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Examining reflected light from exoplanets aids in our understanding of the scattering properties of their atmospheres and will be a primary task of future flagship space- and ground-based telescopes. We introduce an enhanced capability of Planetary Intensity Code for Atmospheric Scattering Observations (PICASO), an open-source radiative transfer model used for exoplanet and brown dwarf atmospheres, to produce reflected light phase curves from three-dimensional atmospheric models. Since PICASO is coupled to the cloud code Virga, we produce phase curves for different cloud condensate species and varying sedimentation efficiencies (fsed) and apply this new functionality to Kepler-7b, a hot Jupiter with phase curve measurements dominated by reflected starlight. We model three different cloud scenarios for Kepler-7b: MgSiO3 clouds only, Mg2SiO4 clouds only, and Mg2SiO4, Al2O3, and TiO2 clouds. All our Virga models reproduce the cloudy region west of the substellar point expected from previous studies, as well as clouds at high latitudes and near the eastern limb, which are primarily composed of magnesium silicates. Al2O3 and TiO2 clouds dominate near the substellar point. We then compare our modeled reflected light phase curves to Kepler observations and find that models with all three cloud condensate species and low sedimentation efficiencies (0.03 - 0.1) match best, though our reflected light phase curves show intensities approximately one-third of those observed by Kepler. We conclude that a better understanding of zonal transport, cloud radiative feedback, and particle scattering properties is needed to further explain the differences between the modeled and observed reflected light fluxes.

Squeezed light plays a vital role in quantum information processing. By nature, it is highly sensitive, which presents significant practical challenges, particularly in remote detection, traditionally requiring complex systems such as active phase locking, clock synchronization, and polarization control. Here, we propose and demonstrate an asynchronous detection method for squeezed light that eliminates the need for these complex systems. By employing radio-frequency heterodyne detection with a locally generated local oscillator and applying a series of digital unitary transformations, we successfully reconstruct squeezed states of light. We validate the feasibility of our approach in two key applications: the distribution of squeezed light over a 10 km fiber channel, and secure quantum key distribution between two labs connected via deployed fiber based on continuous variables using squeezed vacuum states without active modulation. This demonstrates a practical digital reconstruction method for squeezed light, opening new avenues for practical distributed quantum sensing networks and high-performance and long-distance quantum communication using squeezed states and standard telecom technology.

In real-world NLP applications, Large Language Models (LLMs) offer promising solutions due to their extensive training on vast datasets. However, the large size and high computation demands of LLMs limit their practicality in many applications, especially when further fine-tuning is required. To address these limitations, smaller models are typically preferred for deployment. However, their training is hindered by the scarcity of labeled data. In contrast, unlabeled data is often readily which can be leveraged by using LLMs to generate pseudo-labels for training smaller models. This enables the smaller models (student) to acquire knowledge from LLMs(teacher) while reducing computational costs. This process introduces challenges, such as potential noisy pseudo-labels. Selecting high-quality and informative data is therefore critical to enhance model performance while improving the efficiency of data utilization. To address this, we propose LLKD that enables Learning with Less computational resources and less data for Knowledge Distillation from LLMs. LLKD is an adaptive sample selection method that incorporates signals from both the teacher and student. Specifically, it prioritizes samples where the teacher demonstrates high confidence in its labeling, indicating reliable labels, and where the student exhibits a high information need, identifying challenging samples that require further learning. Our comprehensive experiments show that LLKD achieves superior performance across various datasets with higher data efficiency.

The increasing growth of social media provides us with an instant opportunity to be informed of the opinions of a large number of politically active individuals in real-time. We can get an overall idea of the ideologies of these individuals on governmental issues by analyzing the social media texts. Nowadays, different kinds of news websites and popular social media such as Facebook, YouTube, Instagram, etc. are the most popular means of communication for the mass population. So the political perception of the users toward different parties in the country is reflected in the data collected from these social sites. In this work, we have extracted three types of features, such as the stylometric feature, the word-embedding feature, and the TF-IDF feature. Traditional machine learning classifiers and deep learning models are employed to identify political ideology from the text. We have compared our methodology with the research work in different languages. Among them, the word embedding feature with LSTM outperforms all other models with 88.28% accuracy., Comment: 8 Pages !

The Gender Identification (GI) problem is concerned with determining the gender of the author from a given text. It has numerous applications in different fields like forensics, literature, security, marketing, trade, etc. Due to its importance, researchers have put extensive efforts into identifying gender from the text for different languages. Unfortunately, the same statement is not true for the Bangla language despite its being the 7th most spoken language in the world. In this work, we explore Gender Identification from Social media Bangla Text. Specially, we consider two approaches for feature extraction. The first one is Bag-Of-Words(BOW) approach and another one is based on computing features from sentiment and emotions. There is a common stereotype that female authors write in a more emotional way than male authors. One goal of this work is to validate this stereotype for the Bangla language., Comment: 7 pages

This study explores whether the Global Value Chain(GVC) participation of 16 emerging market economies (EMEs) from 1995 to 2018 in the manufacturing sector leads to a rise in carbon emissions embodied in trade. The study covers the ecological dimension of the Global Value Chain and validates the Pollution Haven Hypothesis in developing nations.To address the problem of cross-sectional dependence, autocorrelation, and heteroscedasticity panels, we estimate the above models using the feasible generalized least squares (FGLS) method. Our findings exhibit a continuous growth in carbon emissions of all the EMEs, there exists a positive association between GVC participation and domestic CO2 emissions embodied in gross exports. Also, EMEs' foreign carbon emissions embodied in gross exports directly correlate with backward GVC Participation, suggesting that the cleaner environment in developed countries comes at the expense of a dirtier environment in developing countries., Comment: 20 pages, 9 tables

Transformers are set to become ubiquitous with applications ranging from chatbots and educational assistants to visual recognition and remote sensing. However, their increasing computational and memory demands is resulting in growing energy consumption. Building models with fast and energy-efficient inference is imperative to enable a variety of transformer-based applications. Look Up Table (LUT) based Weightless Neural Networks are faster than the conventional neural networks as their inference only involves a few lookup operations. Recently, an approach for learning LUT networks directly via an Extended Finite Difference method was proposed. We build on this idea, extending it for performing the functions of the Multi Layer Perceptron (MLP) layers in transformer models and integrating them with transformers to propose Quasi Weightless Transformers (QuWeiT). This allows for a computational and energy-efficient inference solution for transformer-based models. On I-ViT-T, we achieve a comparable accuracy of 95.64% on CIFAR-10 dataset while replacing approximately 55% of all the multiplications in the entire model and achieving a 2.2x energy efficiency. We also observe similar savings on experiments with the nanoGPT framework.

