Miracle-dz/UniFAR-FTUs huggingface dataset:
The Ordered Matrix Dirichlet for Modeling Ordinal Dynamics

Many dynamical systems exhibit latent states with intrinsic orderings such as “ally”, “neutral” and “enemy” relationships in international relations. Such latent states are evidenced through entities’ cooperative versus conﬂictual interactions which are similarly ordered. Models of such systems often involve state-to-action emission and state-to-state transition matrices. It is common practice to assume that the rows of these stochastic matrices are independently sampled from a Dirichlet distribution. However, this assumption discards ordinal information and treats states and actions falsely as order-invariant categoricals, which hinders interpretation and evaluation. To address this problem, we propose the Ordered Matrix Dirichlet (OMD): rows are sampled conditionally dependent such that probability mass is shifted to the right of the matrix as we move down rows. This results in a well-ordered mapping between latent states and observed action types. We evaluate the OMD in two settings: a Hidden Markov Model and a novel Bayesian Dynamic Poisson Tucker Model tailored to political event data. Models built on the OMD recover interpretable latent states and show superior forecasting performance in few-shot settings. We detail the wide applicability of the OMD to other domains where models with Dirichlet-sampled matrices are popular (e.g. topic modeling) and publish user-friendly code.
A Survey of Graph Neural Networks for Social Recommender Systems

Social recommender systems (SocialRS) simultaneously leverage user-to-item interactions as well as user-to-user social relations for the task of generating item recommendations to users. Additionally exploiting social relations is clearly effective in understanding users' tastes due to the effects of homophily and social influence. For this reason, SocialRS has increasingly attracted attention. In particular, with the advance of Graph Neural Networks (GNN), many GNN-based SocialRS methods have been developed recently. Therefore, we conduct a comprehensive and systematic review of the literature on GNN-based SocialRS. In this survey, we first identify 80 papers on GNN-based SocialRS after annotating 2151 papers by following the PRISMA framework (Preferred Reporting Items for Systematic Reviews and Meta-Analysis). Then, we comprehensively review them in terms of their inputs and architectures to propose a novel taxonomy: (1) input taxonomy includes 5 groups of input type notations and 7 groups of input representation notations; (2) architecture taxonomy includes 8 groups of GNN encoder, 2 groups of decoder, and 12 groups of loss function notations. We classify the GNN-based SocialRS methods into several categories as per the taxonomy and describe their details. Furthermore, we summarize the benchmark datasets and metrics widely used to evaluate the GNN-based SocialRS methods. Finally, we conclude this survey by presenting some future research directions.
New optical dispersion models for the accurate description of the electrical permittivity in direct and indirect semiconductors

We propose new optical dispersion models to describe the imaginary part of the electrical permittivity of dielectric and semiconductor materials in the fundamental absorption region. We work out our procedure based on the well-known structure of the semi-empirical Tauc–Lorentz dispersion model and the band-fluctuations approach to derive a five-parameter formula that describes the Urbach, Tauc and high-absorption regions of direct and indirect semiconductors. Main features of the dispersion models are the self-consistent generation of the exponential Urbach tail below the bandgap and the incorporation of the Lorentz oscillator behavior due to electronic transitions above the fundamental region. We apply and test these models on optical data of direct (MAPbI3, gallium arsenide and indium phosphide), indirect (gallium phosphide and crystalline silicon), and amorphous hydrogenated silicon semiconductors, accurately describing the spectra of the imaginary part of the electrical permittivity. Lastly, we compare our results with other similarly inspired dispersion models to assess the optical bandgap, Urbach tail and oscillator central resonance energy.
LDP for Inhomogeneous U-Statistics

In this paper we derive a Large Deviation Principle (LDP) for inhomogeneous U/V-statistics of a general order. Using this, we derive a LDP for two types of statistics: random multilinear forms, and number of monochromatic copies of a subgraph. We show that the corresponding rate functions in these cases can be expressed as a variational problem over a suitable space of functions. We use the tools developed to study Gibbs measures with the corresponding Hamiltonians, which include tensor generalizations of both Ising (with non-compact base measure) and Potts models. For these Gibbs measures, we establish scaling limits of log normalizing constants, and weak laws in terms of weak* topology, which are of possible independent interest.
DC-MBR: Distributional Cooling for Minimum Bayesian Risk Decoding

