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This research introduces an innovative forest monitoring system designed to detect and mitigate the threats of forest fires. The proposed system leverages Arduino-based technology integrated with state-of-the-art sensors, including DHT11 for temperature and humidity detection and Flame sensor along with GSM module for gas and smoke detection. The integration of these sensors enables real-time data acquisition and analysis, providing a comprehensive and accurate assessment of environmental conditions within the forest ecosystem. The Arduino platform serves as the central processing unit, orchestrating the communication and synchronization of the sensor data. The DHT11 sensor monitors ambient temperature and humidity levels, crucial indicators for assessing fire risk and identifying potential deforestation activities. Simultaneously, the Flame sensor module detects the occurrence of fire flames nearby thus indicating it by a buzzer. The collected data is processed through an intelligent algorithm that employs machine learning techniques to discern patterns indicative of potential threats. The system is equipped with an adaptive threshold mechanism, allowing it to dynamically adjust to changing environmental conditions. In the event of abnormal readings or anomalies, the system triggers immediate alerts, notifying forest rangers and relevant authorities to facilitate timely response and intervention. The integration of low-cost, easily deployable Arduino-based devices makes this solution scalable and accessible for implementation across diverse forest environments. The proposed system represents a significant step towards leveraging technology to address environmental challenges and protect our forests.

High-dimensional encodings based on temporal modes (TMs) of photonic quantum states provide the foundations for a highly versatile and efficient quantum information science (QIS) framework. Here, we demonstrate a crucial building block for any QIS applications based on TMs: a programmable source of maximally entangled high-dimensional TM states. Our source is based on a parametric down-conversion process driven by a spectrally shaped pump pulse, which facilitates the generation of maximally entangled TM states with a well-defined dimensionality that can be chosen programmatically. We characterize the effective dimensionality of the generated states via measurements of second-order correlation functions and joint spectral intensities, demonstrating the generation of bi-photon TM states with a controlled dimensionality in up to 20 dimensions.

Low Earth orbit (LEO) satellite mega constellations are beginning to include laser inter-satellite links (LISLs) to extend the Internet to the most remote locations on Earth. Since the process of establishing these links incurs a setup delay on the order of seconds, a static network topology is generally established well in advance, which is then used for the routing calculations. However, this involves keeping links active even when they are not being used to forward traffic, leading to poor energy efficiency. Motivated by technological advances that are gradually decreasing the LISL setup delays, we foresee scenarios where it will be possible to compute routes and establish dynamic LISLs on demand. This will require considering setup delays as penalties that will affect the end-to-end latency. In this paper, we present a nonlinear optimization model that considers these penalties in the cost function and propose three heuristic algorithms that solve the problem in a tractable way. The algorithms establish different trade-offs in terms of performance and computational complexity. We extensively analyze metrics including average latency, route change rate, outage probability, and jitter in Starlink's Phase I version 2 constellation. The results show the benefit of adaptive routing schemes according to the link setup delay. In particular, more complex schemes can decrease the average end-to-end latency in exchange for an increase in execution time. On the other hand, depending on the maximum tolerated latency, it is possible to use less computationally complex schemes which will be more scalable for the satellite mega constellations of the future.

The article studies the different physical, vibrational, nonlinear optical, and thermodynamical properties of higher homologs 5O.m (m = 14,16) liquid crystalline compounds using density functional theory. The optimized structure of 5O.m (m= 14,16) liquid crystals was obtained by using density functional theory (DFT) with B3LYP functional and standard basis set 6-31G (d, p). The Infrared spectra (IR), various physical properties such as HOMO-LUMO, nonlinear optical properties (NLO), reactivity parameters, relative energy gaps, and electrostatic potential function are computed and analyzed using the optimized structure of 5O liquid crystal. The time-dependent density functional theory (TD-DFT) has been used to analyze and obtain UV-Vis spectra for both LC compounds. It is observed that the 5O.m (m=14,16) liquid crystals are showing the lower value of HOMO-LUMO energy gaps as 4.17ev which resulted in some highly fascinating optical and physical properties. Using DFT excellent agreement is observed between all spectrum patterns and the simulated UV-Vis and IR spectra. This article, however, also discussed temperature-dependent thermodynamical properties such as zero-point vibrational energy (ZPVE), total thermal energy, total specific heat capacity, rotational constants, and total entropy which enable us to understand the phase transition behavior and specific transition temperatures of different phases, Comment: 24 Pages, 7 figures

Many machine learning models appear to deploy effortlessly under distribution shift, and perform well on a target distribution that is considerably different from the training distribution. Yet, learning theory of distribution shift bounds performance on the target distribution as a function of the discrepancy between the source and target, rarely guaranteeing high target accuracy. Motivated by this gap, this work takes a closer look at the theory of distribution shift for a classifier from a source to a target distribution. Instead of relying on the discrepancy, we adopt an Invariant-Risk-Minimization (IRM)-like assumption connecting the distributions, and characterize conditions under which data from a source distribution is sufficient for accurate classification of the target. When these conditions are not met, we show when only unlabeled data from the target is sufficient, and when labeled target data is needed. In all cases, we provide rigorous theoretical guarantees in the large sample regime.

