Miracle-dz/UniFAR-FTUs huggingface dataset:
Dataset Summarization by K Principal Concepts

We propose the new task of K principal concept identiﬁcation for dataset summarizarion. The objective is to ﬁnd a set of K concepts that best explain the variation within the dataset. Concepts are high-level human interpretable terms such as “tiger”, “kayaking” or “happy”. The K concepts are selected from a (potentially long) input list of candidates, which we denote the concept-bank . The concept-bank may be taken from a generic dictionary or constructed by task-speciﬁc prior knowledge. An image-language embedding method (e.g. CLIP) is used to map the images and the concept-bank into a shared feature space. To select the K concepts that best explain the data, we formulate our problem as a K -uncapacitated facility location problem. An efﬁcient optimization technique is used to scale the local search algorithm to very large concept-banks. The output of our method is a set of K principal concepts that summarize the dataset. Our approach provides a more explicit summary in comparison to selecting K representative images, which are often ambiguous. As a further application of our method, the K principal concepts can be used to classify the dataset into K groups. Extensive experiments demonstrate the efﬁcacy of our approach. bichon frise, bird dog, bloodhound, border collie, borzoi, Boston terrier, boxer, breed, briard, Brittany, bull terrier, bulldog, bullmastiff, cairn terrier, Cape hunting dog, chihuahua, Chinese crested dog, chow chow, cocker spaniel, collie, companion dog, coon hound, corgi, cur, dachshund, Dalmatian, dhole, dingo, Doberman pinscher, dog, elkhound, feist, ﬁghting dog, fox terrier, foxhound, German shepherd, golden retriever, great Dane, great Pyrenees, greyhound, growl, guard dog, gun dogs, harrier, herding dog, hound, hunting dog, husky, Irish setter, Jack Russell terrier, keeshond, kerry blue terrier, King spaniel, Labrador retriever, lap dog, Lhasa apso, malamute, Maltese, mastiff, Mexican mutt, Newfoundland, Norfolk terrier, old English sheepdog, papillon, pedigree, pekingese, pinscher, pit bull, pointer, police dog, poodle, Portuguese water dog, pug, pup, puppy, purebred, rat terrier, rescue dog, retriever, Rhodesian Rottweiler, Saluki, samoyed, scent hound, schnauzer, Scottish terrier, search-and-rescue dog, service dog, setter, Siberian husky, sighthound, sled dog, spaniel, spitz, springer spaniel, corgi, terrier, Westie,
Unveiling the Real Performance of LPDDR5 Memories

LPDDR5 is the latest low-power DRAM standard and expected to be used in various application fields. The vendors have published promising peak bandwidths up to 50 % higher than those of the predecessor LPDDR4. In this paper we evaluate the best-case and worst-case real bandwidth utilization of different LPDDR5 configurations and compare the results to corresponding LPDDR4 configurations. We also show that an upgrade from LPDDR4 to LPDDR5 does not always bring a bandwidth advantage and that some LPDDR5 configurations should be avoided for specific workloads.
MML Probabilistic Principal Component Analysis

Principal component analysis (PCA) is perhaps the most widely method for data dimensionality reduction. A key question in PCA decomposition of data is deciding how many factors to retain. This manuscript describes a new approach to automatically selecting the number of principal components based on the Bayesian minimum message length method of inductive inference. We also derive a new estimate of the isotropic residual variance and demonstrate, via numerical experiments, that it improves on the usual maximum likelihood approach.
PnP-ReG: Learned Regularizing Gradient for Plug-and-Play Gradient Descent

