Miracle-dz/UniFAR-FTUs huggingface dataset:
Active Learning by Feature Mixing

The promise of active learning (AL) is to reduce labelling costs by selecting the most valuable examples to annotate from a pool of unlabelled data. Identifying these examples is especially challenging with high-dimensional data (e.g. images, videos) and in low-data regimes. In this paper, we propose a novel method for batch AL called ALFA-Mix. We identify unlabelled instances with sufficiently-distinct features by seeking inconsistencies in predictions resulting from interventions on their representations. We construct interpolations between representations of labelled and unlabelled instances then examine the predicted labels. We show that inconsistencies in these predictions help discovering features that the model is unable to recognise in the unlabelled instances. We derive an efficient implementation based on a closed-form solution to the optimal interpolation causing changes in predictions. Our method outperforms all recent AL approaches in 30 different settings on 12 benchmarks of images, videos, and non-visual data. The improvements are especially significant in low-data regimes and on self-trained vision transformers, where ALFA-Mix outperforms the state-of-the-art in 59% and 43% of the experiments respectively11The code is available at https://github.com/aminparvaneh/alpha_mix_active_learning.
Pruned Graph Neural Network for Short Story Ordering

Text coherence is a fundamental problem in natural language generation and understanding. Organizing sentences into an order that maximizes coherence is known as sentence ordering. This paper is proposing a new approach based on the graph neural network approach to encode a set of sentences and learn orderings of short stories. We propose a new method for constructing sentence-entity graphs of short stories to create the edges between sentences and reduce noise in our graph by replacing the pronouns with their referring entities. We improve the sentence ordering by introducing an aggregation method based on majority voting of state-of-the-art methods and our proposed one. Our approach employs a BERT-based model to learn semantic representations of the sentences. The results demonstrate that the proposed method significantly outperforms existing baselines on a corpus of short stories with a new state-of-the-art performance in terms of Perfect Match Ratio (PMR) and Kendall's Tau (Tau) metrics. More precisely, our method increases PMR and Tau criteria by more than 5% and 4.3%, respectively. These outcomes highlight the benefit of forming the edges between sentences based on their cosine similarity. We also observe that replacing pronouns with their referring entities effectively encodes sentences in sentence-entity graphs.
Mobile Behavioral Biometrics for Passive Authentication

Current mobile user authentication systems based on PIN codes, fingerprint, and face recognition have several shortcomings. Such limitations have been addressed in the literature by exploring the feasibility of passive authentication on mobile devices through behavioral biometrics. In this line of research, this work carries out a comparative analysis of unimodal and multimodal behavioral biometric traits acquired while the subjects perform different activities on the phone such as typing, scrolling, drawing a number, and tapping on the screen, considering the touchscreen and the simultaneous background sensor data (accelerometer, gravity sensor, gyroscope, linear accelerometer, and magnetometer). Our experiments are performed over HuMIdb, one of the largest and most comprehensive freely available mobile user interaction databases to date. A separate Recurrent Neural Network (RNN) with triplet loss is implemented for each single modality. Then, the weighted fusion of the different modalities is carried out at score level. In our experiments, the most discriminative background sensor is the magnetometer, whereas among touch tasks the best results are achieved with keystroke in a fixed-text scenario. In all cases, the fusion of modalities is very beneficial, leading to Equal Error Rates (EER) ranging from 4% to 9% depending on the modality combination in a 3-second interval.
Cross-lingual Inference with A Chinese Entailment Graph

Predicate entailment detection is a crucial task for question-answering from text, where previous work has explored unsupervised learning of entailment graphs from typed open relation triples. In this paper, we present the first pipeline for building Chinese entailment graphs, which involves a novel high-recall open relation extraction (ORE) method and the first Chinese fine-grained entity typing dataset under the FIGER type ontology. Through experiments on the Levy-Holt dataset, we verify the strength of our Chinese entailment graph, and reveal the cross-lingual complementarity: on the parallel Levy-Holt dataset, an ensemble of Chinese and English entailment graphs outperforms both monolingual graphs, and raises unsupervised SOTA by 4.7 AUC points.
Controlling the shape and topology of two-component colloidal membranes

