Miracle-dz/UniFAR-FTUs huggingface dataset:
User Profiling Using Hinge-loss Markov Random Fields

A variety of approaches have been proposed to automatically infer the profiles of users from their digital footprint in social media. Most of the proposed approaches focus on mining a single type of information, while ignoring other sources of available user-generated content (UGC). In this paper, we propose a mechanism to infer a variety of user characteristics, such as, age, gender and personality traits, which can then be compiled into a user profile. To this end, we model social media users by incorporating and reasoning over multiple sources of UGC as well as social relations. Our model is based on a statistical relational learning framework using Hinge-loss Markov Random Fields (HL-MRFs), a class of probabilistic graphical models that can be defined using a set of first-order logical rules. We validate our approach on data from Facebook with more than 5k users and almost 725k relations. We show how HL-MRFs can be used to develop a generic and extensible user profiling framework by leveraging textual, visual, and relational content in the form of status updates, profile pictures and Facebook page likes. Our experimental results demonstrate that our proposed model successfully incorporates multiple sources of information and outperforms competing methods that use only one source of information or an ensemble method across the different sources for modeling of users in social media.
All-optical reversible controls of integrated photonics by self-assembled azobenzene.

The next frontier in photonics will rely on the synergistic combination of disparate material systems. One unique organic molecule is azobenzene. This molecule can reversibly change conformations when optically excited in the blue (trans-to-cis) or mid-IR (cis-to-trans). Here, we demonstrate SiO2 optical resonators modified with a monolayer of azobenzene-containing 4-(4-diethylaminophenylazo)pyridine (Aazo) with quality factors over 106. Using a pair of lasers, the molecule is reversibly flipped between molecular conformations, inducing resonant wavelength shifts, and multiple switching cycles are demonstrated. The magnitude of the shift scales with the relative surface density of Aazo. The experimental data agrees with theoretical modeling.
Feedforward Inversion Control of DC/DC Dual-Bridge Series Resonant Converter in Buck and Boost Modes

In the paper, a nonlinear inversion technique for the steady-state model of the active dual-bridge series resonant converter is presented. The obtained control strategy allows cycle averaged output current regulation and performs waveform alignment for the controllable achievement of ZVS and synchronous rectification. The control is valid both for voltage buck and boost operating modes, as well as for low-power operation at a fixed frequency. Robustness of the control is studied by simulations with external linear control loops.
Speech Enhancement Based on Denoising Autoencoder With Multi-Branched Encoders

Deep learning-based models have greatly advanced the performance of speech enhancement (SE) systems. However, two problems remain unsolved, which are closely related to model generalizability to noisy conditions: (1) mismatched noisy condition during testing, i.e., the performance is generally sub-optimal when models are tested with unseen noise types that are not involved in the training data; (2) local focus on specific noisy conditions, i.e., models trained using multiple types of noises cannot optimally remove a specific noise type even though the noise type has been involved in the training data. These problems are common in real applications. In this article, we propose a novel denoising autoencoder with a multi-branched encoder (termed DAEME) model to deal with these two problems. In the DAEME model, two stages are involved: training and testing. In the training stage, we build multiple component models to form a multi-branched encoder based on a decision tree (DSDT). The DSDT is built based on prior knowledge of speech and noisy conditions (the speaker, environment, and signal factors are considered in this paper), where each component of the multi-branched encoder performs a particular mapping from noisy to clean speech along the branch in the DSDT. Finally, a decoder is trained on top of the multi-branched encoder. In the testing stage, noisy speech is first processed by each component model. The multiple outputs from these models are then integrated into the decoder to determine the final enhanced speech. Experimental results show that DAEME is superior to several baseline models in terms of objective evaluation metrics, automatic speech recognition results, and quality in subjective human listening tests.
Three-dimensional lattice multiflavor scalar chromodynamics: Interplay between global and gauge symmetries

We study the nature of the finite-temperature transition of the three-dimensional scalar chromodynamics with N_f flavors. These models are constructed by considering maximally O(M)-symmetric multicomponent scalar models, whose symmetry is partially gauged to obtain SU(N_c) gauge theories, with a residual nonabelian global symmetry given by U(N_f) for N_c>2 and Sp(N_f) for N_c=2, so that M = 2 N_c N_f. We find that their finite-temperature transition is continuous for N_f=2 and for all values of Nc we investigated, N_c=2,3,4. Such continuous transitions belong to universality classes related to the global symmetry group of the theory. For N_c=2 it belongs to the SO(5)=Sp(2)/Z_2 universality class, while for N_c>2 it belongs to the SO(3)=SU(2)/Z_2 universality class. For N_f>2, the transition is always of first order. These results match the predictions obtained by using the effective Landau-Ginzburg-Wilson approach in terms of a gauge-invariant order parameter. Our results indicate that the nonabelian gauge degrees of freedom are irrelevant at the transition. These conclusions are supported by an analysis of gauge-field dependent correlation functions, that are always short-ranged, even at the transition.
Accumulated Polar Feature based Deep Learning with Channel Compensation Mechanism for Efficient Automatic Modulation Classification under Time varying Channels

