Miracle-dz/UniFAR-FTUs huggingface dataset:
Image Aesthetics Assessment Using Graph Attention Network

Aspect ratio and spatial layout are two of the principal factors influencing the aesthetic value of a photograph. However, incorporating these into the traditional convolution-based frameworks for the task of image aesthetics assessment is problematic. The aspect ratio of the photographs gets distorted while they are resized/cropped to a fixed dimension to facilitate training batch sampling. On the other hand, the convolutional filters process information locally and are limited in their ability to model the global spatial layout of a photograph. In this work, we present a two-stage framework based on graph neural networks and address both these problems jointly. First, we propose a feature-graph representation in which the input image is modelled as a graph, maintaining its original aspect ratio and resolution. Second, we propose a graph neural network architecture that takes this feature-graph and captures the semantic relationship between different regions of the input image using visual attention. Our experiments show that the proposed framework advances the state-of-the-art results in aesthetic score regression on the Aesthetic Visual Analysis (AVA) benchmark. Our code is publicly available for comparisons and further explorations.1
Training Your Sparse Neural Network Better with Any Mask

Pruning large neural networks to create high-quality, independently trainable sparse masks, which can maintain similar performance to their dense counterparts, is very desirable due to the reduced space and time complexity. As research effort is focused on increasingly sophisticated pruning methods that leads to sparse subnetworks trainable from the scratch, we argue for an orthogonal, under-explored theme: improving training techniques for pruned sub-networks, i.e. sparse training. Apart from the popular belief that only the quality of sparse masks matters for sparse training, in this paper we demonstrate an alternative opportunity: one can carefully customize the sparse training techniques to deviate from the default dense network training protocols, consisting of introducing ``ghost"neurons and skip connections at the early stage of training, and strategically modifying the initialization as well as labels. Our new sparse training recipe is generally applicable to improving training from scratch with various sparse masks. By adopting our newly curated techniques, we demonstrate significant performance gains across various popular datasets (CIFAR-10, CIFAR-100, TinyImageNet), architectures (ResNet-18/32/104, Vgg16, MobileNet), and sparse mask options (lottery ticket, SNIP/GRASP, SynFlow, or even randomly pruning), compared to the default training protocols, especially at high sparsity levels. Code is at https://github.com/VITA-Group/ToST
Learning to learn online with neuromodulated synaptic plasticity in spiking neural networks

We propose that in order to harness our understanding of neuroscience toward machine learning, we must first have powerful tools for training brain-like models of learning. Although substantial progress has been made toward understanding the dynamics of learning in the brain, neuroscience-derived models of learning have yet to demonstrate the same performance capabilities as methods in deep learning such as gradient descent. Inspired by the successes of machine learning using gradient descent, we demonstrate that models of neuromodulated synaptic plasticity from neuroscience can be trained in Spiking Neural Networks (SNNs) with a framework of learning to learn through gradient descent to address challenging online learning problems. This framework opens a new path toward developing neuroscience inspired online learning algorithms.
Algebraic area enumeration for open lattice walks

We calculate the number of open walks of fixed length and algebraic area on a square planar lattice by an extension of the operator method used for the enumeration of closed walks. The open walk area is defined by closing the walks with a straight line across their endpoints and can assume half-integer values in lattice cell units. We also derive the length and area counting of walks with endpoints on specific straight lines and outline an approach for dealing with walks with fully fixed endpoints.
Stability Verification of Neural Network Controllers Using Mixed-Integer Programming

