Miracle-dz/UniFAR-FTUs huggingface dataset:
Graph model for the clustering of dark matter halos

We use network theory to study hierarchical clustering of dark matter halos, the building blocks of cosmic structure. We analyze cosmological simulations of structure formation, which are publicly available, and construct tree graphs for individual main halo systems by connecting their associated subhalos. These graphs exhibit a power-law degree distribution with an exponent of $-2$. We propose a growing graph model based on preferential attachment to realize the power-law distribution exhibited in the simulations. The attachment kernel effectively incorporates effects of minor mergers, major mergers and tidal stripping. The model reproduces many important features of the simulated halos, including the subhalo abundance and the hierarchical clustering. It provides a new way of modeling complex gravitational dynamics of structure formation.
Coarse nodal count and topological persistence

Direct generalizations of Courant’s celebrated nodal domain theorem are often false. A notable example is the case of linear combinations of eigenfunc-tions in dimension 2 or more, known as the “Courant-Herrmann conjecture”. It turns out that a bound which is consistent with Courant’s theorem and Weyl’s law remains true in this case if we ignore nodal domains where the function is small. It also extends in the following sought-after direc-tions: to elliptic operators acting on sections of vector bundles, to higher Betti numbers of nodal domains, and to products of linear combinations of eigenfunctions. In particular, we obtain a coarse version of B´ezout’s theorem for eigenfunctions. Our methods combine new results about persistence modules, a notion originating in topological data analysis, and multiscale polynomial approxi-mation in Sobolev spaces. Joint work with L. Buhovsky, J. Payette, I. Polterovich, L. Polterovich, and V. Stojisavljevic.
Less Is More: Linear Layers on CLIP Features as Powerful VizWiz Model

Current architectures for multi-modality tasks such as visual question answering suffer from their high complexity. As a result, these architectures are difficult to train and require high computational resources. To address these problems we present a CLIP-based architecture that does not require any fine-tuning of the feature extractors. A simple linear classifier is used on the concatenated features of the image and text encoder. During training an auxiliary loss is added which operates on the answer types. The resulting classification is then used as an attention gate on the answer class selection. On the VizWiz 2022 Visual Question Answering Challenge we achieve 60.15 % accuracy on Task 1: Predict Answer to a Visual Question and AP score of 83.78 % on Task 2: Predict Answerability of a Visual Question.
Is Kernel Code Different From Non-Kernel Code? A Case Study of BSD Family Operating Systems

Studies on software evolution explore code churn and code velocity at the abstraction level of a company or an entire project. We argue that this approach misses the differences among abstractions layers and subsystems of large projects. We conduct a case study on four BSD family operating systems: DragonFlyBSD, FreeBSD, NetBSD, and OpenBSD, to investigate the evolution of code churn and code velocity across kernel and non-kernel code. We mine commits for characteristics such as annual growth rate, commit types, change type ratio, and size taxonomy, indicating code churn. Likewise, we investigate code velocity in terms of code review periods, i.e., time-to-first-response, time-to-accept, and time-to-merge.Our study provides evidence that software evolves differently at abstraction layers: kernel and non-kernel. The study finds similarities in the code base growth rate and distribution of commit types (neutral, additive, and subtractive) across BSD subsystems, however, (a) most commits contain either kernel or non-kernel code, (b) kernel commits are larger than non-kernel commits, and (c) code reviews for kernel code take longer than non-kernel code.
Computability Models: Algebraic, Topological and Geometric Algorithms

The discreteness problem for finitely generated subgroups of $PSL(2,\mathbb{R})$ and $PSL(2,\mathbb{C})$ is a long-standing open problem. In this paper we consider whether or not this problem is decidable by an algorithm. Our main result is that the answer depends upon what model of computation is chosen. Since our discussion involves the disparate topics of computability theory and group theory, we include substantial background material.
Local Dependence and Guarding