Significance Three-dimensional shaping of thin sheets and membranes is a ubiquitous feature of materials science and underlies diverse biological processes, ranging from large-scale morphogenesis to molecular drug delivery. It is also a familiar everyday phenomenon, as illustrated by the formation of soap bubbles. We demonstrate how flat disk–shaped colloidal membranes transform into saddle-like surfaces. As membranes coalesce, the saddle-shaped surfaces form catenoid-like structures and other architectures of increasing complexity and topological genus. The unique features of our experiments reveal the three-dimensional details of the pathway by which membranes undergo topological shape changes in real time. Our results demonstrate the topological shaping of membrane-like materials through control of their elasticity and Gaussian curvature modulus.
Similarity Equivariant Linear Transformation of Joint Orientation-Scale Space Representations

Convolution is conventionally defined as a linear operation on functions of one or more variables which commutes with shifts. Group convolution generalizes the concept to linear operations on functions of group elements representing more general geometric transformations and which commute with those transformations. Since similarity transformation is the most general geometric transformation on images that preserves shape, the group convolution that is equivariant to similarity transformation is the most general shape preserving linear operator. Because similarity transformations have four free parameters, group convolutions are defined on four-dimensional, joint orientation-scale spaces. Although prior work on equivariant linear operators has been limited to discrete groups, the similarity group is continuous. In this paper, we describe linear operators on discrete representations that are equivariant to continuous similarity transformation. This is achieved by using a basis of functions that is it joint shiftable-twistable-scalable. These pinwheel functions use Fourier series in the orientation dimension and Laplace transform in the log-scale dimension to form a basis of spatially localized functions that can be continuously interpolated in position, orientation and scale. Although this result is potentially significant with respect to visual computation generally, we present an initial demonstration of its utility by using it to compute a shape equivariant distribution of closed contours traced by particles undergoing Brownian motion in velocity. The contours are constrained by sets of points and line endings representing well known bistable illusory contour inducing patterns.
Computational intelligence approach for prediction of hardness performance in coating process

Nowadays, coated materials are widely used due to their excellent properties especially for the hardness performance. The hardness of coated tools is determined by the coating process parameters. Traditionally, optimization to obtain the best coating performance of the parameters in a coating process was done by trial and error approach. However the traditional approach has raised issues with regards to cost and customization. In this research, these two issues were addressed by using a computational intelligence approach to develop a model for predicting the output responses in order to identify the optimal parameters used in coating process. Previous studies have shown that this approach was successfully adopted for optimization purpose in many types of domains. However, it was not yet applied in the coating process domain. Thus, two methods from computational intelligence approach were applied, namely Support Vector Machine (SVM) and Artificial Neural Network (ANN). The comparisons of the performances of the developed models were conducted based on predictive performance measurements such as percentage error, mean squared error (MSE), co-efficient determination (R2), and model accuracy and complexity. The results showed that, SVM obtained better predictive performances and less complicated in comparison to other prediction models. As a conclusion, SVM has demonstrated its capability in predicting the hardness performance of coating process and outperformed the other models. Besides that, the model is a promising alternative tool for coating process optimization as compared to the traditional approach.
Towards Semantic Communications: A Paradigm Shift