Supersonic magnetohydrodynamic (MHD) turbulence is a ubiquitous state for many astrophysical plasmas. However, even the basic statistics for this type of turbulence remains uncertain. We present results from supersonic MHD turbulence simulations at unparalleled resolutions, with plasma Reynolds numbers of over a million. In the kinetic energy spectrum we find a break between the scales that are dominated by kinetic energy, with spectral index $-2$, and those that become strongly magnetized, with spectral index $-3/2$. By analyzing the Helmholtz decomposed kinetic energy spectrum, we find that the compressible modes are not passively mixed through the cascade of the incompressible modes. At high magnetic Reynolds number, above $10^5$, we find a power law in the magnetic energy spectrum with spectral index $-9/5$. On the strongly magnetized, subsonic scales the plasma tends to self-organize into locally relaxed regions, where there is strong alignment between the current density, magnetic field, velocity field and vorticity field, depleting both the nonlinearities and magnetic terms in the MHD equations, which we attribute to plasma relaxation on scales where the magnetic fluctuations evolve on shorter timescales than the velocity fluctuations. This process constrains the cascade to inhomogenous, volume-poor, fractal surfaces between relaxed regions, which has significant repercussions for understanding the nature of magnetized turbulence in astrophysical plasmas and the saturation of the fluctuation dynamo., Comment: 22 pages. 10 figures. Currently the world's largest MHD turbulence simulation on a grid of 10,080^3 (Re and Rm over a million). Comments welcome, email james.beattie@princeton.edu

Architectural simulators hold a vital role in RISC-V research, providing a crucial platform for workload evaluation without the need for costly physical prototypes. They serve as a dynamic environment for exploring innovative architectural concepts, enabling swift iteration and thorough analysis of performance metrics. As deep learning algorithms become increasingly pervasive, it is essential to benchmark new architectures with machine learning workloads. The diverse computational kernels used in deep learning algorithms highlight the necessity for a comprehensive compilation toolchain to map to target hardware platforms. This study evaluates the performance of a wide array of machine learning workloads on RISC-V architectures using gem5, an open-source architectural simulator. Leveraging an open-source compilation toolchain based on Multi-Level Intermediate Representation (MLIR), the research presents benchmarking results specifically focused on deep learning inference workloads. Additionally, the study sheds light on current limitations of gem5 when simulating RISC-V architectures, offering insights for future development and refinement., Comment: RISC-V Summit Europe 2024

Reinforcement Learning (RL) suffers from sample inefficiency in sparse reward domains, and the problem is further pronounced in case of stochastic transitions. To improve the sample efficiency, reward shaping is a well-studied approach to introduce intrinsic rewards that can help the RL agent converge to an optimal policy faster. However, designing a useful reward shaping function for all desirable states in the Markov Decision Process (MDP) is challenging, even for domain experts. Given that Large Language Models (LLMs) have demonstrated impressive performance across a magnitude of natural language tasks, we aim to answer the following question: `Can we obtain heuristics using LLMs for constructing a reward shaping function that can boost an RL agent's sample efficiency?' To this end, we aim to leverage off-the-shelf LLMs to generate a plan for an abstraction of the underlying MDP. We further use this LLM-generated plan as a heuristic to construct the reward shaping signal for the downstream RL agent. By characterizing the type of abstraction based on the MDP horizon length, we analyze the quality of heuristics when generated using an LLM, with and without a verifier in the loop. Our experiments across multiple domains with varying horizon length and number of sub-goals from the BabyAI environment suite, Household, Mario, and, Minecraft domain, show 1) the advantages and limitations of querying LLMs with and without a verifier to generate a reward shaping heuristic, and, 2) a significant improvement in the sample efficiency of PPO, A2C, and Q-learning when guided by the LLM-generated heuristics.

Hermite and Laguerre $\beta$-ensembles are important and well studied models in random matrix theory with special cases $\beta=1,2,4$ corresponding to eigenvalues of classical random matrix ensembles. It is well known that the largest eigenvalues in these, under appropriate scaling, converge weakly to the Tracy-Widom $\beta$ distribution whose distribution function $F_{\beta}$ has asymptotics given by $1-F_{\beta}(x)=\exp\left(-\frac{2\beta}{3}(1+o(1))x^{3/2}\right)$ as $x\to \infty$ and $F_{\beta}(x)=\exp\left(-\frac{\beta}{24}(1+o(1))|x|^3\right)$ as $x\to -\infty$. Although tail estimates for the largest eigenvalues with correct exponents have been proved for the pre-limiting models, estimates with matching constants had not so far been established for general $\beta$; even in the exactly solvable cases, some of the bounds were missing. In this paper, we prove upper and lower moderate deviation estimates for both tails with matching constants. We illustrate the usefulness of these estimates by considering certain questions in planar exponential last passage percolation (LPP), a well-studied model in the KPZ universality class in which certain statistics have same distributions as largest eigenvalues in Laguerre $\beta$-ensembles (for $\beta=1,2,4$). Using our estimates in conjunction with a combination of old and new results on the LPP geometry, we obtain three laws of iterated logarithm including one which settles a conjecture of Ledoux (J. Theor. Probab., 2018). We expect that the sharp moderate deviation estimates will find many further applications in LPP problems and beyond., Comment: 57 pages, 5 figures

