of more adaptable, efficient, robust and interactive biometric security solutions., Comment: Published at IJCB 2024

Soft interfaces are ubiquitous in nature, governing quintessential hydrodynamics functions, like lubrication, stability and cargo transport. It is shown here how a magnetic force field at a magnetic-nonmagnetic fluid interface results in an ultra-soft interface with nonlinear elasticity and tunable viscous shear properties. The balance between magnetic pressure, viscous stress and Laplace pressure results in a deformed and stable liquid-in-liquid tube with apparent elasticity in the range 2 kPa -10 kPa, possibly extended by a proper choice of liquid properties. Such highly deformable liquid-liquid interfaces of arbitrary shape with vanishing viscous shear open doors to unique microfluidic phenomena, biomaterial flows and complex biosystems mimicking., Comment: 7 pages , 5 figures. arXiv admin note: text overlap with arXiv:2310.14280

Under-canopy agricultural robots require robust navigation capabilities to enable full autonomy but struggle with tight row turning between crop rows due to degraded GPS reception, visual aliasing, occlusion, and complex vehicle dynamics. We propose an imitation learning approach using diffusion policies to learn row turning behaviors from demonstrations provided by human operators or privileged controllers. Simulation experiments in a corn field environment show potential in learning this task with only visual observations and velocity states. However, challenges remain in maintaining control within rows and handling varied initial conditions, highlighting areas for future improvement., Comment: Accepted as Extended Abstract to the IEEE ICRA@40 2024

Incomplete Hippocampal Inversion (IHI), sometimes called hippocampal malrotation, is an atypical anatomical pattern of the hippocampus found in about 20% of the general population. IHI can be visually assessed on coronal slices of T1 weighted MR images, using a composite score that combines four anatomical criteria. IHI has been associated with several brain disorders (epilepsy, schizophrenia). However, these studies were based on small samples. Furthermore, the factors (genetic or environmental) that contribute to the genesis of IHI are largely unknown. Large-scale studies are thus needed to further understand IHI and their potential relationships to neurological and psychiatric disorders. However, visual evaluation is long and tedious, justifying the need for an automatic method. In this paper, we propose, for the first time, to automatically rate IHI. We proceed by predicting four anatomical criteria, which are then summed up to form the IHI score, providing the advantage of an interpretable score. We provided an extensive experimental investigation of different machine learning methods and training strategies. We performed automatic rating using a variety of deep learning models (conv5-FC3, ResNet and SECNN) as well as a ridge regression. We studied the generalization of our models using different cohorts and performed multi-cohort learning. We relied on a large population of 2,008 participants from the IMAGEN study, 993 and 403 participants from the QTIM/QTAB studies as well as 985 subjects from the UKBiobank. We showed that deep learning models outperformed a ridge regression. We demonstrated that the performances of the conv5-FC3 network were at least as good as more complex networks while maintaining a low complexity and computation time. We showed that training on a single cohort may lack in variability while training on several cohorts improves generalization., Comment: Accepted for publication at the Journal of Machine Learning for Biomedical Imaging (MELBA) https://melba-journal.org/2024:016

Ian Macdonald's works changed our perspective on so many parts of algebraic combinatorics and formal power series. This talk will display some selected works of the art of Ian Macdonald, representative of different periods of his oeuvre, and analyze how they resonate, both for the past development of our subject and for its future. This paper was prepared for the occasion of a lecture in tribute to Ian G. Macdonald, delivered at FPSAC 2024 in Bochum, Germany on 22 July 2024. I want to express thanks to the Executive Committee of FPSAC, the Organizing Committee of FPSAC 2024, and to the whole of our FPSAC 2024 community for making this lecture a possibility and for considering me for its delivery. Macdonald is my hero, and to be asked to play such a role in his legacy touches me deeply.

As machine learning models become increasingly prevalent in critical decision-making models and systems in fields like finance, healthcare, etc., ensuring their robustness against adversarial attacks and changes in the input data is paramount, especially in cases where models potentially overfit. This paper proposes a comprehensive framework for assessing the robustness of machine learning models through covariate perturbation techniques. We explore various perturbation strategies to assess robustness and examine their impact on model predictions, including separate strategies for numeric and non-numeric variables, summaries of perturbations to assess and compare model robustness across different scenarios, and local robustness diagnosis to identify any regions in the data where a model is particularly unstable. Through empirical studies on real world dataset, we demonstrate the effectiveness of our approach in comparing robustness across models, identifying the instabilities in the model, and enhancing model robustness., Comment: 31 pages, 11 figures, 14 tables