We study LFD, a base logic of functional dependence introduced by Baltag and van Benthem (2021) and its connections with the guarded fragment GF of first-order logic. Like other logics of dependence, the semantics of LFD uses teams: sets of permissible variable assignments. What sets LFD apart is its ability to express local dependence between variables and local dependence of statements on variables. Known features of LFD include decidability, explicit axiomatization, finite model property, and a bisimulation characterization. Others, including the complexity of satisfiability, remained open so far. More generally, what has been lacking is a good understanding of what makes the LFD approach to dependence computationally well-behaved, and how it relates to other decidable logics. In particular, how do allowing variable dependencies and guarding quantifiers compare as logical devices? We provide a new compositional translation from GF into LFD, and conversely, we translate LFD into GF in an `almost compositional' manner. Using these two translations, we transfer known results about GF to LFD in a uniform manner, yielding, e.g., tight complexity bounds for LFD satisfiability, as well as Craig interpolation. Conversely, e.g., the finite model property of LFD transfers to GF. Thus, local dependence and guarding turn out to be intricately entangled notions.
Solving the Poisson equation using coupled Markov chains

This article shows how coupled Markov chains that meet exactly after a random number of iterations can be used to generate unbiased estimators of the solutions of the Poisson equation. We establish connections to recently-proposed unbiased estimators of Markov chain equilibrium expectations. We further employ the proposed estimators of solutions of the Poisson equation to construct unbiased estimators of the asymptotic variance of Markov chain ergodic averages, involving a random but finite computing time. We formally study the proposed methods under realistic assumptions on the meeting times of the coupled chains and on the existence of moments of test functions under the target distribution. We describe experiments in toy examples such as the autoregressive model, and in more challenging settings.
LinK3D: Linear Keypoints Representation for 3D LiDAR Point Cloud

Feature extraction and matching are the basic parts of many robotic vision tasks, such as 2D or 3D object detection, recognition, and registration. As is known, 2D feature extraction and matching have already achieved great success. Unfortunately, in the field of 3D, the current methods may fail to support the extensive application of 3D LiDAR sensors in robotic vision tasks due to their poor descriptiveness and inefficiency. To address this limitation, we propose a novel 3D feature representation method: <underline>Lin</underline>ear <underline>K</underline>eypoints representation for <underline>3D</underline> LiDAR point cloud, called LinK3D. The novelty of LinK3D lies in that it fully considers the characteristics (such as the sparsity and complexity) of LiDAR point clouds and represents the keypoint with its robust neighbor keypoints, which provide strong constraints in the description of the keypoint. The proposed LinK3D has been evaluated on three public datasets, and the experimental results show that our method achieves great matching performance. More importantly, LinK3D also shows excellent real-time performance, faster than the sensor frame rate at 10 Hz of a typical rotating LiDAR sensor. LinK3D only takes an average of <inline-formula><tex-math notation="LaTeX">$\bm {30}$</tex-math></inline-formula> milliseconds to extract features from the point cloud collected by a 64-beam LiDAR and takes merely about <inline-formula><tex-math notation="LaTeX">$\bm {20}$</tex-math></inline-formula> milliseconds to match two LiDAR scans when executed on a computer with an Intel Core i7 processor. Moreover, our method can be extended to LiDAR odometry task, and shows good scalability.
Indian Legal Text Summarization: A Text Normalization-based Approach

In the Indian court system, pending cases have long been a problem. There are more than 4 crore cases outstanding. Manually summarising hundreds of documents is a time-consuming and tedious task for legal stakeholders. Many state-of-the-art models for text summarization have emerged as machine learning has progressed. Domain-independent models don’t do well with legal texts, and fine-tuning those models for the Indian Legal System is problematic due to a lack of publicly available datasets. To improve the performance of domain-independent models, the authors have proposed a methodology for normalizing legal texts in the Indian context. The authors experimented with two state-of-the-art domain-independent models for legal text summarization, namely BART and PEGASUS. BART and PEGASUS are put through their paces in terms of extractive and abstractive summarization to understand the effectiveness of the text normalization approach. Summarised texts are evaluated by domain experts on multiple parameters and using ROUGE metrics. It shows the proposed text normalization approach is effective in legal texts with domain-independent models.
Exploration of Enterprise Server Data to Assess Ease of Modeling System Behavior