Large language models (LLMs) trained on general domain corpora showed remarkable results on natural language processing (NLP) tasks. However, previous research demonstrated LLMs trained using domain-focused corpora perform better on specialized tasks. Inspired by this insight, we developed INDUS, a comprehensive suite of LLMs tailored for the closely-related domains of Earth science, biology, physics, heliophysics, planetary sciences and astrophysics, and trained using curated scientific corpora drawn from diverse data sources. The suite of models include: (1) an encoder model trained using domain-specific vocabulary and corpora to address NLP tasks, (2) a contrastive-learning based text embedding model trained using a diverse set of datasets to address information retrieval tasks and (3) smaller versions of these models created using knowledge distillation for applications which have latency or resource constraints. We also created three new scientific benchmark datasets, CLIMATE-CHANGE NER (entity-recognition), NASA-QA (extractive QA) and NASA-IR (IR) to accelerate research in these multi-disciplinary fields. We show that our models outperform both general-purpose (RoBERTa) and domain-specific (SCIBERT) encoders on these new tasks as well as existing tasks in the domains of interest. Furthermore, we demonstrate the use of these models in two industrial settings -- as a retrieval model for large-scale vector search applications and in automatic content tagging systems., Comment: EMNLP 2024 (Industry Track)

Recent developments in the magnetization dynamics in spin textures, particularly skyrmions, offer promising new directions for magnetic storage technologies and spintronics. Skyrmions, characterized by their topological protection and efficient mobility at low current density, are increasingly recognized for their potential applications in next-generation logic and memory devices. This study investigates the dynamics of skyrmion magnetization, focusing on the manipulation of their topological states as a basis for bitwise data storage through a modified Landau-Lifshitz-Gilbert equation (LLG). We introduce spin-polarized electrons from a topological ferromagnet that induce an electric dipole moment that interacts with the electric gauge field within the skyrmion domain. This interaction creates an effective magnetic field that results in a torque that can dynamically change the topological state of the skyrmion. In particular, we show that these torques can selectively destroy and create skyrmions, effectively writing and erasing bits, highlighting the potential of using controlled electron injection for robust and scalable skyrmion-based data storage solutions.

We discuss notions of almost complex, complex and K\"{a}hler structures in the realm of non-commutative geometry and investigate them for a class of finite dimensional spectral triples on the three-point space. We classify all the almost complex structures on this non-commutative manifold, which also turn out to be complex structures, but none of them are K\"{a}hler in our sense., Comment: This is an old article written in 2017 and it appeared in print only recently. It forms a short section of the first author's thesis

Let $G$ be a locally compact, $\sigma$-compact, Hausdorff groupoid and $A$ be a separable, $C_0(G^{(0)})$-nuclear, $G$-$C^*$-algebra. We prove the existence of quasi-invariant, completely positive and contractive lifts for equivariant, completely positive and contractive maps from $A$ into a separable, quotient $C^*$-algebra. Along the way, we construct the Busby invariant for $G$-actions., Comment: Preliminary version

Robot-assisted bite acquisition involves picking up food items that vary in their shape, compliance, size, and texture. A fully autonomous strategy for bite acquisition is unlikely to efficiently generalize to this wide variety of food items. We propose to leverage the presence of the care recipient to provide feedback when the system encounters novel food items. However, repeatedly asking for help imposes cognitive workload on the user. In this work, we formulate human-in-the-loop bite acquisition within a contextual bandit framework and propose a novel method, LinUCB-QG, that selectively asks for help. This method leverages a predictive model of cognitive workload in response to different types and timings of queries, learned using data from 89 participants collected in an online user study. We demonstrate that this method enhances the balance between task performance and cognitive workload compared to autonomous and querying baselines, through experiments in a food dataset-based simulator and a user study with 18 participants without mobility limitations., Comment: The second and third authors contributed equally. The last two authors advised equally

In this paper, a decimal first degree cellular automata (FDCA) based clustering algorithm is proposed where clusters are created based on reachability. Cyclic spaces are created and configurations which are in the same cycle are treated as the same cluster. Here, real-life data objects are encoded into decimal strings using G\"odel number based encoding. The benefits of the scheme is, it reduces the encoded string length while maintaining the features properties. Candidate CA rules are identified based on some theoretical criteria such as self-replication and information flow. An iterative algorithm is developed to generate the desired number of clusters over three stages. The results of the clustering are evaluated based on benchmark clustering metrics such as Silhouette score, Davis Bouldin, Calinski Harabasz and Dunn Index. In comparison with the existing state-of-the-art clustering algorithms, our proposed algorithm gives better performance.