Autonomous driving for urban and highway driving applications often requires High Definition (HD) maps to generate a navigation plan. Nevertheless, various challenges arise when generating and maintaining HD maps at scale. While recent online mapping methods have started to emerge, their performance especially for longer ranges is limited by heavy occlusion in dynamic environments. With these considerations in mind, our work focuses on leveraging lightweight and scalable priors-Standard Definition (SD) maps-in the development of online vectorized HD map representations. We first examine the integration of prototypical rasterized SD map representations into various online mapping architectures. Furthermore, to identify lightweight strategies, we extend the OpenLane-V2 dataset with OpenStreetMaps and evaluate the benefits of graphical SD map representations. A key finding from designing SD map integration components is that SD map encoders are model agnostic and can be quickly adapted to new architectures that utilize bird's eye view (BEV) encoders. Our results show that making use of SD maps as priors for the online mapping task can significantly speed up convergence and boost the performance of the online centerline perception task by 30% (mAP). Furthermore, we show that the introduction of the SD maps leads to a reduction of the number of parameters in the perception and reasoning task by leveraging SD map graphs while improving the overall performance. Project Page: https://henryzhangzhy.github.io/sdhdmap/., Comment: Accepted by the 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2024)

Soil moisture estimation is an important task to enable precision agriculture in creating optimal plans for irrigation, fertilization, and harvest. It is common to utilize statistical and machine learning models to estimate soil moisture from traditional data sources such as weather forecasts, soil properties, and crop properties. However, there is a growing interest in utilizing aerial and geospatial imagery to estimate soil moisture. Although these images capture high-resolution crop details, they are expensive to curate and challenging to interpret. Imagine, an AI-enhanced software tool that predicts soil moisture using visual cues captured by smartphones and statistical data given by weather forecasts. This work is a first step towards that goal of developing a multi-modal approach for soil moisture estimation. In particular, we curate a dataset consisting of real-world images taken from ground stations and their corresponding weather data. We also propose MIS-ME - Meteorological & Image based Soil Moisture Estimator, a multi-modal framework for soil moisture estimation. Our extensive analysis shows that MIS-ME achieves a MAPE of 10.14%, outperforming traditional unimodal approaches with a reduction of 3.25% in MAPE for meteorological data and 2.15% in MAPE for image data, highlighting the effectiveness of tailored multi-modal approaches. Our code and dataset will be available at https://github.com/OSU-Complex-Systems/MIS-ME.git., Comment: Accepted by DSAA2024

In this study, we demonstrate the efficacy of the Ultraviolet Imaging Telescope (UVIT) in identifying and characterizing white dwarfs (WDs) within the Milky Way Galaxy. Leveraging the UVIT point source catalogue towards SMC and crossmatching with Gaia DR3 data, we identified 43 single WDs (37 new detections), 13 new WD+main sequence (MS) candidates and 161 UV bright MS stars by analysing their Spectral Energy Distributions (SED). Using the WD evolutionary models, we determine the masses, effective temperatures, and cooling ages of these identified WDs. Masses of these WDs range from 0.2 to 1.3 M$_{\odot}$ and effective temperatures (T$_{eff}$) between 10000 K to 15000 K, with cooling ages spanning 0.1 to 2 Gyr. Notably, we detect hotter WDs compared to literature values, which is attributed to the sensitivity of UVIT. Furthermore, we report the detection of 20 new extremely low-mass (ELM) candidates from our analysis. Future spectroscopic studies of the ELM candidates will help us understand the formation scenarios of these exotic objects. Despite limitations in Gaia DR3 distance measurements for optically faint WDs, we provide a crude estimate of the WD space density within 1kpc as 1.3 $\times$ 10$^{-3}$ pc$^{-3}$, which is higher than previous estimates in the literature. Our results underscore the instrumental capabilities of UVIT and anticipate the forthcoming UV missions like INSIST for systematic WD discovery. Our methodology sets a precedent for future analyses in other UVIT fields to find more WDs and perform spectroscopic studies to verify their candidacy., Comment: Accepted for publication in Astronomy & Astrophysics

Modern artificial intelligence (AI) systems are powered by foundation models. This paper presents a new set of foundation models, called Llama 3. It is a herd of language models that natively support multilinguality, coding, reasoning, and tool usage. Our largest model is a dense Transformer with 405B parameters and a context window of up to 128K tokens. This paper presents an extensive empirical evaluation of Llama 3. We find that Llama 3 delivers comparable quality to leading language models such as GPT-4 on a plethora of tasks. We publicly release Llama 3, including pre-trained and post-trained versions of the 405B parameter language model and our Llama Guard 3 model for input and output safety. The paper also presents the results of experiments in which we integrate image, video, and speech capabilities into Llama 3 via a compositional approach. We observe this approach performs competitively with the state-of-the-art on image, video, and speech recognition tasks. The resulting models are not yet being broadly released as they are still under development.

