Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-Armed Bandits in Brain-Computer Interfaces

The multi-armed bandit (MAB) problem models a decision-maker that optimizes its actions based on current and acquired new knowledge to maximize its reward. This type of online decision is prominent in many procedures of Brain-Computer Interfaces (BCIs) and MAB has previously been used to investigate, e.g., what mental commands to use to optimize BCI performance. However, MAB optimization in the context of BCI is still relatively unexplored, even though it has the potential to improve BCI performance during both calibration and real-time implementation. Therefore, this review aims to further describe the fruitful area of MABs to the BCI community. The review includes a background on MAB problems and standard solution methods, and interpretations related to BCI systems. Moreover, it includes state-of-the-art concepts of MAB in BCI and suggestions for future research.
Semi-WTC: A Practical Semi-supervised Framework for Attack Categorization through Weight-Task Consistency

—Supervised learning has been widely used for attack detection, which requires large amounts of high-quality data and labels. However, the data is often imbalanced and sufﬁcient annotations are difﬁcult to obtain. Moreover, these supervised models are subject to real-world deployment issues, such as defending against unseen artiﬁcial attacks. We propose a semi-supervised ﬁne-grained attack categorization framework consisting of an encoder and a two-branch structure to integrate information from labeled and unlabeled data to tackle these practical challenges. This framework can be generalized to different supervised models. The multilayer perceptron with residual connection and batch normalization is used as the encoder to extract features and reduce the complexity. The Recurrent Prototype Module (RPM) is proposed to train the encoder effectively in a semi-supervised manner. To alleviate the problem of data imbalance, we introduce the Weight-Task Consistency (WTC) into the iterative process of RPM by assigning larger weights to classes with fewer samples in the loss function. In addition, to cope with new attacks in real-world deployment, we further propose an Active Adaption Resampling (AAR) method, which can better discover the distribution of the unseen sample data and adapt the parameters of the encoder. Experimental results show that our model outperforms the state-of-the-art semi-supervised attack detection methods with a general 5% improvement in classiﬁcation accuracy and a 90% reduction in training time.
On Demographic Bias in Fingerprint Recognition

Fingerprint recognition systems have been deployed globally in numerous applications including personal devices, forensics, law enforcement, banking, and national identity systems. For these systems to be socially acceptable and trustworthy, it is critical that they perform equally well across different demographic groups. In this work, we propose a formal statistical framework to test for the existence of bias (demographic differentials) in fingerprint recognition across four major demographic groups (white male, white female, black male, and black female) for two state-of-the-art (SOTA) fingerprint matchers operating in verification and identification modes. Experiments on two different fingerprint databases (with 15,468 and 1,014 subjects) show that demographic differentials in SOTA fingerprint recognition systems decrease as the matcher accuracy increases and any small bias that may be evident is likely due to certain outlier, low-quality fingerprint images.
Saturation and recurrence of quantum complexity in random quantum circuits

Quantum complexity is a measure of the minimal number of elementary operations required to approximately prepare a given state or unitary channel. Recently, this concept has found applica-tions beyond quantum computing—in studying the dynamics of quantum many-body systems and the long-time properties of AdS black holes. In this context Brown and Susskind [1] conjectured that the complexity of a chaotic quantum system grows linearly in time up to times exponential in the system size, saturating at a maximal value, and remaining maximally complex until undergoing recurrences at doubly-exponential times. In this work we prove the saturation and recurrence of the complexity of quantum states and unitaries in a model of chaotic time-evolution based on random quantum circuits, in which a local random unitary transformation is applied to the system at every time step. Importantly, our ﬁndings hold for quite general random circuit models, irrespective of the gate set and geometry of qubit interactions. Our results advance an understanding of the long-time behaviour of chaotic quantum systems and could shed light on the physics of black hole interiors. From a technical perspective our results are based on establishing new quantitative connections between the Haar measure and high-degree approximate designs, as well as the fact that random quantum circuits of suﬃciently high depth converge to approximate designs. begins at t ≈ d 2 and proceeds until saturation. We emphasize that in contrast to the results involving approximate equidistribution, these complexity lower bounds have a trivial ε dependence.
A general scenario of tunneling time in different energy regimes

We theoretically study the tunneling time by investigating a wave packet of Bose-condensed atoms passing through a square barrier. We find that the tunneling time exhibits different scaling laws in different energy regimes. For negative incident energy of the wave packet, counterintuitively, the tunneling time decreases very rapidly with decreasing incident velocity. In contrast, for positive incident energy smaller than the barrier height, the tunneling time increases slowly and then reaches a maximum, which is in agreement with the Larmor clock experiments. The effect of the barrier width related to the uncertainty principle on the maximum tunneling time is also addressed. Our work provides a general scenario of tunneling time that can be used to understand and explain the controversy over tunneling time.
Mimicking Behaviors in Separated Domains