Counterfactual examples are frequently used for model development and evaluation in many natural language processing (NLP) tasks. Although methods for automated counterfactual generation have been explored, such methods depend on models such as pre-trained language models that are then fine-tuned on auxiliary, often task-specific datasets. Collecting and annotating such datasets for counterfactual generation is labor intensive and therefore, infeasible in practice. Therefore, in this work, we focus on a novel problem setting: \textit{zero-shot counterfactual generation}. To this end, we propose a structured way to utilize large language models (LLMs) as general purpose counterfactual example generators. We hypothesize that the instruction-following and textual understanding capabilities of recent LLMs can be effectively leveraged for generating high quality counterfactuals in a zero-shot manner, without requiring any training or fine-tuning. Through comprehensive experiments on various downstream tasks in natural language processing (NLP), we demonstrate the efficacy of LLMs as zero-shot counterfactual generators in evaluating and explaining black-box NLP models., Comment: arXiv admin note: text overlap with arXiv:2309.13340

Whole-arm tactile feedback is crucial for robots to ensure safe physical interaction with their surroundings. This paper introduces CushSense, a fabric-based soft and stretchable tactile-sensing skin designed for physical human-robot interaction (pHRI) tasks such as robotic caregiving. Using stretchable fabric and hyper-elastic polymer, CushSense identifies contacts by monitoring capacitive changes due to skin deformation. CushSense is cost-effective ($\sim$US\$7 per taxel) and easy to fabricate. We detail the sensor design and fabrication process and perform characterization, highlighting its high sensing accuracy (relative error of 0.58%) and durability (0.054% accuracy drop after 1000 interactions). We also present a user study underscoring its perceived safety and comfort for the assistive task of limb manipulation. We open source all sensor-related resources on https://emprise.cs.cornell.edu/cushsense., Comment: 8 pages, 8 figures, ICRA2024

The black box nature of deep learning models complicate their usage in critical applications such as remote sensing. Conformal prediction is a method to ensure trust in such scenarios. Subject to data exchangeability, conformal prediction provides finite sample coverage guarantees in the form of a prediction set that is guaranteed to contain the true class within a user defined error rate. In this letter we show that conformal prediction algorithms are related to the uncertainty of the deep learning model and that this relation can be used to detect if the deep learning model is out-of-calibration. Popular classification models like Resnet50, Densenet161, InceptionV3, and MobileNetV2 are applied on remote sensing datasets such as the EuroSAT to demonstrate how under noisy scenarios the model outputs become untrustworthy. Furthermore an out-of-calibration detection procedure relating the model uncertainty and the average size of the conformal prediction set is presented.

We study thermalization slowing down of a quantum many-body system upon approach to two distinct integrability limits. Motivated by previous studies of classical systems, we identify two thermalization time scales: one quantum Lyapunov time scale is extracted by quantifying operator growth in time in an appropriately defined basis, while another ergodization time scale is related to statistics of fluctuations of the time-evolved operator around its mean value based on the eigenstate thermalization hypothesis. Using a paradigmatic Quantum Ising chain we find that both timescales diverge upon approach to integrability. The relative strength of the divergence of the scales depends on the particular integrable limit. This allows us to define two different universality classes of quantum thermalization: short- and long-range networks.

Plasmoid instability accelerates reconnection in collisional plasmas by transforming a laminar reconnection layer into numerous plasmoids connected by secondary current sheets in two dimensions (2D) and by fostering self-generated turbulent reconnection in three dimensions (3D). In large-scale astrophysical and space systems, plasmoid instability likely initiates in the collisional regime but may transition into the collisionless regime as the fragmentation of the current sheet progresses toward kinetic scales. Hall MHD models are widely regarded as a simplified yet effective representation of the transition from collisional to collisionless reconnection. However, plasmoid instability in 2D Hall MHD simulations often leads to a single-X-line reconnection configuration, which significantly differs from fully kinetic particle-in-cell simulation results. This study shows that single-X-line reconnection is less likely to occur in 3D compared to 2D. Moreover, depending on the Lundquist number and the ratio between the system size and the kinetic scale, Hall MHD can also realize 3D self-generated turbulent reconnection. We analyze the features of the self-generated turbulent state, including the energy power spectra and the scale dependence of turbulent eddy anisotropy.

$\bf{W}^*$ is the category of the archimedean l-groups with distinguished strong order unit and unit-preserving l-group homomorphisms. For $G \in \bf{W}^*$, we have the canonical compact space $YG$, and Yosida representation $G \leq C(YG)$, thus, for $g \in G$, the cozero-set coz(g) in $YG$. The ideals at issue in $G$ include the principal ideals and polars, $G(g)$ and $g^{\perp \perp}$, respectively, and the $\bf{W}^*$-kernels of $\bf{W}^*$-morphisms from $G$. The ``coincidences of types" include these properties of $G$: (M) Each $G(g) = g^{\perp \perp}$; (Y) Each $G(g)$ is a $\bf{W}^*$-kernel; (CR) Each $g^{\perp \perp}$ is a $\bf{W}^*$-kernel (iff each coz(g) is regular open). For each of these, we give numerous ``rephrasings", and examples, and note that (M) = (Y) $\cap$ (CR). This paper is a companion to a paper in preparation by the present authors, which includes the present thrust in contexts less restrictive and more algebraic. Here, the focus on $\bf{W}^*$ brings topology to bear, and sharpens the view.

Regular practice of gratitude has the potential to enhance psychological wellbeing and foster stronger social connections among young adults. However, there is a lack of research investigating user needs and expectations regarding gratitude-promoting applications. To address this gap, we employed a user-centered design approach to develop a mobile application that facilitates gratitude practice. Our formative study involved 20 participants who utilized an existing application, providing insights into their preferences for organizing expressions of gratitude and the significance of prompts for reflection and mood labeling after working hours. Building on these findings, we conducted a deployment study with 26 participants using our custom-designed application, which confirmed the positive impact of structured options to guide gratitude practice and highlighted the advantages of passive engagement with the application during busy periods. Our study contributes to the field by identifying key design considerations for promoting gratitude among young adults.