Metric Temporal Logic (MTL) and Timed Propositional Temporal Logic (TPTL) extend Linear Temporal Logic (LTL) for real-time constraints, with MTL using time-bounded modalities and TPTL employing freeze quantifiers. Satisfiability for both is generally undecidable; however, MTL becomes decidable under certain non-punctual and partially-punctual restrictions. Punctuality can be restored trivially under similar non-punctual restrictions on TPTL even for one variable fragment. Our first contribution is to study more restricted notion of openness for 1-TPTL, under which punctuality can not be recovered. We show that even under such restrictions, the satisfiability checking does not get computationally easier. This implies that 1-TPTL (and hence TPTL) does not enjoy benefits of relaxing punctuality unlike MTL. As our second contribution we introduce a refined, partially adjacent restriction in 1-TPTL (PA-1-TPTL), and prove decidability for its satisfiability checking. We show that this logic is strictly more expressive than partially punctual Metric Temporal Logic, making this as one of the most expressive known boolean-closed decidable timed logic., Comment: arXiv admin note: text overlap with arXiv:1705.01501

The demand for GaN Nanowires (NWs)-based optoelectronic devices has rapidly increased over the past few years due to its superior crystalline quality compare to their planar counterparts. However, NWs-based devices face significant challenges because of number of surface states, basal plane stacking faults and coalescence related defect states. While the origins of most of the defect states have been identified and mitigated using well-established methods, the origins of few defect states remain unknown, and thus their suppression methods have yet to be explored. One such defect state is the 3.45 eV luminescence peak, known as the UX-band. In this report, we have investigated the origin of this peak in NWs grown on a sapphire substrate by using Plasma Assisted Molecular Beam Epitaxy (PAMBE) tool. We have found that the defect states, generated due to oxygen incorporation, especially when the oxygen atoms substitute the Ga atoms, are the primary cause of this band. Actually the excitons are localized at this defect center and the radiative recombination of localized excitons gives the characteristic UX-band. By protecting the NWs from oxygen incorporation through AlN encapsulation process, we have completely suppressed the 3.45 eV peak and proved further that the peak is caused by oxygen induced defect states. Thus we have addressed an issue that persisted over the last three decades, potentially paving the way for efficiency improvements in optoelectronic devices.

Segmenting an object in a video presents significant challenges. Each pixel must be accurately labelled, and these labels must remain consistent across frames. The difficulty increases when the segmentation is with arbitrary granularity, meaning the number of segments can vary arbitrarily, and masks are defined based on only one or a few sample images. In this paper, we address this issue by employing a pre-trained text to image diffusion model supplemented with an additional tracking mechanism. We demonstrate that our approach can effectively manage various segmentation scenarios and outperforms state-of-the-art alternatives., Comment: Technical report. Project page is at \url{https://segment-me-in-time.github.io/}

$FeCr_2S_4$ is a ferrimagnet with two oppositely-ordered spin sublattices (Fe and Cr), connected via superexchange interaction, giving a non-zero net magnetic moment. We show how the magnetic dynamics of the sublattices can be selectively manipulated by resonantly perturbing the Fe sublattice using ultrashort laser pulses. The mid-infrared excitation through intra-atomic Fe $d-d$ transitions triggers a markedly slower magneto-optical Kerr effect dynamics compared to an off-resonant pumping affecting both the two sublattices simultaneously. The analysis and conclusions are supported by probing resonantly and off-resonantly to the Fe sublattice., Comment: 29 pages, 19 figures

Retrieval-augmented generation (RAG) enhances the question-answering (QA) abilities of large language models (LLMs) by integrating external knowledge. However, adapting general-purpose RAG systems to specialized fields such as science and medicine poses unique challenges due to distribution shifts and limited access to domain-specific data. To tackle this, we propose SimRAG, a self-training approach that equips the LLM with joint capabilities of question answering and question generation for domain adaptation. Our method first fine-tunes the LLM on instruction-following, question-answering, and search-related data. Then, it prompts the same LLM to generate diverse domain-relevant questions from unlabeled corpora, with an additional filtering strategy to retain high-quality synthetic examples. By leveraging these synthetic examples, the LLM can improve their performance on domain-specific RAG tasks. Experiments on 11 datasets, spanning two backbone sizes and three domains, demonstrate that SimRAG outperforms baselines by 1.2\%--8.6\%., Comment: Work in Progress

This paper significantly advances the application of Quantum Key Distribution (QKD) in Free- Space Optics (FSO) satellite-based quantum communication. We propose an innovative satellite quantum channel model and derive the secret quantum key distribution rate achievable through this channel. Unlike existing models that approximate the noise in quantum channels as merely Gaussian distributed, our model incorporates a hybrid noise analysis, accounting for both quantum Poissonian noise and classical Additive-White-Gaussian Noise (AWGN). This hybrid approach acknowledges the dual vulnerability of continuous variables (CV) Gaussian quantum channels to both quantum and classical noise, thereby offering a more realistic assessment of the quantum Secret Key Rate (SKR). This paper delves into the variation of SKR with the Signal-to-Noise Ratio (SNR) under various influencing parameters. We identify and analyze critical factors such as reconciliation efficiency, transmission coefficient, transmission efficiency, the quantum Poissonian noise parameter, and the satellite altitude. These parameters are pivotal in determining the SKR in FSO satellite quantum channels, highlighting the challenges of satellitebased quantum communication. Our work provides a comprehensive framework for understanding and optimizing SKR in satellite-based QKD systems, paving the way for more efficient and secure quantum communication networks.

