Miracle-dz/UniFAR-FTUs huggingface dataset:
Coarse embeddability of Wasserstein space and the space of persistence diagrams

We prove an equivalence between open questions about the embeddability of the space of persistence diagrams and the space of probability distributions (i.e.~Wasserstein space). It is known that for many natural metrics, no coarse embedding of either of these two spaces into Hilbert space exists. Some cases remain open, however. In particular, whether coarse embeddings exist with respect to the $p$-Wasserstein distance for $1\leq p\leq 2$ remains an open question for the space of persistence diagrams and for Wasserstein space on the plane. In this paper, we show that embeddability for persistence diagrams implies embeddability for Wasserstein space on $\mathbb{R}^2$, with the converse holding when $p>1$. To prove this, we show that finite subsets of Wasserstein space uniformly coarsely embed into the space of persistence diagrams, and vice versa (when $p>1$).
Adaptive RRI Selection Algorithms for Improved Cooperative Awareness in Decentralized NR-V2X

Decentralized vehicle-to-everything (V2X) networks (i.e., C-V2X Mode-4 and NR-V2X Mode-2) utilize sensing-based semi-persistent scheduling (SPS) where vehicles sense and reserve suitable radio resources for Basic Safety Message (BSM) transmissions at prespecified periodic intervals termed as Resource Reservation Interval (RRI). Vehicles rely on these received periodic BSMs to localize nearby (transmitting) vehicles and infrastructure, referred to as cooperative awareness. Cooperative awareness enables line of sight and non-line of sight localization, extending a vehicle’s sensing and perception range. In this work, we first show that under high vehicle density scenarios, existing SPS (with prespecified RRIs) suffer from poor cooperative awareness, quantified as tracking error. Tracking error is defined as the difference between a vehicle’s true and estimated location as measured by its neighbors. To address the issues of static RRI SPS and improve cooperative awareness, we propose two novel RRI selection algorithms– namely, Channel-aware RRI (Ch-RRI) selection and Age of Information (AoI)-aware RRI (AoI-RRI) selection. Ch-RRI dynamically selects an RRI based on channel resource availability depending upon the (sparse or dense) vehicle densities, whereas AoI-RRI utilizes a novel information freshness metric, called Age of Information (AoI) to select a suitable RRI. Both adaptive RRI algorithms use SPS for selecting transmission opportunities for timely BSM transmissions at the chosen RRI. System-level simulations demonstrate that both proposed schemes outperform the SPS with fixed RRI in terms of improved cooperative awareness. Furthermore, AoI-RRI SPS outperforms Ch-RRI SPS in high densities, whereas Ch-RRI SPS is slightly better than AoI-RRI SPS in low densities.
Identifying the discs, bulges, and intra-halo light of simulated galaxies through structural decomposition

We perform a structural decomposition of galaxies identified in three cosmological hydrodynamical simulations by applying Gaussian Mixture Models (GMMs) to the kinematics of their stellar particles. We study the resulting disc, bulge, and intra-halo light (IHL) components of galaxies whose host dark matter haloes have virial masses in the range M200 = 1011– 1015 M⊙. Our decomposition technique isolates galactic discs whose mass fractions, fdisc, correlate strongly with common alternative morphology indicators; for example, fdisc is approximately equal to κco, the fraction of stellar kinetic energy in co-rotation. The primary aim of our study, however, is to characterise the IHL of galaxies in a consistent manner and over a broad mass range, and to analyse its properties from the scale of galactic stellar haloes up to the intra-cluster light. Our results imply that the IHL fraction, fIHL, has appreciable scatter and is strongly correlated with galaxy morphology: at fixed stellar mass, the IHL of disc galaxies is typically older and less massive than that of spheroids. Above M200 ≈ 1013 M⊙, we find, on average, fIHL ≈ 0.37, albeit with considerable scatter. The transition radius beyond which the IHL dominates the stellar mass of a galaxy is roughly 30 kpc for disc galaxies, but depends strongly on halo mass for spheroids. However, we find that no alternative IHL definitions – whether based on the ex-situ stellar mass, or the stellar mass outside a spherical aperture – reproduce our dynamically-defined IHL masses.
A dynamic construal approach to antonymy

Based on textual and psycholinguistic data, this article presents a Cognitive Semantic treatment of antonymy. It argues that antonymy is a conceptual construal, involving a contentful meaning dimension and a BOUNDED configuration. Form- meaning pairings are antonyms when they are used in binary contrast in discourse. In this respect, all antonym construals are on an equal footing, but nevertheless different lexico-semantic pairings have different levels of lexico-semantic affinity due to factors such as the salience of the contentful dimension, the configuration of the members that form part of the opposition, the symmetry of the members in relation to the BOUNDARY, contextual versatility and frequency.
Dark energy in light of the early JWST observations: case for a negative cosmological constant?