Accurate prediction of battery temperature rise is very essential for designing an efficient thermal management scheme. In this paper, machine learning (ML) based prediction of Vanadium Redox Flow Battery (VRFB) thermal behavior during charge-discharge operation has been demonstrated for the first time. Considering different currents with a specified electrolyte flow rate, the temperature of a kW scale VRFB system is studied through experiments. Three different ML algorithms; Linear Regression (LR), Support Vector Regression (SVR) and Extreme Gradient Boost (XGBoost) have been used for the prediction work. The training and validation of ML algorithms have been done by the practical dataset of a 1kW 6kWh VRFB storage under 40A, 45A, 50A and 60A charge-discharge currents and 10 L min-1 of flow rate. A comparative analysis among the ML algorithms is done in terms of performance metrics such as correlation coefficient (R2), mean absolute error (MAE) and root mean square error (RMSE). It is observed that XGBoost shows the highest accuracy in prediction of around 99%. The ML based prediction results obtained in this work can be very useful for controlling the VRFB temperature rise during operation and act as indicator for further development of an optimized thermal management system., Comment: 21 pages, 5 figures

Continuous gravitational waves (CGWs) from various astrophysical sources are one of the many future probes of upcoming Gravitational Wave (GW) search missions. Neutron stars (NSs) with deformity are one of the leading sources of CGW emissions. In this work, for the first time, a novel attempt to estimate the dark matter (DM) capture rate is performed using CGW as the probe to the local NS population. Competitive bounds on DM capture from the local NS population are reported when compared with DM direct search experiments and other astrophysical observations., Comment: 13 pages, 5 figures

The study of decoherence plays a key role in our understanding of the transition from the quantum to the classical world. Typically, one considers a system coupled to an external bath which forms a model for an open quantum system. While most of the studies pertain to a position coupling between the system and the environment, some involve a momentum coupling, giving rise to an anomalous diffusive model. Here we have gone beyond existing studies and analysed the quantum Langevin dynamics of a harmonically oscillating charged Brownian particle in the presence of a magnetic field and coupled to an Ohmic heat bath via both position and momentum couplings. The presence of both position and momentum couplings leads to a stronger interaction with the environment, resulting in a faster loss of coherence compared to a situation where only position coupling is present. The rate of decoherence can be tuned by controlling the relative strengths of the position and momentum coupling parameters. In addition, the magnetic field results in the slowing down of the loss of information from the system, irrespective of the nature of coupling between the system and the bath. Our results can be experimentally verified by designing a suitable ion trap setup., Comment: 15 pages, 3 figures. Expert comments on our manuscript are always welcome

Precision measurements of space and time, like those made by the detectors of the Laser Interferometer Gravitational-wave Observatory (LIGO), are often confronted with fundamental limitations imposed by quantum mechanics. The Heisenberg uncertainty principle dictates that the position and momentum of an object cannot both be precisely measured, giving rise to an apparent limitation called the Standard Quantum Limit (SQL). Reducing quantum noise below the SQL in gravitational-wave detectors, where photons are used to continuously measure the positions of freely falling mirrors, has been an active area of research for decades. Here we show how the LIGO A+ upgrade reduced the detectors' quantum noise below the SQL by up to 3 dB while achieving a broadband sensitivity improvement, more than two decades after this possibility was first presented.

This paper presents a demonstration of the developed prototype showcasing a way to preserve the Intangible Cultural Heritage of Uttarakhand, India. Aipan is a traditional art form practiced in the Kumaon region in the state of Uttarakhand. It is typically used to decorate floors and walls at places of worship or entrances of homes and is considered auspicious to begin any work or event. This art is associated with a great degree of social, cultural as well as religious significance and is passed from generation to generation. However, in the present era of modernization and technological advancements, this art form now stands on the verge of depletion. This study presents a humble attempt to preserve this vanishing art form through the use of Virtual Reality (VR). Ethnographic studies were conducted in Almora, Nainital, and Haldwani regions of Uttarakhand to trace the origins as well as to gain a deeper understanding of this art form. A total of ten (N =10) Aipan designers were interviewed. Several interesting insights are revealed through these studies that show the potential to be incorporated as a VR experience., Comment: Demonstrated at ISMAR 2020

We consider the spatio-temporal gridded daily diurnal temperature range (DTR) data across India during the 72-year period 1951--2022. We augment this data with information on the El Nino-Southern Oscillation (ENSO) and on the climatic regions (Stamp's and Koeppen's classification) and four seasons of India. We use various matrix theory approaches to trim out strong but routine signals, random matrix theory to remove noise, and novel empirical generalised singular-value distributions to establish retention of essential signals in the trimmed data. We make use of the spatial Bergsma statistics to measure spatial association and identify temporal change points in the spatial-association. In particular, our investigation captures a yet unknown change-point over the 72 years under study with drastic changes in spatial-association of DTR in India. It also brings out changes in spatial association with regard to ENSO. We conclude that while studying/modelling Indian DTR data, due consideration should be granted to the strong spatial association that is being persistently exhibited over decades, and provision should be kept for potential change points in the temporal behaviour, which in turn can bring moderate to dramatic changes in the spatial association pattern. Some of our analysis also reaffirms the conclusions made by other authors, regarding spatial and temporal behavior of DTR, adding our own insights. We consider the data from the yearly, seasonal and climatic zones points of view, and discover several new and interesting statistical structures which should be of interest, especially to climatologists and statisticians. Our methods are not country specific and could be used profitably for DTR data from other geographical areas.