The Plug-and-Play (PnP) framework makes it possible to integrate advanced image denoising priors into optimization algorithms, to efficiently solve a variety of image restoration tasks generally formulated as Maximum A Posteriori (MAP) estimation problems. The Plug-and-Play alternating direction method of multipliers (ADMM) and the Regularization by Denoising (RED) algorithms are two examples of such methods that made a breakthrough in image restoration. However, while the former method only applies to proximal algorithms, it has recently been shown that there exists no regularization that explains the RED algorithm when the denoisers lack Jacobian symmetry, which happen to be the case of most practical denoisers. To the best of our knowledge, there exists no method for training a network that directly represents the gradient of a regularizer, which can be directly used in Plug-and-Play gradient-based algorithms. We show that it is possible to train a network directly modeling the gradient of a MAP regularizer while jointly training the corresponding MAP denoiser. We use this network in gradient-based optimization methods and obtain better results comparing to other generic Plug-and-Play approaches. We also show that the regularizer can be used as a pre-trained network for unrolled gradient descent. Lastly, we show that the resulting denoiser allows for a better convergence of the Plug-and-Play ADMM.
The Relation between Radio Polarization and Gamma-ray Emission in AGN Jets

We have compared the parsec-scale jet linear polarization properties of the Fermi LAT-detected and non-detected sources in the complete flux-density-limited (MOJAVE-1) sample of highly beamed AGN. Of the 123 MOJAVE sources, 30 were detected by the LAT during its first three months of operation. We find that during the era since the launch of Fermi, the unresolved core components of the LAT-detected jets have significantly higher median fractional polarization at 15 GHz. This complements our previous findings that these LAT sources have higher apparent jet speeds, brightness temperatures and Doppler factors, and are preferentially found in higher activity states.
The effect of particle anisotropy on the modulation of turbulent flows

Abstract We investigate the modulation of turbulence caused by the presence of finite-size dispersed particles. Bluff (isotropic) spheres versus slender (anisotropic) fibres are considered to understand the influence of the shape of the objects on altering the carrier flow. While at a fixed mass fraction – but different Stokes number – both objects provide a similar bulk effect characterized by a large-scale energy depletion, a scale-by-scale analysis of the energy transfer reveals that the alteration of the whole spectrum is intrinsically different. For bluff objects, the classical energy cascade shrinks in its extension but is unaltered in the energy content and its typical features, while for slender ones we find an alternative energy flux which is essentially mediated by the fluid–solid coupling.
Ferromagnetism on an atom-thick and extended 2D-metal-organic framework

Ferromagnetism (FM) is the cornerstone of permanent magnets, data storage and other technologies that directly impact our everyday life by their implementation in standard applications and devices. When downscaling bulk materials into their two-dimensional (2D) magnetic isotropic form, the Mermin-Wagner theorem precludes this collective state mediated by short-range exchange interactions at finite temperatures. Interestingly, this prediction fails when significant magnetic anisotropy is present in the material, as recently demonstrated in single layered van der Waals crystals. Before the latter, single layer metal-organic frameworks (MOFs) grown on metallic supports were one of the earliest candidates for achieving 2D-FM. Such high expectations were based on the chemical and spacing control of the 2D-MOF magnetic centers, the tunability of the organic linkers and the rich self-assembled architectures displayed. However, despite many attempts, extended FM in 2D-MOFs has been experimentally elusive. In this work, we demonstrate that extended, cooperative FM takes place in an atom thick 2D-MOF consisting of 9,10-dicyanoanthracene (DCA) molecules and Fe adatoms grown on Au(111). We show this by means of an experimental multitechnique approach that is endorsed by state-of-the art first-principles calculations. Particularly, this 2D ferromagnet follows a first order transition with TC ~ 35 K, which is driven by exchange interactions mainly through the molecular linkers (J=2 meV) and exhibits an out-of-plane square-like hysteresis loop. The strict periodicity of our 2D-MOF allows us to envision the fabrication of ultra-dense single atom magnetic memories and opens the way to explore periodic magnetic 2D-models that could considerably increase the fundamental superparamagnetic limit.
Sentiment Analysis on Inflation after COVID-19