Vehicular Ad-Hoc Networks (VANETs) hold immense potential for improving traffic safety and efficiency. However, traditional centralized approaches for machine learning in VANETs raise concerns about data privacy and security. Federated Learning (FL) offers a solution that enables collaborative model training without sharing raw data. This paper proposes FL-DECO-BC as a novel privacy-preserving, provably secure, and provenance-preserving federated learning framework specifically designed for VANETs. FL-DECO-BC leverages decentralized oracles on blockchain to securely access external data sources while ensuring data privacy through advanced techniques. The framework guarantees provable security through cryptographic primitives and formal verification methods. Furthermore, FL-DECO-BC incorporates a provenance-preserving design to track data origin and history, fostering trust and accountability. This combination of features empowers VANETs with secure and privacy-conscious machine-learning capabilities, paving the way for advanced traffic management and safety applications.

The cross sections for the $^{12}\mathrm{C}(\gamma,\alpha)^{8}\mathrm{Be}\rightarrow 3\alpha$ reaction have been successfully measured using exclusive coincidence between three $\alpha$ particles, minimizing Compton background. Sequential breakup kinematics are evident, and the cross sections are presented as locally averaged histogram values. Theoretical \textsc{Fresco} CDCC-CRC calculations reproduce the experimental data, showing that the process involves electromagnetic coupling to both $^{8}\mathrm{Be}^{0^+}$ and $^{8}\mathrm{Be}^{2^+}$ states. This study confirms that the $^{12}\mathrm{C}(\gamma,\alpha)^{8}\mathrm{Be}\rightarrow 3\alpha$ reaction proceeds via a sequential mechanism, crucial for understanding its significance in radiotherapy dosimetry.

Some, if not all, binary neutron star (BNS) coalescences, and a fraction of neutron - star black hole (NSBH) mergers, are thought to produce sufficient mass-ejection to power Gamma-Ray Bursts (GRBs). However, this fraction, as well as the distribution of beaming angles of BNS-associated GRBs, are poorly constrained from observation. Recent work applied machine learning tools to analyze GRB light curves observed by {\textit{Fermi}}/GBM and {\it Swift}/BAT. GRBs were segregated into multiple distinct clusters, with the tantalizing possibility that one of them (BNS cluster) could be associated with BNSs and another (NSBH cluster) with NSBHs. As a proof of principle, assuming that all GRBs detected by {\it Fermi}/GBM and {\it Swift}/BAT associated with BNSs (NSBHs) lie in the BNS (NSBH) cluster, we estimate their rates ($\mathrm{Gpc}^{-3}\mathrm{yr}^{-1}$). We compare these rates with corresponding BNS and NSBH rates estimated by the LIGO-Virgo-Kagra (LVK) collaboration from the first three observing runs (O1, O2, O3). We find that the BNS rates are consistent with LVK's rate estimates, assuming a uniform distribution of beaming fractions ($f_b \in [0.01, 0.1]$). Conversely, using the LVK's BNS rate estimates, assuming all BNS mergers produce GRBs, we are able to constrain the beaming angle distribution to $\theta_j \in [0.8^{\circ}, 38.8^{\circ}]$ at $90\%$ confidence. We similarly place a lower limit on the fraction of GRB-Bright NSBHs to $f_B \gtrsim 0.10$ ($f_B \gtrsim 0.022$) with {\it Fermi}/GBM ({\it Swift}/BAT) data., Comment: 10 pages, 3 figures

Contextual dueling bandit is used to model the bandit problems, where a learner's goal is to find the best arm for a given context using observed noisy preference feedback over the selected arms for the past contexts. However, existing algorithms assume the reward function is linear, which can be complex and non-linear in many real-life applications like online recommendations or ranking web search results. To overcome this challenge, we use a neural network to estimate the reward function using preference feedback for the previously selected arms. We propose upper confidence bound- and Thompson sampling-based algorithms with sub-linear regret guarantees that efficiently select arms in each round. We then extend our theoretical results to contextual bandit problems with binary feedback, which is in itself a non-trivial contribution. Experimental results on the problem instances derived from synthetic datasets corroborate our theoretical results., Comment: Accepted at ICML 2024 Workshop on Foundations of Reinforcement Learning and Control

Tracking plant features is crucial for various agricultural tasks like phenotyping, pruning, or harvesting, but the unstructured, cluttered, and deformable nature of plant environments makes it a challenging task. In this context, the recent advancements in foundational models show promise in addressing this challenge. In our work, we propose PlantTrack where we utilize DINOv2 which provides high-dimensional features, and train a keypoint heatmap predictor network to identify the locations of semantic features such as fruits and leaves which are then used as prompts for point tracking across video frames using TAPIR. We show that with as few as 20 synthetic images for training the keypoint predictor, we achieve zero-shot Sim2Real transfer, enabling effective tracking of plant features in real environments.

