Miracle-dz/UniFAR-FTUs huggingface dataset:
A New Algorithm for Hidden Markov Models Learning Problem

This research focuses on the algorithms and approaches for learning Hidden Markov Models (HMMs) and compares HMM learning methods and algorithms. HMM is a statistical Markov model in which the system being modeled is assumed to be a Markov process. One of the essential characteristics of HMMs is their learning capabilities. Learning algorithms are introduced to overcome this inconvenience. One of the main problems of the newly proposed algorithms is their validation. This research aims by using the theoretical and experimental analysis to 1) compare HMMs learning algorithms proposed in the literature, 2) provide a validation tool for new HMM learning algorithms, and 3) present a new algorithm called Asexual Reproduction Optimization (ARO) with one of its extensions - Modified ARO (MARO) - as a novel HMM learning algorithm to use the validation tool proposed. According to the literature findings, it seems that populationbased algorithms perform better among HMMs learning approaches than other algorithms. Also, the testing was done in nine benchmark datasets. The results show that MARO outperforms different algorithms in objective functions in terms of accuracy and robustness.
Efficient Designs of SLOPE Penalty Sequences in Finite Dimension

In linear regression, SLOPE is a new convex analysis method that generalizes the Lasso via the sorted L1 penalty: larger fitted coefficients are penalized more heavily. This magnitude-dependent regularization requires an input of penalty sequence $\lambda$, instead of a scalar penalty as in the Lasso case, thus making the design extremely expensive in computation. In this paper, we propose two efficient algorithms to design the possibly high-dimensional SLOPE penalty, in order to minimize the mean squared error. For Gaussian data matrices, we propose a first order Projected Gradient Descent (PGD) under the Approximate Message Passing regime. For general data matrices, we present a zero-th order Coordinate Descent (CD) to design a sub-class of SLOPE, referred to as the k-level SLOPE. Our CD allows a useful trade-off between the accuracy and the computation speed. We demonstrate the performance of SLOPE with our designs via extensive experiments on synthetic data and real-world datasets.
Uncovering Interpretable Internal States of Merging Tasks at Highway on-Ramps for Autonomous Driving Decision-Making

Humans make daily routine decisions based on their internal states in intricate interaction scenarios. This article presents a probabilistically reconstructive learning approach to identify the internal states of multivehicle sequential interactions when merging at highway on-ramps. We treated the merging task’s sequential decision as a dynamic, stochastic process and then integrated the internal states into a hidden Markov model (HMM)-Gaussian mixture regression (GMR) model, a probabilistic combination of an extended GMR and HMM. We also developed a variant of the expectation–maximization (EM) algorithm to estimate the model parameters and verified it based on a real-world dataset. Experiment results reveal that three interpretable internal states can semantically describe the interactive merge procedure at highway on-ramps. This finding provides a basis for developing an efficient model-based decision-making algorithm for autonomous vehicles (AVs) in a partially observable environment. Note to Practitioners—Model-based learning approaches have obtained increasing attention in decision-making design due to their stability and interpretability. This article was built upon two facts: 1) intelligent agents can only receive partially observable environmental information directly through their equipped sensors in the real world and 2) humans mainly utilize the internal states and associated dynamics inferred from observations to make proper decisions in complex environments. Similarly, autonomous vehicles (AVs) need to understand, infer, anticipate, and exploit the internal states of dynamic environments. Applying probabilistic decision-making models to AVs requires updating the internal states’ beliefs and associated dynamics after getting new observations. The designed and verified emission model in hidden Markov model (HMM)-Gaussian mixture regression (GMR) provides a modifiable functional module for online updates of the associated internal states. Experiment results based on the real-world driving dataset demonstrate that the internal states extracted using HMM-GMR can represent the dynamic decision-making process semantically and make an accurate prediction.
Electron-Positron Vacuum Instability in Strong Electric Fields. Relativistic Semiclassical Approach

