Miracle-dz/UniFAR-FTUs huggingface dataset:
Tournaments, Johnson Graphs, and NC-Teaching

Quite recently a teaching model, called"No-Clash Teaching"or simply"NC-Teaching", had been suggested that is provably optimal in the following strong sense. First, it satisfies Goldman and Matthias' collusion-freeness condition. Second, the NC-teaching dimension (= NCTD) is smaller than or equal to the teaching dimension with respect to any other collusion-free teaching model. It has also been shown that any concept class which has NC-teaching dimension $d$ and is defined over a domain of size $n$ can have at most $2^d \binom{n}{d}$ concepts. The main results in this paper are as follows. First, we characterize the maximum concept classes of NC-teaching dimension $1$ as classes which are induced by tournaments (= complete oriented graphs) in a very natural way. Second, we show that there exists a family $(\cC_n)_{n\ge1}$ of concept classes such that the well known recursive teaching dimension (= RTD) of $\cC_n$ grows logarithmically in $n = |\cC_n|$ while, for every $n\ge1$, the NC-teaching dimension of $\cC_n$ equals $1$. Since the recursive teaching dimension of a finite concept class $\cC$ is generally bounded $\log|\cC|$, the family $(\cC_n)_{n\ge1}$ separates RTD from NCTD in the most striking way. The proof of existence of the family $(\cC_n)_{n\ge1}$ makes use of the probabilistic method and random tournaments. Third, we improve the afore-mentioned upper bound $2^d\binom{n}{d}$ by a factor of order $\sqrt{d}$. The verification of the superior bound makes use of Johnson graphs and maximum subgraphs not containing large narrow cliques.
Ordered exponential random walks

We study a $d$-dimensional random walk with exponentially distributed increments conditioned so that the components stay ordered (in the sense of Doob). We find explicitly a positive harmonic function $h$ for the killed process and then construct an ordered process using Doob's $h$-transform. Since these random walks are not nearest-neighbour, the harmonic function is not the Vandermonde determinant. The ordered process is related to the departure process of M/M/1 queues in tandem. We find asymptotics for the tail probabilities of the time until the components in exponential random walks become disordered and a local limit theorem. We find the distribution of the processes of smallest and largest particles as Fredholm determinants.
2022 Expansion formulae for two-center non-integer Slater-type orbital charge densities. I. Complete orthonormal sets of exponential-type orbitals with non-integer principal quantum numbers

The numerical Gram − Schmidt procedure is performed for the non − integer Slater − type orbitals. Formulas for the linear combination coeﬃcients are derived. They are used to transform orthonormal basis functions into the Slater − type orbitals or vice versa. A closed form expression for the orthogonalized Slater − type orbitals is achieved. It is used to generalize complete orthonormal exponential − type orbitals basis sets obtained by Guseinov in [Int. J. Quant. Chem. 90 , 114 (2002)] to non − integer values of principal quantum numbers.
Soft and Hard Constrained Parametric Generative Schemes for Encoding and Synthesizing Airfoils

Traditional airfoil parametric technique has signiﬁcant limitation in modern aerodynamic optimization design. There is a strong demand for developing a parametric method with good intuitiveness, ﬂexibility and representative accuracy. In this paper, two parametric generative schemes based on deep learning methods are proposed to represent the complicate design space under speciﬁc constraints. 1. Soft-constrained scheme: The CVAE-based model trains geometric constraints as part of the network and can provide constrained airfoil synthesis; 2. Hard-constrained scheme: The VAE-based model serves to generate diverse airfoils, while an FFD-based technique projects the generated airfoils to the ﬁnal airfoils satisfying the given constraints. The statistical results show that the reconstructed airfoils are accurate and smooth without extra ﬁlters. The soft constrained scheme tend to synthesize and explore airfoils eﬃciently and eﬀectively, concentrating to the reference airfoil in both geometry space and objective space. The constraints will loose for a little bit because the inherent property of the model. The hard constrained scheme tend to generate and explore airfoils in a wider range for both geometry space and objective space, and the distribution in objective space is closer to normal distribution. The synthesized airfoils through this scheme strictly conform with constraints, though the projection may produce some odd airfoil shapes.
Tossing Black Hole Spin Axes