We implement traditional machine learning and deep learning methods for global tweets from 2017-2022 to build a high-frequency measure of the public’s sentiment index on inflation and analyze its correlation with other online data sources such as google trend and market-oriented inflation index. We use manually labeled trigrams to test the prediction performance of several machine learning models (logistic regression, random forest etc.) and choose Bert model for final demonstration. Then, we sum daily tweets’ sentiment scores gained from Bert model to obtain the predicted inflation sentiment index, and we further analyze the regional and pre/post covid patterns of these inflation indexes. Lastly, we take other empirical inflation-related data as references and prove that twitter-based inflation sentiment analysis method has an outstanding capability to predict inflation. The results suggest that Twitter combined with deep learning methods can be a novel and timely method to utilize existing abundant data sources on inflation expectations and provide daily indicators of consumers’ perception on inflation.
CoLo: A Contrastive Learning Based Re-ranking Framework for One-Stage Summarization

Traditional training paradigms for extractive and abstractive summarization systems always only use token-level or sentence-level training objectives. However, the output summary is always evaluated from summary-level which leads to the inconsistency in training and evaluation. In this paper, we propose a Contrastive Learning based re-ranking framework for one-stage summarization called CoLo. By modeling a contrastive objective, we show that the summarization model is able to directly generate summaries according to the summary-level score without additional modules and parameters. Extensive experiments demonstrate that CoLo boosts the extractive and abstractive results of one-stage systems on CNN/DailyMail benchmark to 44.58 and 46.33 ROUGE-1 score while preserving the parameter efficiency and inference efficiency. Compared with state-of-the-art multi-stage systems, we save more than 100 GPU training hours and obtaining 3x 8x speed-up ratio during inference while maintaining comparable results.
TERMinator: A System for Scientific Texts Processing

This paper is devoted to the extraction of entities and semantic relations between them from scientific texts, where we consider scientific terms as entities. In this paper, we present a dataset that includes annotations for two tasks and develop a system called TERMinator for the study of the influence of language models on term recognition and comparison of different approaches for relation extraction. Experiments show that language models pre-trained on the target language are not always show the best performance. Also adding some heuristic approaches may improve the overall quality of the particular task. The developed tool and the annotated corpus are publicly available at https://github.com/iis-research-team/terminator and may be useful for other researchers.
Canonical bases for cluster algebras

In [GHK11], Conjecture 0.6, the first three authors conjectured that the ring of regular functions on a natural class of affine log Calabi-Yau varieties (those with maximal boundary) has a canonical vector space basis parameterized by the integral tropical points of the mirror. Further, the structure constants for the multiplication rule in this basis should be given by counting broken lines (certain combinatorial objects, morally the tropicalisations of holomorphic discs). Here we prove the conjecture in the case of cluster varieties, where the statement is a more precise form of the Fock-Goncharov dual basis conjecture, [FG06], Conjecture 4.3. In particular, under suitable hypotheses, for each Y the partial compactification of an affine cluster variety U given by allowing some frozen variables to vanish, we obtain canonical bases for H0(Y,OY ) extending to a basis of H0(U,OU ). Each choice of seed canonically identifies the parameterizing sets of these bases with integral points in a polyhedral cone. These results specialize to basis results of combinatorial representation theory. For example, by considering the open double Bruhat cell U in the basic affine space Y we obtain a canonical basis of each irreducible representation of SLr, parameterized by a set which each choice of seed identifies with integral points of a lattice polytope. These bases and polytopes are all constructed essentially without representation theoretic considerations. Along the way, our methods prove a number of conjectures in cluster theory, including positivity of the Laurent phenomenon for cluster algebras of geometric type.
Generalized matrix nearness problems

We show that the global minimum solution of $\lVert A - BXC \rVert$ can be found in closed-form with singular value decompositions and generalized singular value decompositions for a variety of constraints on $X$ involving rank, norm, symmetry, two-sided product, and prescribed eigenvalue. This extends the solution of Friedland--Torokhti for the generalized rank-constrained approximation problem to other constraints as well as provides an alternative solution for rank constraint in terms of singular value decompositions. For more complicated constraints on $X$ involving structures such as Toeplitz, Hankel, circulant, nonnegativity, stochasticity, positive semidefiniteness, prescribed eigenvector, etc, we prove that a simple iterative method is linearly and globally convergent to the global minimum solution.
RALACs: Action Recognition in Autonomous Vehicles using Interaction Encoding and Optical Flow

