Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Averaging Methods for Randomized Second Order Optimization

We consider distributed optimization problems where forming the Hessian is computationally challenging and communication is a significant bottleneck. We develop unbiased parameter averaging methods for randomized second order optimization that employ sampling and sketching of the Hessian. Existing works do not take the bias of the estimators into consideration, which limits their application to massively parallel computation. We provide closed-form formulas for regularization parameters and step sizes that provably minimize the bias for sketched Newton directions. We also extend the framework of second order averaging methods to introduce an unbiased distributed optimization framework for heterogeneous computing systems with varying worker resources. Additionally, we demonstrate the implications of our theoretical findings via large scale experiments performed on a serverless computing platform.
BciPy: Brain-Computer Interface Software in Python

There are high technological and software demands associated with conducting brain-computer interface (BCI) research. In order to accelerate the development and accessibility of BCI, it is worthwhile to focus on open-source and desired tooling. Python, a prominent computer language, has emerged as a language of choice for many research and engineering purposes. In this manuscript, we present BciPy, an open-source, Python-based software for conducting BCI research. It was developed with a focus on restoring communication using event-related potential (ERP) spelling interfaces, however, it may be used for other non-spelling and non-ERP BCI paradigms. Major modules in this system include support for data acquisition, data queries, stimuli presentation, signal processing, signal viewing and modeling, language modeling, task building, and a simple Graphical User Interface (GUI).
Hierarchical Rule Induction Network for Abstract Visual Reasoning

Abstract reasoning refers to the ability to analyze information, discover rules at an intangible level, and solve problems in innovative ways. Raven's Progressive Matrices (RPM) test is typically used to examine the capability of abstract reasoning. In the test, the subject is asked to identify the correct choice from the answer set to fill the missing panel at the bottom right of RPM (e.g., a 3$\times$3 matrix), following the underlying rules inside the matrix. Recent studies, taking advantage of Convolutional Neural Networks (CNNs), have achieved encouraging progress to accomplish the RPM test problems. Unfortunately, simply relying on the relation extraction at the matrix level, they fail to recognize the complex attribute patterns inside or across rows/columns of RPM. To address this problem, in this paper we propose a Hierarchical Rule Induction Network (HriNet), by intimating human induction strategies. HriNet extracts multiple granularity rule embeddings at different levels and integrates them through a gated embedding fusion module. We further introduce a rule similarity metric based on the embeddings, so that HriNet can not only be trained using a tuplet loss but also infer the best answer according to the similarity score. To comprehensively evaluate HriNet, we first fix the defects contained in the very recent RAVEN dataset and generate a new one named Balanced-RAVEN. Then extensive experiments are conducted on the large-scale dataset PGM and our Balanced-RAVEN, the results of which show that HriNet outperforms the state-of-the-art models by a large margin.
Model-independent determination of the nucleon charge radius from lattice QCD

Lattice QCD calculations of nucleon form factors are restricted to discrete values of the Euclidean four-momentum transfer. Therefore, the extraction of radii typically relies on parametrizing and fitting the lattice QCD data to obtain its slope close to zero momentum transfer. We investigate a new method, which allows to compute the nucleon radius directly from existing lattice QCD data, without assuming a functional form for the momentum dependence of the underlying form factor. The method is illustrated for the case of the isovector mean square charge radius of the nucleon $\langle r^2_\mathrm{isov} \rangle$ and the quark-connected contributions to $\langle r^2_p\rangle$ and $\langle r^2_n \rangle$ for the proton and neutron, respectively. Computations are performed using a single gauge ensemble with $N_f=2+1+1$ maximally twisted mass clover-improved fermions at physical quark mass and a lattice spacing of $a=0.08\mathrm{fm}$.
TPLVM: Portfolio Construction by Student's t-process Latent Variable Model

Optimal asset allocation is a key topic in modern finance theory. To realize the optimal asset allocation on investor’s risk aversion, various portfolio construction methods have been proposed. Recently, the applications of machine learning are rapidly growing in the area of finance. In this article, we propose the Student’s t-process latent variable model (TPLVM) to describe non-Gaussian fluctuations of financial timeseries by lower dimensional latent variables. Subsequently, we apply the TPLVM to portfolio construction as an alternative of existing nonlinear factor models. To test the performance of the proposed method, we construct minimum-variance portfolios of global stock market indices based on the TPLVM or Gaussian process latent variable model. By comparing these portfolios, we confirm the proposed portfolio outperforms that of the existing Gaussian process latent variable model.
Reinforcement learning for the manipulation of eye tracking data