In this article, we propose a framework for the stability verification of mixed-integer linear programming (MILP) representable control policies. This framework compares a fixed candidate policy, which admits an efficient parameterization and can be evaluated at a low computational cost, against a fixed baseline policy, which is known to be stable but expensive to evaluate. We provide sufficient conditions for the closed-loop stability of the candidate policy in terms of the worst-case approximation error with respect to the baseline policy, and we show that these conditions can be checked by solving a mixed-integer quadratic program. In addition, we demonstrate that an outer and inner approximation of the stability region of the candidate policy can be computed by solving an MILP. The proposed framework is sufficiently general to accommodate a broad range of candidate policies including ReLU neural networks (NNs), optimal solution maps of parametric quadratic programs, and model predictive control (MPC) policies. We also present an open-source toolbox in Python based on the proposed framework, which allows for the easy verification of custom NN architectures and MPC formulations. We showcase the flexibility and reliability of our framework in the context of a dc–dc power converter case study and investigate its computational complexity.
Unsupervised Voice Activity Detection by Modeling Source and System Information using Zero Frequency Filtering

Voice activity detection (VAD) is an important pre-processing step for speech technology applications. The task consists of deriving segment boundaries of audio signals which contain voicing information. In recent years, it has been shown that voice source and vocal tract system information can be extracted using zero-frequency filtering (ZFF) without making any explicit model assumptions about the speech signal. This paper investigates the potential of zero-frequency filtering for jointly modeling voice source and vocal tract system information, and proposes two approaches for VAD. The first approach demarcates voiced regions using a composite signal composed of different zero-frequency filtered signals. The second approach feeds the composite signal as input to the rVAD algorithm. These approaches are compared with other supervised and unsupervised VAD methods in the literature, and are evaluated on the Aurora-2 database, across a range of SNRs (20 to -5 dB). Our studies show that the proposed ZFF-based methods perform comparable to state-of-art VAD methods and are more invariant to added degradation and different channel characteristics.
Benchopt: Reproducible, efficient and collaborative optimization benchmarks

Numerical validation is at the core of machine learning research as it allows to assess the actual impact of new methods, and to confirm the agreement between theory and practice. Yet, the rapid development of the field poses several challenges: researchers are confronted with a profusion of methods to compare, limited transparency and consensus on best practices, as well as tedious re-implementation work. As a result, validation is often very partial, which can lead to wrong conclusions that slow down the progress of research. We propose Benchopt, a collaborative framework to automate, reproduce and publish optimization benchmarks in machine learning across programming languages and hardware architectures. Benchopt simplifies benchmarking for the community by providing an off-the-shelf tool for running, sharing and extending experiments. To demonstrate its broad usability, we showcase benchmarks on three standard learning tasks: $\ell_2$-regularized logistic regression, Lasso, and ResNet18 training for image classification. These benchmarks highlight key practical findings that give a more nuanced view of the state-of-the-art for these problems, showing that for practical evaluation, the devil is in the details. We hope that Benchopt will foster collaborative work in the community hence improving the reproducibility of research findings.
Anatomy-Guided Weakly-Supervised Abnormality Localization in Chest X-rays

Creating a large-scale dataset of abnormality annotation on medical images is a labor-intensive and costly task. Leveraging weak supervision from readily available data such as radiology reports can compensate lack of large-scale data for anomaly detection methods. However, most of the current methods only use image-level pathological observations, failing to utilize the relevant anatomy mentions in reports. Furthermore, Natural Language Processing (NLP)-mined weak labels are noisy due to label sparsity and linguistic ambiguity. We propose an Anatomy-Guided chest X-ray Network (AGXNet) to address these issues of weak annotation. Our framework consists of a cascade of two networks, one responsible for identifying anatomical abnormalities and the second responsible for pathological observations. The critical component in our framework is an anatomy-guided attention module that aids the downstream observation network in focusing on the relevant anatomical regions generated by the anatomy network. We use Positive Unlabeled (PU) learning to account for the fact that lack of mention does not necessarily mean a negative label. Our quantitative and qualitative results on the MIMIC-CXR dataset demonstrate the effectiveness of AGXNet in disease and anatomical abnormality localization. Experiments on the NIH Chest X-ray dataset show that the learned feature representations are transferable and can achieve the state-of-the-art performances in disease classification and competitive disease localization results. Our code is available at https://github.com/batmanlab/AGXNet
Search for OB associations in Gaia early Data Release 3