Minimum Bayesian Risk Decoding (MBR) emerges as a promising decoding algorithm in Neural Machine Translation. However, MBR performs poorly with label smoothing, which is surprising as label smoothing provides decent improvement with beam search and improves generality in various tasks. In this work, we show that the issue arises from the un-consistency of label smoothing on the token-level and sequence-level distributions. We demonstrate that even though label smoothing only causes a slight change in the token-level, the sequence-level distribution is highly skewed. We coin the issue \emph{autoregressive over-smoothness}. To address this issue, we propose a simple and effective method, Distributional Cooling MBR (DC-MBR), which manipulates the entropy of output distributions by tuning down the Softmax temperature. We theoretically prove the equivalence between pre-tuning label smoothing factor and distributional cooling. Extensive experiments on NMT benchmarks validate that distributional cooling improves MBR in various settings.
On the Field Dependence of Magnetoresistance in Two-Dimensional Systems

Weak localization theory of magnetoresistance in two-dimensional systems is re-investigated. A formulation is developed which is applicable up to magnetic field much larger than the exsisting theory. The result of the calculation is compared with the experiment on GaAs/AlGaAs heterojunction and the agreement between them is satisfactory.
Optical phonetics and visual perception of lexical and phrasal boundaries in English

Detection of lexical and phrasal boundaries in the speech signal is crucial to successful comprehension. Several suprasegmental acoustic cues have been identified for boundary detection in speech (e.g., stress pattern, duration, and pitch). However, the corresponding optical cues have not been described in detail, and it is not known to which optical cues to boundaries speechreaders are attuned. In a production study, three male American English talkers spoke two repetitions each of eight pairs of sentences in two boundary conditions (one‐word versus two‐word sequences, one‐phrase versus two‐phrase sequences). Sentence pairs were constructed such that they differed minimally in the presence of a boundary. Audio, video, and 3D‐movements of the face were recorded. Sentence pairs in both boundary conditions differed on both optical and acoustic duration measurements. A subset of the sentence pairs was also presented in a visual perception study. Pairs were chosen a priori because they differed or did not dif.
Better Hit the Nail on the Head than Beat around the Bush: Removing Protected Attributes with a Single Projection

Bias elimination and recent probing studies attempt to remove specific information from embedding spaces. Here it is important to remove as much of the target information as possible, while preserving any other information present. INLP is a popular recent method which removes specific information through iterative nullspace projections.Multiple iterations, however, increase the risk that information other than the target is negatively affected.We introduce two methods that find a single targeted projection: Mean Projection (MP, more efficient) and Tukey Median Projection (TMP, with theoretical guarantees). Our comparison between MP and INLP shows that (1) one MP projection removes linear separability based on the target and (2) MP has less impact on the overall space.Further analysis shows that applying random projections after MP leads to the same overall effects on the embedding space as the multiple projections of INLP. Applying one targeted (MP) projection hence is methodologically cleaner than applying multiple (INLP) projections that introduce random effects.
The Neural Correlates of Linguistic Structure Building: Comments on Kazanina & Tavano (2022)

A recent perspective paper by Kazanina & Tavano (referred to as the KT perspective in the following) argues how neural oscillations cannot provide a potential neural correlate for syntactic structure building. The view that neural oscillations can provide a potential neural correlate for syntactic structure building is largely attributed to a study by Ding, Melloni, Zhang, Tian, and Poeppel in 2016 (referred to as the DMZTP study).
Optical Flux and Spectral Variability of BL Lacertae during Its Historical High Outburst in 2020

BL Lacertae underwent a series of historical high flux activity over a year from 2020 August in the optical to VHE γ-rays. In this paper, we report on optical flux and spectral variability of the first historical maxima outburst event during October–November in the g, r, and i bands with the 1.26 m telescope at the Xinglong Observatory, China. We detected significant intranight variations with amplitude rising up to ∼30%, where the fastest variability timescale was found to be a few tens of minutes, giving an emitting region size of the order 10−3 pc, which corresponds to ∼100 Schwarzschild radius of the central black hole, likely coming from some jet mini-structures. Unlike on the intranight timescale, a clear frequency-dependent pattern along symmetric timescales (∼11 days) of flux variation is detected on a long timescale. The spectral evolution was predominated by flattening of the spectra with increasing brightness i.e., a bluer-when-brighter trend in 96% of the cases. On the night before the outburst peak, the color indices shown in a color–magnitude diagram, clustered into two distinct branches, within a period of ∼6 hr, which is connected to a hard-soft-hard spectral evolution trend extracted from time-resolved spectra. To the best of our knowledge, such a trend has never been seen in BL Lac or any other blazars before. The results obtained in this study can be explained in the context of shock-induced particle acceleration or magnetic reconnection in the jet where turbulent processes most likely resulted in the asymmetric flux variation on a nightly timescale.
Predicting dominant hand from spatiotemporal context varying physiological data