Enterprise networks are one of the major targets for cyber attacks due to the vast amount of sensitive and valuable data they contain. A common approach to detecting attacks in the enterprise environment relies on modeling the behavior of users and systems to identify unexpected deviations. The feasibility of this approach crucially depends on how well attack-related events can be isolated from benign and mundane system activities. Despite the signiﬁcant focus on end-user systems, the background behavior of servers running critical services for the enterprise is less studied. To guide the design of detection methods tailored for servers, in this work, we examine system event records from 46 servers in a large enterprise obtained over a duration of ten weeks. We analyze the rareness characteristics and the similarity of the provenance relations in the event log data. Our ﬁndings show that server activity, in general, is highly variant over time and dissimilar across diﬀerent types of servers. However, careful consideration of proﬁling window of historical events and service level grouping of servers improve rareness measurements by 24.5%. Further, utilizing better contextual representations, the similarity in provenance relationships could be improved. An important implication of our ﬁndings is that detection techniques developed considering experimental setups with non-representative characteristics may perform poorly in practice.
Vaughan--Lee's nilpotent loop of size $12$ is finitely based

From work of Vaughan–Lee in [7] it follows that if a finite nilpotent loop splits into a direct product of factors of prime power order, then its equational theory has a finite basis. Whether the condition on the direct decomposition is necessary has remained open since. In the same paper, Vaughan–Lee gives an explicit example of a nilpotent loop of order 12 that does not factor into loops of prime power order and asks whether it is finitely based. We give a finite basis for his example by explicitly characterizing its term functions. This also allows us to show that the subpower membership problem for this loop can be solved in polynomial time.
Enhancing Explainability of Hyperparameter Optimization via Bayesian Algorithm Execution

Despite all the beneﬁts of automated hyperparameter optimization (HPO), most modern HPO algorithms are black-boxes themselves. This makes it difﬁcult to understand the decision process which lead to the selected conﬁguration, reduces trust in HPO, and thus hinders its broad adoption. Here, we study the combination of HPO with interpretable machine learning (IML) methods such as partial dependence plots. However, if such methods are naively applied to the experimental data of the HPO process in a post-hoc manner, the underlying sampling bias of the optimizer can distort interpretations. We propose a modiﬁed HPO method which efﬁciently balances the search for the global optimum w.r. t. predictive performance and the reliable estimation of IML explanations of an underlying black-box function by coupling Bayesian optimization and Bayesian Algorithm Execution. On benchmark cases of both synthetic objectives and HPO of a neural network, we demonstrate that our method returns more reliable explanations of the underlying black-box without a loss of optimization performance.
Annular Computational Imaging: Capture Clear Panoramic Images Through Simple Lens

Panoramic Annular Lens (PAL) composed of few lenses has great potential in panoramic surrounding sensing tasks for mobile and wearable devices because of its tiny size and large Field of View (FoV). However, the image quality of tiny-volume PAL confines to optical limit due to the lack of lenses for aberration correction. In this paper, we propose an Annular Computational Imaging (ACI) framework to break the optical limit of light-weight PAL design. To facilitate learning-based image restoration, we introduce a wave-based simulation pipeline for panoramic imaging and tackle the synthetic-to-real gap through multiple data distributions. The proposed pipeline can be easily adapted to any PAL with design parameters and is suitable for loose-tolerance designs. Furthermore, we design the Physics Informed Image Restoration Network (PI$^{2}$RNet) considering the physical priors of panoramic imaging and single-pass physics-informed engine. At the dataset level, we create the DIVPano dataset and the extensive experiments on it illustrate that our proposed network sets the new state of the art in the panoramic image restoration under spatially-variant degradation. In addition, the evaluation of the proposed ACI on a simple PAL with only 3 spherical lenses reveals the delicate balance between high-quality panoramic imaging and compact design. To the best of our knowledge, we are the first to explore Computational Imaging (CI) in PAL.
Multivariate Normality Test with Copula Entropy