The distribution of young stars into OB associations has long been in need of updating. High-precision Gaia early Data Release 3 astrometry, coupled with modern machine-learning methods, allows this to be done. We have compiled a well-defined sample which includes OB stars and young open clusters, in total comprising about 47,700 objects. To break the sample down into groupings resembling associations, we applied the HDBSCAN* clustering algorithm. We used a Monte Carlo method to estimate the kinematic ages of the resulting clusters and the Student’s t-test to assess the significance of the linear correlations between proper motions and coordinates, indicating the presence of possible cluster expansion signatures. The ages of the majority of clusters demonstrating a general expansion at a 1σ confidence level are several tens of Myr, which is in agreement with the expected ages of OB associations. We found 32 open clusters which turned out to be members of the resulting groupings; their ages are consistent with one another within the uncertainties. Comparison of the clusters thus obtained with the historical composition of OB associations in the literature shows a correspondence between their positions in the Galaxy but an apparent absence of good one-to-one stellar matches. Therefore, we suggest that the historical composition of OB associations needs to be revised.
Topology-aware Generalization of Decentralized SGD

This paper studies the algorithmic stability and generalizability of decentralized stochastic gradient descent (D-SGD). We prove that the consensus model learned by D-SGD is $\mathcal{O}{(N^{-1}+m^{-1} +\lambda^2)}$-stable in expectation in the non-convex non-smooth setting, where $N$ is the total sample size, $m$ is the worker number, and $1+\lambda$ is the spectral gap that measures the connectivity of the communication topology. These results then deliver an $\mathcal{O}{(N^{-(1+\alpha)/2}+ m^{-(1+\alpha)/2}+\lambda^{1+\alpha} + \phi_{\mathcal{S}})}$ in-average generalization bound, which is non-vacuous even when $\lambda$ is closed to $1$, in contrast to vacuous as suggested by existing literature on the projected version of D-SGD. Our theory indicates that the generalizability of D-SGD is positively correlated with the spectral gap, and can explain why consensus control in initial training phase can ensure better generalization. Experiments of VGG-11 and ResNet-18 on CIFAR-10, CIFAR-100 and Tiny-ImageNet justify our theory. To our best knowledge, this is the first work on the topology-aware generalization of vanilla D-SGD. Code is available at https://github.com/Raiden-Zhu/Generalization-of-DSGD.
Polarimetric phase retrieval: uniqueness and algorithms

This work introduces a novel Fourier phase retrieval model, called polarimetric phase retrieval that enables a systematic use of polarization information in Fourier phase retrieval problems. We provide a complete characterization of uniqueness properties of this new model by unraveling equivalencies with a peculiar polynomial factorization problem. We introduce two diﬀerent but complementary categories of reconstruction methods. The ﬁrst one is algebraic and relies on the use of approximate greatest common divisor computations using Sylvester matrices. The second one carefully adapts existing algorithms for Fourier phase retrieval, namely semideﬁnite positive relaxation and Wirtinger-Flow, to solve the polarimetric phase retrieval problem. Finally, a set of numerical experiments permits a detailed assessment of the numerical behavior and relative performances of each proposed reconstruction strategy. We further highlight a reconstruction strategy that combines both approaches for scalable, computationally eﬃcient and asymptotically MSE optimal performance.
Burst2Vec: An Adversarial Multi-Task Approach for Predicting Emotion, Age, and Origin from Vocal Bursts

We present Burst2Vec, our multi-task learning approach to predict emotion, age, and origin (i. e., native country/language) from vocal bursts. Burst2Vec utilises pre-trained speech representations to capture acoustic information from raw waveforms and incorporates the concept of model debiasing via adversarial training. Our models achieve a relative 30 % performance gain over baselines using pre-extracted features and score the highest amongst all participants in the ICML ExVo 2022 Multi-Task Challenge.
A Piecewise Convexification Method for Non-Convex Multi-Objective Optimization Programs with Box Constraints

