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The magnetic properties of bulk RuO$_2$ remain a subject of active debate, despite its pivotal role in the emergence of altermagnetism. The latter is a novel paradigm in magnetic phases, characterized by the absence of net magnetization due to anti-parallel alignment of magnetic moments, yet displaying finite spin-splitting in the electronic band structure. This unique behavior unlocks opportunities for advanced applications in information technology devices. Recent experimental and theoretical investigations suggest that bulk RuO$_2$, contrary to prior assumptions, is non-magnetic. In this work, we propose the fabrication of RuO$_2$ thin films to robustly stabilize the altermagnetic phase. Unlike their bulk counterparts, thin films experience substantial strain relaxation, leading to a dramatic impact on the electronic structure that triggers a transition towards an altermagnetic behavior, which mimics the impact of an artificially applied Hubbard-U correction to account for electronic correlations. Our findings promote the use and exploration of thin films for the realization of spintronic devices based on altermagnets.

In many problem settings that require spatio-temporal forecasting, the values in the time-series not only exhibit spatio-temporal correlations but are also influenced by spatial diffusion across locations. One such example is forecasting the concentration of fine particulate matter (PM2.5) in the atmosphere which is influenced by many complex factors, the most important ones being diffusion due to meteorological factors as well as transport across vast distances over a period of time. We present a novel Spatio-Temporal Graph Neural Network architecture, that specifically captures these dependencies to forecast the PM2.5 concentration. Our model is based on an encoder-decoder architecture where the encoder and decoder parts leverage gated recurrent units (GRU) augmented with a graph neural network (TransformerConv) to account for spatial diffusion. Our model can also be seen as a generalization of various existing models for time-series or spatio-temporal forecasting. We demonstrate the model's effectiveness on two real-world PM2.5 datasets: (1) data collected by us using a recently deployed network of low-cost PM$_{2.5}$ sensors from 511 locations spanning the entirety of the Indian state of Bihar over a period of one year, and (2) another publicly available dataset that covers severely polluted regions from China for a period of 4 years. Our experimental results show our model's impressive ability to account for both spatial as well as temporal dependencies precisely., Comment: 9 pages, 4 figures, International Conference on Data Science and Management of Data (CODS-COMAD), IIT Jodhpur, 2024

We study the Boolean Satisfiability problem (SAT) in the framework of diversity, where one asks for multiple solutions that are mutually far apart (i.e., sufficiently dissimilar from each other) for a suitable notion of distance/dissimilarity between solutions. Interpreting assignments as bit vectors, we take their Hamming distance to quantify dissimilarity, and we focus on problem of finding two solutions. Specifically, we define the problem MAX DIFFER SAT (resp. EXACT DIFFER SAT) as follows: Given a Boolean formula $\phi$ on $n$ variables, decide whether $\phi$ has two satisfying assignments that differ on at least (resp. exactly) $d$ variables. We study classical and parameterized (in parameters $d$ and $n-d$) complexities of MAX DIFFER SAT and EXACT DIFFER SAT, when restricted to some formula-classes on which SAT is known to be polynomial-time solvable. In particular, we consider affine formulas, $2$-CNF formulas and hitting formulas. For affine formulas, we show the following: Both problems are polynomial-time solvable when each equation has at most two variables. EXACT DIFFER SAT is NP-hard, even when each equation has at most three variables and each variable appears in at most four equations. Also, MAX DIFFER SAT is NP-hard, even when each equation has at most four variables. Both problems are W[1]-hard in the parameter $n-d$. In contrast, when parameterized by $d$, EXACT DIFFER SAT is W[1]-hard, but MAX DIFFER SAT admits a single-exponential FPT algorithm and a polynomial-kernel. For 2-CNF formulas, we show the following: Both problems are polynomial-time solvable when each variable appears in at most two clauses. Also, both problems are W[1]-hard in the parameter $d$ (and therefore, it turns out, also NP-hard), even on monotone inputs (i.e., formulas with no negative literals). Finally, for hitting formulas, we show that both problems are polynomial-time solvable., Comment: 27 pages, 5 figures; this is full version of the corresponding paper accepted and presented at the 35th International Symposium on Algorithms and Computation (ISAAC 2024)

Hydrogen is considered an alternative source of energy to fossil fuels for the fulfilment of current energy demands. Photocatalysis initiates the hydrogen evolution reaction which is believed to be the greenest approach to produce hydrogen through clean, safe, and environmentally friendly methods. In this Review, we focus mainly on the comprehensive analysis of the 2D and 3D bulk materials on the basis of their superior photocatalytic activities. However, several literatures have reported the superiority of 2D material over the bulk counterpart in terms of photocatalytic performance owing to their ultrathin layered structures, offer a higher surface-to-volume ratio, flexibility, large active sites for incoming H2O molecules, etc. We have thoroughly analysed the drawbacks of various hydrogen production methods focusing on the photocatalysis mechanism and the processes of evolution of hydrogen. In addition to this, a short overview of the various solid-state materials for photocatalysis that have been developed so far and their mechanisms are discussed. Lastly, we have discussed the recent developments in 2D materials and their composites as promising photocatalysts.