The estimation of the Hubble constant in the past few decades has increasingly become more accurate with the advance of new techniques. But its value seems to depend on the epoch at which the measurements are made. The Planck estimate of the Hubble constant from the observations of the cosmic microwave background radiation in the early universe is about 67 km/s/Mpc, whereas that obtained from the distance indicators at the current epoch is about 73-74 km/s/Mpc. This discrepancy between the two groups of measurement is termed as the Hubble tension which has gained much attention in the past few decades with growing significance as measurements from both, the early and the late universe, studies continue to produce results with increasing precision. In this work, we propose a modification to gravity by considering a finite range gravitational field as an alternate explanation for this discrepancy in the value of the Hubble constant., Comment: 12 pages, 2 figures, 28 equations

We present a conceptual design for a fiber positioning system for multi-object high-resolution spectroscopy, designed to be compatible with the upcoming large telescopes with a wide field of view. The design incorporates multiple Atmospheric Dispersion Correctors (ADCs) and tip-tilt mirrors that receive non-telecentric input from individual targets and direct it to the ADCs. Here, we introduce a mechanical design for the fiber positioner that accommodates the optics and operates in a curved focal plane with a Radius of Curvature (R) of 3m. This mechanical design provides four degrees of freedom to access the focal volume, enhancing targeting efficiency. The proposed design and an efficient target allocation algorithm ensure a targeting efficiency of approximately 80-100% for a primary observation session. We also present a methodology for target assignment, positioning, and quantification based on sequential and Monte Carlo (MC) algorithms. This method has been tested on realistic fields with varying target densities to validate its performance.

Wi-Fi-based indoor localization has been extensively studied for context-aware services. As a result, the accurate Wi-Fi-based indoor localization introduces a great location privacy threat. However, the existing solutions for location privacy protection are hard to implement on current devices. They require extra hardware deployment in the environment or hardware modifications at the transmitter or receiver side. To this end, we propose DOLOS, a system that can protect the location privacy of the Wi-Fi user with a novel signal obfuscation approach. DOLOSis a software-only solution that can be deployed on existing protocol-compliant Wi-Fi user devices. We provide this obfuscation by invalidating a simple assumption made by most localization systems -- "direct path signal arrives earlier than all the reflections to distinguish this direct path prior to estimating the location". However, DOLOS creates a novel software fix that allows the user to transmit the signal wherein this direct path arrives later, creating ambiguity in the location estimates. Our experimental results demonstrate DOLOS can degrade the localization accuracy of state-of-art systems by 6x for a single AP and 2.5x for multiple AP scenarios, thereby protecting the Wi-Fi user's location privacy without compromising the Wi-Fi communication performance.

We tackle the challenge of predicting models' Out-of-Distribution (OOD) performance using in-distribution (ID) measurements without requiring OOD data. Existing evaluations with "Effective Robustness", which use ID accuracy as an indicator of OOD accuracy, encounter limitations when models are trained with diverse supervision and distributions, such as class labels (Vision Models, VMs, on ImageNet) and textual descriptions (Visual-Language Models, VLMs, on LAION). VLMs often generalize better to OOD data than VMs despite having similar or lower ID performance. To improve the prediction of models' OOD performance from ID measurements, we introduce the Lowest Common Ancestor (LCA)-on-the-Line framework. This approach revisits the established concept of LCA distance, which measures the hierarchical distance between labels and predictions within a predefined class hierarchy, such as WordNet. We assess 75 models using ImageNet as the ID dataset and five significantly shifted OOD variants, uncovering a strong linear correlation between ID LCA distance and OOD top-1 accuracy. Our method provides a compelling alternative for understanding why VLMs tend to generalize better. Additionally, we propose a technique to construct a taxonomic hierarchy on any dataset using K-means clustering, demonstrating that LCA distance is robust to the constructed taxonomic hierarchy. Moreover, we demonstrate that aligning model predictions with class taxonomies, through soft labels or prompt engineering, can enhance model generalization. Open source code in our Project Page: https://elvishelvis.github.io/papers/lca/., Comment: ICML 2024 Oral Presentation; Project Page: https://elvishelvis.github.io/papers/lca/