Devising a strategy to make a system mimic behaviors from another system is a problem that naturally arises in many areas of Computer Science. In this work, we interpret this problem in the context of intelligent agents, from the perspective of ltlf , a formalism commonly used in AI for expressing finite-trace properties. Our model consists of two separated dynamic domains, DA and DB, and an LTLf specification that formalizes the notion of mimicking by mapping properties on behaviors (traces) of DA into properties on behaviors of DB. The goal is to synthesize a strategy that step-by-step maps every behavior of DA into a behavior of DB so that the specification is met. We consider several forms of mapping specifications, ranging from simple ones to full LTLf , and for each, we study synthesis algorithms and computational properties.
Discovery of optical and infrared accretion disc wind signatures in the black hole candidate MAXI J1348-630

MAXI J1348-630 is a low mass X-ray binary discovered in 2019 during a bright outburst. During this event, the system sampled both hard and soft states following the standard evolution. We present multi-epoch optical and near-infrared spectroscopy obtained with X-shooter at the Very Large Telescope. Our dataset includes spectra taken during the brightest phases of the outburst as well as the decay towards quiescence. We study the evolution of the main emission lines, paying special attention to the presence of features commonly associated with accretion disc winds, such as blue-shifted absorptions, broad emission line wings and flat-top profiles. We find broad emission line wings in H-alpha during the hard-to-soft transition and blue-shifted absorption troughs at ~-500 km/s in H-beta, HeI-5876, H-alpha and Pa-beta during the bright soft-intermediate state. In addition, flat-top profiles are seen throughout the outburst. We interpret these observables as signatures of a cold (i.e. optical to infrared) accretion disc wind present in the system. We discuss the properties of the wind and compare them with those seen in other X-ray transients. In particular, the wind velocity that we observe is low when compared to those of other systems, which might be a direct consequence of the relatively low binary inclination, as suggested by several observables. This study strengthen the hypothesis that cold winds are a common feature in low mass X-ray binaries and that they can also be detected in low inclination objects via high-quality optical and infrared spectroscopy.
Transformer-based Program Synthesis for Low-Data Environments

Recent advancements in large pre-trained transformer models (GPT2/3, T5) have found use in program synthesis to generate programs that satisfy a set of input/output examples. However, these models perform poorly on long-horizon and low-data tasks, and often don't seem to understand the semantics of the languages they generate. We investigate an approach that tackles both of these issues, by using attributed context-free-grammars of programming languages to generate programs, and then analyzing generated programs so that they can be annotated with compile and runtime attributes, such as types, so that information about the program can be remembered during long-horizon generation. We firstly find that synthesized datasets can be made efficiently and can provide transformer models with enough data in order to perform well on some synthesis tasks. We also find that giving models access to program attributes is especially effective in low-data environments, and tends improve the quality and reduce errors of transformer-generated programs.
Unconventional Visual Sensors for Autonomous Vehicles

—Autonomous vehicles rely on perception systems to understand their surroundings for further navigation missions. Cameras are essential for perception systems due to the advantages of object detection and recognition provided by modern computer vision algorithms, comparing to other sensors, such as LiDARs and radars. However, limited by its inherent imaging principle, a standard RGB camera may perform poorly in a variety of adverse scenarios, including but not limited to: low illumination, high contrast, bad weather such as fog/rain/snow, etc. Meanwhile, estimating the 3D information from the 2D image detection is generally more difﬁcult when compared to LiDARs or radars. Several new sensing technologies have emerged in recent years to address the limitations of conventional RGB cameras. In this paper, we review the principles of four novel image sensors: infrared cameras, range-gated cameras, polarization cameras, and event cameras. Their comparative advantages, existing or potential applications, and corresponding data processing algorithms are all presented in a systematic manner. We expect that this study will assist practitioners in the autonomous driving society with new perspectives and insights.
Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning

Speakers' referential expressions often depart from communicative ideals in ways that help illuminate the nature of pragmatic language use. Patterns of overmodification, in which a speaker uses a modifier that is redundant given their communicative goal, have proven especially informative in this regard. It seems likely that these patterns are shaped by the environment a speaker is exposed to in complex ways. Unfortunately, systematically manipulating these factors during human language acquisition is impossible. In this paper, we propose to address this limitation by adopting neural networks (NN) as learning agents. By systematically varying the environments in which these agents are trained, while keeping the NN architecture constant, we show that overmodification is more likely with environmental features that are infrequent or salient. We show that these findings emerge naturally in the context of a probabilistic model of pragmatic communication.
Multicast Scheduling for Multi-Message over Multi-Channel: A Permutation-based Wolpertinger Deep Reinforcement Learning Method