Motivated by recent developments in tetraquark studies, we investigate the mass spectra and decay properties of light-strange tetraquarks ($sq\bar{s}\bar{q},ss\bar{q}\bar{q}$) in diquark-antidiquark formalism. By considering different internal quark structures and internal color structures, mass spectra are generated in semi-relativistic and non-relativistic frameworks. For decay widths, the annihilation model and spectator model have been incorporated. Several resonances have been explored as potential candidates for these tetraquarks. Concurrently, mass spectra and several decay channels of kaons ($s\bar{q},q\bar{s}$) are also investigated. This study is carried out in the hope of helping improve the understanding of tetraquarks in the light-light sector., Comment: 31 Pages, 18 Figures. arXiv admin note: text overlap with arXiv:2410.14246

Pb-based perovskites are considered to be the most efficient materials for energy harvest. However, real-time application is limited because of their toxicity. As a result, lead-free perovskites that offer similar advantages are potential alternatives. Here, we have chosen LiSnX$_3$ (X = Cl, Br, and I) for further calculation and explore its possibilities for harvesting clean and green energy. Our objective is to examine strategies for optimizing the parameters that control the energy-harvesting capabilities, particularly the interplay between structural variations and electrical properties. The density functional theory (DFT) has been employed for the theoretical simulation. Within the DFT framework, we have studied the effect of applied pressure (0 to 20 GPa) and elemental substitution on their physical properties. We hereby report the variation of lattice parameters, elastic constants, band gaps, and piezoelectric constants. MD simulation with time steps of up to 5 ps was performed to verify structural stability at room temperature. We report the semi-conducting characteristic of LiSnX$_3$ and the high piezoelectric response up to 20.7 Cm2. The presence of high piezoelectric coefficients suggests that manipulation of the structure of LiSnX$_3$ may provide an alternative way to harvest energy through electromechanical processes.

Wide bandgap semiconductors (WBGs) are predicted to be the potential materials for energy generation and storing. In this work, we used density functional theory (DFT) that incorporates generalized gradient approximation (GGA) and meta-generalized gradient approximation (mGGA) methods to explore the various properties of the LiSnCl3 and LiSnBr3 perovskites. The structural stabilities, charge transfer, electronic, optical, mechanical, and piezoelectric properties are studied. Herein, we report that these rarely studied materials are eco-friendly and look promising for optoelectronics and piezoelectric applications.

Recently, a new magnetic phase, termed altermagnetism, has caught the attention of the magnetism and spintronics community. This newly discovered magnetic phenomenon differs from traditional ferromagnetism and antiferromagnetic. It generally lacks net magnetization and is characterized by unusual non-relativistic spin-splitting and broken time-reversal symmetry. This leads to novel transport properties such as the anomalous Hall effect, the crystal Nernst effect, and spin-dependent phenomena that cannot be fully explained by traditional magnetic theories. Spin-dependent phenomena such as spin currents, spin-splitter torques, and high-frequency dynamics emerge as key characteristics in altermagnets. This paper reviews the main aspects pertaining to altermagnets by providing an overview of theoretical investigations and experimental realizations. We discuss the most recent developments in altermagnetism, its comparison to other magnetic orders, and future prospects for exploiting its unique properties in next-generation devices.

The light, strange baryons have been studied through various approaches and attempted to be looked for rigorously in experiments. The screened potential has been applied to heavy baryon sector as well as meson systems in earlier works. Here, this article attempts to compare the results for linear and screened potential for light strange baryons. Also, the Regge trajectories depict the linear nature., Comment: 11 pages, 5 figures, 2 tables, To be published in IJMPA, ICNFP-2022

We present a theory of SNS junctions, a normal metal sandwiched between two superconductors, along the crossover from the BCS to the BEC regime. We calculate the Josephson current as a function of the chemical potential relative to the band edge in the superconducting region, $\mu_S$, where the BEC phase is indicated by $\mu_S <0$. The chemical potential relative to the band edge in the normal metal, $\mu_N$, allows us to tune the junction between the SNS case ($\mu_N>0$) and the SIS case, where the superconductors are separated by a tunneling barrier. We find that there are Andreev levels in the BEC regime, as long as there is sufficient density of states in the normal region, i.e. when $\mu_N>\Delta$, where $\Delta$ is the amplitude of the superconducting order parameter. For 1D SNS junctions, we find the Josephson current $I_S$ carried by these Andreev levels to be a function of the ratio $\Delta/\Delta_d$, where $\Delta_d$ is the Andreev level spacing. At zero temperature, the Josephson current has a maximum on the BCS side of the transition where $\Delta$ is maximal. At finite temperature, however, we find that the maximum moves to the BEC side of the crossover. We identify the mechanism for this phenomenon to be the decrease in the number of Andreev levels at the BCS-BEC crossover, accompanied by an increase in excitation energy to the unoccupied levels, making it less likely that these states are thermally occupied. Thereby, at finite temperature, the Josephson current is more strongly reduced on the BCS side of the crossover, resulting in a maximal Josephson current at the BCS-BEC crossover., Comment: 5 pages, 6 figures