Total-Body PET (TB-PET) scanners represent a significant advancement in medical diagnostics, exemplified by the uEXPLORER, the world's first TB-PET system with an axial span of 194 cm, which exhibits exceptional sensitivity and spatial resolution. This study employs the Monte Carlo simulation toolkit Geant4 to evaluate various configurations and materials of detector crystals. We concentrate on three critical parameters: sensitivity, intrinsic coincidence time resolution (CTR), and energy resolution across three crystal designs: 1)standard LYSO crystals as the baseline; 2)0.1% Mg, 1% Ce doped $Gd_3Al_2Ga_3O_{12}$ (Mg,Ce:GAGG) as an alternative material; and 3)pyramid-shaped LYSO crystals, which maintain the same dimensions as the standard LYSO. The research is grounded in the geometric configuration of the uEXPLORER. Our findings reveal that pyramid-shaped LYSO crystals exhibit superior performance, achieving an impressive CTR of 42 ps. In contrast, PET detectors utilizing doped GAGG crystals demonstrate a 6% reduction in intrinsic CTR compared to LYSO. However, Mg,Ce:GAGG crystals surpass LYSO in energy resolution by 25%, while cuboidal LYSO crystals achieve approximately 37% greater sensitivity than their Mg,Ce:GAGG counterparts. These results underscore the impact of different crystal materials and geometries on PET scanner performance, emphasizing the trade-offs among sensitivity, coincidence time resolution (CTR), and energy resolution. Such insights are pivotal for informing the design of future TB-PET systems., Comment: 12 pages, 13 total figures, 7 captioned figures

In this study, we investigate the under-explored intervention planning aimed at disseminating accurate information within dynamic opinion networks by leveraging learning strategies. Intervention planning involves identifying key nodes (search) and exerting control (e.g., disseminating accurate or official information through the nodes) to mitigate the influence of misinformation. However, as the network size increases, the problem becomes computationally intractable. To address this, we first introduce a ranking algorithm to identify key nodes for disseminating accurate information, which facilitates the training of neural network classifiers that provide generalized solutions for the search and planning problems. Second, we mitigate the complexity of label generation, which becomes challenging as the network grows, by developing a reinforcement learning-based centralized dynamic planning framework. We analyze these NN-based planners for opinion networks governed by two dynamic propagation models. Each model incorporates both binary and continuous opinion and trust representations. Our experimental results demonstrate that the ranking algorithm-based classifiers provide plans that enhance infection rate control, especially with increased action budgets for small networks. Further, we observe that the reward strategies focusing on key metrics, such as the number of susceptible nodes and infection rates, outperform those prioritizing faster blocking strategies. Additionally, our findings reveal that graph convolutional network-based planners facilitate scalable centralized plans that achieve lower infection rates (higher control) across various network configurations, including Watts-Strogatz topology, varying action budgets, varying initial infected nodes, and varying degrees of infected nodes., Comment: Accepted at NeurIPS 2024

Integrating diverse data modalities is crucial for enhancing the performance of personalized recommendation systems. Traditional models, which often rely on singular data sources, lack the depth needed to accurately capture the multifaceted nature of item features and user behaviors. This paper introduces a novel framework for multi-behavior recommendations, leveraging the fusion of triple-modality, which is visual, textual, and graph data through alignment with large language models (LLMs). By incorporating visual information, we capture contextual and aesthetic item characteristics; textual data provides insights into user interests and item features in detail; and graph data elucidates relationships within the item-behavior heterogeneous graphs. Our proposed model called Triple Modality Fusion (TMF) utilizes the power of LLMs to align and integrate these three modalities, achieving a comprehensive representation of user behaviors. The LLM models the user's interactions including behaviors and item features in natural languages. Initially, the LLM is warmed up using only natural language-based prompts. We then devise the modality fusion module based on cross-attention and self-attention mechanisms to integrate different modalities from other models into the same embedding space and incorporate them into an LLM. Extensive experiments demonstrate the effectiveness of our approach in improving recommendation accuracy. Further ablation studies validate the effectiveness of our model design and benefits of the TMF.

We employ the Hopfield model as a simplified framework to explore both the memory deficits and the biochemical processes characteristic of Alzheimer's disease. By simulating neuronal death and synaptic degradation through increasing the number of stored patterns and introducing noise into the synaptic weights, we demonstrate hallmark symptoms of dementia, including memory loss, confusion, and delayed retrieval times. As the network's capacity is exceeded, retrieval errors increase, mirroring the cognitive confusion observed in Alzheimer's patients. Additionally, we simulate the impact of synaptic degradation by varying the sparsity of the weight matrix, showing impaired memory recall and reduced retrieval success as noise levels increase. Furthermore, we extend our model to connect memory loss with biochemical processes linked to Alzheimer's. By simulating the role of reduced insulin sensitivity over time, we show how it can trigger increased calcium influx into mitochondria, leading to misfolded proteins and the formation of amyloid plaques. These findings, modeled over time, suggest that both neuronal degradation and metabolic factors contribute to the progressive decline seen in Alzheimer's disease. Our work offers a computational framework for understanding the dual impact of synaptic and metabolic dysfunction in neurodegenerative diseases., Comment: 8 pages, 4 figures

6G, the next generation of mobile networks, is set to offer even higher data rates, ultra-reliability, and lower latency than 5G. New 6G services will increase the load and dynamism of the network. Network Function Virtualization (NFV) aids with this increased load and dynamism by eliminating hardware dependency. It aims to boost the flexibility and scalability of network deployment services by separating network functions from their specific proprietary forms so that they can run as virtual network functions (VNFs) on commodity hardware. It is essential to design an NFV orchestration and management framework to support these services. However, deploying bulky monolithic VNFs on the network is difficult, especially when underlying resources are scarce, resulting in ineffective resource management. To address this, microservices-based NFV approaches are proposed. In this approach, monolithic VNFs are decomposed into micro VNFs, increasing the likelihood of their successful placement and resulting in more efficient resource management. This article discusses the proposed framework for resource allocation for microservices-based services to provide end-to-end Quality of Service (QoS) using the Double Deep Q Learning (DDQL) approach. Furthermore, to enhance this resource allocation approach, we discussed and addressed two crucial sub-problems: the need for a dynamic priority technique and the presence of the low-priority starvation problem. Using the Deep Deterministic Policy Gradient (DDPG) model, an Adaptive Scheduling model is developed that effectively mitigates the starvation problem. Additionally, the impact of incorporating traffic load considerations into deployment and scheduling is thoroughly investigated.

Compound Finance is a decentralized lending protocol that enables the secure and efficient borrowing and lending of cryptocurrencies, utilizing smart contracts and dynamic interest rates based on supply and demand to facilitate transactions. The protocol enables users to supply different crypto assets and accrue interest, while borrowers can avail themselves of loans secured by collateralized assets. Our collaboration with Compound Finance focuses on harnessing the power of the Chainrisk simulation engine to optimize risk parameters of the Compound V3 (Comet) protocol. This report delineates a comprehensive methodology aimed at calculating key risk metrics of the protocol. This optimization framework is pivotal for mitigating systemic risks and enhancing the overall stability of the protocol. By leveraging Chainrisk's Cloud Platform, we conduct millions of simulations to evaluate the protocol's Value at Risk (VaR) and Liquidations at Risk (LaR), ultimately providing recommendations for parameter adjustments.