Early data from the James Webb Space Telescope (JWST) has uncovered the existence of a surprisingly abundant population of very massive galaxies at extremely high redshift, which are hard to accommodate within the standard ΛCDM cosmology. We explore whether the JWST observations may be pointing towards more complex dynamics in the dark energy (DE) sector. Motivated by the ubiquity of anti-de Sitter vacua in string theory, we consider a string-inspired scenario where the DE sector consists of a negative cosmological constant (nCC) and a evolving component with positive energy density on top, whose equation of state is allowed to cross the phantom divide. We show that such a scenario can drastically alter the growth of structure compared to ΛCDM, and accommodate the otherwise puzzling JWST observations if the dynamical component evolves from the quintessence-like regime in the past to the phantom regime today: in particular, we demonstrate that the presence of a nCC (which requires a higher density for the evolving component) plays a crucial role in enhancing the predicted cumulative comoving stellar mass density. Our work reinforces the enormous potential held by observations of the abundance of high-z galaxies in probing cosmological models and new fundamental physics, including string-inspired ingredients.
Resilient Scheduling of Control Software Updates in Radial Power Distribution Systems

In response to newly found security vulnerabilities, or as part of a moving target defense, a fast and safe control software update scheme for networked control systems is highly desirable. We here develop such a scheme for intelligent electronic devices (IEDs) in power distribution systems, which is a solution to the so-called software update rollout problem. This problem seeks to minimize the makespan of the software rollout, while guaranteeing safety in voltage and current at all buses and lines despite possible worst-case update failure where malfunctioning IEDs may inject harmful amounts of power into the system. Based on the nonlinear DistFlow equations, we derive linear relations relating software update decisions to the worst-case voltages and currents, leading to a decision model both tractable and more accurate than previous models based on the popular linearized DistFlow equations. Under reasonable protection assumptions, the rollout problem can be formulated as a vector bin packing problem and instances can be built and solved using scalable computations. Using realistic benchmarks including one with 10,476 buses, we demonstrate that the proposed method can generate safe and effective rollout schedules in real-time.
Interiors of singularity-free rotating black holes

General Relativity provides us with some solutions for rotating black holes. However, there are some problems associated with them: the appearance of singularities, the possibility of violations of the cosmic censorship conjecture, the existence of regions where the mass acts repulsively and the violation of causality. Many authors consider that these problems reveal the existence of certain limits in the applicability of General Relativity. For instance, it is believed that the same existence of singularities in the classical black hole solutions is a weakness of the theory and that a full Quantum Gravity Theory would provide us with singularity-free black hole models. In this paper, the generic properties of the interiors of singularity-free rotating black holes are analyzed. Remarkably, it is shown that they are devoid of any of the aforementioned problems of the classical solutions.
Estimate-Then-Optimize versus Integrated-Estimation-Optimization versus Sample Average Approximation: A Stochastic Dominance Perspective

In data-driven stochastic optimization, model parameters of the underlying distribution need to be estimated from data in addition to the optimization task. Recent literature considers integrating the estimation and optimization processes by selecting model parameters that lead to the best empirical objective performance. This integrated approach, which we call integrated-estimation-optimization (IEO), can be readily shown to outperform simple estimate-then-optimize (ETO) when the model is misspecified. In this paper, we show that a reverse behavior appears when the model class is well-specified and there is sufficient data. Specifically, for a general class of nonlinear stochastic optimization problems, we show that simple ETO outperforms IEO asymptotically when the model class covers the ground truth, in the strong sense of stochastic dominance of the regret. Namely, the entire distribution of the regret, not only its mean or other moments, is always better for ETO compared to IEO. Our results also apply to constrained, contextual optimization problems where the decision depends on observed features. Whenever applicable, we also demonstrate how standard sample average approximation (SAA) performs the worst when the model class is well-specified in terms of regret, and best when it is misspecified. Finally, we provide experimental results to support our theoretical comparisons and illustrate when our insights hold in finite-sample regimes and under various degrees of misspecification.
Learning-Augmented B-Trees

We study learning-augmented binary search trees (BSTs) and B-Trees via Treaps with composite priorities. The result is a simple search tree where the depth of each item is determined by its predicted weight $w_x$. To achieve the result, each item $x$ has its composite priority $-\lfloor\log\log(1/w_x)\rfloor + U(0, 1)$ where $U(0, 1)$ is the uniform random variable. This generalizes the recent learning-augmented BSTs [Lin-Luo-Woodruff ICML`22], which only work for Zipfian distributions, to arbitrary inputs and predictions. It also gives the first B-Tree data structure that can provably take advantage of localities in the access sequence via online self-reorganization. The data structure is robust to prediction errors and handles insertions, deletions, as well as prediction updates.
The phase transition for the Gaussian free field is sharp