—Multicasting is an efﬁcient technique to simultaneously transmit common messages from the base station (BS) to multiple mobile users (MUs). The multicast scheduling problem for multiple messages over multiple channels, which jointly minimizes the energy consumption of the BS and the latency of serving asynchronized requests from the MUs, is formulated as an inﬁnite-horizon Markov decision process (MDP) with large discrete action space and multiple time-varying constraints, which has not been efﬁciently addressed in the literatures. By studying the intrinsic features of this MDP under stationary policies and reﬁning the reward function, we ﬁrst simplify it to an equivalent form with a much smaller state space. Then, we propose a modiﬁed deep reinforcement learning (DRL) algorithm, namely the permutation-based Wolpertinger deep deterministic policy gradient (PW-DDPG), to solve the simpliﬁed problem. Speciﬁcally, PW-DDPG utilizes a permutation-based action embedding module to address the large discrete action space issue and a feasible exploration module to deal with the time-varying constraints. Moreover, as a benchmark, an upper bound of the considered MDP is derived by solving an integer programming problem. Numerical results validate that the proposed algorithm achieves close performance to the derived benchmark.
Backdoor Attacks on Bayesian Neural Networks using Reverse Distribution

Due to cost and time-to-market constraints, many industries outsource the training process of machine learning models (ML) to third-party cloud service providers, popularly known as ML-as-a-Service (MLaaS). MLaaS creates opportunity for an adversary to provide users with backdoored ML models to produce incorrect predictions only in extremely rare (attacker-chosen) scenarios. Bayesian neural networks (BNN) are inherently immune against backdoor attacks since the weights are designed to be marginal distributions to quantify the uncertainty. In this paper, we propose a novel backdoor attack based on ef-fective learning and targeted utilization of reverse distribution. This paper makes three important contributions. (1) To the best of our knowledge, this is the ﬁrst backdoor attack that can effectively break the robustness of BNNs. (2) We produce reverse distributions to cancel the original distributions when the trigger is activated. (3) We propose an efﬁcient solution for merging probability distributions in BNNs. Experimental results on diverse benchmark datasets demonstrate that our proposed attack can achieve the attack success rate (ASR) of 100%, while the ASR of the state-of-the-art attacks is lower than 60%.
Multi-Messenger Constraints on the Hubble Constant Through Combination of Gravitational Waves, Gamma-Ray Bursts and Kilonovae from Neutron Star Mergers

The simultaneous detection of gravitational waves and light from the binary neutron star merger GW170817 led to independent measurements of distance and redshift, providing a direct estimate of the Hubble constant H0 that does not rely on a cosmic distance ladder, nor assumes a specific cosmological model. By using gravitational waves as “standard sirens”, this approach holds promise to arbitrate the existing tension between the H0 value inferred from the cosmic microwave background and those obtained from local measurements. However, the known degeneracy in the gravitational-wave analysis between distance and inclination of the source led to a H0 value from GW170817 that was not precise enough to resolve the existing tension. In this review, we summarize recent works exploiting the viewing-angle dependence of the electromagnetic signal, namely the associated short gamma-ray burst and kilonova, to constrain the system inclination and improve on H0. We outline the key ingredients of the different methods, summarize the results obtained in the aftermath of GW170817 and discuss the possible systematics introduced by each of these methods.
On the efficiency of Stochastic Quasi-Newton Methods for Deep Learning

While first-order methods are popular for solving optimization problems that arise in large-scale deep learning problems, they come with some acute deficiencies. To diminish such shortcomings, there has been recent interest in applying second-order methods such as quasi-Newton based methods which construct Hessians approximations using only gradient information. The main focus of our work is to study the behaviour of stochastic quasi-Newton algorithms for training deep neural networks. We have analyzed the performance of two well-known quasi-Newton updates, the limited memory Broyden-Fletcher-Goldfarb-Shanno (BFGS) and the Symmetric Rank One (SR1). This study fills a gap concerning the real performance of both updates and analyzes whether more efficient training is obtained when using the more robust BFGS update or the cheaper SR1 formula which allows for indefinite Hessian approximations and thus can potentially help to better navigate the pathological saddle points present in the non-convex loss functions found in deep learning. We present and discuss the results of an extensive experimental study which includes the effect of batch normalization and network's architecture, the limited memory parameter, the batch size and the type of sampling strategy. we show that stochastic quasi-Newton optimizers are efficient and able to outperform in some instances the well-known first-order Adam optimizer run with the optimal combination of its numerous hyperparameters.
An Efficient Piggybacking Design Framework with Sub-packetization $l\le r$ for All-Node Repair

