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Suspensions of finite-size solid particles in a turbulent pipe flow are found in many industrial and technical flows. Due to the ample parameter space consisting of particle size, concentration, density and Reynolds number, a complete picture of the particle-fluid interaction is still lacking. Pressure drop predictions are often made using viscosity models only considering the bulk solid volume fraction. For the case of turbulent pipe flow laden with neutrally buoyant spherical particles, we investigate the pressure drop and overall drag (friction factor), fluid velocity and particle distribution in the pipe. We use a combination of experimental (MRV) and numerical (DNS) techniques and a continuum flow model. We find that the particle size and the bulk flow rate influence the mean fluid velocity, velocity fluctuations and the particle distribution in the pipe for low flow rates. However, the effects of the added solid particles diminish as the flow rate increases. We created a master curve for drag change compared to single-phase flow for the particle-laden cases. This curve can be used to achieve more accurate friction factor predictions than the traditional modified viscosity approach that does not account for particle size.

The emergence of Deep Convolutional Neural Networks (DCNNs) has been a pervasive tool for accomplishing widespread applications in computer vision. Despite its potential capability to capture intricate patterns inside the data, the underlying embedding space remains Euclidean and primarily pursues contractive convolution. Several instances can serve as a precedent for the exacerbating performance of DCNNs. The recent advancement of neural networks in the hyperbolic spaces gained traction, incentivizing the development of convolutional deep neural networks in the hyperbolic space. In this work, we propose Hyperbolic DCNN based on the Poincar\'{e} Disc. The work predominantly revolves around analyzing the nature of expansive convolution in the context of the non-Euclidean domain. We further offer extensive theoretical insights pertaining to the universal consistency of the expansive convolution in the hyperbolic space. Several simulations were performed not only on the synthetic datasets but also on some real-world datasets. The experimental results reveal that the hyperbolic convolutional architecture outperforms the Euclidean ones by a commendable margin.

While Large Language Models (LLMs) have demonstrated remarkable performance in certain dimensions, their ability to express implicit language cues that human use for effective communication remains unclear. This paper presents ExpressivityArena, a Python library for measuring the implicit communication abilities of LLMs. We provide a comprehensive framework to evaluate expressivity of arbitrary LLMs and explore its practical implications. To this end, we refine the definition and measurements of ``expressivity,'' and use our framework in a set of small experiments. These experiments test LLMs in creative and logical tasks such as poetry, coding, and emotion-based responses. They are then evaluated by an automated grader, through ExpressivityArena, which we verify to be the most pragmatic for testing expressivity. Building on these experiments, we deepen our understanding of the expressivity of LLMs by assessing their ability to remain expressive in conversations. Our findings indicate that LLMs are capable of generating and understanding expressive content, however, with some limitations. These insights will inform the future development and deployment of expressive LLMs. We provide the code for ExpressivityArena alongside our paper., Comment: 8 pages, 22 figures

The widespread availability of maps has enabled the development of numerous location-based applications, including navigation, ride-sharing, fitness tracking, gaming, robotics, and augmented reality. Today, the maps that power these services are predominantly controlled by a few large corporations and mostly cover outdoor spaces. As the use of these applications expands and indoor localization technologies advance, we are seeing the need for a scalable, federated location management system that can extend into private spaces. We introduce OpenFLAME (Open Federated Localization and Mapping Engine), the first federated and decentralized localization service. OpenFLAME links servers that handle localization for specific regions, providing applications with a seamless global view. Creating a federated localization system poses challenges, such as discovering the appropriate servers for a region and integrating services managed by independent providers. To address these issues and ensure scalability, we leverage Domain Name System (DNS) for service discovery and implement map abstractions to retrieve and merge locations across different maps. Our trace-driven study demonstrates that federated localization across remote servers is feasible with acceptable query latencies. To highlight the potential of the system, we developed an augmented reality navigation application for a large indoor space, showing that OpenFLAME can successfully power location-based applications.

Understanding identifiability of latent content and style variables from unaligned multi-domain data is essential for tasks such as domain translation and data generation. Existing works on content-style identification were often developed under somewhat stringent conditions, e.g., that all latent components are mutually independent and that the dimensions of the content and style variables are known. We introduce a new analytical framework via cross-domain \textit{latent distribution matching} (LDM), which establishes content-style identifiability under substantially more relaxed conditions. Specifically, we show that restrictive assumptions such as component-wise independence of the latent variables can be removed. Most notably, we prove that prior knowledge of the content and style dimensions is not necessary for ensuring identifiability, if sparsity constraints are properly imposed onto the learned latent representations. Bypassing the knowledge of the exact latent dimension has been a longstanding aspiration in unsupervised representation learning -- our analysis is the first to underpin its theoretical and practical viability. On the implementation side, we recast the LDM formulation into a regularized multi-domain GAN loss with coupled latent variables. We show that the reformulation is equivalent to LDM under mild conditions -- yet requiring considerably less computational resource. Experiments corroborate with our theoretical claims.

Frustrated magnets can elude the paradigm of conventional symmetry breaking and instead exhibit signatures of emergent symmetries at low temperatures. Such symmetries arise from "accidental" degeneracies within the ground state manifold and have been explored in a number of disparate models, in both two and three dimensions. Here we report the systematic construction of a family of classical spin models that, for a wide variety of lattice geometries with triangular motifs in one, two and three spatial dimensions, such as the kagome or hyperkagome lattices, exhibit an emergent, continuous U(1) symmetry. This is particularly surprising because the underlying Hamiltonian actually has very little symmetry - a bond-directional, off-diagonal exchange model inspired by the microscopics of spin-orbit entangled materials (the $\Gamma^{\prime}$-model). The construction thus allows for a systematic study of the interplay between the emergent continuous U(1) symmetry and the underlying discrete Hamiltonian symmetries in different lattices across different spatial dimensions. We discuss the impact of thermal and quantum fluctuations in lifting the accidental ground state degeneracy via the thermal and quantum order-by-disorder mechanisms, and how spatial dimensionality and lattice symmetries play a crucial role in shaping the physics of the model. Complementary Monte Carlo simulations, for representative one-, two-, and three-dimensional lattice geometries, provide a complete account of the thermodynamics and confirm our analytical expectations., Comment: 14 pages, 14 figures