The instability of electron-positron vacuum in strong electric fields is studied. First, falling to the Coulomb center is discussed at Z>137/2 for a spinless boson and at Z>137 for electron. Subsequently, focus is concentrated on description of deep electron levels and spontaneous positron production in the field of a finite-size nucleus with the charge Z>Zcr≃170. Next, these effects are studied in application to the low-energy heavy-ion collisions. Subsequently, we consider phenomenon of “electron condensation” on levels of upper continuum crossed the boundary of the lower continuum ϵ=−m in the field of a supercharged nucleus with Z≫Zcr. Finally, attention is focused on many-particle problems of polarization of the quantum electrodynamics (QED) vacuum and electron condensation at ultra-short distances from a source of charge. We argue for a principal difference of cases, when the size of the source is larger than the pole size rpole, at which the dielectric permittivity of the vacuum reaches zero and smaller rpole. Some arguments are presented in favor of the logical consistency of QED. All of the problems are considered within the same relativistic semiclassical approach.
Higgs alignment and the top quark

There is a surprising connection between the top quark and Higgs alignment in Gildener-Weinberg multi-Higgs doublet models. Were it not for the top quark and its large mass, the coupling of the 125 GeV Higgs boson $H$ to gauge bosons and fermions would be indistinguishable from those of the Standard Model Higgs. The top quark's coupling to a single Higgs doublet breaks this perfect alignment in higher orders of the Coleman-Weinberg loop expansion of the effective potential. But the effect is still small, $< {\cal{O}}(1\%)$, and probably experimentally inaccessible.
β-MODIFICATION OF ISOTACTIC POLYPROPYLENE: PREPARATION, STRUCTURE, PROCESSING, PROPERTIES, AND APPLICATION

The methods of preparation and formation of supermolecular structures in quiescent and sheared melts and the properties of the β-modification of isotactic polypropylene (β-iPP) are reviewed. The introduction of selective β-nucleants is the most reliable method for preparation of samples rich in β-modification or of pure β-iPP. The advantages and drawbacks of the known β-nucleating agents are summarized. It is emphasized that pure β-iPP can be prepared under laboratory and processing conditions in the presence of highly active and selective β-nucleants. Nevertheless, there are no literature data—apart from that of the author's groups—which evidenced unambiguously the formation of pure β-iPP. It hints at the insufficient selectivity of β-nucleants used or at the inappropriate crystallization or melting conditions applied by other scientists. The structure formation during the high-temperature hedritic crystallization is discussed comprehensively and illustrated by polarized light microscopy and scanning electron microscopy micrographs. Some specific features of β-iPP, namely the high- and low-temperature growth transition, the restricted temperature range of the formation of pure β-iPP, and the unique melting and recrystallization characteristics (melting and annealing memory effect) are summarized. It was emphasized that impact strength and toughness of β-iPP markedly exceed those of α-iPP. Processing of β-nucleated iPP and application of β-nucleated iPP is described briefly.
MaskNet: Introducing Feature-Wise Multiplication to CTR Ranking Models by Instance-Guided Mask

Click-Through Rate(CTR) estimation has become one of the most fundamental tasks in many real-world applications and it's important for ranking models to effectively capture complex high-order features. Shallow feed-forward network is widely used in many state-of-the-art DNN models such as FNN, DeepFM and xDeepFM to implicitly capture high-order feature interactions. However, some research has proved that addictive feature interaction, particular feed-forward neural networks, is inefficient in capturing common feature interaction. To resolve this problem, we introduce specific multiplicative operation into DNN ranking system by proposing instance-guided mask which performs element-wise product both on the feature embedding and feed-forward layers guided by input instance. We also turn the feed-forward layer in DNN model into a mixture of addictive and multiplicative feature interactions by proposing MaskBlock in this paper. MaskBlock combines the layer normalization, instance-guided mask, and feed-forward layer and it is a basic building block to be used to design new ranking model under various configurations. The model consisting of MaskBlock is called MaskNet in this paper and two new MaskNet models are proposed to show the effectiveness of MaskBlock as basic building block for composing high performance ranking systems. The experiment results on three real-world datasets demonstrate that our proposed MaskNet models outperform state-of-the-art models such as DeepFM and xDeepFM significantly, which implies MaskBlock is an effective basic building unit for composing new high performance ranking systems.
Room-temperature atmospheric argon plasma jet sustained with submicrosecond high-voltage pulses