The detection of double black hole (BH+BH) mergers provides a unique possibility to understand their physical properties and origin. To date, the LIGO–Virgo–KAGRA network of high-frequency gravitational-wave observatories has announced the detection of more than 85 BH+BH merger events. An important diagnostic feature that can be extracted from the data is the distribution of effective inspiral spins of the BHs. This distribution is in clear tension with theoretical expectations from both an isolated binary star origin, which traditionally predicts close-to-aligned BH component spins, and formation via dynamical interactions in dense stellar environments that predicts a symmetric distribution of effective inspiral spins. Here it is demonstrated that isolated binary evolution can convincingly explain the observed data if BHs have their spin axis tossed during their formation process in the core collapse of a massive star, similarly to the process evidently acting in newborn neutron stars. BH formation without spin-axis tossing, however, has difficulties reproducing the observed data—even if alignment of spins prior to the second core collapse is disregarded. Based on simulations with only a minimum of assumptions, constraints from empirical data can be made on the spin magnitudes of the first- and second-born BHs, thereby serving to better understand massive binary star evolution prior to the formation of BHs.
Direct numerical simulations of shocklet-containing turbulent channel counter-flows

Counter-ﬂow or counter-current conﬁgurations can maintain high turbulence intensities and exhibit a signiﬁcant level of mixing. We have previously introduced a wall-bounded counter-ﬂow turbulent channel conﬁguration (Physical Review Fluids, 6(9), p.094603. ) as an efﬁcient framework to study compressibility effects on turbulence. Here, we extend our previous direct numerical simulation study to a relatively higher Mach number ( M = 0 . 7) to investigate strong compressibility effects (also by reducing the Prandtl number from Pr = 0 . 7 to 0 . 2), and the formation and evolution of unsteady shocklet structures. It is found that the conﬁguration is able to produce highly turbulent ﬂows with embedded shocklets and signiﬁcant asymmetry in probability density functions of dilatation. A peak turbulent Mach number close to unity is obtained, for which the contribution of the dilatational dissipation to total dissipation is nevertheless found to be limited to 6%.
Speaker Recognition in the Wild

In this paper, we propose a pipeline to ﬁnd the number of speakers, as well as audios belonging to each of these now identiﬁed speakers in a source of audio data where number of speakers or speaker labels are not known a priori. We used this approach as a part of our Data Preparation pipeline for Speech Recognition in Indic Languages. 1 To understand and evaluate the accuracy of our proposed pipeline, we introduce two metrics- Cluster Purity, and Cluster Uniqueness. Cluster Purity quantiﬁes how ”pure” a cluster is. Cluster Uniqueness, on the other hand, quantiﬁes what percentage of clusters belong only to a single dominant speaker. We discuss more on these metrics in section 4. Since we develop this utility to aid us in identifying data based on speaker IDs before training an Automatic Speech Recognition (ASR) model, and since most of this data takes considerable effort to scrape, we also conclude that 98% of data gets mapped to the top 80% of clusters (computed by removing any clusters with less than a ﬁxed number of utterances- we do this to get rid of some very small clusters and use this threshold as 30), in the test set chosen.
Byzantine Fault Tolerance in Distributed Machine Learning : a Survey