Animating hand-drawn sketches using traditional tools is challenging and complex. Sketches provide a visual basis for explanations, and animating these sketches offers an experience of real-time scenarios. We propose an approach for animating a given input sketch based on a descriptive text prompt. Our method utilizes a parametric representation of the sketch's strokes. Unlike previous methods, which struggle to estimate smooth and accurate motion and often fail to preserve the sketch's topology, we leverage a pre-trained text-to-video diffusion model with SDS loss to guide the motion of the sketch's strokes. We introduce length-area (LA) regularization to ensure temporal consistency by accurately estimating the smooth displacement of control points across the frame sequence. Additionally, to preserve shape and avoid topology changes, we apply a shape-preserving As-Rigid-As-Possible (ARAP) loss to maintain sketch rigidity. Our method surpasses state-of-the-art performance in both quantitative and qualitative evaluations.

Federated Learning (FL) has emerged as a promising method to collaboratively learn from decentralized and heterogeneous data available at different clients without the requirement of data ever leaving the clients. Recent works on FL have advocated taking a Bayesian approach to FL as it offers a principled way to account for the model and predictive uncertainty by learning a posterior distribution for the client and/or server models. Moreover, Bayesian FL also naturally enables personalization in FL to handle data heterogeneity across the different clients by having each client learn its own distinct personalized model. In particular, the hierarchical Bayesian approach enables all the clients to learn their personalized models while also taking into account the commonalities via a prior distribution provided by the server. However, despite their promise, Bayesian approaches for FL can be computationally expensive and can have high communication costs as well because of the requirement of computing and sending the posterior distributions. We present a novel Bayesian FL method using an efficient second-order optimization approach, with a computational cost that is similar to first-order optimization methods like Adam, but also provides the various benefits of the Bayesian approach for FL (e.g., uncertainty, personalization), while also being significantly more efficient and accurate than SOTA Bayesian FL methods (both for standard as well as personalized FL settings). Our method achieves improved predictive accuracies as well as better uncertainty estimates as compared to the baselines which include both optimization based as well as Bayesian FL methods.

We explore the potential of gravitational waves (GWs) to probe the pre-BBN era of the early universe, focusing on the effects of energy injection. Specifically, we examine a hidden sector alongside the Standard Model that undergoes a strong first-order phase transition (FOPT), producing a GW signal. Once the phase transition has completed, energy injection initiates reheating in the hidden sector, which positions the hidden sector field so that additional phase transitions can occur. This can result in a total of three distinct phase transitions with a unique three-peak GW spectrum. Among these transitions, the first and third are of the standard type, while the intermediate second transition is inverted, moving from a broken to an unbroken phase. Using polynomial potentials as a framework, we derive analytical relations among the phase transition parameters and the resulting GW spectrum. Our results indicate that the second and third transitions generate GWs with higher amplitudes than the first, with a peak frequency ratio differing by up to an order of magnitude. This three-peak GW spectrum is detectable by upcoming facilities such as LISA, BBO, and UDECIGO. Notably, the phenomenon is robust across various potentials and model parameters, suggesting that hidden sector GWs provide a powerful tool for exploring new physics scenarios in the pre-BBN era.

For a set $A$ of positive integers with $\gcd(A)=1$, let $\langle A \rangle$ denote the set of all finite linear combinations of elements of $A$ over the non-negative integers. The it is well known that only finitely many positive integers do not belong to $\langle A \rangle$. The Frobenius number and the genus associated with the set $A$ is the largest number and the cardinality of the set of integers non-representable by $A$. By a generalized Fibonacci sequence $\{V_n\}_{n \ge 1}$ we mean any sequence of positive integers satisfying the recurrence $V_n=V_{n-1}+V_{n-2}$ for $n \ge 3$. We study the problem of determining the Frobenius number and genus for sets $A=\{V_n,V_{n+d},V_{n+2d},\ldots\}$ for arbitrary $n$, where $d$ odd or $d=2$., Comment: 18 pages, 9 references

A graph $G$ is cordial if there exists a function $f$ from the vertices of $G$ to $\{0,1\}$ such that the number of vertices labelled $0$ and the number of vertices labelled $1$ differ by at most $1$, and if we assign to each edge $xy$ the label $|f(x)-f(y)|$, the number of edges labelled $0$ and the number of edges labelled $1$ also differ at most by $1$. We introduce two measures of how close a graph is to being cordial, and compute these measures for a variety of classes of graphs., Comment: 13 pages, 3 tables, 11 references