In this paper, we present an approach based on reinforcement learning for eye tracking data manipulation. It is based on two opposing agents, where one tries to classify the data correctly and the second agent looks for patterns in the data, which get manipulated to hide specific information. We show that our approach is successfully applicable to preserve the privacy of a subject. In addition, our approach allows to evaluate the importance of temporal, as well as spatial, information of eye tracking data for specific classification goals. In general, this approach can also be used for stimuli manipulation, making it interesting for gaze guidance. For this purpose, this work provides the theoretical basis, which is why we have also integrated a section on how to apply this method for gaze guidance.
Tight Quantum Lower Bound for Approximate Counting with Quantum States

We prove tight lower bounds for the following variant of the counting problem considered by Aaronson et al. The task is to distinguish whether an input set $x\subseteq [n]$ has size either $k$ or $k'=(1+\epsilon)k$. We assume the algorithm has access to * the membership oracle, which, for each $i\in [n]$, can answer whether $i\in x$, or not; and * the uniform superposition $|\psi_x\rangle = \sum_{i\in x} |i\rangle/\sqrt{|x|}$ over the elements of $x$. Moreover, we consider three different ways how the algorithm can access this state: ** the algorithm can have copies of the state $|\psi_x\rangle$; ** the algorithm can execute the reflecting oracle which reflects about the state $|\psi_x\rangle$; ** the algorithm can execute the state-generating oracle (or its inverse) which performs the transformation $|0\rangle\mapsto |\psi_x\rangle$. Without the second type of resources (related to $|\psi_x\rangle$), the problem is well-understood, see Brassard et al. The study of the problem with the second type of resources was recently initiated by Aaronson et al. We completely resolve the problem for all values of $1/k \le \epsilon\le 1$, giving tight trade-offs between all types of resources available to the algorithm. Thus, we close the main open problems from Aaronson et al. The lower bounds are proven using variants of the adversary bound by Belovs and employing analysis closely related to the Johnson association scheme.
Lightweight Lexical Test Prioritization for Immediate Feedback

The practice of unit testing enables programmers to obtain automated feedback on whether a currently edited program is consistent with the expectations specified in test cases. Feedback is most valuable when it happens immediately, as defects can be corrected instantly before they become harder to fix. With growing and longer running test suites, however, feedback is obtained less frequently and lags behind program changes. The objective of test prioritization is to rank tests so that defects, if present, are found as early as possible or with the least costs. While there are numerous static approaches that output a ranking of tests solely based on the current version of a program, we focus on change-based test prioritization, which recommends tests that likely fail in response to the most recent program change. The canonical approach relies on coverage data and prioritizes tests that cover the changed region, but obtaining and updating coverage data is costly. More recently, information retrieval techniques that exploit overlapping vocabulary between change and tests have proven to be powerful, yet lightweight. In this work, we demonstrate the capabilities of information retrieval for prioritizing tests in dynamic programming languages using Python as example. We discuss and measure previously understudied variation points, including how contextual information around a program change can be used, and design alternatives to the widespread \emph{TF-IDF} retrieval model tailored to retrieving failing tests. To obtain program changes with associated test failures, we designed a tool that generates a large set of faulty changes from version history along with their test results. Using this data set, we compared existing and new lexical prioritization strategies using four open-source Python projects, showing large improvements over untreated and random test orders and results consistent with related work in statically typed languages. We conclude that lightweight IR-based prioritization strategies are effective tools to predict failing tests in the absence of coverage data or when static analysis is intractable like in dynamic languages. This knowledge can benefit both individual programmers that rely on fast feedback, as well as operators of continuous integration infrastructure, where resources can be freed sooner by detecting defects earlier in the build cycle.
Learning Optimal Control with MPC Layer

This paper explores the potential to combine the Model Predictive Control (MPC) and Reinforcement Learning (RL). This is achieved based on the technique Cvxpy, which explores the differentiable optimization problems and embeds it as a layer in machine learning. As the function approximaters in RL, the MPC problem constructed by Cvxpy is deployed into all frameworks of RL algorithms, including value-based RL, policy gradient, actor-critic RL. We detail the combination method and provide the novel algorithm structure w.r. t some typical RL algorithms. The major advantage of our MPC layer in RL algorithm is flexibility and fast convergent rate. We provide some practical tricks, which contains initial parameter training in advance and derivative computation by Lagrange formula. We use openAI and pytorch to execute some experiments for the new algorithms.
Learning Zero-Sum Simultaneous-Move Markov Games Using Function Approximation and Correlated Equilibrium