Byzantine Fault Tolerance (BFT) is one of the most challenging problems in Distributed Machine Learning (DML), defined as the resilience of a fault-tolerant system in the presence of malicious components. Byzantine failures are still difficult to deal with due to their unrestricted nature, which results in the possibility of generating arbitrary data. Significant research efforts are constantly being made to implement BFT in DML. Some recent studies have considered various BFT approaches in DML. However, some aspects are limited, such as the few approaches analyzed, and there is no classification of the techniques used in the studied approaches. In this paper, we present a survey of recent work surrounding BFT in DML, mainly in first-order optimization methods, especially Stochastic Gradient Descent(SGD). We highlight key techniques as well as fundamental approaches. We provide an illustrative description of the techniques used in BFT in DML, with a proposed classification of BFT approaches in the context of their fundamental techniques. This classification is established on specific criteria such as communication process, optimization method, and topology setting, which characterize future work methods addressing open challenge
LUNA: Learning Slot-Turn Alignment for Dialogue State Tracking

Dialogue state tracking (DST) aims to predict the current dialogue state given the dialogue history. Existing methods generally exploit the utterances of all dialogue turns to assign value for each slot. This could lead to suboptimal results due to the information introduced from irrelevant utterances in the dialogue history, which may be useless and can even cause confusion. To address this problem, we propose LUNA, a SLot-TUrN Alignment enhanced approach. It first explicitly aligns each slot with its most relevant utterance, then further predicts the corresponding value based on this aligned utterance instead of all dialogue utterances. Furthermore, we design a slot ranking auxiliary task to learn the temporal correlation among slots which could facilitate the alignment. Comprehensive experiments are conducted on three multi-domain task-oriented dialogue datasets, MultiWOZ 2.0, MultiWOZ 2.1, and MultiWOZ 2.2. The results show that LUNA achieves new state-of-the-art results on these datasets.
Rethinking Classifier and Adversarial Attack

Various defense models have been proposed to resist adversarial attack algorithms, but existing adversarial robustness evaluation methods always overestimate the adversarial robustness of these models (i.e., not approaching the lower bound of robustness). To solve this problem, this paper uses the proposed decouple space method to divide the classifier into two parts: non-linear and linear. Then, this paper defines the representation vector of the original example (and its space, i.e., the representation space) and uses the iterative optimization of Absolute Classification Boundaries Initialization (ACBI) to obtain a better attack starting point. Particularly, this paper applies ACBI to nearly 50 widely-used defense models (including 8 architectures). Experimental results show that ACBI achieves lower robust accuracy in all cases.
Multi-Agent Deep Reinforcement Learning in Vehicular OCC

Optical camera communications (OCC) has emerged as a key enabling technology for the seamless operation of future autonomous vehicles. In this paper, we introduce a spectral efficiency optimization approach in vehicular OCC. Specifically, we aim at optimally adapting the modulation order and the relative speed while respecting bit error rate and latency constraints. As the optimization problem is NP-hard problem, we model the optimization problem as a Markov decision process (MDP) to enable the use of solutions that can be applied online. We then relaxed the constrained problem by employing Lagrange relaxation approach before solving it by multi-agent deep reinforcement learning (DRL). We verify the performance of our proposed scheme through extensive simulations and compare it with various variants of our approach and a random method. The evaluation shows that our system achieves significantly higher sum spectral efficiency compared to schemes under comparison.
Orbital equivalence classes of finite coverings of geodesic flows

Let $M$ be a closed 3-manifold admitting a finite cover of index n along the fibers over the unit tangent bundle of a closed surface. We prove that if n is odd, there is only one Anosov flow on M up to orbital equivalence, and if n is even, there are two orbital equivalence classes of Anosov flows on M.
Unsupervised Mismatch Localization in Cross-Modal Sequential Data