: This paper presents a piecewise convexiﬁcation method for solving non-convex multi-objective optimization problems with box constraints. Based on the ideas of the α -based Branch and Bound ( α BB) method of global optimization and the interval subdivision, a series of convex relaxation sub-multiobjective problems for this non-convex multi-objective optimization problem are ﬁrstly obtained, and these sub-problems constitute a piecewise convexiﬁcation problem of the original problem on the whole box. We then construct the (approximate, weakly) eﬃcient solution set of this piecewise convexiﬁcation problem, and use these sets to approximate the globally (weakly) eﬃcient solution set of the original problem. Furthermore, we propose a piecewise convexiﬁcation algorithm and show that this algorithm can also obtain approximate globally eﬃcient solutions by calculating a ﬁnite subset of the eﬃcient solution set of the multi-objective convex sub-problems only. Finally, its performance is demonstrated with various test instances.
Humans are not Boltzmann Distributions: Challenges and Opportunities for Modelling Human Feedback and Interaction in Reinforcement Learning

Reinforcement learning (RL) commonly assumes access to well-specified reward functions, which many practical applications do not provide. Instead, recently, more work has explored learning what to do from interacting with humans. So far, most of these approaches model humans as being (nosily) rational and, in particular, giving unbiased feedback. We argue that these models are too simplistic and that RL researchers need to develop more realistic human models to design and evaluate their algorithms. In particular, we argue that human models have to be personal, contextual, and dynamic. This paper calls for research from different disciplines to address key questions about how humans provide feedback to AIs and how we can build more robust human-in-the-loop RL systems.
Anisotropic polaritons in 2D vdW materials

Perhaps the most significant progress to the field of infrared optics and nanophotonics has been made through the real space realisation of polaritons in two-dimensional materials that provide maximum light confinement functionalities. The recent breakthrough discovery of in-plane hyperbolicity in the natural van der Waals material has revealed a most exciting optical property which enable an in-plane anisotropic dispersion. Yet, the most intriguing feature of in-plane anisotropic dispersion is the
Prisoners of Their Own Devices: How Models Induce Data Bias in Performative Prediction

The unparalleled ability of machine learning algorithms to learn patterns from data also enables them to incorporate biases embedded within. A biased model can then make decisions that disproportionately harm certain groups in society. Much work has been devoted to measuring unfairness in static ML environments, but not in dynamic, performative prediction ones, in which most real-world use cases operate. In the latter, the predictive model itself plays a pivotal role in shaping the distribution of the data. However, little attention has been heeded to relating unfairness to these interactions. Thus, to further the understanding of unfairness in these settings, we propose a taxonomy to characterize bias in the data, and study cases where it is shaped by model behaviour. Using a real-world account opening fraud detection case study as an example, we study the dangers to both performance and fairness of two typical biases in performative prediction: distribution shifts, and the problem of selective labels.
Phosphatidic acid inhibits SCAB1-mediated F-actin bundling in Arabidopsis

ABSTRACT Stomatal closure-associated actin-binding protein 1 (SCAB1) regulates stomatal closure by mediating actin filament reorganization in Arabidopsis thaliana. Our previous study showed that phosphatidylinositol 3-phosphate (PI3P) binds to SCAB1 and inhibits its oligomerization, thereby inhibiting its activity on F-actin in guard cells during stomatal closure. In this study, we show that another phospholipid, phosphatidic acid (PA), also binds to SCAB1 and inhibits its actin-bundling activity but not its actin-binding activity. F-actin bundling was promoted in vivo by treating Col-0 seedlings with n-butanol, a suppressor of PA production, but this effect was absent in the scab1 mutant. These results indicate that the signaling molecule PA is involved in the modulation of SCAB1 activity in F-actin reorganization.
Mapping frontoinsular cortex from diffusion microstructure