Materials hosting flat bands at the vicinity of the Fermi level promote exotic symmetry broken states. Common to many of these are van Hove singularities at saddle points of the dispersion or even higher-order van Hove singularities where the dispersion is flattened further. The band structure of kagome metals hosts both a flat band and two regular saddle points flanking a Dirac node. We investigate the kagome ferromagnetic metal Co$_3$Sn$_2$S$_2$ using scanning tunneling spectroscopy. We identify a new mechanism by which a triangular distortion on its kagome Co$_3$Sn surface termination considerably flattens the saddle point dispersion, and induces an isolated higher-order van Hove singularity (HOvHS) with algebraically divergent density of states pinned to the Fermi energy. The distortion-induced HOvHS precipitates a Pomeranchuk instability of the Fermi surface, resulting in the formation of a series of nematic electronic states. We visualize the nematic order across an energy shell of about 100 meV in both real-, reciprocal-, and momentum-spaces, as a cascade of wavefunction distributions which spontaneously break the remaining rotational symmetry of the underlying distorted kagome lattice, without generating any additional translational symmetry breaking. It signifies the spontaneous removal of a subset of saddle points from the Fermi energy to lower energies. By tracking the electronic wavefunction structure across the deformed Fermi surface we further identify a charge pumping-like evolution of the wavefunction center of mass. The mechanism we find for the generation of higher-order saddle points under a kagome distortion may be common to other kagome materials, and potentially other lattice structures, suggesting a generic new avenue for inducing unconventional electronic instabilities towards exotic states of matter.

Following significant progress in the visualization and characterization of hybrid superconducting-semiconducting systems, greatly propelled by reports of Majorana zero modes in nanowire devices, considerable attention has been devoted to investigating the electronic structure at the buried superconducting-semiconducting interface and the nature of the induced superconducting correlations. The properties of that interface and the structure of the electronic wave functions that occupy it determine the functionality and the topological nature of the induced superconducting state. Here, we introduce a novel hybrid platform for proximity-inducing superconductivity in InAs$_{0.6}$Sb$_{0.4}$ nanowires, leveraging a unique architecture and material combination. By dispersing these nanowires over a superconducting Indium film we exploit Indium's high critical temperature of 3.7~K and the anticipated high spin-orbit and Zeeman couplings of InAs$_{0.6}$Sb$_{0.4}$. This design preserves the pristine top facet of the nanowires, making it highly compatible with scanning tunneling spectroscopy. Using this architecture we demonstrate that the mechanical contact supports Cooper-pair transparency as high as 90\%, comparable with epitaxial interfaces. The anisotropic angular response to an applied magnetic field shows the quasi-two-dimensional nature of the parent superconductivity in the Indium film and the quasi-one-dimensional nature of the induced superconductivity in the nanowires. Our platform offers robust and advantageous foundations for studying the emergence of topological superconductivity and the interplay of superconductivity and magnetism using atomic-scale spectroscopic tools., Comment: 30 pages, 20 figures

Understanding the thermal conductivity of chromium doped V2O3 is crucial for optimizing the design of selectors for memory and neuromorphic devices. We utilized the time-domain thermoreflectance technique to measure the thermal conductivity of chromium doped V2O3 across varying concentrations, spanning the doping induced metal-insulator transition. In addition, different oxygen stoichiometries and film thicknesses were investigated in their crystalline and amorphous phases. Chromium doping concentration (0%-30%) and the degree of crystallinity emerged as the predominant factors influencing the thermal properties, while the effect of oxygen flow (600-1400 ppm) during deposition proved to be negligible. Our observations indicate that even in the metallic phase of V2O3, the lattice contribution is the dominant factor in thermal transport with no observable impact from the electrons on heat transport. Finally, the thermal conductivity of both amorphous and crystalline V2O3 was measured at cryogenic temperatures (80-450 K). Our thermal conductivity measurements as a function of temperature reveal that both phases exhibit behavior similar to amorphous materials, indicating pronounced phonon scattering effects in the crystalline phase of V2O3., Comment: Submitted to APL

Understanding how species interactions shape biodiversity is a core challenge in ecology. While much focus has been on long-term stability, there is rising interest in transient dynamics-the short-lived periods when ecosystems respond to disturbances and adjust toward stability. These transitions are crucial for predicting ecosystem reactions and guiding effective conservation. Our study introduces a model that uses convex combinations to blend pairwise and higher-order interactions, offering a more realistic view of natural ecosystems. We find pairwise interactions slow the journey to stability, while higher-order interactions speed it up. Employing global stability analysis and numerical simulations, we establish that as the proportion of higher-order interactions (HOIs) increases, mean transient times exhibit a significant reduction, thereby underscoring the essential role of HOIs in enhancing biodiversity stabilization. Our results reveal a robust correlation between the most negative real part of the eigenvalues of the Jacobian matrix associated with the linearized system at the coexistence equilibrium and the mean transient times. This indicates that a more negative leading eigenvalue correlates with accelerated convergence to stable coexistence abundances. This insight is vital for comprehending ecosystem resilience and recovery, emphasizing the key role of HOIs in promoting stabilization. Amid growing interest in transient dynamics and its implications for biodiversity and ecological stability, our study enhances the understanding of how species interactions affect both transient and long-term ecosystem behavior. By addressing a critical gap in ecological theory and offering a practical framework for ecosystem management, our work advances knowledge of transient dynamics, ultimately informing effective conservation strategies., Comment: 13 pages, 6 figures