We introduce deterministic suffix-reading automata (DSA), a new automaton model over finite words. Transitions in a DSA are labeled with words. From a state, a DSA triggers an outgoing transition on seeing a word ending with the transition's label. Therefore, rather than moving along an input word letter by letter, a DSA can jump along blocks of letters, with each block ending in a suitable suffix. This feature allows DSAs to recognize regular languages more concisely, compared to DFAs. In this work, we focus on questions around finding a "minimal" DSA for a regular language. The number of states is not a faithful measure of the size of a DSA, since the transition-labels contain strings of arbitrary length. Hence, we consider total-size (number of states + number of edges + total length of transition-labels) as the size measure of DSAs. We start by formally defining the model and providing a DSA-to-DFA conversion that allows to compare the expressiveness and succinctness of DSA with related automata models. Our main technical contribution is a method to derive DSAs from a given DFA: a DFA-to-DSA conversion. We make a surprising observation that the smallest DSA derived from the canonical DFA of a regular language L need not be a minimal DSA for L. This observation leads to a fundamental bottleneck in deriving a minimal DSA for a regular language. In fact, we prove that given a DFA and a number k, the problem of deciding if there exists an equivalent DSA of total-size at most k is NP-complete., Comment: In Proceedings GandALF 2024, arXiv:2410.21884

This paper presents a deep learning framework for the multi-class classification of gastrointestinal abnormalities in Video Capsule Endoscopy (VCE) frames. The aim is to automate the identification of ten GI abnormality classes, including angioectasia, bleeding, and ulcers, thereby reducing the diagnostic burden on gastroenterologists. Utilizing an ensemble of DenseNet and ResNet architectures, the proposed model achieves an overall accuracy of 94\% across a well-structured dataset. Precision scores range from 0.56 for erythema to 1.00 for worms, with recall rates peaking at 98% for normal findings. This study emphasizes the importance of robust data preprocessing techniques, including normalization and augmentation, in enhancing model performance. The contributions of this work lie in developing an effective AI-driven tool that streamlines the diagnostic process in gastroenterology, ultimately improving patient care and clinical outcomes., Comment: 10 pages, 5 figures, CVIP challenge report including the validation results

In large deep neural networks that seem to perform surprisingly well on many tasks, we also observe a few failures related to accuracy, social biases, and alignment with human values, among others. Therefore, before deploying these models, it is crucial to characterize this failure landscape for engineers to debug or audit models. Nevertheless, it is infeasible to exhaustively test for all possible combinations of factors that could lead to a model's failure. In this paper, we improve the "Failures are fated, but can be faded" framework (arXiv:2406.07145)--a post-hoc method to explore and construct the failure landscape in pre-trained generative models--with a variety of deep reinforcement learning algorithms, screening tests, and LLM-based rewards and state generation. With the aid of limited human feedback, we then demonstrate how to restructure the failure landscape to be more desirable by moving away from the discovered failure modes. We empirically demonstrate the effectiveness of the proposed method on diffusion models. We also highlight the strengths and weaknesses of each algorithm in identifying failure modes., Comment: 13 pages, 11 figures. arXiv admin note: substantial text overlap with arXiv:2406.07145

Under some initial and boundary conditions, the rapid reaction-thermal diffusion process taking place during frontal polymerization (FP) destabilizes the planar mode of front propagation, leading to spatially varying, complex hierarchical patterns in thermoset polymeric materials. Although modern reaction-diffusion models can predict the patterns resulting from unstable FP, the inverse design of patterns, which aims to retrieve process conditions that produce a desired pattern, remains an open challenge due to the non-unique and non-intuitive mapping between process conditions and manufactured patterns. In this work, we propose a probabilistic generative model named univariate conditional variational autoencoder (UcVAE) for the inverse design of hierarchical patterns in FP-based manufacturing. Unlike the cVAE, which encodes both the design space and the design target, the UcVAE encodes only the design space. In the encoder of the UcVAE, the number of training parameters is significantly reduced compared to the cVAE, resulting in a shorter training time while maintaining comparable performance. Given desired pattern images, the trained UcVAE can generate multiple process condition solutions that produce high-fidelity hierarchical patterns.

Software Defined Networking (SDN) has evolved to revolutionize next-generation networks, offering programmability for on-the-fly service provisioning, primarily supported by the OpenFlow (OF) protocol. The limited storage capacity of Ternary Content Addressable Memory (TCAM) for storing flow tables in OF switches introduces vulnerabilities, notably the Low-Rate Flow Table Overflow (LOFT) attacks. LOFT exploits the flow table's storage capacity by occupying a substantial amount of space with malicious flow, leading to a gradual degradation in the flow-forwarding performance of OF switches. To mitigate this threat, we propose FloRa, a machine learning-based solution designed for monitoring and detecting LOFT attacks in SDN. FloRa continuously examines and determines the status of the flow table by closely examining the features of the flow table entries. Upon detecting an attack FloRa promptly activates the detection module. The module monitors flow properties, identifies malicious flows, and blacklists them, facilitating their eviction from the flow table. Incorporating novel features such as Packet Arrival Frequency, Content Relevance Score, and Possible Spoofed IP along with Cat Boost employed as the attack detection method. The proposed method reduces CPU overhead, memory overhead, and classification latency significantly and achieves a detection accuracy of 99.49%, which is more than the state-of-the-art methods to the best of our knowledge. This approach not only protects the integrity of the flow tables but also guarantees the uninterrupted flow of legitimate traffic. Experimental results indicate the effectiveness of FloRa in LOFT attack detection, ensuring uninterrupted data forwarding and continuous availability of flow table resources in SDN., Comment: IEEE Transactions on Network and Service Management (2024)

The concept of evolutionarily stability and its relation with the fixed points of the replicator equation are important aspects of evolutionary game dynamics. In the light of the fact that oscillating state of a population and individuals (or players) of different roles are quite natural occurrences, we ask the question how the concept of evolutionarily stability can be generalized so as to associate game-theoretic meaning to oscillatory behaviours of players asymmetrically interacting, i.e., if there are both intraspecific and interspecific interactions between two subpopulations in the population. We guide our scheme of generalization such that the evolutionary stability is related to the dynamic stability of the corresponding periodic orbits of a time-discrete replicator dynamics. We name the generalization of evolutionarily stable state as two-species heterogeneity stable orbit. Furthermore, we invoke the principle of decrease of relative entropy in order to associate the generalization of evolutionary stability with an information-theoretic meaning. This particular generalization is aptly termed as two-species information stable orbit.