The Machine learning (ML) is a rapidly evolving field of technology that has the potential to greatly impact society in a variety of ways. However, there are also concerns about the potential negative effects of ML on society, such as job displacement and privacy issues. This research aimed to conduct a comprehensive analysis of the current and future impact of ML on society. The research included a thorough literature review, case studies, and surveys to gather data on the economic impact of ML, ethical and privacy implications, and public perceptions of the technology. The survey was conducted on 150 respondents from different areas. The case studies conducted were on the impact of ML on healthcare, finance, transportation, and manufacturing. The findings of this research revealed that the majority of respondents have a moderate level of familiarity with the concept of ML, believe that it has the potential to benefit society, and think that society should prioritize the development and use of ML. Based on these findings, it was recommended that more research is conducted on the impact of ML on society, stronger regulations and laws to protect the privacy and rights of individuals when it comes to ML should be developed, transparency and accountability in ML decision-making processes should be increased, and public education and awareness about ML should be enhanced., Comment: 12 pages

Current finger-vein or palm-vein recognition systems usually require direct contact of the subject with the apparatus. This can be problematic in environments where hygiene is of primary importance. In this work we present a contactless vascular biometrics sensor platform named \sweet which can be used for hand vascular biometrics studies (wrist, palm, and finger-vein) and surface features such as palmprint. It supports several acquisition modalities such as multi-spectral Near-Infrared (NIR), RGB-color, Stereo Vision (SV) and Photometric Stereo (PS). Using this platform we collect a dataset consisting of the fingers, palm and wrist vascular data of 120 subjects and develop a powerful 3D pipeline for the pre-processing of this data. We then present biometric experimental results, focusing on Finger-Vein Recognition (FVR). Finally, we discuss fusion of multiple modalities, such palm-vein combined with palm-print biometrics. The acquisition software, parts of the hardware design, the new FV dataset, as well as source-code for our experiments are publicly available for research purposes.

We investigate the melting transition of non-supercoiled circular DNA of different lengths, employing Brownian dynamics simulation. In the absence of supercoiling, we find that melting of circular DNA is driven by a large bubble, which agrees with the previous predictions of circular DNA melting in the presence of supercoiling. By analyzing sector-wise changes in average base-pair distance, our study reveals that the melting behavior of circular DNA closely resembles that of linear DNA. Additionally, we find a marked difference in the thermal stability of circular DNA over linear DNA at very short length scales, an effect that diminishes as the length of circular DNA increases. The stability of smaller circular DNA is linked to the occurrence of transient small bubbles, characterized by a lower probability of growth., Comment: 11 figures

We present results from X-ray imaging of high-aspect-ratio magnetic reconnection experiments driven at the National Ignition Facility. Two parallel, self-magnetized, elongated laser-driven plumes are produced by tiling 40 laser beams. A magnetic reconnection layer is formed by the collision of the plumes. A gated X-ray framing pinhole camera with micro-channel plate (MCP) detector produces multiple images through various filters of the formation and evolution of both the plumes and current sheet. As the diagnostic integrates plasma self-emission along the line of sight, 2-dimensional electron temperature maps $\langle T_e \rangle_Y$ are constructed by taking the ratio of intensity of these images obtained with different filters. The plumes have a characteristic temperature $\langle T_e \rangle_Y = 240 \pm 20$ eV at 2 ns after the initial laser irradiation and exhibit a slow cooling up to 4 ns. The reconnection layer forms at 3 ns with a temperature $\langle T_e \rangle_Y = 280 \pm 50$ eV as the result of the collision of the plumes. The error bars of the plumes and current sheet temperatures separate at $4$ ns, showing the heating of the current sheet from colder inflows. Using a semi-analytical model, we find that the observed heating of the current sheet is consistent with being produced by electron-ion drag, rather than the conversion of magnetic to kinetic energy., Comment: Submitted to Physics of Plasmas. 19 pages (total), 14 figures, 2 tables

The attention module in vision transformers(ViTs) performs intricate spatial correlations, contributing significantly to accuracy and delay. It is thereby important to modulate the number of attentions according to the input feature complexity for optimal delay-accuracy tradeoffs. To this end, we propose PIVOT - a co-optimization framework which selectively performs attention skipping based on the input difficulty. For this, PIVOT employs a hardware-in-loop co-search to obtain optimal attention skip configurations. Evaluations on the ZCU102 MPSoC FPGA show that PIVOT achieves 2.7x lower EDP at 0.2% accuracy reduction compared to LVViT-S ViT. PIVOT also achieves 1.3% and 1.8x higher accuracy and throughput than prior works on traditional CPUs and GPUs. The PIVOT project can be found at https://github.com/Intelligent-Computing-Lab-Yale/PIVOT., Comment: Accepted to 61st ACM/IEEE Design Automation Conference (DAC '24), June 23--27, 2024, San Francisco, CA, USA (6 Pages)