We theoretically investigate the transport and shot noise properties of a one-dimensional semiconducting nanowire with Rashba spin-orbit coupling~(SOC) placed in closed proximity to a bulk $s$-wave superconductor and an altermagnet with $d$-wave symmetry. Such heterostructure with vanishing net magnetization manifests itself as an alternative route to anchor Majorana zero modes~(MZMs) characterized by appropriate topological index~(winding number $W$). Interestingly, this system also hosts accidental zero modes~(AZMs) emerged with vanishing topological index indicating their non-topological nature. Furthermore, by incorporating three terminal setup, we explore the transport and shot noise signatures of these zero modes. At zero temperature, we obtain zero bias peak (ZBP) in differential conductance to be quantized with value $|W|\times 2 e^{2}/h$ for MZMs. On the other hand, AZMs exhibit non-quantized value at zero bias. Moreover, zero temperature shot noise manifests negative~(positive) value for MZMs~(AZMs) within the bulk gap. At finite temperature, shot noise exhibits negative value~(negative to positive transition) concerning MZMs~(AZMs). Thus, the obtained signatures clearly distinguish between the MZMs and non-topological AZMs. We extend our analysis by switching on the next to nearest neighbour hopping amplitude and SOC. Our conclusion remains unaffected for this case as well. Hence, our work paves the way to differentiate between emergent MZMs and AZMs in a semiconductor/ superconductor/ altermagnet heterostructure., Comment: 5+9 Pages, 4+5 PDF Figures, Comments are welcome

E-commerce platforms have a vast catalog of items to cater to their customers' shopping interests. Most of these platforms assist their customers in the shopping process by offering optimized recommendation carousels, designed to help customers quickly locate their desired items. Many models have been proposed in academic literature to generate and enhance the ranking and recall set of items in these carousels. Conventionally, the accompanying carousel title text (header) of these carousels remains static. In most instances, a generic text such as "Items similar to your current viewing" is utilized. Fixed variations such as the inclusion of specific attributes "Other items from a similar seller" or "Items from a similar brand" in addition to "frequently bought together" or "considered together" are observed as well. This work proposes a novel approach to customize the header generation process of these carousels. Our work leverages user-generated reviews that lay focus on specific attributes (aspects) of an item that were favorably perceived by users during their interaction with the given item. We extract these aspects from reviews and train a graph neural network-based model under the framework of a conditional ranking task. We refer to our innovative methodology as Dynamic Text Snippets (DTS) which generates multiple header texts for an anchor item and its recall set. Our approach demonstrates the potential of utilizing user-generated reviews and presents a unique paradigm for exploring increasingly context-aware recommendation systems., Comment: 7 pages, 3 figures, PAIS 2024 (ECAI)

Virtual Reality (VR) is quickly establishing itself in various industries, including training, education, medicine, and entertainment, in which users are frequently required to carry out multiple complex cognitive and physical activities. However, the relationship between cognitive activities, physical activities, and familiar feelings of cybersickness is not well understood and thus can be unpredictable for developers. Researchers have previously provided labeled datasets for predicting cybersickness while users are stationary, but there have been few labeled datasets on cybersickness while users are physically walking. Thus, from 39 participants, we collected head orientation, head position, eye tracking, images, physiological readings from external sensors, and the self-reported cybersickness severity, physical load, and mental load in VR. Throughout the data collection, participants navigated mazes via real walking and performed tasks challenging their attention and working memory. To demonstrate the dataset's utility, we conducted a case study of training classifiers in which we achieved 95% accuracy for cybersickness severity classification. The noteworthy performance of the straightforward classifiers makes this dataset ideal for future researchers to develop cybersickness detection and reduction models. To better understand the features that helped with classification, we performed SHAP(SHapley Additive exPlanations) analysis, highlighting the importance of eye tracking and physiological measures for cybersickness prediction while walking. This open dataset can allow future researchers to study the connection between cybersickness and cognitive loads and develop prediction models. This dataset will empower future VR developers to design efficient and effective Virtual Environments by improving cognitive load management and minimizing cybersickness.

Wearable Internet of Things (IoT) devices are gaining ground for continuous physiological data acquisition and health monitoring. These physiological signals can be used for security applications to achieve continuous authentication and user convenience due to passive data acquisition. This paper investigates an electrocardiogram (ECG) based biometric user authentication system using features derived from the Convolutional Neural Network (CNN) and self-supervised contrastive learning. Contrastive learning enables us to use large unlabeled datasets to train the model and establish its generalizability. We propose approaches enabling the CNN encoder to extract appropriate features that distinguish the user from other subjects. When evaluated using the PTB ECG database with 290 subjects, the proposed technique achieved an authentication accuracy of 99.15%. To test its generalizability, we applied the model to two new datasets, the MIT-BIH Arrhythmia Database and the ECG-ID Database, achieving over 98.5% accuracy without any modifications. Furthermore, we show that repeating the authentication step three times can increase accuracy to nearly 100% for both PTBDB and ECGIDDB. This paper also presents model optimizations for embedded device deployment, which makes the system more relevant to real-world scenarios. To deploy our model in IoT edge sensors, we optimized the model complexity by applying quantization and pruning. The optimized model achieves 98.67% accuracy on PTBDB, with 0.48% accuracy loss and 62.6% CPU cycles compared to the unoptimized model. An accuracy-vs-time-complexity tradeoff analysis is performed, and results are presented for different optimization levels.

Inspired by biology and implemented using nanotechnology, the self-assembly of patchy particles has emerged as a pivotal mechanism for constructing complex structures that mimic natural systems with diverse functionalities. Here, we explore the dissipative self-assembly of patchy particles under nonequilibrium conditions, with the aim of overcoming the constraints imposed by equilibrium assembly. Utilizing extensive Monte Carlo (MC) and Molecular Dynamics (MD) simulations, we provide insight into the effects of external forces that mirror natural and chemical processes on the assembly rates and the stability of the resulting assemblies comprising $8$, $10$, and $13$ patchy particles. Implemented by a favorable bond-promoting drive in MC or a pulsed square wave potential in MD, our simulations reveal the role these external drives play in accelerating assembly kinetics and enhancing structural stability, evidenced by a decrease in the time to first assembly and an increase in the duration the system remains in an assembled state. Through the analysis of an order parameter, entropy production, bond dynamics, and interparticle forces, we unravel the underlying mechanisms driving these advancements. We also validated our key findings by simulating a larger system of $100$ patchy particles. Our comprehensive results not only shed light on the impact of external stimuli on self-assembly processes but also open a promising pathway for expanding the application by leveraging patchy particles for novel nanostructures., Comment: 74 pages and 21 figures

Noise is a vital factor in determining the accuracy of processing the information of the quantum channel. One must consider classical noise effects associated with quantum noise sources for more realistic modelling of quantum channels. A hybrid quantum noise model incorporating both quantum Poisson noise and classical additive white Gaussian noise (AWGN) can be interpreted as an infinite mixture of Gaussians with weightage from the Poisson distribution. The entropy measure of this function is difficult to calculate. This research developed how the infinite mixture can be well approximated by a finite mixture distribution depending on the Poisson parametric setting compared to the number of mixture components. The mathematical analysis of the characterization of hybrid quantum noise has been demonstrated based on Gaussian and Poisson parametric analysis. This helps in the pattern analysis of the parametric values of the component distribution, and it also helps in the calculation of hybrid noise entropy to understand hybrid quantum noise better.