Understanding the evolution of galaxies cannot exclude the important role played by the central supermassive black hole and the circumgalactic medium (CGM). Simulations have strongly suggested the negative feedback of AGN Jet/wind/outflows on the ISM/CGM of a galaxy leading to the eventual decline of star formation. However, no "smoking gun" evidence exists so far where relics of feedback, observed in any band, are consistent with the time scale of a major decline in star formation, in any sample of galaxies. Relics of any AGN-driven outflows will be observed as a faint and fuzzy structure which may be difficult to characterise by automated algorithms but trained citizen scientists can possibly perform better through their intuitive vision with additional heterogeneous data available anywhere on the Internet. RAD@home, launched on 15th April 2013, is not only the first Indian Citizen Science Research (CSR) platform in astronomy but also the only CSR publishing discoveries using any Indian telescope. We briefly report 11 CSR discoveries collected over the last eleven years. While searching for such relics we have spotted cases of offset relic lobes from elliptical and spiral, episodic radio galaxies with overlapping lobes as the host galaxy is in motion, large diffuse spiral-shaped emission, cases of jet-galaxy interaction, kinks and burls on the jets, a collimated synchrotron thread etc. Such exotic sources push the boundaries of our understanding of classical Seyferts and radio galaxies with jets and the process of discovery prepares the next generation for science with the upgraded GMRT and Square Kilometre Array Observatory (SKAO)., Comment: 14 pages, 8 figures. Accepted for publication in the Springer-Nature conference proceedings for "ISRA 2023: The Relativistic Universe: From Classical to Quantum Proceedings of the International Symposium on Recent Developments in Relativistic Astrophysics". Comments and collaborations, most welcome! Please visit #RADatHomeIndia website at radathomeindia.org

In this paper, we study the problem of Participatory Budgeting (PB) with approval ballots, inspired by Multi-Winner Voting schemes. We present generalized preference aggregation methods for participatory budgeting, especially for finding seemingly fair budget allocations. To achieve this, we generalize such preference aggregation methods from the well-known methods, namely the Sequential Chamberlin Courant rule and the Sequential Monroe Rule in the realm of social choice theory. Further, we provide an experimental evaluation of the preference aggregation methods using an impartial culture method of preference generation and study the extent to which such polynomial time algorithms satisfy one of the most popular notions of fairness called proportional representation., Comment: 8 pages, 3 figures

We introduce Pixtral-12B, a 12--billion-parameter multimodal language model. Pixtral-12B is trained to understand both natural images and documents, achieving leading performance on various multimodal benchmarks, surpassing a number of larger models. Unlike many open-source models, Pixtral is also a cutting-edge text model for its size, and does not compromise on natural language performance to excel in multimodal tasks. Pixtral uses a new vision encoder trained from scratch, which allows it to ingest images at their natural resolution and aspect ratio. This gives users flexibility on the number of tokens used to process an image. Pixtral is also able to process any number of images in its long context window of 128K tokens. Pixtral 12B substanially outperforms other open models of similar sizes (Llama-3.2 11B \& Qwen-2-VL 7B). It also outperforms much larger open models like Llama-3.2 90B while being 7x smaller. We further contribute an open-source benchmark, MM-MT-Bench, for evaluating vision-language models in practical scenarios, and provide detailed analysis and code for standardized evaluation protocols for multimodal LLMs. Pixtral-12B is released under Apache 2.0 license.

The non-stoichiometric Fe$_2$P-type (FeMnP$_{0.5}$Si$_{0.5}$)$_{1-x}$(FeV)$_{x}$ alloys ( $x=0, 0.01$, $0.02$, and $0.03$) have been investigated as potential candidates for magnetic refrigeration near room temperature. The magnetic ordering temperature decreases with increasing FeV concentration, $x$, which can be ascribed to decreased ferromagnetic coupling strength between the magnetic atoms. The strong magnetoelastic coupling in these alloys results in large values of the isothermal entropy change ($\Delta S_M$); $15.7$ J/kgK, at $2$ T magnetic field for the $x = 0$ alloy. $\Delta S_M$ decreases with increasing $x$. Results from M{\"o}ssbauer spectroscopy reveal that the average hyperfine field (in the ferromagnetic state) and average center shift (in the paramagnetic state) have the same decreasing trend as $\Delta S_M$. The thermal hysteresis ($\Delta T_{hyst}$) of the magnetic phase transition decreases with increasing $x$, while the mechanical stability of the alloys improves due to the reduced lattice volume change across the magnetoelastic phase transition. The adiabatic temperature change $\Delta T_{ad}$, which highly depends on $\Delta T_{hyst}$, is $1.7$ K at $1.9$ T applied field for the $x = 0.02$ alloy.

Active galactic nuclei (AGN) feedback and its impact on their host galaxies are critical to our understanding of galaxy evolution. Here, we present a combined analysis of new high resolution ultraviolet (UV) data from the Ultraviolet Imaging Telescope (UVIT) on AstroSat and archival optical spectroscopic data from VLT/MUSE, for the Seyfert galaxy, NGC 1365. Concentrating on the central 5 kpc region, the UVIT images in the far and near UV show bright star forming knots in the circumnuclear ring as well as a faint central source. After correcting for extinction, we found the star formation rate (SFR) surface density of the circumnuclear 2 kpc ring to be similar to other starbursts, despite the presence of an AGN outflow, as seen in [OIII] 5007 Angstrom. On the other hand, we found fainter UV and thus lower SFR in the direction south-east of the AGN relative to north-west in agreement with observations at other wavelengths from JWST and ALMA. The AGN outflow velocity is found to be lesser than the escape velocity, suggesting that the outflowing gas will rain back into the galaxy. The deep UV data has also revealed diffuse UV emission in the direction of the AGN outflow. By combining [OIII] and UV data, we found the diffuse emission to be of AGN origin., Comment: 12 Pages, 7 figures, Accepted for publication in ApJ