Health metrics from wrist-worn devices demand an automatic dominant hand prediction to keep an accurate operation. The prediction would improve reliability, enhance the consumer experience, and encourage further development of healthcare applications. This paper aims to evaluate the use of physiological and spatiotemporal context information from a two-hand experiment to predict the wrist placement of a commercial smartwatch. The main contribution is a methodology to obtain an effective model and features from low sample rate physiological sensors and a self-reported context survey. Results show an effective dominant hand prediction using data from a single subject under real-life conditions.
DialogCC: An Automated Pipeline for Creating High-Quality Multi-Modal Dialogue Dataset

As sharing images in an instant message is a crucial factor, there has been active research on learning an image-text multi-modal dialogue models. However, training a well-generalized multi-modal dialogue model remains challenging due to the low quality and limited diversity of images per dialogue in existing multi-modal dialogue datasets. In this paper, we propose an automated pipeline to construct a multi-modal dialogue dataset, ensuring both dialogue quality and image diversity without requiring minimum human effort. In our pipeline, to guarantee the coherence between images and dialogue, we prompt GPT-4 to infer potential image-sharing moments - specifically, the utterance, speaker, rationale, and image description. Furthermore, we leverage CLIP similarity to maintain consistency between aligned multiple images to the utterance. Through this pipeline, we introduce DialogCC, a high-quality and diverse multi-modal dialogue dataset that surpasses existing datasets in terms of quality and diversity in human evaluation. Our comprehensive experiments highlight that when multi-modal dialogue models are trained using our dataset, their generalization performance on unseen dialogue datasets is significantly enhanced. We make our source code and dataset publicly available.
The Thermomajorization Polytope and Its Degeneracies

Drawing inspiration from transportation theory, in this work, we introduce the notions of “well-structured” and “stable” Gibbs states and we investigate their implications for quantum thermodynamics and its resource theory approach via thermal operations. It is found that, in the quasi-classical realm, global cyclic state transfers are impossible if and only if the Gibbs state is stable. Moreover, using a geometric approach by studying the so-called thermomajorization polytope, we prove that any subspace in equilibrium can be brought out of equilibrium via thermal operations. Interestingly, the case of some subsystem being in equilibrium can be witnessed via the degenerate extreme points of the thermomajorization polytope, assuming that the Gibbs state of the system is well structured. These physical considerations are complemented by simple new constructions for the polytope’s extreme points, as well as for an important class of extremal Gibbs-stochastic matrices.
Design and Planning of Flexible Mobile Micro-Grids Using Deep Reinforcement Learning

Ongoing risks from climate change have impacted the livelihood of global nomadic communities, and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced Plug and Play control strategies have been recently developed with such a decentralized framework in mind, more easily allowing for the interconnection of nomadic communities, both to each other and to the main grid. In light of the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) is implemented for the design and planning problem tackled. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important implications for both nomadic communities and policymakers focused on enabling their energy access.
Modification of L2 vowel production by perception training as evaluated by acoustic analysis and native speakers

Recent cross‐language studies have shown that there is a link between speech perception and speech production when learning second language (L2) consonants. This paper reports additional evidence for such a link from a study which examines whether identification training of L2 vowels improves the trainees’ production ability. Twenty native speakers of Japanese served as subjects. Half of the subjects received a pretest, identification training on English /ɑ, ae, ʌ/ and a post‐test. For the other half of the subjects, only the pretest and post‐test were administered. In addition to the perception test, productions of the English vowels were recorded in the pre‐ and post‐test. These productions were later examined in two ways; acoustical analysis and evaluation by native English speakers. Identification ability significantly improved from pretest to post‐test for the trained group, but not for the untrained group as reported by Strange and Akahane‐Yamada [J. Acoust. Soc. Am. 102, 3137 (1997)]. More important.
Gap Preserving Reductions between Reconfiguration Problems