Ethanethiol (C$_2$H$_5$SH), a molecule detected in the interstellar medium (ISM), indicates the rich chemistry involving sulfur atoms. However, its behavior at low temperatures remains elusive, particularly the reported transition from an amorphous phase to a crystal. This study employs classical molecular dynamics (MD) simulations to reproduce the liquid-state properties of ethanethiol and to simulate the initial amorphous state of ethanethiol films deposited on a KBr substrate. The amorphous ethanethiol did not show spontaneous crystallization upon increasing temperature. Also, ethanethiol ice crystals exhibit melting behavior on KBr substrate at elevated temperatures. Our MD simulations of thin ice samples do not show any signature reversible phase change. It will be interesting to continue this study with a thicker sample, which is beyond our current computational means. These findings underscore the complexity of icy mantle morphology on cold ISM dust grains., Comment: Manuscript accepted for publication in "Astrophysics and Space Science Proceedings", Springer nature (Symposium: ISRA 2023)

We explore the phenomena of prethermalization in a many-body classical system of rotors under aperiodic drives characterised by waiting time distribution (WTD), where the waiting time is defined as the time between two consecutive kicks. We consider here two types of aperiodic drives: random and quasi-periodic. We observe a short-lived pseudo-thermal regime with algebraic suppression of heating for the random drive where WTD has an infinite tail, as observed for Poisson and binomial kick sequences. On the other hand, quasi-periodic drive characterised by a WTD with a sharp cut-off, observed for Thue-Morse sequence of kick, leads to prethermal region where heating is exponentially suppressed. The kinetic energy growth is analyzed using an average surprise associated with WTD quantifying the randomness of drive. In all of the aperiodic drives we obtain the chaotic heating regime for late time, however, the diffusion constant gets renormalized by the average surprise of WTD in comparison to the periodic case., Comment: 12 pages, 12 figures

In this work, for the first time, a density functional theory (DFT) based comprehensive theoretical study is performed on the surface electronic properties of Bi2Se3 nanosheet in the presence of a surface capping layer as well as mechanical strain. The study systematically introduces a biaxial compressive and tensile strain up to 5% in natural, Sb2Se3 surface capped, and SbBiSe3 surface capped Bi2Se3, and the subsequent effects on the electronic properties are assessed from the surface energy band (E-k) structure, the density of states (DOS), band edge energy and bandgap variations, surface conducting state localization, and Fermi surface spin-textures. The key findings of this work are systematically analyzed from conducting surface state hybridization through bulk in the presence of surface capping layers and applied biaxial strain. The result demonstrates that the interplay of surface capping and strain can simultaneously tune the surface electronic structure, spin-momentum locking results from change in electronic localization and interactions. In essence, this work presents an extensive theoretical and design-level insight into the surface capping and biaxial strain co-engineering in Bi2Se3, which can potentially facilitate different topological transport for modern optoelectronics, spintronics, valleytronics, bulk photovoltaics applications of engineered nanostructured topological materials in the future.

We report a milestone in achieving large-scale, ultrathin (~5 nm) superconducting NbN thin films on 300 mm Si wafers using a high-volume manufacturing (HVM) industrial physical vapor deposition (PVD) system. The NbN thin films possess remarkable structural uniformity and consistently high superconducting quality across the entire 300 mm Si wafer, by incorporating an AlN buffer layer. High-resolution X-ray diffraction and transmission electron microscopy analyses unveiled enhanced crystallinity of (111)-oriented {\delta}-phase NbN with the AlN buffer layer. Notably, NbN films deposited on AlN-buffered Si substrates exhibited a significantly elevated superconducting critical temperature (~2 K higher for the 10 nm NbN) and a higher upper critical magnetic field or Hc2 (34.06 T boost in Hc2 for the 50 nm NbN) in comparison with those without AlN. These findings present a promising pathway for the integration of quantum-grade superconducting NbN films with the existing 300 mm CMOS Si platform for quantum information applications.

Online shopping platforms, such as Amazon, offer services to billions of people worldwide. Unlike web search or other search engines, product search engines have their unique characteristics, primarily featuring short queries which are mostly a combination of product attributes and structured product search space. The uniqueness of product search underscores the crucial importance of the query understanding component. However, there are limited studies focusing on exploring this impact within real-world product search engines. In this work, we aim to bridge this gap by conducting a comprehensive study and sharing our year-long journey investigating how the query understanding service impacts Amazon Product Search. Firstly, we explore how query understanding-based ranking features influence the ranking process. Next, we delve into how the query understanding system contributes to understanding the performance of a ranking model. Building on the insights gained from our study on the evaluation of the query understanding-based ranking model, we propose a query understanding-based multi-task learning framework for ranking. We present our studies and investigations using the real-world system on Amazon Search.

Effective fraud detection and analysis of government-issued identity documents, such as passports, driver's licenses, and identity cards, are essential in thwarting identity theft and bolstering security on online platforms. The training of accurate fraud detection and analysis tools depends on the availability of extensive identity document datasets. However, current publicly available benchmark datasets for identity document analysis, including MIDV-500, MIDV-2020, and FMIDV, fall short in several respects: they offer a limited number of samples, cover insufficient varieties of fraud patterns, and seldom include alterations in critical personal identifying fields like portrait images, limiting their utility in training models capable of detecting realistic frauds while preserving privacy. In response to these shortcomings, our research introduces a new benchmark dataset, IDNet, designed to advance privacy-preserving fraud detection efforts. The IDNet dataset comprises 837,060 images of synthetically generated identity documents, totaling approximately 490 gigabytes, categorized into 20 types from $10$ U.S. states and 10 European countries. We evaluate the utility and present use cases of the dataset, illustrating how it can aid in training privacy-preserving fraud detection methods, facilitating the generation of camera and video capturing of identity documents, and testing schema unification and other identity document management functionalities., Comment: 40 pages