In this letter, an experimental study is presented to characterize a room-temperature plasma jet in atmospheric argon generated with submicrosecond voltage pulses at 4kHz. Distinct from sinusoidally produced argon discharges that are prone to thermal runaway instabilities, the pulsed atmospheric argon plasma jet is stable and cold with an electron density 3.9 times greater than that in a comparable sinusoidal jet. Its optical emission is also much stronger. Electrical measurement suggests that the discharge event is preceded with a prebreakdown phase and its plasma stability is facilitated by the short voltage pulses.
Revisiting the strong shock problem: Converging and diverging shocks in different geometries

Self-similar solutions to converging (implosions) and diverging (explosions) shocks have been studied before, in planar, cylindrical or spherical symmetry. Here we offer a unified treatment of these apparently disconnected problems . We study the flow of an ideal gas with adiabatic index γ with initial density ρ ∼ r , containing a strong shock wave. We characterize the self-similar solutions in the entirety of the parameter space γ, ω, and draw the connections between the different geometries. We find that the so-called ”gap” in diverging shocks does not exist in planar geometry, and that only type II self-similar solutions are valid in converging shocks. We also find that in some cases, a converging shock might not create a reflected shock after its convergence. Finally, we derive analytical approximations for the similarity exponent in the entirety of parameter space.
Does Standard Backpropagation Forget Less Catastrophically Than Adam?

Catastrophic forgetting remains a severe hin-drance to the broad application of artiﬁcial neural networks (ANNs), however, it continues to be a poorly understood phenomenon. Despite the extensive amount of work on catastrophic forgetting, we argue that it is still unclear how exactly the phenomenon should be quantiﬁed, and, moreover, to what degree all of the choices we make when designing learning systems affect the amount of catastrophic forgetting. We use various testbeds from the reinforcement learning and supervised learning literature to (1) provide evidence that the choice of which modern gradient-based optimization algorithm is used to train an ANN has a signiﬁcant impact on the amount of catastrophic forgetting and show that–surprisingly–in many instances classical algorithms such as vanilla SGD experience less catastrophic forgetting than the more modern algorithms such as Adam. We empirically compare four different existing metrics for quantifying catastrophic forgetting and (2) show that the degree to which the learning systems experience catastrophic forgetting is suf-ﬁciently sensitive to the metric used that a change from one principled metric to another is enough to change the conclusions of a study dramatically. Our results suggest that a much more rigorous experimental methodology is required when looking at catastrophic forgetting. Based on our results, we recommend inter-task forgetting in supervised learning must be measured with both retention and relearning metrics concurrently, and intra-task forgetting in reinforcement learning must–at the very least–be measured with pairwise interference.
Revisiting Smoothed Online Learning

In this paper, we revisit the problem of smoothed online learning, in which the online learner suffers both a hitting cost and a switching cost, and target two performance metrics: competitive ratio and dynamic regret with switching cost. To bound the competitive ratio, we assume the hitting cost is known to the learner in each round, and investigate the simple idea of balancing the two costs by an optimization problem. Surprisingly, we find that minimizing the hitting cost alone is $\max(1, \frac{2}{\alpha})$-competitive for $\alpha$-polyhedral functions and $1 + \frac{4}{\lambda}$-competitive for $\lambda$-quadratic growth functions, both of which improve state-of-the-art results significantly. Moreover, when the hitting cost is both convex and $\lambda$-quadratic growth, we reduce the competitive ratio to $1 + \frac{2}{\sqrt{\lambda}}$ by minimizing the weighted sum of the hitting cost and the switching cost. To bound the dynamic regret with switching cost, we follow the standard setting of online convex optimization, in which the hitting cost is convex but hidden from the learner before making predictions. We modify Ader, an existing algorithm designed for dynamic regret, slightly to take into account the switching cost when measuring the performance. The proposed algorithm, named as Smoothed Ader, attains an optimal $O(\sqrt{T(1+P_T)})$ bound for dynamic regret with switching cost, where $P_T$ is the path-length of the comparator sequence. Furthermore, if the hitting cost is accessible in the beginning of each round, we obtain a similar guarantee without the bounded gradient condition, and establish an $\Omega(\sqrt{T(1+P_T)})$ lower bound to confirm the optimality.
Parameter-free Locally Accelerated Conditional Gradients