In this paper, we define weighted failure rate and their different means from the stand point of an application. We begin by emphasizing that the formation of n independent component series system having weighted failure rates with sum of weight functions being unity is same as a mixture of n distributions. We derive some parametric and non-parametric characterization results. We discuss on the form invariance property of baseline failure rate for a specific choice of weight function. Some bounds on means of aging functions are obtained. Here, we establish that weighted IFRA class is not closed under formation of coherent systems unlike the IFRA class. An interesting application of the present work is credited to the fact that the quantile version of means of failure rate is obtained as a special case of weighted means of failure rate.

Meal preparation is an important instrumental activity of daily living~(IADL). While existing research has explored robotic assistance in meal preparation tasks such as cutting and cooking, the crucial task of peeling has received less attention. Robot-assisted peeling, conventionally a bimanual task, is challenging to deploy in the homes of care recipients using two wheelchair-mounted robot arms due to ergonomic and transferring challenges. This paper introduces a robot-assisted peeling system utilizing a single robotic arm and an assistive cutting board, inspired by the way individuals with one functional hand prepare meals. Our system incorporates a multimodal active perception module to determine whether an area on the food is peeled, a human-in-the-loop long-horizon planner to perform task planning while catering to a user's preference for peeling coverage, and a compliant controller to peel the food items. We demonstrate the system on 12 food items representing the extremes of different shapes, sizes, skin thickness, surface textures, skin vs flesh colors, and deformability.

We study the emergence and stability of a prethermal phase in an integrable many-body system subjected to a Fibonacci drive. Despite not being periodic, Fibonacci drives have been shown to introduce dynamical constraints due to their self-similar structure, unlike random driving protocols. From perturbative analysis, this has been argued to result in an exponentially long prethermal phase in the high frequency limit of driving. Examining higher order terms in the perturbative expansion, we show that the perturbative description breaks down eventually in such systems at a finite universal order, which depends solely on features of the Fibonacci sequence. This leads to an onset of energy absorption at long time scales for intermediate and low driving frequencies. Interestingly, in spite of the breakdown of an effective Hamiltonian in the perturbative analysis, we still observe slow logarithmic heating time-scales, unlike purely random drives., Comment: 15 pages, 8 captioned figures

We report the observation of a coalescing compact binary with component masses $2.5\text{-}4.5~M_\odot$ and $1.2\text{-}2.0~M_\odot$ (all measurements quoted at the 90% credible level). The gravitational-wave signal GW230529_181500 was observed during the fourth observing run of the LIGO-Virgo-KAGRA detector network on 2023 May 29 by the LIGO Livingston Observatory. The primary component of the source has a mass less than $5~M_\odot$ at 99% credibility. We cannot definitively determine from gravitational-wave data alone whether either component of the source is a neutron star or a black hole. However, given existing estimates of the maximum neutron star mass, we find the most probable interpretation of the source to be the coalescence of a neutron star with a black hole that has a mass between the most massive neutron stars and the least massive black holes observed in the Galaxy. We provisionally estimate a merger rate density of $55^{+127}_{-47}~\text{Gpc}^{-3}\,\text{yr}^{-1}$ for compact binary coalescences with properties similar to the source of GW230529_181500; assuming that the source is a neutron star-black hole merger, GW230529_181500-like sources constitute about 60% of the total merger rate inferred for neutron star-black hole coalescences. The discovery of this system implies an increase in the expected rate of neutron star-black hole mergers with electromagnetic counterparts and provides further evidence for compact objects existing within the purported lower mass gap., Comment: 45 pages (10 pages author list, 13 pages main text, 1 page acknowledgements, 13 pages appendices, 8 pages bibliography), 17 figures, 16 tables. Update to match version published in The Astrophysical Journal Letters. Data products available from https://zenodo.org/records/10845779

Optimising stellarators for quasisymmetry leads to strongly reduced collisional transport and energetic particle losses compared to unoptimised configurations. Though stellarators with precise quasisymmetry have been obtained in the past, it remains unclear how broad the parameter space is where good quasisymmetry may be achieved. We study the range of aspect ratio and rotational transform values for which stellarators with excellent quasisymmetry on the boundary can be obtained. A large number of Fourier harmonics is included in the boundary representation, which is made computationally tractable by the use of adjoint methods to enable fast gradient-based optimisation, and by the direct optimisation of vacuum magnetic fields, which converge more robustly compared to solutions from magnetohydrostatics. Several novel configurations are presented, including stellarators with record levels of quasisymmetry on a surface; three field period quasiaxisymmetric stellarators with substantial magnetic shear, and compact quasisymmetric stellarators at low aspect ratios similar to tokamaks., Comment: 17 pages, 7 figures