As a signature of competing correlations, stripes occur in a variety of strongly correlated systems, such as high temperature superconductors (SCs) and quantum Hall effect. We study a double layer SC in the presence of a parallel magnetic field $B$ within the Bogoliubov-de Gennes framework. We find that for low $B$ the system remains in the ``Bardeen-Cooper-Schrieffer (BCS) phase" with a spatially uniform gap, but with increasing $B$, a transition occurs into a phase which contains stripes of the BCS phase separated by regions where the interlayer phase difference rotates by $2\pi$ due to the presence of inter-layer vortices. This stripe phase is predicted to manifest through oscillations in the amplitude of the SC gap and an alternating pattern of supercurrents. We will comment on the relation to previous works based on the Landau-Ginzburg formalism as well as on the possible experimental realization and signature of this phase., Comment: 6 pages, 2 figures

Mobile device hackers often target ambient sensing, human activity identification, and interior floor mapping. In addition to overt signals like microphones and cameras, covert channels like WiFi, Bluetooth, and augmented GPS signal strengths have been employed to gather this information. Until date, passive, receive-only satellite GPS sensing relied solely on signal strength and location information. This paper demonstrates that semi-processed GPS data (39 features) accessible to apps since Android 7 with precise location permissions can be used as a highly accurate leaky channel for sensing ambient, recognising human activity, and mapping indoor spaces (99%+ accuracy). This report describes a longitudinal research that used semi-processed GPS readings from mobile devices throughout a 40,000 sq. km region for a year. Data was acquired from aeroplanes, cruise ships, and high-altitude places. To retain crucial information, we analyse all satellite GPS signals and select the best characteristics using cross-correlation analysis. Our work, AndroCon, combines lin-ear discriminant analysis, unscented Kalman filtering, gradient boosting, and random forest learning to provide an accurate ambient and human activity sensor. At AndroCon, basic ML algorithms are used for discreet and somewhat explainable outcomes. We can readily recognise challenging situations, such as being in a subway, when someone is waving a hand in front of a mobile device, in front of a stairway, or with others present (not always carrying phones). This is the most extensive study on satellite GPS-based sensing as of yet., Comment: 18 pages

Complex dialog systems often use retrieved evidence to facilitate factual responses. Such RAG (Retrieval Augmented Generation) systems retrieve from massive heterogeneous data stores that are usually architected as multiple indexes or APIs instead of a single monolithic source. For a given query, relevant evidence needs to be retrieved from one or a small subset of possible retrieval sources. Complex queries can even require multi-step retrieval. For example, a conversational agent on a retail site answering customer questions about past orders will need to retrieve the appropriate customer order first and then the evidence relevant to the customer's question in the context of the ordered product. Most RAG Agents handle such Chain-of-Thought (CoT) tasks by interleaving reasoning and retrieval steps. However, each reasoning step directly adds to the latency of the system. For large models this latency cost is significant -- in the order of multiple seconds. Multi-agent systems may classify the query to a single Agent associated with a retrieval source, though this means that a (small) classification model dictates the performance of a large language model. In this work we present REAPER (REAsoning-based PlannER) - an LLM based planner to generate retrieval plans in conversational systems. We show significant gains in latency over Agent-based systems and are able to scale easily to new and unseen use cases as compared to classification-based planning. Though our method can be applied to any RAG system, we show our results in the context of a conversational shopping assistant.

High-temperature cuprates superconductors are characterised by the complex interplay between superconductivity (SC) and charge density wave (CDW) in the context of intertwined competing orders. In contrast to abundant studies for hole-doped cuprates, the exact nature of CDW and its relationship to SC was much less explored in electron-doped counterparts. Here, we performed resonant inelastic x-ray scattering (RIXS) experiments to investigate the relationship between CDW and SC in electron-doped La$_{2-x}$Ce$_x$CuO$_4$. The short-range CDW order with a correlation length $\sim35$~\AA~was found in a wide range of temperature and doping concentration. Near the optimal doping, the CDW order is weakened inside the SC phase, implying an intimate relationship between the two orders. This interplay has been commonly reported in hole-doped La-based cuprates near the optimal doping. We reconciled the diverging behaviour of CDW across the superconducting phase in various cuprate materials by introducing the CDW correlation length as a key parameter. Our study paves the way for establishing a unified picture to describe the phenomenology of CDW and its relationship with SC in the cuprate family., Comment: 24 pages 5 figures; Supplementary Materials available upon request

Estimating many-body effects that deviate from an independent particle approach, has long been a key research interest in condensed matter physics. Layered cuprates are prototypical systems, where electron-electron interactions are found to strongly affect the dynamics of single-particle excitations. It is however, still unclear how the electron correlations influence charge excitations, such as plasmons, which have been variously treated with either weak or strong correlation models. In this work, we demonstrate the hybridised nature of collective valence charge fluctuations leading to dispersing acoustic-like plasmons in hole-doped La$_{1.84}$Sr$_{0.16}$CuO$_{4}$ and electron-doped La$_{1.84}$Ce$_{0.16}$CuO$_{4}$ using the two-particle probe, resonant inelastic x-ray scattering. We then describe the plasmon dispersions in both systems, within both the weak mean-field Random Phase Approximation (RPA) and strong coupling $t$-$J$-$V$ models. The $t$-$J$-$V$ model, which includes the correlation effects implicitly, accurately describes the plasmon dispersions as resonant excitations outside the single-particle intra-band continuum. In comparison, a quantitative description of the plasmon dispersion in the RPA approach is obtained only upon explicit consideration of re-normalized electronic band parameters. Our comparative analysis shows that electron correlations significantly impact the low-energy plasmon excitations across the cuprate doping phase diagram, even at long wavelengths. Thus, complementary information on the evolution of electron correlations, influenced by the rich electronic phases in condensed matter systems, can be extracted through the study of two-particle charge response., Comment: 6 Figures

Realizing tunable functional materials with built-in nanoscale heat flow directionality represents a significant challenge that could advance thermal management strategies. Here we use spatiotemporally-resolved thermoreflectance to visualize lateral thermal transport anisotropy in self-assembled supercrystals of anisotropic Au nanocrystals. Correlative electron and thermoreflectance microscopy reveal that nano- to meso-scale heat predominantly flows along the long-axis of the anisotropic nanocrystals, and does so across grain boundaries and curved assemblies while voids disrupt heat flow. We finely control the anisotropy via the aspect ratio of constituent nanorods, and it exceeds the aspect ratio for nano-bipyramid supercrystals and certain nanorod arrangements. Finite element simulations and effective medium modeling rationalize the emergent anisotropic behavior in terms of a simple series resistance model, further providing a framework for estimating thermal anisotropy as a function of material and structural parameters. Self-assembly of colloidal nanocrystals promises an interesting route to direct heat flow in a wide range of applications that utilize this important class of materials.