In this paper, we proposed a multivariate normality test based on copula entropy. The test statistic is defined as the difference between the copula entropies of unknown distribution and the Gaussian distribution with same covariances. The estimator of the test statistic is presented based on the nonparametric estimator of copula entropy. Two simulation experiments were conducted to compare the proposed test with the five existing ones. Experiment results show the advantage of our test over the others.
Accelerating Federated Learning via Sampling Anchor Clients with Large Batches

Using large batches in recent federated learning studies has improved convergence rates, but it requires additional computation overhead compared to using small batches. To overcome this limitation, we propose a uniﬁed framework FedAMD, which disjoints the participants into anchor and miner groups based on time-varying probabilities. Each client in the anchor group computes the gradient using a large batch, which is regarded as its bullseye. Clients in the miner group perform multiple local updates using serial mini-batches, and each local update is also indirectly regulated by the global target derived from the average of clients’ bullseyes. As a result, the miner group follows a near-optimal update towards the global minimizer, adapted to update the global model. Measured by (cid:15) -approximation, FedAMD achieves a convergence rate of O (1 /(cid:15) ) under non-convex objectives by sampling an anchor with a constant probability. The theoretical result considerably surpasses the state-of-the-art algorithm BVR-L-SGD at O (1 /(cid:15) 3 / 2 ) , while FedAMD reduces at least O (1 /(cid:15) ) communication overhead. Empirical studies on real-world datasets validate the effectiveness of FedAMD and demonstrate the superiority of our proposed algorithm.
Federated Bayesian Neural Regression: A Scalable Global Federated Gaussian Process

In typical scenarios where the Federated Learning (FL) framework applies, it is common for clients to have insufficient training data to produce an accurate model. Thus, models that provide not only point estimations, but also some notion of confidence are beneficial. Gaussian Process (GP) is a powerful Bayesian model that comes with naturally well-calibrated variance estimations. However, it is challenging to learn a stand-alone global GP since merging local kernels leads to privacy leakage. To preserve privacy, previous works that consider federated GPs avoid learning a global model by focusing on the personalized setting or learning an ensemble of local models. We present Federated Bayesian Neural Regression (FedBNR), an algorithm that learns a scalable stand-alone global federated GP that respects clients' privacy. We incorporate deep kernel learning and random features for scalability by defining a unifying random kernel. We show this random kernel can recover any stationary kernel and many non-stationary kernels. We then derive a principled approach of learning a global predictive model as if all client data is centralized. We also learn global kernels with knowledge distillation methods for non-identically and independently distributed (non-i.i.d.) clients. Experiments are conducted on real-world regression datasets and show statistically significant improvements compared to other federated GP models.
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback

Deep neural networks (DNNs) demonstrates significant advantages in improving ranking performance in retrieval tasks. Driven by the recent developments in optimization and generalization of DNNs, learning a neural ranking model online from its interactions with users becomes possible. However, the required exploration for model learning has to be performed in the entire neural network parameter space, which is prohibitively expensive and limits the application of such online solutions in practice. In this work, we propose an efficient exploration strategy for online interactive neural ranker learning based on bootstrapping. Our solution is based on an ensemble of ranking models trained with perturbed user click feedback. The proposed method eliminates explicit confidence set construction and the associated computational overhead, which enables the online neural rankers training to be efficiently executed in practice with theoretical guarantees. Extensive comparisons with an array of state-of-the-art OL2R algorithms on two public learning to rank benchmark datasets demonstrate the effectiveness and computational efficiency of our proposed neural OL2R solution.
Posterior covariance information criterion for arbitrary loss functions