Piggybacking design has been widely applied in distributed storage systems since it can greatly reduce the repair bandwidth with small sub-packetization. Compared with other existing erasure codes, piggybacking is more convenient to operate and the I/O cost is lower. In this paper, we propose a new efficient design which can further reduce the repair bandwidth with the sub-packetization $l\le r$ where $r = n-k$. Generally, we let $l\le 8$. Compared with other analogous designs, our design has lower $l$ and the value of $l$ is more flexible. Moreover, our design can repair all nodes with small repair bandwidth. Therefore our piggybacking design is more feasible.
Statistical Uncertainty Principle in Stochastic Dynamics

Maximum entropy principle identifies forces conjugated to observables and the thermodynamic relations between them, independent upon their underlying mechanistic details. For data about state distributions or transition statistics, the principle can be derived from limit theorems of infinite data sampling. This derivation reveals its empirical origin and clarify the meaning of applying it to large but finite data. We derive an uncertainty principle for the statistical variations of the observables and the inferred forces. We use a toy model for molecular motor as an example.
Modelling the Daily Electricity Demand of Electric Vessels in Plymouth

The international Maritime Organization (IMO) has set the target of reducing the emissions from the shipping sector to at least 50% of the 2008 levels. One potential method to cut emissions is to convert vessels to battery powered propulsion in a similar manner to that which has been adopted for motor vehicles. Although, battery powered propulsion will not be suitable for all vessels, the conversion of those that are will lead to an increase in the energy demand from the national grid. This study uses historic port call data is used to model the timings of arrivals and the number of vessels in the port of Plymouth to predict the increase in additional energy demand required for battery powered vessels through a period of 24 hours as a greater proportion of the fleet move to battery powered propulsion.
Combining deep learning and adaptive sparse modeling for low-dose CT reconstruction

Traditional model-based image reconstruction (MBIR) methods combine forward and noise models with simple object priors. Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addressing the challenges when reconstructing images with measurement undersampling or various types of noise. In this work, we propose a hybrid supervised-unsupervised learning framework for X-ray computed tomography (CT) image reconstruction. The proposed learning formulation leverages both sparsity or unsupervised learning-based priors and neural network reconstructors to simulate a fixed-point iteration process. Each proposed trained block consists of a deterministic MBIR solver and a neural network. The information flows in parallel through these two reconstructors and is then optimally combined, and multiple such blocks are cascaded to form a reconstruction pipeline. We demonstrate the efficacy of this learned hybrid model for low-dose CT image reconstruction with limited training data, where we use the NIH AAPM Mayo Clinic Low Dose CT Grand Challenge dataset for training and testing. In our experiments, we study combinations of supervised deep network reconstructors and sparse representations-based (unsupervised) learned or analytical priors. Our results demonstrate the promising performance of the proposed framework compared to recent reconstruction methods.
Learning Bayesian Networks with Annealing Machine

Recent studies have reported that annealing machines are capable of solving combinatorial optimization problems with high accuracy. Annealing machines can potentially be applied to score-based Bayesian network structure learning. However, the bit capacity of an annealing machine is currently limited. To utilize the annealing technology, converting score-based learning problems into quadratic unconstrained binary optimizations within the bit capacity is necessary. In this paper, we propose an efficient conversion method with the advanced identification of candidate parent sets and their decomposition. We also provide an integer programming problem to find the decomposition that minimizes the number of required bits. Experimental results on $7$ benchmark datasets with variables from $75$ to $223$ show that our approach requires less bits than the $100$K bit capacity of the fourth-generation Fujitsu Digital Annealer, a fully coupled annealing machine developed with semiconductor technology. Moreover, we demonstrate that the Digital Annealer with our conversion method outperforms existing algorithms on score maximization. These results highlight the utility of annealing processors in learning Bayesian networks.
Spurious Local Minima of Deep ReLU Neural Networks in the Neural Tangent Kernel Regime

In this paper, we theoretically prove that the deep ReLU neural networks do not lie in spurious local minima in the loss landscape under the Neural Tangent Kernel (NTK) regime, that is, in the gradient descent training dynamics of the deep ReLU neural networks whose parameters are initialized by a normal distribution in the limit as the widths of the hidden layers tend to inﬁnity.