Estimating spectral gaps of quantum many-body Hamiltonians is a highly challenging computational task, even under assumptions of locality and translation-invariance. Yet, the quest for rigorous gap certificates is motivated by their broad applicability, ranging from many-body physics to quantum computing and classical sampling techniques. Here we present a general method for obtaining lower bounds on the spectral gap of frustration-free quantum Hamiltonians in the thermodynamic limit. We formulate the gap certification problem as a hierarchy of optimization problems (semidefinite programs) in which the certificate -- a proof of a lower bound on the gap -- is improved with increasing levels. Our approach encompasses existing finite-size methods, such as Knabe's bound and its subsequent improvements, as those appear as particular possible solutions in our optimization, which is thus guaranteed to either match or surpass them. We demonstrate the power of the method on one-dimensional spin-chain models where we observe an improvement by several orders of magnitude over existing finite size criteria in both the accuracy of the lower bound on the gap, as well as the range of parameters in which a gap is detected., Comment: 21 pages, 4 figures, comments welcome

Understanding and optimizing the relaxation dynamics of many-body systems is essential for both foundational studies in quantum thermodynamics, as well as for applications including quantum simulation and quantum computing. Efficient thermal state preparation of a given many-body Hamiltonian depends on the spectrum of Lindbladian defined via jump operators. In this work we provide a systematic framework allowing construction of an optimal Lindbladian resulting in fast thermal state preparation for a considered Hamiltonian. Importantly, the optimal Lindbladian respects the symmetries of the target equilibrium state. We demonstrate the potential of our framework and optimization with the kinetic Ising model and the Lipkin-Meshkov-Glick model, showcasing efficient thermal state preparation., Comment: 17 pages, 4 figures

We present a benchmark for Planning And Reasoning Tasks in humaN-Robot collaboration (PARTNR) designed to study human-robot coordination in household activities. PARTNR tasks exhibit characteristics of everyday tasks, such as spatial, temporal, and heterogeneous agent capability constraints. We employ a semi-automated task generation pipeline using Large Language Models (LLMs), incorporating simulation in the loop for grounding and verification. PARTNR stands as the largest benchmark of its kind, comprising 100,000 natural language tasks, spanning 60 houses and 5,819 unique objects. We analyze state-of-the-art LLMs on PARTNR tasks, across the axes of planning, perception and skill execution. The analysis reveals significant limitations in SoTA models, such as poor coordination and failures in task tracking and recovery from errors. When LLMs are paired with real humans, they require 1.5x as many steps as two humans collaborating and 1.1x more steps than a single human, underscoring the potential for improvement in these models. We further show that fine-tuning smaller LLMs with planning data can achieve performance on par with models 9 times larger, while being 8.6x faster at inference. Overall, PARTNR highlights significant challenges facing collaborative embodied agents and aims to drive research in this direction., Comment: Alphabetical author order

Kakeya sets are compact subsets of $\mathbb{R}^n$ that contain a unit line segment pointing in every direction. The Kakeya conjecture states that such sets must have Hausdorff dimension $n$. The property of stickiness was first discovered by Katz-{\L}aba-Tao in their 1999 breakthrough paper on the Kakeya problem. Then Wang-Zahl formalized the definition of a sticky Kakeya set, and proposed a special case of the Kakeya conjecture for such sets. Specifically this conjecture states that sticky Kakeya sets in $\mathbb{R}^n$ have Hausdorff dimension $n$ and Wang-Zahl went on to prove the conjecture for $n=3$. A planebrush is a geometric object which is a higher dimensional analogue of Wolff's hairbrush. Using the planebrush argument, Katz-Zahl showed that Kakeya sets in $\mathbb{R}^4$ have Hausdorff dimension at least 3.059. If we restrict our attention to sticky Kakeya sets, we can improve upon this bound by combining the planebrush result with additional stickiness property. To be precise, we will show in this paper that sticky Kakeya sets in $\mathbb{R}^4$ have dimension at least 3.25., Comment: 31 pages, 8 figures

Automatic Speech Recognition (ASR) systems have been examined and shown to exhibit biases toward particular groups of individuals, influenced by factors such as demographic traits, accents, and speech styles. Noise can disproportionately impact speakers with certain accents, dialects, or speaking styles, leading to biased error rates. In this work, we introduce a novel framework DENOASR, which is a selective denoising technique to reduce the disparity in the word error rates between the two gender groups, male and female. We find that a combination of two popular speech denoising techniques, viz. DEMUCS and LE, can be effectively used to mitigate ASR disparity without compromising their overall performance. Experiments using two state-of-the-art open-source ASRs - OpenAI WHISPER and NVIDIA NEMO - on multiple benchmark datasets, including TIE, VOX-POPULI, TEDLIUM, and FLEURS, show that there is a promising reduction in the average word error rate gap across the two gender groups. For a given dataset, the denoising is selectively applied on speech samples having speech intelligibility below a certain threshold, estimated using a small validation sample, thus ameliorating the need for large-scale human-written ground-truth transcripts. Our findings suggest that selective denoising can be an elegant approach to mitigate biases in present-day ASR systems., Comment: Paper accepted at IEEE ICKG 2024