Combinatorial reconfiguration is a growing research field studying problems on the transformability between a pair of solutions of a search problem. We consider the approximability of optimization variants of reconfiguration problems; e.g., for a Boolean formula $\varphi$ and two satisfying truth assignments $\sigma_{\sf s}$ and $\sigma_{\sf t}$ for $\varphi$, Maxmin SAT Reconfiguration requires to maximize the minimum fraction of satisfied clauses of $\varphi$ during transformation from $\sigma_{\sf s}$ to $\sigma_{\sf t}$. Solving such optimization variants approximately, we may obtain a reconfiguration sequence comprising almost-satisfying truth assignments. In this study, we prove a series of gap-preserving reductions to give evidence that a host of reconfiguration problems are PSPACE-hard to approximate, under some plausible assumption. Our starting point is a new working hypothesis called the Reconfiguration Inapproximability Hypothesis (RIH), which asserts that a gap version of Maxmin CSP Reconfiguration is PSPACE-hard. This hypothesis may be thought of as a reconfiguration analogue of the PCP theorem. Our main result is PSPACE-hardness of approximating Maxmin $3$-SAT Reconfiguration of bounded occurrence under RIH. The crux of its proof is a gap-preserving reduction from Maxmin Binary CSP Reconfiguration to itself of bounded degree. Because a simple application of the degree reduction technique using expander graphs due to Papadimitriou and Yannakakis does not preserve the perfect completeness, we modify the alphabet as if each vertex could take a pair of values simultaneously. To accomplish the soundness requirement, we further apply an explicit family of near-Ramanujan graphs and the expander mixing lemma. As an application of the main result, we demonstrate that under RIH, optimization variants of popular reconfiguration problems are PSPACE-hard to approximate.
Bounds for the Grundy chromatic number of graphs in terms of domination number

For any graph $G$, the Grundy (or First-Fit) chromatic number of $G$, denoted by $\Gamma(G)$ (also $\chi_{_{\sf FF}}(G)$), is defined as the maximum number of colors used by the First-Fit (greedy) coloring of the vertices of $G$. Determining the Grundy number is $NP$-complete, and obtaining bounds for $\Gamma(G)$ in terms of the known graph parameters is an active research topic. By a star partition of $G$ we mean any partition of $V(G)$ into say $V_1, \ldots, V_k$ such that each $G[V_i]$ contains a vertex adjacent to any other vertex in $V_i$. In this paper using the star partition of graphs we obtain the first upper bounds for the Grundy number in terms of the domination number. We also prove some bounds in terms of the domination number and girth of graphs.
Ergodicity and stability of hybrid systems with threshold type state-dependent switching

To deal with stochastic hybrid systems with general state-dependent switching, we pro-pose an approximation method by a sequence of stochastic hybrid systems with threshold type switching. The convergence rate in the Wasserstein distance is estimated in terms of the diﬀerence between transition rate matrices. Our method is based on an elaborate construction of coupling processes in terms of Skorokhod’s representation theorem for jumping processes. Moreover, we establish explicit criteria on the ergodicity and stability for stochastic hybrid systems with threshold type switching. Some examples are given to illustrate the sharpness of these criteria
An Efficient Evolutionary Deep Learning Framework Based on Multi-source Transfer Learning to Evolve Deep Convolutional Neural Networks

Convolutional neural networks (CNNs) have constantly achieved better performance over years by introducing more complex topology, and enlarging the capacity towards deeper and wider CNNs. This makes the manual design of CNNs extremely difficult, so the automated design of CNNs has come into the research spot-light, which has obtained CNNs that outperform manually-designed CNNs. However, the computational cost is still the bottleneck of automatically designing CNNs. In this paper, inspired by transfer learning, a new evolutionary computation based framework is proposed to efficiently evolve CNNs without compromising the classification accuracy. The proposed framework leverages multi-source domains, which are smaller datasets than the target domain datasets, to evolve a generalised CNN block only once. And then, a new stacking method is proposed to both widen and deepen the evolved block, and a grid search method is proposed to find optimal stacking solutions. The experimental results show the proposed method acquires good CNNs faster than 15 peer competitors within less than 40 GPU-hours. Regarding the classification accuracy, the proposed method gains its strong competitiveness against the peer competitors, which achieves the best error rates of 3.46%, 18.36% and 1.76% for the CIFAR-10, CIFAR-100 and SVHN datasets, respectively.
Differentially-Private Bayes Consistency

We construct a universally Bayes consistent learning rule that satisﬁes differential privacy (DP). We ﬁrst handle the setting of binary classiﬁcation and then extend our rule to the more general setting of density estimation (with respect to the total variation metric). The existence of a universally consistent DP learner reveals a stark difference with the distribution-free PAC model. Indeed, in the latter DP learning is extremely limited: even one-dimensional linear classiﬁers are not privately learnable in this stringent model. Our result thus demonstrates that by allowing the learning rate to depend on the target distribution, one can circumvent the above-mentioned impossibility result and in fact learn arbitrary distributions by a single DP algorithm. As an application, we prove that any VC class can be privately learned in a semi-supervised setting with a near-optimal labeled sample complexity of ˜ O ( d/ε ) labeled examples (and with an unlabeled sample complexity that can depend on the target distribution).