Content mismatch usually occurs when data from one modality is translated to another, e.g. language learners producing mispronunciations (errors in speech) when reading a sentence (target text) aloud. However, most existing alignment algorithms assume that the content involved in the two modalities is perfectly matched, thus leading to difficulty in locating such mismatch between speech and text. In this work, we develop an unsupervised learning algorithm that can infer the relationship between content-mismatched cross-modal sequential data, especially for speech-text sequences. More specifically, we propose a hierarchical Bayesian deep learning model, dubbed mismatch localization variational autoencoder (ML-VAE), which decomposes the generative process of the speech into hierarchically structured latent variables, indicating the relationship between the two modalities. Training such a model is very challenging due to the discrete latent variables with complex dependencies involved. To address this challenge, we propose a novel and effective training procedure that alternates between estimating the hard assignments of the discrete latent variables over a specifically designed mismatch localization finite-state acceptor (ML-FSA) and updating the parameters of neural networks. In this work, we focus on the mismatch localization problem for speech and text, and our experimental results show that ML-VAE successfully locates the mismatch between text and speech, without the need for human annotations for model training.
A reliable switching amplifier for active magnetic bearings

In this paper a concept for a reliable switching amplifier for active magnetic bearings is presented. Depending on the reliability required, it consists of two or more left and right switching amplifier half-bridges. These so called "hot swap modules" (HSMs) are connected in parallel, each with its own error detection and decoupling circuitry. If an error occurs in an HSM, it is autonomously switched off and disconnects itself from the AMB-actuator, while the remaining, fully functioning HSMs take over the required current. The faulty HSM can be replaced by a functioning one during full operation of the AMB in a hot-swap procedure. For recognition of HSM-errors the functions of several components on each HSM are monitored, for example IGBT and diodes of the half-bridge and the correct operation of the error detection circuitry. To test the basic functionality of this concept along with the behaviour during component-faults extensive simulations with PSpice were carried out. Simulation results allow an efficient and reliable design of the error detection circuitry.
Stochastic Relative Degree and Path-Wise Control of Nonlinear Stochastic Systems

In this article, we address the path-wise control of systems described by a set of nonlinear stochastic differential equations. For this class of systems, we introduce a notion of stochastic relative degree and a change of coordinates, which transforms the dynamics to a stochastic normal form. The normal form is instrumental for the design of a state-feedback control, which linearizes and makes the dynamics deterministic. We observe that this control is idealistic, i.e., it is not practically implementable because it employs a feedback of the Brownian motion (which is never available) to cancel the noise. Using the idealistic control as a starting point, we introduce a hybrid control architecture, which achieves practical path-wise control. This hybrid controller uses measurements of the state to perform periodic compensations for the noise contribution to the dynamics. We prove that the hybrid controller retrieves the idealistic performances in the limit as the compensating period approaches zero. We address the problem of asymptotic output tracking, solving it in the idealistic and in the practical framework. We finally validate the theory by means of a numerical example.
Hardware System Implementation for Human Detection using HOG and SVM Algorithm

— Human detection is a popular issue and has been widely used in many applications. However, including complexities in computation, leading to the human detection system implemented hardly in real-time applications. This paper presents the architecture of hardware, a human detection system that was simulated in the ModelSim tool. As a co-processor, this system was built to off-load to Central Processor Unit (CPU) and speed up the computation timing. The 130x66 RGB pixels of static input image attracted features and classify by using the Histogram of Oriented Gradient (HOG) algorithm and Support Vector Machine (SVM) algorithm, respectively. As a result, the accuracy rate of this system reaches 84.35 percent. And the timing for detection decreases to 0.757 ms at 50MHz frequency (54 times faster when this system was implemented in software by using the Matlab tool).
One Size Does Not Fit All: The Case for Personalised Word Complexity Models

Complex Word Identification (CWI) aims to detect words within a text that a reader may find difficult to understand. It has been shown that CWI systems can improve text simplification, readability prediction and vocabulary acquisition modelling. However, the difficulty of a word is a highly idiosyncratic notion that depends on a reader's first language, proficiency and reading experience. In this paper, we show that personal models are best when predicting word complexity for individual readers. We use a novel active learning framework that allows models to be tailored to individuals and release a dataset of complexity annotations and models as a benchmark for further research.
Visually plausible human-object interaction capture from wearable sensors