Abstract We developed a novel method for mapping the location, surface area, thickness, and volume of frontoinsular cortex (FI) using structural and diffusion magnetic resonance imaging. FI lies in the ventral part of anterior insular cortex and is characterized by its distinctive population von Economo neurons (VENs). Functional neuroimaging studies have revealed its involvement in affective processing, and histopathology has implicated VEN loss in behavioral-variant frontotemporal dementia and chronic alcoholism; however, structural neuroimaging of FI has been relatively limited. We delineated FI by jointly modeling cortical surface geometry and its coincident diffusion microstructure parameters. We found that neurite orientation dispersion in cortical gray matter can be used to map FI in specific individuals, and the derived measures reflect a range of behavioral factors in young adults from the Human Connectome Project (N=1052). FI volume was larger in the left hemisphere than the right (31%), and the percentage volume of FI was larger in women than men (15.3%). FI volume was associated with measures of decision-making (delay discounting, substance abuse), emotion (negative intrusive thinking and perception of hostility), and social behavior (theory of mind and working memory for faces). The common denominator is that larger FI size is related to greater self-control and social awareness.
FreSCo: Frequency-Domain Scan Context for LiDAR-based Place Recognition with Translation and Rotation Invariance

Place recognition plays a crucial role in relocalization and loop closure detection tasks for robots and vehicles. This paper seeks a well-defined global descriptor for LiDAR-based place recognition. Compared to local descriptors, global descriptors show remarkable performance in urban road scenes but are usually viewpoint-dependent. To this end, we propose a simple yet robust global descriptor dubbed FreSCo that decomposes the viewpoint difference during revisit and achieves both translation and rotation invariance by leveraging Fourier Transform and circular shift technique. Besides, a fast two-stage pose estimation method is proposed to estimate the relative pose after place retrieval by utilizing the compact 2D point clouds extracted from the original data. Experiments show that FreSCo exhibited superior performance than contemporaneous methods on sequences of different scenes from multiple datasets. Code will be publicly available at https://github.com/soytony/FreSCo.
On the Complexity of Adversarial Decision Making

A central problem in online learning and decision making -- from bandits to reinforcement learning -- is to understand what modeling assumptions lead to sample-efficient learning guarantees. We consider a general adversarial decision making framework that encompasses (structured) bandit problems with adversarial rewards and reinforcement learning problems with adversarial dynamics. Our main result is to show -- via new upper and lower bounds -- that the Decision-Estimation Coefficient, a complexity measure introduced by Foster et al. in the stochastic counterpart to our setting, is necessary and sufficient to obtain low regret for adversarial decision making. However, compared to the stochastic setting, one must apply the Decision-Estimation Coefficient to the convex hull of the class of models (or, hypotheses) under consideration. This establishes that the price of accommodating adversarial rewards or dynamics is governed by the behavior of the model class under convexification, and recovers a number of existing results -- both positive and negative. En route to obtaining these guarantees, we provide new structural results that connect the Decision-Estimation Coefficient to variants of other well-known complexity measures, including the Information Ratio of Russo and Van Roy and the Exploration-by-Optimization objective of Lattimore and Gy\"{o}rgy.
Alternating-sum statistics for certain sets of integers

We introduce a class of set families that includes the collection of primitive sets, pairwise coprime sets, and product-free sets. If F is a set family in our class, we let F n,k be the number of elements in F ∩ 2 { 1 , 2 ,. ,n } with cardinality exactly k and show that where K F is a constant depending on the family F but not on n . This constant equals − 1 in the case of primitive sets and 0 in the case of pairwise coprime sets as well as the case of product-free sets. We generalise primitive sets by saying that a set is s -multiple if it does not contain more than s multiples of any integer. We show that if P s,n,k is the number of s -multiple subsets of { 1 , 2 , . , n } with cardinality k , then generalising the identity above in the special case of primitive sets.