We investigate the mass spectra and decay properties of pions and all light tetraquarks using both semi-relativistic and non-relativistic frameworks. By applying a Cornell-like potential and a spin-dependent potential, we generate the mass spectra. The decay properties of tetraquarks are evaluated using the annihilation model and the spectator model. Potential tetraquark candidates are interpreted for quantum numbers $J^{PC} = 0^{++}, 0^{-+}, 1^{-+}, 1^{+-}, 1^{--}, 2^{+-}, 2^{-+},$ and $2^{--}$. Additionally, we compare our results with existing experimental data and theoretical predictions to validate our findings. This study aims to enhance the understanding of tetraquarks in the light-light sector., Comment: 10 pages, 6 figures

Paints and coatings undergo a variety of physical and chemical changes under environmental exposures. Accurate prediction of these changes is important for the applications of coatings. This work presents a novel approach to modeling accelerated weathering of coating by combining concurrent physical and chemical processes. The model integrates key factors influencing coating degradation and employs a multi-scale framework to capture macro-scale physical changes and micro-level chemical transformations. The chemical component/model simulates photo-degradation reactions using kinetic equations, while the physical component uses Monte Carlo simulations where repeated random events develop surface erosion. The surface topography and chemistry of coating is generated statistically through the physical model. The variations in surface topography and chemistry of coating are correlated with the chemical changes from the chemical model, resulting in estimation of both physical and chemical changes in the coating during real accelerated weathering time. Results demonstrate accurate predictions of chemistry changes, surface degradation profiles, roughness, gloss loss, and relative fracture toughness, which are validated successfully with the experimentally available data. This integrated approach provides insight into coating failure mechanisms, enabling accurate service life prediction, and serve as a tool for formulating durable coatings and optimizing testing protocols.

This work presents a detailed study of the electronic structure, phonon dispersion, Z2 invariant calculation, and Fermi surface of the newly discovered kagome superconductor CsV3Sb5, using density functional theory (DFT). The phonon dispersion in the pristine state reveals two negative modes at the M and L points of the Brillouin zone, indicating lattice instability. CsV3Sb5 transitions into a structurally stable 2x2x1 charge density wave (CDW) phase, confirmed by positive phonon modes. The electronic band structure shows several Dirac points near the Fermi level, with a narrow gap opening due to spin-orbit coupling (SOC), though the effect of SOC on other bands is minimal. In the pristine phase, this material exhibits a quasi-2D cylindrical Fermi surface, which undergoes reconstruction in the CDW phase. We calculated quantum oscillation frequencies using Onsager's relation, finding good agreement with experimental results in the CDW phase. To explore the topological properties of CsV3Sb5, we computed the Z2 invariant in both pristine and CDW phases, resulting in a value of (u0; u1u2u3) = (1; 000), suggesting the strong topological nature of this material. Our detailed analysis of phonon dispersion, electronic bands, Fermi surface mapping, and Z2 invariant provides insights into the topological properties, CDW order, and unconventional superconductivity in AV3Sb5 (A = K, Rb, and Cs)., Comment: To be published in APL Quantum

This study conducts a comprehensive first-principles investigation of the IrMnAl Heusler alloy, highlighting its magnetic properties and assessing the effects of pressure-induced phase shift. The limitations of Generalized Gradient Approximation in accurately representing the compounds magnetic behavior is addressed by employing the GGA+U approach, which more effectively captures the electronic features. At 5.6 GPa pressure, we observe a novel structural phase transition from the cubic F4-3m to the P4-3m space group. The calculated Curie temperature, combined with the analysis of Mn-Mn exchange interaction, sheds light on the weak ferromagnetism observed in the new phase of the compound.

We propose a novel mechanism, dark matter internal pair production (DIPP), to detect dark matter candidates at beam dump facilities. When energetic dark matter scatters in a material, it can create a lepton-antilepton pair by exchanging a virtual photon with the nucleus, similar to the neutrino trident process. We demonstrate this process for dark matter coupled to dark photons in experiments such as DarkQuest, SBND, and DUNE ND experiments. Since the pair-produced lepton-antilepton pair carries a large fraction of the center-of-mass energy and also has similar energy profiles, they can be clearly distinguished from backgrounds. We utilize the above features to show that DIPP is effective in probing various dark matter models, especially at DUNE ND and DarkQuest, by looking for electron-positron and muon-antimuon signatures. We also consider a scenario with dark sector couplings to quarks and muons only to show that DIPP can probe a wide range of dark matter models with various final states., Comment: 7 pages, 4 figures