Automatic text summarization in Nepali language is an unexplored area in natural language processing (NLP). Although considerable research has been dedicated to extractive summarization, the area of abstractive summarization, especially for low-resource languages such as Nepali, remains largely unexplored. This study explores the use of multilingual transformer models, specifically mBART and mT5, for generating headlines for Nepali news articles through abstractive summarization. The research addresses key challenges associated with summarizing texts in Nepali by first creating a summarization dataset through web scraping from various Nepali news portals. These multilingual models were then fine-tuned using different strategies. The performance of the fine-tuned models were then assessed using ROUGE scores and human evaluation to ensure the generated summaries were coherent and conveyed the original meaning. During the human evaluation, the participants were asked to select the best summary among those generated by the models, based on criteria such as relevance, fluency, conciseness, informativeness, factual accuracy, and coverage. During the evaluation with ROUGE scores, the 4-bit quantized mBART with LoRA model was found to be effective in generating better Nepali news headlines in comparison to other models and also it was selected 34.05% of the time during the human evaluation, outperforming all other fine-tuned models created for Nepali News headline generation.

Training of a tokenizer plays an important role in the performance of deep learning models. This research aims to understand the performance of tokenizers in five state-of-the-art (SOTA) large language models (LLMs) in the Assamese language of India. The research is important to understand the multi-lingual support for a low-resourced language such as Assamese. Our research reveals that the tokenizer of SUTRA from Two AI performs the best with an average Normalized Sequence Length (NSL) value of 0.45, closely followed by the tokenizer of GPT-4o from Open AI with an average NSL value of 0.54, followed by Gemma 2, Meta Llama 3.1, and Mistral Large Instruct 2407 with an average NSL value of 0.82, 1.4, and 1.48 respectively.

Evolutionarily stable strategy (ESS) is the defining concept of evolutionary game theory. It has a fairly unanimously accepted definition for the case of symmetric games which are played in a homogeneous population where all individuals are in same role. However, in asymmetric games, which are played in a population with multiple subpopulations (each of which has individuals in one particular role), situation is not as clear. Various generalizations of ESS defined for such cases differ in how they correspond to fixed points of replicator equation which models evolutionary dynamics of frequencies of strategies in the population. Moreover, some of the definitions may even be equivalent, and hence, redundant in the scheme of things. Along with reporting some new results, this paper is partly indented as a contextual mini-review of some of the most important definitions of ESS in asymmetric games. We present the definitions coherently and scrutinize them closely while establishing equivalences -- some of them hitherto unreported -- between them wherever possible. Since it is desirable that a definition of ESS should correspond to asymptotically stable fixed points of replicator dynamics, we bring forward the connections between various definitions and their dynamical stabilities. Furthermore, we find the use of principle of relative entropy to gain information-theoretic insights into the concept of ESS in asymmetric games, thereby establishing a three-fold connection between game theory, dynamical system theory, and information theory in this context. We discuss our conclusions also in the backdrop of asymmetric hypermatrix games where more than two individuals interact simultaneously in the course of getting payoffs., Comment: 22 pages, 3 figures

A core task in multi-modal learning is to integrate information from multiple feature spaces (e.g., text and audio), offering modality-invariant essential representations of data. Recent research showed that, classical tools such as {\it canonical correlation analysis} (CCA) provably identify the shared components up to minor ambiguities, when samples in each modality are generated from a linear mixture of shared and private components. Such identifiability results were obtained under the condition that the cross-modality samples are aligned/paired according to their shared information. This work takes a step further, investigating shared component identifiability from multi-modal linear mixtures where cross-modality samples are unaligned. A distribution divergence minimization-based loss is proposed, under which a suite of sufficient conditions ensuring identifiability of the shared components are derived. Our conditions are based on cross-modality distribution discrepancy characterization and density-preserving transform removal, which are much milder than existing studies relying on independent component analysis. More relaxed conditions are also provided via adding reasonable structural constraints, motivated by available side information in various applications. The identifiability claims are thoroughly validated using synthetic and real-world data.

In our recent investigation, we utilized a century's worth of archival optical data to search for a decade-long periodicity from the blazar PG 1553+113, finding a hint of a 22-year period. Building on this foundation, the current study extends our analysis to include 10 blazars from the Fermi Large Area Telescope 2FHL catalog to uncover similar long-term periodic behavior. To ensure the reliability of our findings, we consider the impact of observational limitations, such as temporal gaps and uneven sampling, which could potentially introduce artifacts or false periodic signals. Our analysis reveals that 4 of these blazars (AP Librae, MKN 421, MKN 501, PG 1246+586) exhibit decade-long periods in their optical light curves, albeit 3 of them may be influenced by noise. However, a likely genuine period of approximately 51 $\pm$ 9 yr is identified for MKN 421., Comment: 14 pages, 13 figures, Submitted to The Astrophysical Journal

Authors of COVID-19 papers produced during the pandemic were overwhelmingly not subject matter experts. Such a massive inflow of scholars from different expertise areas is both an asset and a potential problem. Domain-informed scientific collaboration is the key to preparing for future crises.

Laser site-selective spectroscopy and high-resolution absorption measurements have been used to determine 51 crystal-field energy levels for one of the Ho$^{3+}$ centres in Y$_{2}$SiO$_{5}$. This centre is denoted as Site 2 and has been tentatively assigned as the seven-fold coordinated centre. High resolution absorption measurements reveal complex hyperfine patterns that obey and approximate selection rule. The application of a magnetic field along the three optical axes reveals the presence of avoided crossings below 0.5 Tesla, in both the ground and excited states.