In next-generation communications, massive machine-type communications (MTC) induce severe burden on base stations. To address such issue, automatic modulation classification (AMC) can reduce signaling overhead through blindly recognizing the modulation types without handshaking, thus playing an important role in intelligent modems. The emerging deep learning (DL) technique stores intelligence in the network, resulting in superior performance over traditional approaches. However, DL-based approaches suffer from computational complexity and offline training overhead, which severely hinder practical applications. Furthermore, the required massive resources for online retraining should be further considered under time-varying fading channel, which has not been studied in the prior arts. In this work, an accumulated polar feature-based DL with channel compensation mechanism is proposed to cope with the aforementioned issues. Firstly, the simulation results show that learning features from polar domain with historical information can approach optimal performance and reduce offline training overhead by 37.8 times. Secondly, the proposed neural network-based channel estimator (NN-CE) can learn the channel response and compensate for the distorted channel with 13% improvement. Moreover, in applying the novel NN-CE in the time-varying fading channel, two efficient mechanisms of online retraining are proposed, which reduce transmission overhead and retraining overhead by 90% and 76%, respectively. Finally, the performance of the proposed approach is evaluated and compared with prior arts on a public dataset to demonstrate its great efficiency.
Application of ICA on Self-Interference Cancellation of In-Band Full Duplex Systems

In this letter, we propose a modified version of Fast Independent Component Analysis (FICA) algorithm to solve the self-interference cancellation (SIC) problem in In-band Full Duplex (IBFD) communication systems. The complex mixing problem is mathematically formulated to suit the real-valued blind source separation (BSS) algorithms. In addition, we propose a method to estimate the ambiguity factors associated with ICA lumped together with the channels and residual separation error. Experiments were performed on an FD platform where FICA-based BSS was applied for SIC in the frequency domain. Experimental results show superior performance compared to least squares SIC by up to 6 dB gain in the SNR.
Tight Localizations of Feedback Sets

The classical NP–hard feedback arc set problem (FASP) and feedback vertex set problem (FVSP) ask for a minimum set of arcs ε ⊆ E or vertices ν ⊆ V whose removal G ∖ ε, G ∖ ν makes a given multi–digraph G=(V, E) acyclic, respectively. Though both problems are known to be APX–hard, constant ratio approximations or proofs of inapproximability are unknown. We propose a new universal O(|V||E|4)–heuristic for the directed FASP. While a ratio of r ≈ 1.3606 is known to be a lower bound for the APX–hardness, at least by empirical validation we achieve an approximation of r ≤ 2. Most of the relevant applications, such as circuit testing, ask for solving the FASP on large sparse graphs, which can be done efficiently within tight error bounds with our approach.
Adapting deep learning for sentiment classification of code-switched informal short text

Nowadays, an abundance of short text is being generated that uses nonstandard writing styles influenced by regional languages. Such informal and code-switched content are under-resourced in terms of labeled datasets and language models even for popular tasks like sentiment classification. In this work, we (1) present a labeled dataset called MultiSenti for sentiment classification of code-switched informal short text, (2) explore the feasibility of adapting resources from a resource-rich language for an informal one, and (3) propose a deep learning-based model for sentiment classification of code-switched informal short text. We aim to achieve this without any lexical normalization, language translation, or code-switching indication. The performance of the proposed models is compared with three existing multilingual sentiment classification models. The results show that the proposed model performs better in general and adapting character-based embeddings yield equivalent performance while being computationally more efficient than training word-based domain-specific embeddings.
Computationally Efficient NER Taggers with Combined Embeddings and Constrained Decoding

Current State-of-the-Art models in Named Entity Recognition (NER) are neural models with a Conditional Random Field (CRF) as the final network layer, and pre-trained "contextual embeddings". The CRF layer is used to facilitate global coherence between labels, and the contextual embeddings provide a better representation of words in context. However, both of these improvements come at a high computational cost. In this work, we explore two simple techniques that substantially improve NER performance over a strong baseline with negligible cost. First, we use multiple pre-trained embeddings as word representations via concatenation. Second, we constrain the tagger, trained using a cross-entropy loss, during decoding to eliminate illegal transitions. While training a tagger on CoNLL 2003 we find a $786$\% speed-up over a contextual embeddings-based tagger without sacrificing strong performance. We also show that the concatenation technique works across multiple tasks and datasets. We analyze aspects of similarity and coverage between pre-trained embeddings and the dynamics of tag co-occurrence to explain why these techniques work. We provide an open source implementation of our tagger using these techniques in three popular deep learning frameworks --- TensorFlow, Pytorch, and DyNet.
Sharp relations between volume growth, isoperimetry and resistance in vertex-transitive graphs