The last seventy years have witnessed the transition of communication from Shannon's theoretical concept to current high-efficient practical systems. Classical communication systems address the capability-deficiency issue mainly by module-stacking and technique-densification with ever-increasing complexity. In such a traditional viewpoint, classical source coding only uses explicit probabilistic models to compress data, regardless of the meaning of transmitted source messages. Also, channel coded transmission does not identify the source content. In this sense, state-of-the-art communication systems work merely at the technical level as summarized by Weaver. Unlike the traditional system design philosophy, this article proposes a new route to boost the system capabilities towards intelligence-endogenous and primitive-concise communications. The communication paradigm upgrades to the semantic level, which is radically different since all the key techniques imply the use of meanings of transmitted data, thus deeply changing the design of the communication system. This paradigm shifting unveils a promising direction due to its ability to offer an identical quality of service with much lower data transmission requirement. Different from other similar works, this article constitutes a brief tutorial on the framework of semantic communications, its gain analyzed from the information theory perspective, a method to calculate the semantic compression bound, and an exemplary use case of semantic communications.
The baryonic-to-halo mass relation from mass and energy cascade in self-gravitating collisionless dark matter flow

The relation between properties of galaxies and dark matter (DM) halos they reside in can be valuable to understand the structure formation and evolution. In particular, the baryonic-to-halo mass ratio (BHMR) and its evolution may provide many important insights. We ﬁrst review unique properties of self-gravitating collisionless dark matter ﬂow (SG-CFD), followed by their applications in deriving BHMR. To maximize system entropy, the long-range interaction requires a broad size of halos to be formed. These halos facilitate an inverse mass and energy cascade from small to large scales that involves a constant rate of energy cascade 𝜀 𝑢 ≈ − 4 . 6 × 10 − 7 𝑚 2 / 𝑠 3 . The mass and energy cascade represent an intermediate statistically steady state of dark matter ﬂow. In addition, dark matter ﬂow exhibits scale-dependent ﬂow behaviors that is incompressible on small scale and irrotational on large scale. Considering a given halo with a total baryonic mass 𝑚 𝑏 , halo mass 𝑚 ℎ , halo virial size 𝑟 ℎ , and ﬂat rotation speed 𝑣 𝑓 , the baryonic-to-halo mass relation can be analytically derived by combining the baryonic Tully-Fisher relation and the rate of energy cascade 𝜀 𝑢 in small and large halos. We found a maximum BHMR ratio 0.076 for halos with a critical mass 𝑚 ℎ𝑐 ∼ 10 12 𝑀 (cid:12) at z=0. That ratio is much lower for both smaller and larger halos such that two regimes can be identiﬁed: i) for incompressible small halos with mass 𝑚 ℎ < 𝑚 ℎ𝑐 , we have 𝜀 𝑢 ∝ 𝑣 𝑓 / 𝑟 ℎ , 𝑣 𝑓 ∝ 𝑟 ℎ , and 𝑚 𝑏 ∝ ( 𝑚 ℎ ) 4 / 3 ; ii) for large halos with mass 𝑚 ℎ > 𝑚 ℎ𝑐 , we have 𝜀 𝑢 ∝ 𝑣 3 𝑓 / 𝑟 ℎ , 𝑣 𝑓 ∝ 𝑟 1 / 3 ℎ , and 𝑚 𝑏 ∝ ( 𝑚 ℎ ) 4 / 9 . Combined with double- λ halo mass function, the average BHMR ratio in all halos ( 0.024 at z=0) can be analytically derived, along with its redshift evolution. The fraction of total baryons in all galaxies is 7.6% at z=0 and increases with time ∝ 𝑡 1 / 3 . The SPARC (Spitzer Photometry & Accurate Rotation Curves) data with 175 late-type galaxies were used for derivation and comparison.
Weak itinerant magnetic phases of 
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La2Ni7 is an intermetallic compound that is thought to have itinerant magnetism with a small moment ordering below 65 K. A recent study of single crystal samples by Ribeiro et. al. [Phys. Rev. B 105, 014412 (2022)] determined detailed anisotropic H-T phase diagrams and revealed three zero-field magnetic phase transitions at T1 ~ 61.0 K, T2 ~ 56.5K, and T3 ~ 42 K. In that study only the highest temperature phase is shown to have a clear ferromagnetic component. Here we present a single crystal neutron diffraction study determining the propagation vector and magnetic moment direction of the three magnetically ordered phases, two incommensurate and one commensurate, as a function of temperature. The higher temperature phases have similar, incommensurate propagation vectors, but with different ordered moment directions. At lower temperatures the magnetic order becomes commensurate with magnetic moments along the c direction as part of a first-order magnetic phase transition. We find that the low-temperature commensurate magnetic order is consistent with a proposal from earlier DFT calculations.
Snowmass2021 Cosmic Frontier White Paper: Fundamental Physics and Beyond the Standard Model