In everyday lives, humans naturally modify the surrounding environment through interactions, e.g . , moving a chair to sit on it. To reproduce such interactions in virtual spaces ( e.g ., metaverse), we need to be able to capture and model them, including changes in the scene geometry, ideally from ego-centric input alone (head camera and body-worn inertial sensors). This is an extremely hard problem, especially since the object/scene might not be visible from the head camera ( e.g ., a human not looking at a chair while sitting down, or not looking at the door handle while opening a door). In this paper, we present HOPS, the first method to capture interactions such as dragging objects and opening doors from ego-centric data alone. Central to our method is reasoning about human-object interactions, allowing to track objects even when they are not visible from the head camera. HOPS localizes and registers both the human and the dynamic object in a pre-scanned static scene. HOPS is an important first step towards advanced AR/VR applications based on immersive virtual universes, and can provide human-centric training data Abstract. In this supplementary material, we first (Sec. 7) provide more details about our method. In Sec. 8, we provide implementation details. In Sec. 9, we provide additional experiments and details about our experimental setup. Our project page also includes a supplementary video illustrating our approach, comparisons and qualitative results for multiple sequences. In addition, the video presents failure cases and points out directions for future work. Our work considers the problem of modeling the interaction between a human and an object in the scene. Such interactions are dynamic by nature. The results of our approach are thus best appreciated in motion and we strongly encourage the reader to look at the video.
User-Driven Research of Medical Note Generation Software

A growing body of work uses Natural Language Processing (NLP) methods to automatically generate medical notes from audio recordings of doctor-patient consultations. However, there are very few studies on how such systems could be used in clinical practice, how clinicians would adjust to using them, or how system design should be influenced by such considerations. In this paper, we present three rounds of user studies, carried out in the context of developing a medical note generation system. We present, analyse and discuss the participating clinicians' impressions and views of how the system ought to be adapted to be of value to them. Next, we describe a three-week test run of the system in a live telehealth clinical practice. Major findings include (i) the emergence of five different note-taking behaviours; (ii) the importance of the system generating notes in real time during the consultation; and (iii) the identification of a number of clinical use cases that could prove challenging for automatic note generation systems.
General sum stochastic games with networked information flows

Inspired by applications such as supply chain management, epidemics, and social networks, we formulate a stochastic game model that addresses three key features common across these domains: 1) network-structured player interactions, 2) pair-wise mixed cooperation and competition among players, and 3) limited global information toward individual decision-making. In combination, these features pose significant challenges for black box approaches taken by deep learning-based multi-agent reinforcement learning (MARL) algorithms and deserve more detailed analysis. We formulate a networked stochastic game with pair-wise general sum objectives and asymmetrical information structure, and empirically explore the effects of information availability on the outcomes of different MARL paradigms such as individual learning and centralized learning decentralized execution.
The interventional Bayesian Gaussian equivalent score for Bayesian causal inference with unknown soft interventions

Describing the causal relations governing a system is a fundamental task in many scientific fields, ideally addressed by experimental studies. However, obtaining data under intervention scenarios may not always be feasible, while discovering causal relations from purely observational data is notoriously challenging. In certain settings, such as genomics, we may have data from heterogeneous study conditions, with soft (partial) interventions only pertaining to a subset of the study variables, whose effects and targets are possibly unknown. Combining data from experimental and observational studies offers the opportunity to leverage both domains and improve on the identifiability of causal structures. To this end, we define the interventional BGe score for a mixture of observational and interventional data, where the targets and effects of intervention may be unknown. To demonstrate the approach we compare its performance to other state-of-the-art algorithms, both in simulations and data analysis applications. Prerogative of our method is that it takes a Bayesian perspective leading to a full characterisation of the posterior distribution of the DAG structures. Given a sample of DAGs one can also automatically derive full posterior distributions of the intervention effects. Consequently the method effectively captures the uncertainty both in the structure and the parameter estimates. Codes to reproduce the simulations and analyses are publicly available at github.com/jackkuipers/iBGe