We prove sharp bounds on the probability that the simple random walk on a vertex-transitive graph escapes the ball of radius r before returning to its starting point. In particular, this shows that if the ball of radius r has size slightly greater than r^2 then this probability is bounded from below. On the other hand, we show that if the ball of radius r has volume slightly less than r^3 then this probability decays logarithmically for all larger balls. These results represent a finitary refinement of Varopoulos's theorem that a random walk on a vertex-transitive graph is recurrent if and only if the graph has at most quadratic volume growth. They also combine to show that there is a gap for escape probabilities: there exists a universal constant c such that the random walk on an arbitrary vertex-transitive graph is either recurrent or has a probability of at least c of escaping to infinity. We also prove versions of these results for finite graphs, in particular confirming and strengthening a conjecture of Benjamini and Kozma from 2002. Amongst other things, we also generlise our results to give a sharp finitary version of the characterisation of p-parabolic vertex-transitive graphs, and prove a number of sharp isoperimetric inequalities for vertex-transitive graphs.
The VLT-FLAMES Tarantula Survey

Analysis of late O-type stars observed in the Large Magellanic Cloud (LMC) by the VLT-FLAMES Tarantula Survey revealed a discrepancy between the physical properties estimated from model-atmosphere analysis and those expected from their morphological classifications. Here we revisit the analysis of 32 of these puzzling objects using new hydrogen-helium-silicon FASTWIND models and a different fitting approach to re-evaluate their physical properties. Our new analysis confirms that these stars indeed have properties that are typical of late O-type dwarfs. We also present the first estimates of silicon abundances for O-type stars in the 30 Dor clusters NGC 2060 and NGC 2070, with a weighted mean abundance for our sample of ɛSi = 7.05 ± 0.03. Our values are ~0.20 dex lower than those previously derived for B-type stars in the LMC clusters N 11 and NGC 2004 using TLUSTY models. Various possibilities (e.g. differences in the analysis methods, effects of microturbulence, and real differences between stars in different clusters) were considered to account for these results. We also used our grid of FASTWIND models to reassess the impact of using the Galactic classification criteria for late O-type stars in the LMC by scrutinising their sensitivity to different stellar properties. At the cool edge of the O star regime the He II λ4686/He I λ4713 ratio used to assign luminosity class for Galactic stars can mimic giants or bright giants in the LMC, even for objects with high gravities (log g > 4.0 dex). We argue that this line ratio is not a reliable luminosity diagnostic for late O-type stars in the LMC, and that the Si IV λ4089/He I λ4026 ratio is more robust for these types.
EcoNAS: Finding Proxies for Economical Neural Architecture Search

Neural Architecture Search (NAS) achieves significant progress in many computer vision tasks. While many methods are proposed to improve the efficiency of NAS, the search progress is still laborious because training and evaluating plausible architectures over large search space is time-consuming. Assessing network candidates under a proxy (i.e., computationally reduced setting) thus becomes inevitable. In this paper, we observe that most existing proxies exhibit different behaviors in maintaining the rank consistency among network candidates. In particular, some proxies can be more reliable – the rank of candidates does not differ much comparing their reduced setting performance and final performance. In this paper, we systematically investigate some widely adopted reduction factors and report our observations. Inspired by these observations, we present a reliable proxy and further formulate a hierarchical proxy strategy that spends more computations on candidate networks that are potentially more accurate, while discards unpromising ones in early stage with a fast proxy. This leads to an economical evolutionary-based NAS (EcoNAS), which achieves an impressive 400×search time reduction in comparison to the evolutionary-based state of the art [19] (8 v.s. 3150 GPU days). Some new proxies led by our observations can also be applied to accelerate other NAS methods while still able to discover good candidate networks with performance matching those found by previous proxy strategies. Codes and models will be released to facilitate future research.
A Study of Bug Resolution Characteristics in Popular Programming Languages

This paper presents a large-scale study that investigates the bug resolution characteristics among popular Github projects written in different programming languages. We explore correlations but, of course, we cannot infer causation. Specifically, we analyse bug resolution data from approximately 70 million Source Line of Code, drawn from 3 million commits to 600 GitHub projects, primarily written in 10 programming languages. We find notable variations in apparent bug resolution time and patch (fix) size. While interpretation of results from such large-scale empirical studies is inherently difficult, we believe that the differences in medians are sufficiently large to warrant further investigation, replication, re-analysis and follow up research. For example, in our corpus, the median apparent bug resolution time (elapsed time from raise to resolve) for Ruby was 4X that for Go and 2.5X for Java. We also found that patches tend to touch more files for the corpus of strongly typed and for statically typed programs. However, we also found evidence for a lower elapsed resolution time for bug resolution committed to projects constructed from statically typed languages. These findings, if replicated in subsequent follow on studies, may shed further empirical light on the debate about the importance of static typing.
A Convective Dredge-up Model as the Origin of Hydrogen in DBA White Dwarfs