We develop provably efficient reinforcement learning algorithms for two-player zero-sum finite-horizon Markov games with simultaneous moves. To incorporate function approximation, we consider a family of Markov games where the reward function and transition kernel possess a linear structure. Both the offline and online settings of the problems are considered. In the offline setting, we control both players and aim to find the Nash Equilibrium by minimizing the duality gap. In the online setting, we control a single player playing against an arbitrary opponent and aim to minimize the regret. For both settings, we propose an optimistic variant of the least-squares minimax value iteration algorithm. We show that our algorithm is computationally efficient and provably achieves an $\tilde O(\sqrt{d^3 H^3 T} )$ upper bound on the duality gap and regret, where $d$ is the linear dimension, $H$ the horizon and $T$ the total number of timesteps. Our results do not require additional assumptions on the sampling model. Our setting requires overcoming several new challenges that are absent in Markov decision processes or turn-based Markov games. In particular, to achieve optimism with simultaneous moves, we construct both upper and lower confidence bounds of the value function, and then compute the optimistic policy by solving a general-sum matrix game with these bounds as the payoff matrices. As finding the Nash Equilibrium of a general-sum game is computationally hard, our algorithm instead solves for a Coarse Correlated Equilibrium (CCE), which can be obtained efficiently. To our best knowledge, such a CCE-based scheme for optimism has not appeared in the literature and might be of interest in its own right.
SpotTheFake: An Initial Report on a New CNN-Enhanced Platform for Counterfeit Goods Detection

The counterfeit goods trade represents nowadays more than 3.3% of the whole world trade and thus it's a problem that needs now more than ever a lot of attention and a reliable solution that would reduce the negative impact it has over the modern society. This paper presents the design and early stage development of a novel counterfeit goods detection platform that makes use of the outstsanding learning capabilities of the classical VGG16 convolutional model trained through the process of "transfer learning" and a multi-stage fake detection procedure that proved to be not only reliable but also very robust in the experiments we have conducted so far using an image dataset of various goods which we gathered ourselves.
Secure-by-synthesis network with active deception and temporal logic specifications

This paper is concerned with the synthesis of strategies in network systems with active cyber deception. Active deception in a network employs decoy systems and other defenses to conduct defensive planning against the intrusion of malicious attackers who have been confirmed by sensing systems. In this setting, the defender's objective is to ensure the satisfaction of security properties specified in temporal logic formulas. We formulate the problem of deceptive planning with decoy systems and other defenses as a two-player games with asymmetrical information and Boolean payoffs in temporal logic. We use level-2 hypergame with temporal logic objectives to capture the incomplete/incorrect knowledge of the attacker about the network system as a payoff misperception. The true payoff function is private information of the defender. Then, we extend the solution concepts of $omega$-regular games to analyze the attacker's rational strategy given her incomplete information. By generalizing the solution of level-2 hypergame in the normal form to extensive form, we extend the solutions of games with safe temporal logic objectives to decide whether the defender can ensure security properties to be satisfied with probability one, given any possible strategy that is perceived to be rational by the attacker. Further, we use the solution of games with co-safe (reachability) temporal logic objectives to determine whether the defender can engage the attacker, by directing the attacker to a high-fidelity honeypot. The effectiveness of the proposed synthesis methods is illustrated with synthetic network systems with honeypots.
Computing rank‐revealing factorizations of matrices stored out‐of‐core

This paper describes efficient algorithms for computing rank‐revealing factorizations of matrices that are too large to fit in main memory (RAM), and must instead be stored on slow external memory devices such as disks (out‐of‐core or out‐of‐memory). Traditional algorithms for computing rank‐revealing factorizations (such as the column pivoted QR factorization and the singular value decomposition) are very communication intensive as they require many vector‐vector and matrix‐vector operations, which become prohibitively expensive when data is not in RAM. Randomization allows to reformulate new methods so that large contiguous blocks of the matrix are processed in bulk. The paper describes two distinct methods. The first is a blocked version of column pivoted Householder QR, organized as a “left‐looking” method to minimize the number of the expensive write operations. The second method results employs a UTV factorization. It is organized as an algorithm‐by‐blocks to overlap computations and I/O operations. As it incorporates power iterations, it is much better at revealing the numerical rank. Numerical experiments on several computers demonstrate that the new algorithms are almost as fast when processing data stored on slow memory devices as traditional algorithms are for data stored in RAM.
Extreme Classification via Adversarial Softmax Approximation