We prove that the phase transition for the Gaussian free field (GFF) is sharp. In comparison to a previous argument due to Rodriguez in 2017 which characterized a $0-1$ law for the Massive Gaussian Free Field by analyzing crossing probabilities below a threshold $h_{**}$, we implement a strategy due to Duminil-Copin and Manolescu in 2016, which establishes that two parameters are equal, one of which encapsulates the probability of obtaining an infinite connected component under free boundary conditions, while the other encapsulates the natural logarithm of the probability of obtaining a connected component from the origin to the box of length $n$ which is also taken under free boundary conditions. We quantify the probability of obtaining crossings in easy and hard directions, without imposing conditions that the graph is invariant with respect to reflections, in addition to making use of a differential inequality adapted for the GFF. The sharpness of the phase transition is characterized by the fact that below a certain height parameter of the GFF, the probability of obtaining an infinite cluster a.s. decays exponentially fast, while above the parameter, the probability of obtaining an infinite cluster occurs a.s. with good probability.
On the Connection between Pre-training Data Diversity and Fine-tuning Robustness

Pre-training has been widely adopted in deep learning to improve model performance, especially when the training data for a target task is limited. In our work, we seek to understand the implications of this training strategy on the generalization properties of downstream models. More specifically, we ask the following question: how do properties of the pre-training distribution affect the robustness of a fine-tuned model? The properties we explore include the label space, label semantics, image diversity, data domains, and data quantity of the pre-training distribution. We find that the primary factor influencing downstream effective robustness (Taori et al., 2020) is data quantity, while other factors have limited significance. For example, reducing the number of ImageNet pre-training classes by 4x while increasing the number of images per class by 4x (that is, keeping total data quantity fixed) does not impact the robustness of fine-tuned models. We demonstrate our findings on pre-training distributions drawn from various natural and synthetic data sources, primarily using the iWildCam-WILDS distribution shift as a test for downstream robustness.
Explainable Depression Detection via Head Motion Patterns

While depression has been studied via multimodal non-verbal behavioural cues, head motion behaviour has not received much attention as a biomarker. This study demonstrates the utility of fundamental head-motion units, termed kinemes, for depression detection by adopting two distinct approaches, and employing distinctive features: (a) discovering kinemes from head motion data corresponding to both depressed patients and healthy controls, and (b) learning kineme patterns only from healthy controls, and computing statistics derived from reconstruction errors for both the patient and control classes. Employing machine learning methods, we evaluate depression classification performance on the BlackDog and AVEC2013 datasets. Our findings indicate that: (1) head motion patterns are effective biomarkers for detecting depressive symptoms, and (2) explanatory kineme patterns consistent with prior findings can be observed for the two classes. Overall, we achieve peak F1 scores of 0.79 and 0.82, respectively, over BlackDog and AVEC2013 for binary classification over episodic thin-slices, and a peak F1 of 0.72 over videos for AVEC2013.
Challenges for Monocular 6D Object Pose Estimation in Robotics

Object pose estimation is a core perception task that enables, for example, object grasping and scene understanding. The widely available, inexpensive and high-resolution RGB sensors and CNNs that allow for fast inference based on this modality make monocular approaches especially well suited for robotics applications. We observe that previous surveys on object pose estimation establish the state of the art for varying modalities, single- and multi-view settings, and datasets and metrics that consider a multitude of applications. We argue, however, that those works' broad scope hinders the identification of open challenges that are specific to monocular approaches and the derivation of promising future challenges for their application in robotics. By providing a unified view on recent publications from both robotics and computer vision, we find that occlusion handling, novel pose representations, and formalizing and improving category-level pose estimation are still fundamental challenges that are highly relevant for robotics. Moreover, to further improve robotic performance, large object sets, novel objects, refractive materials, and uncertainty estimates are central, largely unsolved open challenges. In order to address them, ontological reasoning, deformability handling, scene-level reasoning, realistic datasets, and the ecological footprint of algorithms need to be improved.
Concept-Based Explainability for an EEG Transformer Model

Deep learning models are complex due to their size, structure, and inherent randomness in training procedures. Additional complexity arises from the selection of datasets and inductive biases. Addressing these challenges for explainability, Kim et al. (2018) introduced Concept Activation Vectors (CAVs), which aim to understand deep models’ internal states in terms of human-aligned concepts. These concepts correspond to directions in latent space, identified using linear discriminants. Although this method was first applied to image classification, it was later adapted to other domains, including natural language processing. In this work, we attempt to apply the method to electroencephalogram (EEG) data for explainability in Kostas et al.’s BENDR (2021), a large-scale transformer model. A crucial part of this endeavor involves defining the explanatory concepts and selecting relevant datasets to ground concepts in the latent space. Our focus is on two mechanisms for EEG concept formation: the use of externally labeled EEG datasets, and the application of anatomically defined concepts. The former approach is a straightforward generalization of methods used in image classification, while the latter is novel and specific to EEG. We present evidence that both approaches to concept formation yield valuable insights into the representations learned by deep EEG models.
FaFCNN: A General Disease Classification Framework Based on Feature Fusion Neural Networks