Gravitational wave detectors are formidable tools to explore strong-field gravity, especially black holes and neutron stars. These compact objects are extraordinarily efficient at producing electromagnetic and gravitational radiation. As such, they are ideal laboratories for fundamental physics and have an immense discovery potential. The detection of black hole binaries by third-generation Earth-based detectors, space-based detectors and pulsar timing arrays will provide exquisite tests of general relativity. Loud"golden"events and extreme mass-ratio inspirals can strengthen the observational evidence for horizons by mapping the exterior spacetime geometry, inform us on possible near-horizon modifications, and perhaps reveal a breakdown of Einstein's gravity. Measurements of the black-hole spin distribution and continuous gravitational-wave searches can turn black holes into efficient detectors of ultralight bosons across ten or more orders of magnitude in mass. A precise monitoring of the phase of inspiralling binaries can constrain the existence of additional propagating fields and characterize the environment in which the binaries live, bounding the local dark matter density and properties. Gravitational waves from compact binaries will probe general relativity and fundamental physics in previously inaccessible regimes, and allow us to address fundamental issues in our current understanding of the cosmos.
Effect of the modified gravity on the large scale structure formation

We investigate the formation of the large scale structures in the present accelerated era in f ( R ) gravity background. This is done by considering the linear growth of matter perturbations at low redshift z < 1. The eﬀect of f ( R ) alters the behaviour of the matter density perturbations from the matter dominated universe to the late-time accelerated universe which is encoded in the Newtonian gravitational constant as G → G eff . The modiﬁed gravitational constant ( G eff ) depends on the form of f ( R ). The late-time accelerated expansion aﬀects the formation of large scale structures by slowing down the growth of matter density. On the other hand, f ( R ) increases the growth rate of the matter density perturbations. We have found that the source term in f ( R ) background, G eff Ω m overcomes the accelerated expansion and the eﬀect of accelerated expansion suppresses the formation of the large scale structures in the asymptotic future.
Snowmass2021 Cosmic Frontier White Paper: Cosmological Simulations for Dark Matter Physics

Over the past several decades, unexpected astronomical discoveries have been fueling a new wave of particle model building and are inspiring the next generation of ever-more-sophisticated simulations to reveal the nature of Dark Matter (DM). This coincides with the advent of new observing facilities coming online, including JWST, the Rubin Observatory, the Nancy Grace Roman Space Telescope, and CMB-S4. The time is now to build a novel simulation program to interpret observations so that we can identify novel signatures of DM microphysics across a large dynamic range of length scales and cosmic time. This white paper identifies the key elements that are needed for such a simulation program. We identify areas of growth on both the particle theory side as well as the simulation algorithm and implementation side, so that we can robustly simulate the cosmic evolution of DM for well-motivated models. We recommend that simulations include a fully calibrated and well-tested treatment of baryonic physics, and that outputs should connect with observations in the space of observables. We identify the tools and methods currently available to make predictions and the path forward for building more of these tools. A strong cosmic DM simulation program is key to translating cosmological observations to robust constraints on DM fundamental physics, and provides a connection to lab-based probes of DM physics.
Median of Means Principle for Bayesian Inference