Patents are the currency of innovation, and like any currency, they need to be managed and protected (Gavin Potenza). Patents, as legal documents that secure intellectual property rights, play a critical role in technological innovation. The growing complexity of patent documents and the surge in patent applications have created a need for automated solutions in patent analysis. In this work, we present PatExpert, an autonomous multi-agent conversational framework designed to streamline and optimize patent-related tasks. The framework consists of a metaagent that coordinates task-specific expert agents for various patent-related tasks and a critique agent for error handling and feedback provision. The meta-agent orchestrates specialized expert agents, each fine-tuned for specific tasks such as patent classification, acceptance, claim generation, abstractive summarization, multi-patent analysis, and scientific hypothesis generation. For multi-patent analysis, the framework incorporates advanced methods like Graph Retrieval-Augmented Generation (GRAG) to enhance response accuracy and relevance by combining semantic similarity with knowledge graphs. Error handling is managed by critique agents (Gold-LLM-as-a-Judge and Reward-LLM-as-a-Judge), which evaluate output responses for accuracy and provide iterative feedback. The framework also prioritizes explainability, ensuring transparent justifications for decisions made during patent analysis. Its comprehensive capabilities make it a valuable tool for automating complex patent workflows, enhancing efficiency, accuracy, and compliance in patent-related tasks. Empirical evidence demonstrates significant improvements in patent processing tasks, concluding that the framework offers a robust solution for automating and optimizing patent analysis., Comment: Accepted at Workshop on Open-World Agents (OWA-NeurIPS 2024) : Synergizing Reasoning and Decision-Making in Open-World Environments

A human interacting with a robot often forms predictions of what the robot will do next. For instance, based on the recent behavior of an autonomous car, a nearby human driver might predict that the car is going to remain in the same lane. It is important for the robot to understand the human's prediction for safe and seamless interaction: e.g., if the autonomous car knows the human thinks it is not merging -- but the autonomous car actually intends to merge -- then the car can adjust its behavior to prevent an accident. Prior works typically assume that humans make precise predictions of robot behavior. However, recent research on human-human prediction suggests the opposite: humans tend to approximate other agents by predicting their high-level behaviors. We apply this finding to develop a second-order theory of mind approach that enables robots to estimate how humans predict they will behave. To extract these high-level predictions directly from data, we embed the recent human and robot trajectories into a discrete latent space. Each element of this latent space captures a different type of behavior (e.g., merging in front of the human, remaining in the same lane) and decodes into a vector field across the state space that is consistent with the underlying behavior type. We hypothesize that our resulting high-level and course predictions of robot behavior will correspond to actual human predictions. We provide initial evidence in support of this hypothesis through a proof-of-concept user study.

Characterizing materials with electron micrographs poses significant challenges for automated labeling due to the complex nature of nanomaterial structures. To address this, we introduce a fully automated, end-to-end pipeline that leverages recent advances in Generative AI. It is designed for analyzing and understanding the microstructures of semiconductor materials with effectiveness comparable to that of human experts, contributing to the pursuit of Artificial General Intelligence (AGI) in nanomaterial identification. Our approach utilizes Large MultiModal Models (LMMs) such as GPT-4V, alongside text-to-image models like DALLE-3. We integrate a GPT-4 guided Visual Question Answering (VQA) method to analyze nanomaterial images, generate synthetic nanomaterial images via DALLE-3, and employ in-context learning with few-shot prompting in GPT-4V for accurate nanomaterial identification. Our method surpasses traditional techniques by enhancing the precision of nanomaterial identification and optimizing the process for high-throughput screening., Comment: Published at Deployable AI (DAI) Workshop at AAAI-2024

Black box neural networks are an indispensable part of modern robots. Nevertheless, deploying such high-stakes systems in real-world scenarios poses significant challenges when the stakeholders, such as engineers and legislative bodies, lack insights into the neural networks' decision-making process. Presently, explainable AI is primarily tailored to natural language processing and computer vision, falling short in two critical aspects when applied in robots: grounding in decision-making tasks and the ability to assess trustworthiness of their explanations. In this paper, we introduce a trustworthy explainable robotics technique based on human-interpretable, high-level concepts that attribute to the decisions made by the neural network. Our proposed technique provides explanations with associated uncertainty scores by matching neural network's activations with human-interpretable visualizations. To validate our approach, we conducted a series of experiments with various simulated and real-world robot decision-making models, demonstrating the effectiveness of the proposed approach as a post-hoc, human-friendly robot learning diagnostic tool., Comment: 19 pages, 25 figures

Clustering, as an unsupervised technique, plays a pivotal role in various data analysis applications. Among clustering algorithms, Spectral Clustering on Euclidean Spaces has been extensively studied. However, with the rapid evolution of data complexity, Euclidean Space is proving to be inefficient for representing and learning algorithms. Although Deep Neural Networks on hyperbolic spaces have gained recent traction, clustering algorithms or non-deep machine learning models on non-Euclidean Spaces remain underexplored. In this paper, we propose a spectral clustering algorithm on Hyperbolic Spaces to address this gap. Hyperbolic Spaces offer advantages in representing complex data structures like hierarchical and tree-like structures, which cannot be embedded efficiently in Euclidean Spaces. Our proposed algorithm replaces the Euclidean Similarity Matrix with an appropriate Hyperbolic Similarity Matrix, demonstrating improved efficiency compared to clustering in Euclidean Spaces. Our contributions include the development of the spectral clustering algorithm on Hyperbolic Spaces and the proof of its weak consistency. We show that our algorithm converges at least as fast as Spectral Clustering on Euclidean Spaces. To illustrate the efficacy of our approach, we present experimental results on the Wisconsin Breast Cancer Dataset, highlighting the superior performance of Hyperbolic Spectral Clustering over its Euclidean counterpart. This work opens up avenues for utilizing non-Euclidean Spaces in clustering algorithms, offering new perspectives for handling complex data structures and improving clustering efficiency., Comment: Currently under review in IEEE T-PAMI

Meta-heuristic algorithmic development has been a thrust area of research since its inception. In this paper, a novel meta-heuristic optimization algorithm, Olive Ridley Survival (ORS), is proposed which is inspired from survival challenges faced by hatchlings of Olive Ridley sea turtle. A major fact about survival of Olive Ridley reveals that out of one thousand Olive Ridley hatchlings which emerge from nest, only one survive at sea due to various environmental and other factors. This fact acts as the backbone for developing the proposed algorithm. The algorithm has two major phases: hatchlings survival through environmental factors and impact of movement trajectory on its survival. The phases are mathematically modelled and implemented along with suitable input representation and fitness function. The algorithm is analysed theoretically. To validate the algorithm, fourteen mathematical benchmark functions from standard CEC test suites are evaluated and statistically tested. Also, to study the efficacy of ORS on recent complex benchmark functions, ten benchmark functions of CEC-06-2019 are evaluated. Further, three well-known engineering problems are solved by ORS and compared with other state-of-the-art meta-heuristics. Simulation results show that in many cases, the proposed ORS algorithm outperforms some state-of-the-art meta-heuristic optimization algorithms. The sub-optimal behavior of ORS in some recent benchmark functions is also observed.