Projection-free conditional gradient (CG) methods are the algorithms of choice for constrained optimization setups in which projections are often computationally prohibitive but linear optimization over the constraint set remains computationally feasible. Unlike in projection-based methods, globally accelerated convergence rates are in general unattainable for CG. However, a very recent work on Locally accelerated CG (LaCG) has demonstrated that local acceleration for CG is possible for many settings of interest. The main downside of LaCG is that it requires knowledge of the smoothness and strong convexity parameters of the objective function. We remove this limitation by introducing a novel, Parameter-Free Locally accelerated CG (PF-LaCG) algorithm, for which we provide rigorous convergence guarantees. Our theoretical results are complemented by numerical experiments, which demonstrate local acceleration and showcase the practical improvements of PF-LaCG over non-accelerated algorithms, both in terms of iteration count and wall-clock time.
Twin-width and permutations

Inspired by a width invariant on permutations defined by Guillemot and Marx, Bonnet, Kim, Thomass\‘e, and Watrigant introduced the twin-width of graphs, which is a parameter describing its structural complexity. This invariant has been further extended to binary structures, in several (basically equivalent) ways. We prove that a class of binary relational structures (that is: edge-colored partially directed graphs) has bounded twin-width if and only if it is a first-order transduction of a~proper permutation class. As a by-product, we show that every class with bounded twin-width contains at most $2^{O(n)}$ pairwise non-isomorphic $n$-vertex graphs.
Multi-Stage Decentralized Matching Markets: Uncertain Preferences and Strategic Behaviors

Matching markets are often organized in a multi-stage and decentralized manner. Moreover, participants in real-world matching markets often have uncertain preferences. This article develops a framework for learning optimal strategies in such settings, based on a nonparametric statistical approach and variational analysis. We propose an efficient algorithm, built upon concepts of “lower uncertainty bound” and “calibrated decentralized matching,” for maximizing the participants’ expected payoff. We show that there exists a welfare-versus-fairness trade-off that is characterized by the uncertainty level of acceptance. Participants will strategically act in favor of a low uncertainty level to reduce competition and increase expected payoff. We study signaling mechanisms that help to clear the congestion in such decentralized markets and find that the effects of signaling are heterogeneous, showing a dependence on the participants and matching stages. We prove that participants can be better off with multi-stage matching compared to single-stage matching. The deferred acceptance procedure assumes no limit on the number of stages and attains efficiency and fairness but may make some participants worse off than multi-stage matching. We demonstrate aspects of the theoretical predictions through simulations and an experiment using real data from college admissions.
Large deviations of Schramm-Loewner evolutions: A survey

These notes survey the first results on large deviations of Schramm-Loewner evolutions (SLE) with emphasis on interrelations between rate functions and applications to complex analysis. More precisely, we describe the large deviations of SLE$_\kappa$ when the $\kappa$ parameter goes to zero in the chordal and multichordal case and to infinity in the radial case. The rate functions, namely Loewner and Loewner-Kufarev energies, are closely related to the Weil-Petersson class of quasicircles and real rational functions.
Introducing physical warp drives