Unsupervised Domain Adaptation (UDA) aims to adapt models from labeled source domains to unlabeled target domains. When adapting to adverse scenes, existing UDA methods fail to perform well due to the lack of instructions, leading their models to overlook discrepancies within all adverse scenes. To tackle this, we propose CoDA which instructs models to distinguish, focus, and learn from these discrepancies at scene and image levels. Specifically, CoDA consists of a Chain-of-Domain (CoD) strategy and a Severity-Aware Visual Prompt Tuning (SAVPT) mechanism. CoD focuses on scene-level instructions to divide all adverse scenes into easy and hard scenes, guiding models to adapt from source to easy domains with easy scene images, and then to hard domains with hard scene images, thereby laying a solid foundation for whole adaptations. Building upon this foundation, we employ SAVPT to dive into more detailed image-level instructions to boost performance. SAVPT features a novel metric Severity that divides all adverse scene images into low-severity and high-severity images. Then Severity directs visual prompts and adapters, instructing models to concentrate on unified severity features instead of scene-specific features, without adding complexity to the model architecture. CoDA achieves SOTA performances on widely-used benchmarks under all adverse scenes. Notably, CoDA outperforms the existing ones by 4.6%, and 10.3% mIoU on the Foggy Driving, and Foggy Zurich benchmarks, respectively. Our code is available at https://github.com/Cuzyoung/CoDA

We propose a road layout and traffic model, based on last passage percolation (LPP). An easy naive argument shows that coalescence of traffic trajectories is essential to be considered when observing traffic networks around us. This is a fundamental feature in first passage percolation (FPP) models where nearby geodesics naturally coalesce in search of the easiest passage through the landscape. Road designers seek the same in pursuing cost savings, hence FPP geodesics are straightforward candidates to model road layouts. Unfortunately no detailed knowledge is rigorously available on FPP geodesics. To address this, we use exponential LPP instead to build a stochastic model of road traffic and prove certain characteristics thereof. Cars start from every point of the lattice and follow half-infinite geodesics in random directions. Exponential LPP is known to be in the KPZ universality class and it is widely expected that FPP shares very similar properties, hence our findings should equally apply to FPP-based modelling. We address several traffic-related quantities of this model and compare our theorems to real life road networks., Comment: 38 pages, 23 figures

The advent of Vision Language Models (VLM) has allowed researchers to investigate the visual understanding of a neural network using natural language. Beyond object classification and detection, VLMs are capable of visual comprehension and common-sense reasoning. This naturally led to the question: How do VLMs respond when the image itself is inherently unreasonable? To this end, we present IllusionVQA: a diverse dataset of challenging optical illusions and hard-to-interpret scenes to test the capability of VLMs in two distinct multiple-choice VQA tasks - comprehension and soft localization. GPT4V, the best performing VLM, achieves 62.99% accuracy (4-shot) on the comprehension task and 49.7% on the localization task (4-shot and Chain-of-Thought). Human evaluation reveals that humans achieve 91.03% and 100% accuracy in comprehension and localization. We discover that In-Context Learning (ICL) and Chain-of-Thought reasoning substantially degrade the performance of Gemini-Pro in the localization task. Tangentially, we discover a potential weakness in the ICL capabilities of VLMs: they fail to locate optical illusions even when the correct answer is in the context window as a few-shot example.

With the advancement in capabilities of Large Language Models (LLMs), one major step in the responsible and safe use of such LLMs is to be able to detect text generated by these models. While supervised AI-generated text detectors perform well on text generated by older LLMs, with the frequent release of new LLMs, building supervised detectors for identifying text from such new models would require new labeled training data, which is infeasible in practice. In this work, we tackle this problem and propose a domain generalization framework for the detection of AI-generated text from unseen target generators. Our proposed framework, EAGLE, leverages the labeled data that is available so far from older language models and learns features invariant across these generators, in order to detect text generated by an unknown target generator. EAGLE learns such domain-invariant features by combining the representational power of self-supervised contrastive learning with domain adversarial training. Through our experiments we demonstrate how EAGLE effectively achieves impressive performance in detecting text generated by unseen target generators, including recent state-of-the-art ones such as GPT-4 and Claude, reaching detection scores of within 4.7% of a fully supervised detector.

Quantile crossing has been an ever-present thorn in the side of quantile regression. This has spurred research into obtaining densities and coefficients that obey the quantile monotonicity property. While important contributions, these papers do not provide insight into how exactly these constraints influence the estimated coefficients. This paper extends non-crossing constraints and shows that by varying a single hyperparameter ($\alpha$) one can obtain commonly used quantile estimators. Namely, we obtain the quantile regression estimator of Koenker and Bassett (1978) when $\alpha=0$, the non crossing quantile regression estimator of Bondell et al. (2010) when $\alpha=1$, and the composite quantile regression estimator of Koenker (1984) and Zou and Yuan (2008) when $\alpha\rightarrow\infty$. As such, we show that non-crossing constraints are simply a special type of fused-shrinkage., Comment: 37 pages, 16 figures, 4 tables, 7348 words

The Adam optimizer, often used in Machine Learning for neural network training, corresponds to an underlying ordinary differential equation (ODE) in the limit of very small learning rates. This work shows that the classical Adam algorithm is a first-order implicit-explicit (IMEX) Euler discretization of the underlying ODE. Employing the time discretization point of view, we propose new extensions of the Adam scheme obtained by using higher-order IMEX methods to solve the ODE. Based on this approach, we derive a new optimization algorithm for neural network training that performs better than classical Adam on several regression and classification problems.