When applied to autonomous vehicle (AV) settings, action recognition can enhance an environment model's situational awareness. This is especially prevalent in scenarios where traditional geometric descriptions and heuristics in AVs are insufficient. However, action recognition has traditionally been studied for humans, and its limited adaptability to noisy, un-clipped, un-pampered, raw RGB data has limited its application in other fields. To push for the advancement and adoption of action recognition into AVs, this work proposes a novel two-stage action recognition system, termed RALACs. RALACs formulates the problem of action recognition for road scenes, and bridges the gap between it and the established field of human action recognition. This work shows how attention layers can be useful for encoding the relations across agents, and stresses how such a scheme can be class-agnostic. Furthermore, to address the dynamic nature of agents on the road, RALACs constructs a novel approach to adapting Region of Interest (ROI) Alignment to agent tracks for downstream action classification. Finally, our scheme also considers the problem of active agent detection, and utilizes a novel application of fusing optical flow maps to discern relevant agents in a road scene. We show that our proposed scheme can outperform the baseline on the ICCV2021 Road Challenge dataset and by deploying it on a real vehicle platform, we provide preliminary insight to the usefulness of action recognition in decision making.
Cosmological constraints on the DGP braneworld model with gamma-ray bursts

We investigate observational constraints on the Dvali, Gabadadze and Porrati (DGP) model with Gamma-ray bursts (GRBs) at high redshift obtained directly from the Union2 Type Ia supernovae data set. With cosmology-independent GRBs, the Union2 set, as well as the cosmic microwave background observation from the WMAP7 result, the baryon acoustic oscillation, the baryon mass fraction in clusters and the observed H(z) data, we obtain that the best-fit values of the DGP model are {Ω M0 , Ω rc } = {0.235 +0.015 -0.014 , 0.138 +0.051 -0.048 }, which favor a flat universe, and the -0.014 -0.048 transition redshift of the DGP model is z T = 0.67 +0.03 -0.04 . These results lead to more stringent constraints than the previous results for the DGP model.
Bayesian Neural Network Versus Ex-Post Calibration For Prediction Uncertainty

Probabilistic predictions from neural networks which account for predictive uncertainty during classification is crucial in many real-world and high-impact decision making settings. However, in practice most datasets are trained on non-probabilistic neural networks which by default do not capture this inherent uncertainty. This well-known problem has led to the development of post-hoc calibration procedures, such as Platt scaling (logistic), isotonic and beta calibration, which transforms the scores into well calibrated empirical probabilities. A plausible alternative to the calibration approach is to use Bayesian neural networks, which directly models a predictive distribution. Although they have been applied to images and text datasets, they have seen limited adoption in the tabular and small data regime. In this paper, we demonstrate that Bayesian neural networks yields competitive performance when compared to calibrated neural networks and conduct experiments across a wide array of datasets.
Diffusion Adversarial Representation Learning for Self-supervised Vessel Segmentation

Vessel segmentation in medical images is one of the important tasks in the diagnosis of vascular diseases and therapy planning. Although learning-based segmentation approaches have been extensively studied, a large amount of ground-truth labels are required in supervised methods and confusing background structures make neural networks hard to segment vessels in an unsupervised manner. To address this, here we introduce a novel diffusion adversarial representation learning (DARL) model that leverages a denoising diffusion probabilistic model with adversarial learning, and apply it to vessel segmentation. In particular, for self-supervised vessel segmentation, DARL learns the background signal using a diffusion module, which lets a generation module effectively provide vessel representations. Also, by adversarial learning based on the proposed switchable spatially-adaptive denormalization, our model estimates synthetic fake vessel images as well as vessel segmentation masks, which further makes the model capture vessel-relevant semantic information. Once the proposed model is trained, the model generates segmentation masks in a single step and can be applied to general vascular structure segmentation of coronary angiography and retinal images. Experimental results on various datasets show that our method significantly outperforms existing unsupervised and self-supervised vessel segmentation methods.
Enhanced Hong-Ou-Mandel manifolds and figures of merit for linear chains of identical microring resonators