Designing algorithms for space bounded models with restoration requirements on the space used by the algorithm is an important challenge posed about the catalytic computation model introduced by Buhrman et al. (2014). Motivated by the scenarios where we do not need to restore unless is useful, we define $ACL(A)$ to be the class of languages that can be accepted by almost-catalytic Turing machines with respect to $A$ (which we call the catalytic set), that uses at most $c\log n$ work space and $n^c$ catalytic space. We show that if there are almost-catalytic algorithms for a problem with catalytic set as $A \subseteq \Sigma^*$ and its complement respectively, then the problem can be solved by a ZPP algorithm. Using this, we derive that to design catalytic algorithms, it suffices to design almost-catalytic algorithms where the catalytic set is the set of strings of odd weight ($PARITY$). Towards this, we consider two complexity measures of the set $A$ which are maximized for $PARITY$ - random projection complexity (${\cal R}(A)$) and the subcube partition complexity (${\cal P}(A)$). By making use of error-correcting codes, we show that for all $k \ge 1$, there is a language $A_k \subseteq \Sigma^*$ such that $DSPACE(n^k) \subseteq ACL(A_k)$ where for every $m \ge 1$, $\mathcal{R}(A_k \cap \{0,1\}^m) \ge \frac{m}{4}$ and $\mathcal{P}(A_k \cap \{0,1\}^m)=2^{m/4}$. This contrasts the catalytic machine model where it is unclear if it can accept all languages in $DSPACE(\log^{1+\epsilon} n)$ for any $\epsilon > 0$. Improving the partition complexity of the catalytic set $A$ further, we show that for all $k \ge 1$, there is a $A_k \subseteq \{0,1\}^*$ such that $\mathsf{DSPACE}(\log^k n) \subseteq ACL(A_k)$ where for every $m \ge 1$, $\mathcal{R}(A_k \cap \{0,1\}^m) \ge \frac{m}{4}$ and $\mathcal{P}(A_k \cap \{0,1\}^m)=2^{m/4+\Omega(\log m)}$., Comment: 18 pages

Data sparsity is a key challenge limiting the power of AI tools across various domains. The problem is especially pronounced in domains that require active user input rather than measurements derived from automated sensors. It is a critical barrier to harnessing the full potential of AI in domains requiring active user engagement, such as self-reported mood check-ins, where capturing a continuous picture of emotional states is essential. In this context, sparse data can hinder efforts to capture the nuances of individual emotional experiences such as causes, triggers, and contributing factors. Existing methods for addressing data scarcity often rely on heuristics or large established datasets, favoring deep learning models that lack adaptability to new domains. This paper proposes a novel probabilistic framework that integrates user-centric feedback-based learning, allowing for personalized predictions despite limited data. Achieving 60% accuracy in predicting user states among 64 options (chance of 1/64), this framework effectively mitigates data sparsity. It is versatile across various applications, bridging the gap between theoretical AI research and practical deployment.

GdRu$_2$Si$_2$ has recently drawn significant attention as a centrosymmetric magnet capable of hosting a short period skyrmion square lattice (SkL) in the absence of Dzyaloshinskii Moriya interaction (DMI). In this system, Gd atoms are arranged on a square lattice forming 2D layers separated by the Ru-Si network in the out-of-plane direction. In the low T regime, the ground state for zero/smaller external magnetic field ($\vec{B}_\perp$) along the out-of-plane direction is a single helical state, characterized by one modulation vector $\vec{Q}$ along one of the in-plane directions of the square lattice. For some critical range of higher $\vec{B}_\perp$, the helical state transforms into a SkL state that can be viewed as the overlap of two helical states defined with $\vec{Q}$ vectors in two in-plane directions, with the same magnitude of $\vec{Q}$ as for the single helical state. So far in the literature, importance has been given to this in-plane $\vec{Q}$ vector in understanding the magnetic phases of the system, considering the out-of-plane magnetic coupling to be weak, which therefore has been ignored. Our calculation of the Gd-Gd magnetic exchange interactions ($J_{ij}$) however shows the strongest $J_{ij}$ to occur between second neighbour Gd atoms along the [111] body-diagonal direction of the unit cell. This along with the body-centred tetragonal structure of the Gd sublattice points to the presence of a hitherto ignored modulation vector, $\vec{Q}_{[111]}$, along the [111] direction in the helical ground state. Atomistic Spin Dynamics (ASD) simulations show the importance of this interaction. This interlayer modulation vector $\vec{Q}_{[111]}$, along with the intralayer $\vec{Q}_{[100]}$, determines the total magnetic ordering of the system. Our data shows that the magnetic phases in GdRu$_2$Si$_2$ are far more complex than what has been previously discussed., Comment: 39 pages, 8 figures in the manuscript

This paper presents, for the first time, an open-source implementation of the 3GPP Uplink Time Difference of Arrival (UL-TDoA) positioning method using the OpenAirInterface (OAI) framework. UL-TDoA is a critical positioning technique in 5G networks, leveraging the time differences of signal arrival at multiple base stations to determine the precise location of User Equipment (UE). This implementation aims to democratize access to advanced positioning technology by integrating UL-TDoA capabilities into both the Radio Access Network (RAN) and Core Network (CN) components of OAI, providing a comprehensive and 3GPP-compliant solution. The development includes the incorporation of essential protocol procedures, message flows, and interfaces as defined by 3GPP standards. Validation is conducted using two distinct methods: an OAI-RF simulator-based setup for controlled testing and an O-RAN-based Localization Testbed at EURECOM in real-world conditions. The results demonstrate the viability of this open-source UL-TDoA implementation, enabling precise positioning in various environments. By making this implementation publicly available, the study paves the way for widespread research, development, and innovation in the field of 5G positioning technologies, fostering collaboration and accelerating the advancement of cellular network positioning.