A BSTRACT . We propose a novel computationally low-cost method for estimating the predictive risks of Bayesian methods for arbitrary loss functions. The proposed method utilises posterior covariance and provides estimators of the Gibbs and the plugin generalization errors. We present theoretical guarantees of the proposed method, clarifying the connection between the widely applicable information criterion, the Bayesian sensitivity analysis, and the inﬁnitesimal jackknife approximation of Bayesian leave-one-out cross validation. An application to differentially-private learning is also discussed.
Matching options to tasks using Option-Indexed Hierarchical Reinforcement Learning

The options framework in Hierarchical Reinforcement Learning breaks down overall goals into a combination of options or simpler tasks and associated policies, allowing for abstraction in the action space. Ideally, these options can be reused across different higher-level goals; indeed, such reuse is necessary to realize the vision of a continual learning agent that can effectively leverage its prior experience. Previous approaches have only proposed limited forms of transfer of prelearned options to new task settings. We propose a novel option indexing approach to hierarchical learning (OI-HRL), where we learn an affinity function between options and the items present in the environment. This allows us to effectively reuse a large library of pretrained options, in zero-shot generalization at test time, by restricting goal-directed learning to only those options relevant to the task at hand. We develop a meta-training loop that learns the representations of options and environments over a series of HRL problems, by incorporating feedback about the relevance of retrieved options to the higher-level goal. We evaluate OI-HRL in two simulated settings - the CraftWorld and AI2THOR environments - and show that we achieve performance competitive with oracular baselines, and substantial gains over a baseline that has the entire option pool available for learning the hierarchical policy.
Entanglement Negativity and Defect Extremal Surface

We study entanglement negativity for evaporating black hole based on the holographic model with defect brane. We introduce a defect extremal surface formula for entanglement negativity. Based on partial reduction, we show the equivalence between defect extremal surface formula and island formula for entanglement negativity in AdS$_3$/BCFT$_2$. Extending the study to the model of eternal black hole plus CFT bath, we find that black hole-black hole negativity decreases until vanishing, left black hole-left radiation negativity is always a constant, radiation-radiation negativity increases and then saturates at a time later than Page time. In all the time dependent cases, defect extremal surface formula agrees with island formula.
Relative Policy-Transition Optimization for Fast Policy Transfer

We consider the problem of policy transfer between two Markov Decision Processes (MDPs). We introduce a lemma based on existing theoretical results in reinforcement learning to measure the relativity gap between two arbitrary MDPs, that is the difference between any two cumulative expected returns defined on different policies and environment dynamics. Based on this lemma, we propose two new algorithms referred to as Relative Policy Optimization (RPO) and Relative Transition Optimization (RTO), which offer fast policy transfer and dynamics modelling, respectively. RPO transfers the policy evaluated in one environment to maximize the return in another, while RTO updates the parameterized dynamics model to reduce the gap between the dynamics of the two environments. Integrating the two algorithms results in the complete Relative Policy-Transition Optimization (RPTO) algorithm, in which the policy interacts with the two environments simultaneously, such that data collections from two environments, policy and transition updates are completed in one closed loop to form a principled learning framework for policy transfer. We demonstrate the effectiveness of RPTO on a set of MuJoCo continuous control tasks by creating policy transfer problems via variant dynamics.
Svadhyaya system for the Second Diagnosing COVID-19 using Acoustics Challenge 2021

This report describes the system used for detecting COVID-19 positives using three different acoustic modalities, namely speech, breathing, and cough in the second DiCOVA challenge. The proposed system is based on the combination of 4 different approaches, each focusing more on one aspect of the problem, and reaches the blind test AUCs of 86.41, 77.60, and 84.55, in the breathing, cough, and speech tracks, respectively, and the AUC of 85.37 in the fusion of these three tracks.