The Alcubierre warp drive is an exotic solution in general relativity. It allows for superluminal travel at the cost of enormous amounts of matter with negative mass density. For this reason, the Alcubierre warp drive has been widely considered unphysical. In this study, we develop a model of a general warp drive spacetime in classical relativity that encloses all existing warp drive definitions and allows for new metrics without the most serious issues present in the Alcubierre solution. We present the first general model for subliminal positive-energy, spherically symmetric warp drives; construct superluminal warp-drive solutions which satisfy quantum inequalities; provide optimizations for the Alcubierre metric that decrease the negative energy requirements by two orders of magnitude; and introduce a warp drive spacetime in which space capacity and the rate of time can be chosen in a controlled manner. Conceptually, we demonstrate that any warp drive, including the Alcubierre drive, is a shell of regular or exotic material moving inertially with a certain velocity. Therefore, any warp drive requires propulsion. We show that a class of subluminal, spherically symmetric warp drive spacetimes, at least in principle, can be constructed based on the physical principles known to humanity today
EFFECTS OF ALCOHOLS ON GAS HOLDUP AND VOLUMETRIC LIQUID-PHASE MASS TRANSFER COEFFICIENT IN GEL-PARTICLE-SUSPENDED BUBBLE COLUMN

The effects of alcohols, column dimensions, gas velocity, physical properties of liquids, and gel particles on the gas holdup eG and the volumetric liquid-phase mass transfer coefficient kLa in a gel-particle-suspended bubble column under liquid-solid batch operation were studied experimentally. It was shown that addition of alcohols to water generally increases eG. However, kLa values in aqueous solutions of alcohols became larger or smaller than those in water, according to the kind and concentration of the alcohol added to water. It was also shown that the presence of suspended gel-particles in the bubble column reduces values of eG and kLa. Based on these observations, empirical equations for eG in the transition regime in an ethanol solution, for eG in the heterogeneous flow regime applicable to various alcohol solutions and for kLa in both flow regimes were proposed.
Double-descent curves in neural networks: a new perspective using Gaussian processes

Double-descent curves in neural networks describe the phenomenon that the generalisation error initially descends with increasing parameters, then grows after reaching an optimal number of parameters which is less than the number of data points, but then descends again in the overparameterized regime. In this paper, we use techniques from random matrix theory to characterize the spectral distribution of the empirical feature covariance matrix as a width-dependent perturbation of the spectrum of the neural network Gaussian process (NNGP) kernel, thus establishing a novel connection between the NNGP literature and the random matrix theory literature in the context of neural networks. Our analytical expressions allow us to explore the generalisation behavior of the corresponding kernel and GP regression. Furthermore, they offer a new interpretation of double-descent in terms of the discrepancy between the width-dependent empirical kernel and the width-independent NNGP kernel.
Deep Convolutional and Recurrent Networks for Polyphonic Instrument Classification from Monophonic Raw Audio Waveforms

Sound Event Detection and Audio Classification tasks are traditionally addressed through time-frequency representations of audio signals such as spectrograms. However, the emergence of deep neural networks as efficient feature extractors has enabled the direct use of audio signals for classification purposes. In this paper, we attempt to recognize musical instruments in polyphonic audio by only feeding their raw waveforms into deep learning models. Various recurrent and convolutional architectures incorporating residual connections are examined and parameterized in order to build end-to-end classifiers with low computational cost and only minimal preprocessing. We obtain competitive classification scores and useful instrument-wise insight through the IRMAS test set, utilizing a parallel CNN-BiGRU model with multiple residual connections, while maintaining a significantly reduced number of trainable parameters.
On Riemannian Stochastic Approximation Schemes with Fixed Step-Size

This paper studies fixed step-size stochastic approximation (SA) schemes, including stochastic gradient schemes, in a Riemannian framework. It is motivated by several applications, where geodesics can be computed explicitly, and their use accelerates crude Euclidean methods. A fixed step-size scheme defines a family of time-homogeneous Markov chains, parametrized by the step-size. Here, using this formulation, non-asymptotic performance bounds are derived, under Lyapunov conditions. Then, for any step-size, the corresponding Markov chain is proved to admit a unique stationary distribution, and to be geometrically ergodic. This result gives rise to a family of stationary distributions indexed by the step-size, which is further shown to converge to a Dirac measure, concentrated at the solution of the problem at hand, as the step-size goes to 0. Finally, the asymptotic rate of this convergence is established, through an asymptotic expansion of the bias, and a central limit theorem.