The rapid advancements in computer graphics have greatly enhanced the quality of computer-generated images (CGI), making them increasingly indistinguishable from authentic images captured by digital cameras (ADI). This indistinguishability poses significant challenges, especially in an era of widespread misinformation and digitally fabricated content. This research proposes a novel approach to classify CGI and ADI using Swin Transformers and preprocessing techniques involving RGB and CbCrY color frame analysis. By harnessing the capabilities of Swin Transformers, our method foregoes handcrafted features instead of relying on raw pixel data for model training. This approach achieves state-of-the-art accuracy while offering substantial improvements in processing speed and robustness against joint image manipulations such as noise addition, blurring, and JPEG compression. Our findings highlight the potential of Swin Transformers combined with advanced color frame analysis for effective and efficient image authenticity detection., Comment: 7 pages, 5 figures, 3 tables

\textbf{Purpose} This study aims to address the growing challenge of distinguishing computer-generated imagery (CGI) from authentic digital images in the RGB color space. Given the limitations of existing classification methods in handling the complexity and variability of CGI, this research proposes a Swin Transformer-based model for accurate differentiation between natural and synthetic images. \textbf{Methods} The proposed model leverages the Swin Transformer's hierarchical architecture to capture local and global features crucial for distinguishing CGI from natural images. The model's performance was evaluated through intra-dataset and inter-dataset testing across three distinct datasets: CiFAKE, JSSSTU, and Columbia. The datasets were tested individually (D1, D2, D3) and in combination (D1+D2+D3) to assess the model's robustness and domain generalization capabilities. \textbf{Results} The Swin Transformer-based model demonstrated high accuracy, consistently achieving a range of 97-99\% across all datasets and testing scenarios. These results confirm the model's effectiveness in detecting CGI, showcasing its robustness and reliability in both intra-dataset and inter-dataset evaluations. \textbf{Conclusion} The findings of this study highlight the Swin Transformer model's potential as an advanced tool for digital image forensics, particularly in distinguishing CGI from natural images. The model's strong performance across multiple datasets indicates its capability for domain generalization, making it a valuable asset in scenarios requiring precise and reliable image classification., Comment: 12 pages, 4 figures, 3 tables

Detecting test-time distribution shift has emerged as a key capability for safely deployed machine learning models, with the question being tackled under various guises in recent years. In this paper, we aim to provide a consolidated view of the two largest sub-fields within the community: out-of-distribution (OOD) detection and open-set recognition (OSR). In particular, we aim to provide rigorous empirical analysis of different methods across settings and provide actionable takeaways for practitioners and researchers. Concretely, we make the following contributions: (i) We perform rigorous cross-evaluation between state-of-the-art methods in the OOD detection and OSR settings and identify a strong correlation between the performances of methods for them; (ii) We propose a new, large-scale benchmark setting which we suggest better disentangles the problem tackled by OOD detection and OSR, re-evaluating state-of-the-art OOD detection and OSR methods in this setting; (iii) We surprisingly find that the best performing method on standard benchmarks (Outlier Exposure) struggles when tested at scale, while scoring rules which are sensitive to the deep feature magnitude consistently show promise; and (iv) We conduct empirical analysis to explain these phenomena and highlight directions for future research. Code: https://github.com/Visual-AI/Dissect-OOD-OSR, Comment: Accepted to IJCV, preprint version; v2: add supplementary

The current technology landscape lacks a foundational AI model for solving process engineering calculations. In this work, we introduce a novel autonomous agent framework leveraging Retrieval-Augmented Instruction-Tuning (RAIT) to enhance open, customizable small code language models (SLMs) for these calculations. By combining instruction tuned code SLMs with Retrieval-Augmented Code Generation (RACG) using external tools, the agent generates, debugs, and optimizes code from natural language specifications. Our approach addresses the limitations of the current lack of a foundational AI model for specialized process engineering tasks and offers benefits of explainability, knowledge editing, and cost-effectiveness. Additionally, we curate custom datasets of chemical and process engineering problems and solutions to overcome data scarcity. Experimental results show that our framework matches the performance of large-scale proprietary models on benchmark datasets, proving its effectiveness and usability., Comment: Accepted for publication at ML4CCE workshop at ECML PKDD 2024. Please find the link: https://ml4cce-ecml.com/#agenda

We present a novel framework for analyzing and interpreting electron microscopy images in semiconductor manufacturing using vision-language instruction tuning. The framework employs a unique teacher-student approach, leveraging pre-trained multimodal large language models such as GPT-4 to generate instruction-following data for zero-shot visual question answering (VQA) and classification tasks, customizing smaller multimodal models (SMMs) for microscopy image analysis, resulting in an instruction-tuned language-and-vision assistant. Our framework merges knowledge engineering with machine learning to integrate domain-specific expertise from larger to smaller multimodal models within this specialized field, greatly reducing the need for extensive human labeling. Our study presents a secure, cost-effective, and customizable approach for analyzing microscopy images, addressing the challenges of adopting proprietary models in semiconductor manufacturing., Comment: Paper published at AAAI 2024 Spring Symposium Series

Semiconductors, crucial to modern electronics, are generally under-researched in foundational models. It highlights the need for research to enhance the semiconductor device technology portfolio and aid in high-end device fabrication. In this paper, we introduce sLAVA, a small-scale vision-language assistant tailored for semiconductor manufacturing, with a focus on electron microscopy image analysis. It addresses challenges of data scarcity and acquiring high-quality, expert-annotated data. We employ a teacher-student paradigm, using a foundational vision language model like GPT-4 as a teacher to create instruction-following multimodal data for customizing the student model, sLAVA, for electron microscopic image analysis tasks on consumer hardware with limited budgets. Our approach allows enterprises to further fine-tune the proposed framework with their proprietary data securely within their own infrastructure, protecting intellectual property. Rigorous experiments validate that our framework surpasses traditional methods, handles data shifts, and enables high-throughput screening., Comment: Paper published at ICML 2024 Workshop on Foundation Models in the Wild

In the field of chemistry, the objective is to create novel molecules with desired properties, facilitating accurate property predictions for applications such as material design and drug screening. However, existing graph deep learning methods face limitations that curb their expressive power. To address this, we explore the integration of vast molecular domain knowledge from Large Language Models (LLMs) with the complementary strengths of Graph Neural Networks (GNNs) to enhance performance in property prediction tasks. We introduce a Multi-Modal Fusion (MMF) framework that synergistically harnesses the analytical prowess of GNNs and the linguistic generative and predictive abilities of LLMs, thereby improving accuracy and robustness in predicting molecular properties. Our framework combines the effectiveness of GNNs in modeling graph-structured data with the zero-shot and few-shot learning capabilities of LLMs, enabling improved predictions while reducing the risk of overfitting. Furthermore, our approach effectively addresses distributional shifts, a common challenge in real-world applications, and showcases the efficacy of learning cross-modal representations, surpassing state-of-the-art baselines on benchmark datasets for property prediction tasks., Comment: Paper Accepted at Workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models at NeurIPS 2023