There are two fundamental problems in applying deep learning/machine learning methods to disease classification tasks, one is the insufficient number and poor quality of training samples; another one is how to effectively fuse multiple source features and thus train robust classification models. To address these problems, inspired by the process of human learning knowledge, we propose the Feature-aware Fusion Correlation Neural Network (FaFCNN), which introduces a feature-aware interaction module and a feature alignment module based on domain adversarial learning. This is a general framework for disease classification, and FaFCNN improves the way existing methods obtain sample correlation features. The experimental results show that training using augmented features obtained by pre-training gradient boosting decision tree yields more performance gains than random-forest based methods. On the low-quality dataset with a large amount of missing data in our setup, FaFCNN obtains a consistently optimal performance compared to competitive baselines. In addition, extensive experiments demonstrate the robustness of the proposed method and the effectiveness of each component of the model.
Self-Supervised and Semi-Supervised Polyp Segmentation using Synthetic Data

Early detection of colorectal polyps is of utmost importance for their treatment and for colorectal cancer prevention. Computer vision techniques have the potential to aid professionals in the diagnosis stage, where colonoscopies are manually carried out to examine the entirety of the patient's colon. The main challenge in medical imaging is the lack of data, and a further challenge specific to polyp segmentation approaches is the difficulty of manually labeling the available data: the annotation process for segmentation tasks is very time-consuming. While most recent approaches address the data availability challenge with sophisticated techniques to better exploit the available labeled data, few of them explore the self-supervised or semi-supervised paradigm, where the amount of labeling required is greatly reduced. To address both challenges, we leverage synthetic data and propose an end-to-end model for polyp segmentation that integrates real and synthetic data to artificially increase the size of the datasets and aid the training when unlabeled samples are available. Concretely, our model, PI-CUT-Seg, transforms synthetic images with an image-to-image translation module and combines the resulting images with real images to train a segmentation model, where we use model predictions as pseudolabels to better leverage unlabeled samples. Additionally, we propose PL-CUT-Seg+, an improved version of the model that incorporates targeted regularization to address the domain gap between real and synthetic images. The models are evaluated on standard benchmarks for polyp segmentation and reach state-of-the-art results in the self- and semi-supervised setups.
Active Flow Control for Bluff Body Drag Reduction Using Reinforcement Learning with Partial Measurements

Active flow control for drag reduction with reinforcement learning (RL) is performed in the wake of a 2D square bluff body at laminar regimes with vortex shedding. Controllers parameterised by neural networks are trained to drive two blowing and suction jets that manipulate the unsteady flow. RL with full observability (sensors in the wake) successfully discovers a control policy which reduces the drag by suppressing the vortex shedding in the wake. However, a non-negligible performance degradation (~50% less drag reduction) is observed when the controller is trained with partial measurements (sensors on the body). To mitigate this effect, we propose an energy-efficient, dynamic, maximum entropy RL control scheme. First, an energy-efficiency-based reward function is proposed to optimise the energy consumption of the controller while maximising drag reduction. Second, the controller is trained with an augmented state consisting of both current and past measurements and actions, which can be formulated as a nonlinear autoregressive exogenous model, to alleviate the partial observability problem. Third, maximum entropy RL algorithms (Soft Actor Critic and Truncated Quantile Critics) which promote exploration and exploitation in a sample efficient way are used and discover near-optimal policies in the challenging case of partial measurements. Stabilisation of the vortex shedding is achieved in the near wake using only surface pressure measurements on the rear of the body, resulting in similar drag reduction as in the case with wake sensors. The proposed approach opens new avenues for dynamic flow control using partial measurements for realistic configurations.
DETERMINATION OF PESTICIDES IN VEGETABLES AND FRUIT FROM SUPERMARKETS OF LUCKNOW CITY USING GAS CHROMATOGRAPHY-MASS SPECTROSCOPY.

Abhishek Kumar Singh 1,2 , Anshuman Srivastava 1 , Chandra Prabha Pandey 2 and V. P sharma 3 . 1. Pesticide Toxicology Laboratory, Regulatory Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), MG Marg, Lucknow, Uttar Pradesh 226001, India. 2. Department of Chemistry, Babu Banarasi Das University, Faizabad Road, Lucknow Uttar Pradesh 226028, India. 3. Regulatory Toxicology Group, CSIR-Indian Institute of Toxicology Research (CSIR-IITR), MG Marg, Lucknow, Uttar Pradesh 226001, India. Manuscript Info Abstract . Manuscript History