We revisit the problem of the formation of DB white dwarfs, as well as the origin of hydrogen in DBA stars, using a new set of envelope model calculations with stratified and mixed hydrogen/helium compositions. We first describe an approximate model to simulate the so-called convective dilution process, where a thin, superficial hydrogen radiative layer is gradually eroded by the underlying and more massive convective helium envelope, thus transforming a DA white dwarf into a DB star. We show that this convective dilution process is able to account for the large increase in the number of DB white dwarfs below Teff ∼ 20,000 K, but that the residual hydrogen abundances expected from this process are still orders of magnitude lower than those observed in DBA white dwarfs. Scenarios involving the accretion of hydrogen from the interstellar medium or other external bodies have often been invoked to explain these overabundances of hydrogen. In this paper, we describe a new paradigm where hydrogen, initially diluted within the thick stellar envelope, is still present and slowly diffuses upward in the deeper layers of a Teff ∼ 20,000 K white dwarf. When the convective dilution process occurs, the bottom of the mixed H/He convection zone sinks deep into the star, resulting in large amounts of hydrogen being dredged up to the stellar surface, a phenomenon similar to that invoked in the context of DQ white dwarfs.
A Spectral Hidden Markov Model for Nonstationary Oscillatory Processes

We propose to model time-varying periodic and oscillatory processes by means of a hidden Markov model where the states are defined through the spectral properties of a periodic regime. The number of states is unknown along with the relevant periodicities, the role and number of which may vary across states. We address this inference problem by a Bayesian nonparametric hidden Markov model assuming a sticky hierarchical Dirichlet process for the switching dynamics between different states while the periodicities characterizing each state are explored by means of a trans-dimensional Markov chain Monte Carlo sampling step. We develop the full Bayesian inference algorithm and illustrate the use of our proposed methodology for different simulation studies as well as an application related to respiratory research which focuses on the detection of apnea instances in human breathing traces.
Existence and uniqueness of Green's function to a nonlinear Yamabe problem

For a given finite subset $S$ of a compact Riemannian manifold $(M,g)$ whose Schouten curvature tensor belongs to a given cone, we establish a necessary and sufficient condition for the existence and uniqueness of a conformal metric on $M \setminus S$ such that each point of $S$ corresponds to an asymptotically flat end and that the Schouten tensor of the conformal metric belongs to the boundary of the given cone. We further show that these metrics arise as suitably rescaled limit for certain blow-up solutions to the corresponding nonlinear Yamabe problems. As a by-product, we define a purely local notion of Ricci lower bound for continuous metrics which are conformal to smooth metrics and prove a corresponding volume comparison theorem.
Quantum Sensing and Control of Spin-State Dynamics in the Radical-Pair Mechanism

Radical pairs and the dynamics they undergo are prevalent in many chemical and biological systems. Specifically, it has been proposed that the radical pair mechanism results from a relatively strong hyperfine interaction with its intrinsic nuclear spin environment. While the existence of this mechanism is undisputed, the nanoscale details remain to be experimentally shown. We analyze here the role of a quantum sensor in detecting the spin dynamics (non-Markovian) of individual radical pairs in the presence of a weak magnetic field. We show how quantum control methods can be used to set apart the dynamics of radical pair mechanism at various stages of the evolution. We envisage these findings having far-reaching implications to the understanding of the physical mechanism in magnetoreception and other bio-chemical processes with a microscopic detail.
Estimation of the spatial weighting matrix for regular lattice data—An adaptive lasso approach with cross‐sectional resampling

Spatial autoregressive models typically rely on the assumption that the spatial dependence structure is known in advance and is represented by a deterministic spatial weights matrix, although it is unknown in most empirical applications. Thus, we investigate the estimation of sparse spatial dependence structures for regular lattice data. In particular, an adaptive least absolute shrinkage and selection operator (lasso) is used to select and estimate the individual nonzero connections of the spatial weights matrix. To recover the spatial dependence structure, we propose cross‐sectional resampling, assuming that the random process is exchangeable. The estimation procedure is based on a two‐step approach to circumvent simultaneity issues that typically arise from endogenous spatial autoregressive dependencies. The two‐step adaptive lasso approach with cross‐sectional resampling is verified using Monte Carlo simulations. Eventually, we apply the procedure to model nitrogen dioxide (NO2) concentrations and show that estimating the spatial dependence structure contrary to using prespecified weights matrices improves the prediction accuracy considerably.