Spatio-temporal forecasting plays a crucial role in various sectors such as transportation systems, logistics, and supply chain management. However, existing methods are limited by their ability to handle large, complex datasets. To overcome this limitation, we introduce a hybrid approach that combines the strengths of open-source large and small-scale language models (LLMs and LMs) with traditional forecasting methods. We augment traditional methods with dynamic prompting and a grouped-query, multi-head attention mechanism to more effectively capture both intra-series and inter-series dependencies in evolving nonlinear time series data. In addition, we facilitate on-premises customization by fine-tuning smaller open-source LMs for time series trend analysis utilizing descriptions generated by open-source large LMs on consumer-grade hardware using Low-Rank Adaptation with Activation Memory Reduction (LoRA-AMR) technique to reduce computational overhead and activation storage memory demands while preserving inference latency. We combine language model processing for time series trend analysis with traditional time series representation learning method for cross-modal integration, achieving robust and accurate forecasts. The framework effectiveness is demonstrated through extensive experiments on various real-world datasets, outperforming existing methods by significant margins in terms of forecast accuracy., Comment: Paper published at the Deployable AI (DAI) workshop at AAAI-2024

Does $20$ have a friend? Or is it a solitary number? A folklore conjecture asserts that $20$ has no friends i.e. it is a solitary number. In this article, we prove that, a friend $N$ of $20$ is of the form $N=2\cdot5^{2a}m^2$ and it has atleast six distinct prime divisors. Also we prove that $N$ must be atleast $2\cdot 10^{12}$. Furthermore, we show that $\Omega(N)\geq 2\omega(N)+6a-5$ and if $\Omega(m)\leq K$ then $N< 10\cdot 6^{(2^{K-2a+3}-1)^2}$, where $\Omega(n)$ and $\omega(n)$ denote the total number of prime divisors and the number of distinct prime divisors of the integer $n$ respectively. In addition, we deduce that, not all exponents of odd prime divisors of friend $N$ of $20$ are congruent to $-1$ modulo $f$, where $f$ is the order of $5$ in $(\mathbb{Z}/p\mathbb{Z})^\times$ such that $3\mid f$ and $p$ is a prime congruent to $1$ modulo $6$., Comment: 24 pages, 5 figures, Suggestions are welcomed

In the chemical and process industries, Process Flow Diagrams (PFDs) and Piping and Instrumentation Diagrams (P&IDs) are critical for design, construction, and maintenance. Recent advancements in Generative AI, such as Large Multimodal Models (LMMs) like GPT4 (Omni), have shown promise in understanding and interpreting process diagrams for Visual Question Answering (VQA). However, proprietary models pose data privacy risks, and their computational complexity prevents knowledge editing for domain-specific customization on consumer hardware. To overcome these challenges, we propose a secure, on-premises enterprise solution using a hierarchical, multi-agent Retrieval Augmented Generation (RAG) framework for open-domain question answering (ODQA) tasks, offering enhanced data privacy, explainability, and cost-effectiveness. Our novel multi-agent framework employs introspective and specialized sub-agents using open-source, small-scale multimodal models with the ReAct (Reason+Act) prompting technique for PFD and P&ID analysis, integrating multiple information sources to provide accurate and contextually relevant answers. Our approach, supported by iterative self-correction, aims to deliver superior performance in ODQA tasks. We conducted rigorous experimental studies, and the empirical results validated the proposed approach effectiveness., Comment: Our paper is accepted for publication at ML4CCE workshop at ECML PKDD 2024

Characterizing materials with electron micrographs is a crucial task in fields such as semiconductors and quantum materials. The complex hierarchical structure of micrographs often poses challenges for traditional classification methods. In this study, we propose an innovative backbone architecture for analyzing electron micrographs. We create multi-modal representations of the micrographs by tokenizing them into patch sequences and, additionally, representing them as vision graphs, commonly referred to as patch attributed graphs. We introduce the Hierarchical Network Fusion (HNF), a multi-layered network structure architecture that facilitates information exchange between the multi-modal representations and knowledge integration across different patch resolutions. Furthermore, we leverage large language models (LLMs) to generate detailed technical descriptions of nanomaterials as auxiliary information to assist in the downstream task. We utilize a cross-modal attention mechanism for knowledge fusion across cross-domain representations(both image-based and linguistic insights) to predict the nanomaterial category. This multi-faceted approach promises a more comprehensive and accurate representation and classification of micrographs for nanomaterial identification. Our framework outperforms traditional methods, overcoming challenges posed by distributional shifts, and facilitating high-throughput screening., Comment: Our paper is published at the workshop on Robustness of Few-shot and Zero-shot Learning in Foundation Models at NeurIPS 2023

Spatio-temporal forecasting is crucial in transportation, logistics, and supply chain management. However, current methods struggle with large, complex datasets. We propose a dynamic, multi-modal approach that integrates the strengths of traditional forecasting methods and instruction tuning of small language models for time series trend analysis. This approach utilizes a mixture of experts (MoE) architecture with parameter-efficient fine-tuning (PEFT) methods, tailored for consumer hardware to scale up AI solutions in low resource settings while balancing performance and latency tradeoffs. Additionally, our approach leverages related past experiences for similar input time series to efficiently handle both intra-series and inter-series dependencies of non-stationary data with a time-then-space modeling approach, using grouped-query attention, while mitigating the limitations of traditional forecasting techniques in handling distributional shifts. Our approach models predictive uncertainty to improve decision-making. Our framework enables on-premises customization with reduced computational and memory demands, while maintaining inference speed and data privacy/security. Extensive experiments on various real-world datasets demonstrate that our framework provides robust and accurate forecasts, significantly outperforming existing methods., Comment: Published at the ICLR 2024 Workshop on Practical ML for Low Resource Settings(PML4LRS)