We present an exact analytic expression for the Hong-Ou-Mandel (HOM) curve for any number of identical Micro-Ring Resonators (MRRs) in a linear chain. We investigate the extreme stability of this HOM curve, showing that the HOM effect in linear arrays of MRRs is highly robust. We further use this expression to derive three figures of merit for the HOM curve of linear chains of MRRs: the minimum tau value ($\tau_{c}$), the curvature ($\bar{\xi}_N$), and the $5\%$ tolerance in tau ($\delta\tau_{N}$). We promote these metrics to characterize the pros and cons of various linear chains of MRRs and inform design and fabrication.
Visual Detection of Diver Attentiveness for Underwater Human-Robot Interaction

Many underwater tasks, such as cable-and-wreckage inspection, search-and-rescue, benefit from robust human-robot interaction (HRI) capabilities. With the recent advancements in vision-based underwater HRI methods, autonomous underwater vehicles (AUVs) can communicate with their human partners even during a mission. However, these interactions usually require active participation especially from humans (e.g., one must keep looking at the robot during an interaction). Therefore, an AUV must know when to start interacting with a human partner, i.e., if the human is paying attention to the AUV or not. In this paper, we present a diver attention estimation framework for AUVs to autonomously detect the attentiveness of a diver and then navigate and reorient itself, if required, with respect to the diver to initiate an interaction. The core element of the framework is a deep neural network (called DATT-Net) which exploits the geometric relation among 10 facial keypoints of the divers to determine their head orientation. Our on-the-bench experimental evaluations (using unseen data) demonstrate that the proposed DATT-Net architecture can determine the attentiveness of human divers with promising accuracy. Our real-world experiments also confirm the efficacy of DATT-Net which enables real-time inference and allows the AUV to position itself for an AUV-diver interaction.
Scalably learning quantum many-body Hamiltonians from dynamical data

The physics of a closed quantum mechanical system is governed by its Hamiltonian. However, in most practical situations, this Hamiltonian is not precisely known, and ultimately all there is are data obtained from measurements on the system. In this work, we introduce a highly scalable, data-driven approach to learning families of interacting many-body Hamiltonians from dynamical data, by bringing together techniques from gradient-based optimization from machine learning with efﬁcient quantum state representations in terms of tensor networks. Our approach is highly practical, experimentally friendly, and intrinsically scalable to allow for system sizes of above 100 spins. In particular, we demonstrate on synthetic data that the algorithm works even if one is restricted to one simple initial state, a small number of single-qubit observables, and time evolution up to relatively short times. For the concrete example of the one-dimensional Heisenberg model our algorithm exhibits an error constant in the system size and scaling as the inverse square root of the size of the data set.
Spiking Neural Networks for event-based action recognition: A new task to understand their advantage

Spiking Neural Networks (SNN) are characterised by their unique temporal dynamics, but the properties and advantages of such computations are still not well understood. In order to provide answers, in this work we demonstrate how Spiking neurons can enable temporal feature extraction in feed-forward neural networks without the need for recurrent synapses, showing how their bio-inspired computing principles can be successfully exploited beyond energy efficiency gains and evidencing their differences with respect to conventional neurons. This is demonstrated by proposing a new task, DVS-Gesture-Chain (DVS-GC), which allows, for the first time, to evaluate the perception of temporal dependencies in a real event-based action recognition dataset. Our study proves how the widely used DVS Gesture benchmark could be solved by networks without temporal feature extraction, unlike the new DVS-GC which demands an understanding of the ordering of the events. Furthermore, this setup allowed us to unveil the role of the leakage rate in spiking neurons for temporal processing tasks and demonstrated the benefits of"hard reset"mechanisms. Additionally, we also show how time-dependent weights and normalization can lead to understanding order by means of temporal attention.