We present a polynomial-time pseudo-deterministic algorithm for constructing irreducible polynomial of degree $d$ over finite field $\mathbb{F}_q$. A pseudo-deterministic algorithm is allowed to use randomness, but with high probability it must output a canonical irreducible polynomial. Our construction runs in time $\tilde{O}(d^4 \log^4{q})$. Our construction extends Shoup's deterministic algorithm (FOCS 1988) for the same problem, which runs in time $\tilde{O}(d^4 p^{\frac{1}{2}} \log^4{q})$ (where $p$ is the characteristic of the field $\mathbb{F}_q$). Shoup had shown a reduction from constructing irreducible polynomials to factoring polynomials over finite fields. We show that by using a fast randomized factoring algorithm, the above reduction yields an efficient pseudo-deterministic algorithm for constructing irreducible polynomials over finite fields.

Explaining the decisions of AI has become vital for fostering appropriate user trust in these systems. This paper investigates explanations for a structured prediction task called ``text-to-SQL Semantic Parsing'', which translates a natural language question into a structured query language (SQL) program. In this task setting, we designed three levels of model explanation, each exposing a different amount of the model's decision-making details (called ``algorithm transparency''), and investigated how different model explanations could potentially yield different impacts on the user experience. Our study with $\sim$100 participants shows that (1) the low-/high-transparency explanations often lead to less/more user reliance on the model decisions, whereas the medium-transparency explanations strike a good balance. We also show that (2) only the medium-transparency participant group was able to engage further in the interaction and exhibit increasing performance over time, and that (3) they showed the least changes in trust before and after the study., Comment: 15 pages, 18 figure, Preprint

In this study, we analyze the strong decays of singly charmed baryons into another singly charmed baryon and a light pseudo scalar meson by means of heavy hadron chiral perturbation theory (HHChPT) which combines heavy quark symmetry with chiral symmetry. HHChPT worked excellently for describing the strong decays of $1S$-wave charmed baryons. The strong decay widths of well-established singly charmed baryons are well reproduced. Further, the strong decay width of $1P$ states are calculated in this approach by using the calculated masses in the relativistic flux tube (RFT) model. By analysing the mass spectra from the RFT model with strong decays in HHChPT, we investigate the spin-parity assignments. We identify $\Sigma_{c}(2800)$ and $\Xi_{c}(2939)$ as $1P$-wave states having $J^{P}=\frac{3}{2}^{-}$., Comment: 15 pages, submitted to International Journal of Modern Physics A, conference proceedings of XII International Conference on New Frontiers in Physics 2023

This paper introduces the hypothetical track-length fitting algorithm, a novel method for measuring the kinetic energies of ionizing particles in liquid argon time projection chambers (LArTPCs). The algorithm finds the most probable offset in track length for a track-like object by comparing the measured ionization density as a function of position with a theoretical prediction of the energy loss as a function of the energy, including models of electron recombination and detector response. The algorithm can be used to measure the energies of particles that interact before they stop, such as charged pions that are absorbed by argon nuclei. The algorithm's energy measurement resolutions and fractional biases are presented as functions of particle kinetic energy and number of track hits using samples of stopping secondary charged pions in data collected by the ProtoDUNE-SP detector, and also in a detailed simulation. Additional studies describe impact of the dE/dx model on energy measurement performance. The method described in this paper to characterize the energy measurement performance can be repeated in any LArTPC experiment using stopping secondary charged pions.

The preparation of non-trivial states is crucial to the study of quantum many-body physics. Such states can be prepared with adiabatic quantum algorithms, which are restricted by the minimum spectral gap along the path. In this letter, we propose an efficient method to adiabatically prepare tensor networks states (TNSs). We maximize the spectral gap leveraging degrees of freedom in the parent Hamiltonian construction. We demonstrate this efficient adiabatic algorithm for preparing TNS, through examples of random TNS in one dimension, AKLT, and GHZ states. The Hamiltonian optimization applies to both injective and non-injective tensors, in the latter case by exploiting symmetries present in the tensors., Comment: 19 pages, 9 figures

Brain tumor segmentation plays a crucial role in computer-aided diagnosis. This study introduces a novel segmentation algorithm utilizing a modified nnU-Net architecture. Within the nnU-Net architecture's encoder section, we enhance conventional convolution layers by incorporating omni-dimensional dynamic convolution layers, resulting in improved feature representation. Simultaneously, we propose a multi-scale attention strategy that harnesses contemporary insights from various scales. Our model's efficacy is demonstrated on diverse datasets from the BraTS-2023 challenge. Integrating omni-dimensional dynamic convolution (ODConv) layers and multi-scale features yields substantial improvement in the nnU-Net architecture's performance across multiple tumor segmentation datasets. Remarkably, our proposed model attains good accuracy during validation for the BraTS Africa dataset. The ODconv source code along with full training code is available on GitHub., Comment: 9 pages, 3 figures. Accepted at MICCAI 2023, to be published in Springer LNCS. GitHub: https://github.com/i-sahajmistry/nnUNet_BraTS2023