Training a classifier over a large number of classes, known as 'extreme classification', has become a topic of major interest with applications in technology, science, and e-commerce. Traditional softmax regression induces a gradient cost proportional to the number of classes $C$, which often is prohibitively expensive. A popular scalable softmax approximation relies on uniform negative sampling, which suffers from slow convergence due a poor signal-to-noise ratio. In this paper, we propose a simple training method for drastically enhancing the gradient signal by drawing negative samples from an adversarial model that mimics the data distribution. Our contributions are three-fold: (i) an adversarial sampling mechanism that produces negative samples at a cost only logarithmic in $C$, thus still resulting in cheap gradient updates; (ii) a mathematical proof that this adversarial sampling minimizes the gradient variance while any bias due to non-uniform sampling can be removed; (iii) experimental results on large scale data sets that show a reduction of the training time by an order of magnitude relative to several competitive baselines.
Higher order co-occurrence tensors for hypergraphs via face-splitting

A popular trick for computing a pairwise co-occurrence matrix is the product of an incidence matrix and its transpose. We present an analog for higher order tuple co-occurrences using the face-splitting product, or alternately known as the transpose Khatri-Rao product. These higher order co-occurrences encode the commonality of tokens in the company of other tokens, and thus generalize the mutual information commonly studied. We demonstrate this tensor's use via a popular NLP model, and hypergraph models of similarity.
Discernible Compressed Images via Deep Perception Consistency

Image compression, as one of the fundamental low-level image processing tasks, is very essential for computer vision. Conventional image compression methods tend to obtain compressed images by minimizing their appearance discrepancy with the corresponding original images, but pay little attention to their efficacy in downstream perception tasks, e.g., image recognition and object detection. In contrast, this paper aims to produce compressed images by pursuing both appearance and perception consistency. Based on the encoder-decoder framework, we propose using a pre-trained CNN to extract features of original and compressed images. In addition, the maximum mean discrepancy (MMD) is employed to minimize the difference between feature distributions. The resulting compression network can generate images with high image quality and preserve the consistent perception in the feature domain, so that these images can be well recognized by pre-trained machine learning models. Experiments on benchmarks demonstrate the superiority of the proposed algorithm over comparison methods.
Silhouette: Efficient Protected Shadow Stacks for Embedded Systems

Microcontroller-based embedded systems are increasingly used for applications that can have serious and immediate consequences if compromised--including automobile control systems, smart locks, drones, and implantable medical devices. Due to resource and execution-time constraints, C is the primary language used for programming these devices. Unfortunately, C is neither type-safe nor memory-safe, and control-flow hijacking remains a prevalent threat. This paper presents Silhouette: a compiler-based defense that efficiently guarantees the integrity of return addresses--significantly reducing the attack surface for control-flow hijacking. Silhouette provides an incorruptible shadow stack for return addresses using special store instructions found on ARM processors. Specifically, Silhouette transforms regular store instructions into unprivileged equivalents, restricting the untrusted subset of the program's memory accesses to unprivileged memory while simultaneously allowing security instrumentation to access security-critical data structures--all without the need for expensive context switching, hardware changes, or information hiding. Combined with its checks on forward control flow and memory access configuration, Silhouette ensures that all functions return to the correct caller. We implemented Silhouette for the ARMv7-M architecture, but our techniques are applicable to other common embedded ARM architectures. Our evaluation shows that Silhouette incurs a geometric mean of 1.2% and 3.6% performance overhead on two benchmark suites. Furthermore, we prototyped Silhouette-Invert, an alternative implementation of Silhouette, which incurs just 0.2% and 1.9% performance overhead, at the cost of a minor hardware change.
Learning to Generate Multiple Style Transfer Outputs for an Input Sentence

Text style transfer refers to the task of rephrasing a given text in a different style. While various methods have been proposed to advance the state of the art, they often assume the transfer output follows a delta distribution, and thus their models cannot generate different style transfer results for a given input text. To address the limitation, we propose a one-to-many text style transfer framework. In contrast to prior works that learn a one-to-one mapping that converts an input sentence to one output sentence, our approach learns a one-to-many mapping that can convert an input sentence to multiple different output sentences, while preserving the input content. This is achieved by applying adversarial training with a latent decomposition scheme. Specifically, we decompose the latent representation of the input sentence to a style code that captures the language style variation and a content code that encodes the language style-independent content. We then combine the content code with the style code for generating a style transfer output. By combining the same content code with a different style code, we generate a different style transfer output. Extensive experimental results with comparisons to several text style transfer approaches on multiple public datasets using a diverse set of performance metrics validate effectiveness of the proposed approach.