Referring Expression Segmentation (RES) aims to provide a segmentation mask of the target object in an image referred to by the text (i.e., referring expression). Existing methods require large-scale mask annotations. Moreover, such approaches do not generalize well to unseen/zero-shot scenarios. To address the aforementioned issues, we propose a weakly-supervised bootstrapping architecture for RES with several new algorithmic innovations. To the best of our knowledge, ours is the first approach that considers only a fraction of both mask and box annotations (shown in Figure 1 and Table 1) for training. To enable principled training of models in such low-annotation settings, improve image-text region-level alignment, and further enhance spatial localization of the target object in the image, we propose Cross-modal Fusion with Attention Consistency module. For automatic pseudo-labeling of unlabeled samples, we introduce a novel Mask Validity Filtering routine based on a spatially aware zero-shot proposal scoring approach. Extensive experiments show that with just 30% annotations, our model SafaRi achieves 59.31 and 48.26 mIoUs as compared to 58.93 and 48.19 mIoUs obtained by the fully-supervised SOTA method SeqTR respectively on RefCOCO+@testA and RefCOCO+testB datasets. SafaRi also outperforms SeqTR by 11.7% (on RefCOCO+testA) and 19.6% (on RefCOCO+testB) in a fully-supervised setting and demonstrates strong generalization capabilities in unseen/zero-shot tasks., Comment: Accepted at ECCV 2024

Sample Return Capsules (SRCs) entering Earth's atmosphere at hypervelocity from interplanetary space are a valuable resource for studying meteor phenomena. The 24 September 2023 arrival of the OSIRIS-REx (Origins, Spectral Interpretation, Resource Identification, and Security-Regolith Explorer) SRC provided an unprecedented chance for geophysical observations of a well-characterized source with known parameters, including timing and trajectory. A collaborative effort involving researchers from 16 institutions executed a carefully planned geophysical observational campaign at strategically chosen locations, deploying over 400 ground-based sensors encompassing infrasound, seismic, distributed acoustic sensing (DAS), and GPS technologies. Additionally, balloons equipped with infrasound sensors were launched to capture signals at higher altitudes. This campaign (the largest of its kind so far) yielded a wealth of invaluable data anticipated to fuel scientific inquiry for years to come. The success of the observational campaign is evidenced by the near-universal detection of signals across instruments, both proximal and distal. This paper presents a comprehensive overview of the collective scientific effort, field deployment, and preliminary findings. The early findings have the potential to inform future space missions and terrestrial campaigns, contributing to our understanding of meteoroid interactions with planetary atmospheres. Furthermore, the dataset collected during this campaign will improve entry and propagation models as well as augment the study of atmospheric dynamics and shock phenomena generated by meteoroids and similar sources., Comment: 87 pages, 14 figures

We theoretically investigate the transport signature of single and multiple Floquet Majorana end modes~(FMEMs), appearing in an experimentally feasible setup with Rashba nanowire~(NW) placed in closed proximity to a conventional $s$-wave superconductor, in the presence of an external Zeeman field. Periodic drive causes the anomalous $\pi$-modes to emerge in addition to the regular $0$-modes in the driven system where the former does not exhibit any static analog. For single $0$- and/or $\pi$-FMEM, differential conductance exhibits a quantized value of $2e^{2}/h$ while we consider the sum over all the photon sectors, supporting Floquet sum rule. We examine the stability of this summed conductance against random onsite disorder. We further investigate the summed conductance in several cases hosting multiple~(more than one) $0$- or $\pi$-modes at the end of the NW. In these cases, we obtain quantized values of $n\times 2e^{2}/h$ of summed conductance with $n$ being the number of modes~($0$ / $\pi$) located at one end of NW. We repeat our analysis for another experimentally realizable model system known as helical Shiba chain. Moreover, we corroborate our results by computing the differential conductance for FMEMs using non-equilibrium Green's function method. Our work opens up the possibility of studying the transport signatures of FMEMs in these realistic models., Comment: 20 Pages, 12 PDF Figures, Comments are welcome

Leveraging Large Language Models' remarkable proficiency in text-based tasks, recent works on Multi-modal LLMs (MLLMs) extend them to other modalities like vision and audio. However, the progress in these directions has been mostly focused on tasks that only require a coarse-grained understanding of the audio-visual semantics. We present Meerkat, an audio-visual LLM equipped with a fine-grained understanding of image and audio both spatially and temporally. With a new modality alignment module based on optimal transport and a cross-attention module that enforces audio-visual consistency, Meerkat can tackle challenging tasks such as audio referred image grounding, image guided audio temporal localization, and audio-visual fact-checking. Moreover, we carefully curate a large dataset AVFIT that comprises 3M instruction tuning samples collected from open-source datasets, and introduce MeerkatBench that unifies five challenging audio-visual tasks. We achieve state-of-the-art performance on all these downstream tasks with a relative improvement of up to 37.12%., Comment: Accepted at ECCV 2024

Music is a universal language that can communicate emotions and feelings. It forms an essential part of the whole spectrum of creative media, ranging from movies to social media posts. Machine learning models that can synthesize music are predominantly conditioned on textual descriptions of it. Inspired by how musicians compose music not just from a movie script, but also through visualizations, we propose MeLFusion, a model that can effectively use cues from a textual description and the corresponding image to synthesize music. MeLFusion is a text-to-music diffusion model with a novel "visual synapse", which effectively infuses the semantics from the visual modality into the generated music. To facilitate research in this area, we introduce a new dataset MeLBench, and propose a new evaluation metric IMSM. Our exhaustive experimental evaluation suggests that adding visual information to the music synthesis pipeline significantly improves the quality of generated music, measured both objectively and subjectively, with a relative gain of up to 67.98% on the FAD score. We hope that our work will gather attention to this pragmatic, yet relatively under-explored research area., Comment: Accepted at CVPR 2024 as Highlight paper. Webpage: https://schowdhury671.github.io/melfusion_cvpr2024/