Koopman operator theory has proven to be highly significant in system identification, even for challenging scenarios involving nonlinear time-varying systems (NTVS). In this context, we examine a network of connected agents, each with limited observation capabilities, aiming to estimate the dynamics of an NTVS collaboratively. Drawing inspiration from Koopman operator theory, deep neural networks, and distributed consensus, we introduce a distributed algorithm for deep Koopman learning of the dynamics of an NTVS. This approach enables individual agents to approximate the entire dynamics despite having access to only partial state observations. We guarantee consensus not only on the estimated dynamics but also on its structure, i.e., the matrices encountered in the linear equation of the lifted Koopman system. We provide theoretical insights into the convergence of the learning process and accompanying numerical simulations.

Three extraordinary physics discoveries were made from colonial India, which did not have any previous tradition of research in modern physics: Saha ionization equation (1920), Bose statistics (1924), Raman effect (1928). All the three discoverers were founding faculty members of the new small physics department of Calcutta University, which started functioning from 1916. These discoveries were all in the general topic of interaction between matter and radiation. In Part 1, we have described the social and the intellectual environment in which these discoveries were made. Now, in Part 2, we shall first give a background of the revolutionary developments taking place in physics at that time. Then we shall provide a non-technical account of the three discoveries and point out the kind of impact these discoveries made in the subsequent development of physics., Comment: 13 pages, 8 figures. Accepted for publication in "Resonance Journal of Science Education", Indian Academy of Sciences

Three extraordinary physics discoveries were made from colonial India, which did not have any previous tradition of research in modern physics: Saha ionization equation (1920), Bose statistics (1924), Raman effect (1928). All the three discoverers were founding faculty members of the new small physics department of Calcutta University, which started functioning from 1916. These discoveries were all in the general topic of interaction between matter and radiation. In Part 1 of this article, we describe the social and the intellectual environment in which these discoveries were made. Part 2 will focus on the science involved in these discoveries., Comment: 11 pages, 4 figures. Accepted for publication in "Resonance Journal of Science Education", Indian Academy of Sciences

Accurate direction of arrival (DoA) and time of arrival (ToA) estimation is an stringent requirement for several wireless systems like sonar, radar, communications, and dual-function radar communication (DFRC). Due to the use of high carrier frequency and bandwidth, most of these systems are designed with multiple antennae and subcarriers. Although the resolution is high in the large array regime, the DoA-ToA estimation accuracy of the practical on-grid estimation methods still suffers from estimation inaccuracy due to the spectral leakage effect. In this article, we propose DoA-ToA estimation methods for multi-antenna multi-carrier systems with an orthogonal frequency division multiplexing (OFDM) signal. In the first method, we apply discrete Fourier transform (DFT) based coarse signature estimation and propose a low complexity multistage fine-tuning for extreme enhancement in the estimation accuracy. The second method is based on compressed sensing, where we achieve the super-resolution by taking a 2D-overcomplete angle-delay dictionary than the actual number of antenna and subcarrier basis. Unlike the vectorized 1D-OMP method, we apply the low complexity 2D-OMP method on the matrix data model that makes the use of CS methods practical in the context of large array regimes. Through numerical simulations, we show that our proposed methods achieve the similar performance as that of the subspace-based 2D-MUSIC method with a significant reduction in computational complexity., Comment: 5 pages, 4 figures, 1 table

Significant efforts have been made in recent years to measure the properties of charmed baryons. In this work, we study the $\Xi_{c}'$ and $\Omega_{c}$ baryons in the relativistic flux tube model with a quark-diquark picture of a baryon. The modified Regge relation between mass and angular momentum is used to predict the spin-average masses. The spin-dependent interactions are also included to compute the spin-dependent splitting. We calculate the masses of states belonging to the $1F$-wave and $1G$-wave of $\Xi_{c}'$ and $\Omega_{c}$ baryons, which are not yet detected experimentally. Our mass predictions can offer valuable insights to experimental facilities in their search to discover the higher excited states of $\Xi_{c}'$ and $\Omega_{c}$ baryons., Comment: 9 pages, 1 figure, to be published in International Journal of Modern Physics A, This work is presented at $11^{th}$ international conference on new frontiers in physics (ICNFP 2022)