The topic of robustness is experiencing a resurgence of interest in the statistical and machine learning communities. In particular, robust algorithms making use of the so-called median of means estimator were shown to satisfy strong performance guarantees for many problems, including estimation of the mean, covariance structure as well as linear regression. In this work, we propose an extension of the median of means principle to the Bayesian framework, leading to the notion of the robust posterior distribution. In particular, we (a) quantify robustness of this posterior to outliers, (b) show that it satisﬁes a version of the Bernstein-von Mises theorem that con-nects Bayesian credible sets to the traditional conﬁdence intervals, and (c) demonstrate that our approach performs well in applications.
Multilayer crisscross error and erasure correction

In this work, multilayer crisscross error and erasures are considered, which affect entire rows and columns in the matrices of a list of matrices. To measure such errors and erasures, the multi-cover metric is introduced. Several bounds are derived, including a Singleton bound, and maximum multi-cover distance (MMCD) codes are defined as those attaining it. Duality, puncturing and shortening of linear MMCD codes are studied. It is shown that the dual of a linear MMCD code is not necessarily MMCD, and those satisfying this duality condition are defined as dually MMCD codes. Finally, some constructions of codes in the multi-cover metric are given, including dually MMCD codes, together with efficient decoding algorithms for them.
PNM: Pixel Null Model for General Image Segmentation

A major challenge in image segmentation is classifying object boundaries. Recent efforts propose to refine the segmentation result with boundary masks. However, models are still prone to misclassifying boundary pixels even when they correctly capture the object contours. In such cases, even a perfect boundary map is unhelpful for segmentation refinement. In this paper, we argue that assigning proper prior weights to error-prone pixels such as object boundaries can significantly improve the segmentation quality. Specifically, we present the \textit{pixel null model} (PNM), a prior model that weights each pixel according to its probability of being correctly classified by a random segmenter. Empirical analysis shows that PNM captures the misclassification distribution of different state-of-the-art (SOTA) segmenters. Extensive experiments on semantic, instance, and panoptic segmentation tasks over three datasets (Cityscapes, ADE20K, MS COCO) confirm that PNM consistently improves the segmentation quality of most SOTA methods (including the vision transformers) and outperforms boundary-based methods by a large margin. We also observe that the widely-used mean IoU (mIoU) metric is insensitive to boundaries of different sharpness. As a byproduct, we propose a new metric, \textit{PNM IoU}, which perceives the boundary sharpness and better reflects the model segmentation performance in error-prone regions.
Calibration of Derivative Pricing Models: a Multi-Agent Reinforcement Learning Perspective

One of the most fundamental questions in quantitative finance is the existence of continuous-time diffusion models that fit market prices of a given set of options. Traditionally, one employs a mix of intuition, theoretical and empirical analysis to find models that achieve exact or approximate fits. Our contribution is to show how a suitable game theoretical formulation of this problem can help solve this question by leveraging existing developments in modern deep multi-agent reinforcement learning to search in the space of stochastic processes. Our experiments show that we are able to learn local volatility, as well as path-dependence required in the volatility process to minimize the price of a Bermudan option. Our algorithm can be seen as a particle method à la Guyon et Henry-Labordere where particles, instead of being designed to ensure , are learning RL-driven agents cooperating towards more general calibration targets.
The physics case of a 3 TeV muon collider stage

In the path towards a muon collider with center of mass energy of 10 TeV or more, a stage at 3 TeV emerges as an appealing option. Reviewing the physics potential of such muon collider is the main purpose of this document. In order to outline the progression of the physics performances across the stages, a few sensitivity projections for higher energy are also presented. There are many opportunities for probing new physics at a 3 TeV muon collider. Some of them are in common with the extensively documented physics case of the CLIC 3 TeV energy stage, and include measuring the Higgs trilinear coupling and testing the possible composite nature of the Higgs boson and of the top quark at the 20 TeV scale. Other opportunities are unique of a 3 TeV muon collider, and stem from the fact that muons are collided rather than electrons. This is exemplified by studying the potential to explore the microscopic origin of the current $g$-2 and $B$-physics anomalies, which are both related with muons.