Ni-Mo superalloys have emerged as materials of choice for a diverse array of applications owing to their superior mechanical properties, exceptional corrosion and oxidation resistance, electrocatalytic behavior, and surface stability. Understanding and optimizing the surface composition of Ni-Mo alloys is critical for enhancing their performance in practical applications. Traditional experimental surface analysis techniques, while informative, are often prohibitive in terms of cost and time. Likewise, theoretical approaches such as first-principle calculations demand substantial computational resources and it is difficult to simulate large structures. This study introduces an alternative approach utilizing hybrid Monte-Carlo / Molecular Dynamics (MC/MD) simulations to investigate the surface composition of Ni-Mo alloys. We report the development of an optimized Embedded-Atom Method (EAM) potential specifically for Ni-Mo alloys, carefully parameterized using empirical lattice constants and formation energies of elemental and face-centered cubic (FCC) Ni-Mo solid solution alloys. The reliability of the EAM potential is corroborated via the evaluation of equations of state, with a particular focus on reproducing structural properties. Utilizing this validated potential, MC/MD simulations were performed to understand the depth-wise variations in the compositions of Ni-Mo alloy nanoparticles and extended surfaces. These simulations reveal a preferential segregation of nickel on surface, and molybdenum in sub-surface layer. Due to this preferential segregation, it is imperative to consider surface segregation while tailoring the surface properties for targeted applications.

Designing algorithms for space bounded models with restoration requirements on the space used by the algorithm is an important challenge posed about the catalytic computation model introduced by Buhrman et al. (2014). Motivated by the scenarios where we do not need to restore unless is useful, we define $ACL(A)$ to be the class of languages that can be accepted by almost-catalytic Turing machines with respect to $A$ (which we call the catalytic set), that uses at most $c\log n$ work space and $n^c$ catalytic space. We show that if there are almost-catalytic algorithms for a problem with catalytic set as $A \subseteq \Sigma^*$ and its complement respectively, then the problem can be solved by a ZPP algorithm. Using this, we derive that to design catalytic algorithms, it suffices to design almost-catalytic algorithms where the catalytic set is the set of strings of odd weight ($PARITY$). Towards this, we consider two complexity measures of the set $A$ which are maximized for $PARITY$ - random projection complexity (${\cal R}(A)$) and the subcube partition complexity (${\cal P}(A)$). By making use of error-correcting codes, we show that for all $k \ge 1$, there is a language $A_k \subseteq \Sigma^*$ such that $DSPACE(n^k) \subseteq ACL(A_k)$ where for every $m \ge 1$, $\mathcal{R}(A_k \cap \{0,1\}^m) \ge \frac{m}{4}$ and $\mathcal{P}(A_k \cap \{0,1\}^m)=2^{m/4}$. This contrasts the catalytic machine model where it is unclear if it can accept all languages in $DSPACE(\log^{1+\epsilon} n)$ for any $\epsilon > 0$. Improving the partition complexity of the catalytic set $A$ further, we show that for all $k \ge 1$, there is a $A_k \subseteq \{0,1\}^*$ such that $\mathsf{DSPACE}(\log^k n) \subseteq ACL(A_k)$ where for every $m \ge 1$, $\mathcal{R}(A_k \cap \{0,1\}^m) \ge \frac{m}{4}$ and $\mathcal{P}(A_k \cap \{0,1\}^m)=2^{m/4+\Omega(\log m)}$., Comment: 22 pages, A new lower bound on the subcube partition complexity of Hamming balls (Proposition 2.6 and Lemma 2.7), improving the bound and fixing an error in the previous version

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

Large Deep Learning models are compressed and deployed for specific applications. However, current Deep Learning model compression methods do not utilize the information about the target application. As a result, the compressed models are application agnostic. Our goal is to customize the model compression process to create a compressed model that will perform better for the target application. Our method, Application Specific Compression (ASC), identifies and prunes components of the large Deep Learning model that are redundant specifically for the given target application. The intuition of our work is to prune the parts of the network that do not contribute significantly to updating the data representation for the given application. We have experimented with the BERT family of models for three applications: Extractive QA, Natural Language Inference, and Paraphrase Identification. We observe that customized compressed models created using ASC method perform better than existing model compression methods and off-the-shelf compressed models., Comment: Accepted in the Proceedings of the 8th Joint International Conference on Data Science & Management of Data (12th ACM IKDD CODS and 30th COMAD) for the Short Research Paper track, 5 pages

Diffusion-based models demonstrate impressive generation capabilities. However, they also have a massive number of parameters, resulting in enormous model sizes, thus making them unsuitable for deployment on resource-constraint devices. Block-wise generation can be a promising alternative for designing compact-sized (parameter-efficient) deep generative models since the model can generate one block at a time instead of generating the whole image at once. However, block-wise generation is also considerably challenging because ensuring coherence across generated blocks can be non-trivial. To this end, we design a retrieval-augmented generation (RAG) approach and leverage the corresponding blocks of the images retrieved by the RAG module to condition the training and generation stages of a block-wise denoising diffusion model. Our conditioning schemes ensure coherence across the different blocks during training and, consequently, during generation. While we showcase our approach using the latent diffusion model (LDM) as the base model, it can be used with other variants of denoising diffusion models. We validate the solution of the coherence problem through the proposed approach by reporting substantive experiments to demonstrate our approach's effectiveness in compact model size and excellent generation quality.