Miracle-dz/UniFAR-FTUs huggingface dataset:
Transition kernel couplings of the Metropolis–Hastings algorithm

Couplings play a central role in the analysis of Markov chain convergence to stationarity and in the construction of novel Markov chain Monte Carlo diagnostics, estimators, and variance reduction techniques. The quality of the resulting bounds or methods typically depends on how quickly the coupling induces meeting between chains, a property sometimes referred to as its eﬃciency. The design of eﬃcient Markovian couplings remains a diﬃcult open question, especially for discrete time processes. In pursuit of this goal, in this paper we fully characterize the couplings of the Metropolis–Hastings (MH) transition kernel, providing necessary and suﬃcient conditions in terms of the underlying proposal and acceptance distributions. We apply these results to characterize the set of maximal couplings of the MH kernel, resolving open questions posed in O’Leary et al. [2020] on the structure and properties of these couplings. These results represent an advance in the understanding of the MH kernel and a step toward the formulation of eﬃcient couplings for this popular family of algorithms.
QoS-aware Link Scheduling Strategy for Data Transmission in SDVN

The vehicular ad-hoc network (VANET) based on dedicated short-range communication (DSRC) is a distributed communication system, in which all the nodes share the wireless channel with carrier sense multiple access/collision avoid (CSMA/CA) protocol. However, the backoff mechanism of CSMA/CA in the channel contention might cause uncertain transmission delay and impede a certain quality of service (QoS) of applications. Moreover, there still exists a possibility of parlous data-packets collision, especially for broadcast or non-acknowledgement (NACK) transmissions. The original contributions of this paper are summarized as follows: (1) Model the packets collision probability of broadcast or NACK transmission in VANET with the combination theory and investigate the potential influence of miss my packets (MMP) problem. (2) Based on the software define vehicular network (SDVN) framework and QoS requirement, a novel link-level scheduling strategy, which determines the start-sending time for each connection, is proposed to maximize packets delivery ratio (PDR). Alternatively, maximizing PDR has been converted to the overlap minimization among transmission durations. (3) Meanwhile, an innovative transmission scheduling greedy search (TSGS) algorithm is originally proposed to mitigate computational complexity. Extensive simulations have been done in a unified platform Veins combining SUMO and OMNET++. And numerous results show that the proposed algorithm can effectively improve the PDR by at least 15%, enhance the collision-avoidance performance by almost 40%, and reduce the MMP ratio by about 3% compared with the random transmitting, meanwhile meet the QoS requirement.
Use of the digestive gland of the oyster Crassostrea rhizophorae (Guilding, 1828) as a bioindicator of Zn, Cd and Cu contamination in estuarine sediments (south-east Brazil)

This study demonstrates the accumulation of Zn, Cd and Cu in the digestive gland of the oyster Crassostrea rhizophorae in response to the contamination of sediments and discusses the potential use of this specific organ in monitoring metal contamination in tropical areas. Sediment and oyster samples were collected from coastal Rio de Janeiro sites with different levels of human impact: Sepetiba Bay, Guanabara Bay and the Paraty coast. Metal concentrations were measured using inductively coupled plasma optical emission spectrometry (ICP OES). Significant statistical differences (p<0.001; p<0.05) were observed for Zn and Cd concentrations in the digestive gland; the highest Zn concentrations were found at Sepetiba, followed by Guanabara and Paraty. The highest digestive gland Cd concentrations were found at Paraty, followed by Sepetiba and Guanabara. These concentrations were proportional to those found in the sediments. There was no significant difference (p>0.05) in Cu among the sampling sites. The highest digestive-gland Cu concentration was also found at Sepetiba, followed by Guanabara. The biosediment accumulation factor indicated a gradient of sediment contamination for Zn and Cd. The digestive gland of C. rhizophorae can be a potential indicator of trace metal contamination in sediments from tropical estuarine environments.
Characterizing and Comparing External Measures for the Assessment of Cluster Analysis and Community Detection

In the context of cluster analysis and graph partitioning, many external evaluation measures have been proposed in the literature to compare two partitions of the same set. This makes the task of selecting the most appropriate measure for a given situation a challenge for the end user. However, this issue is overlooked in the literature. Researchers tend to follow tradition and use the standard measures of their field, although they often became standard only because previous researchers started consistently using them. In this work, we propose a new empirical evaluation framework to solve this issue, and help the end user selecting an appropriate measure for their application. For a collection of candidate measures, it first consists in describing their behavior by computing them for a generated dataset of partitions, obtained by applying a set of predefined parametric partition transformations. Second, our framework performs a regression analysis to characterize the measures in terms of how they are affected by these parameters and transformations. This allows both describing and comparing the measures. Our approach is not tied to any specific measure or application, so it can be applied to any situation. We illustrate its relevance by applying it to a selection of standard measures, and show how it can be put in practice through two concrete use cases.
Metalearning: Sparse Variable-Structure Automata

Dimension of the encoder output (i.e., the code layer) in an autoencoder is a key hyper-parameter for representing the input data in a proper space. This dimension must be carefully selected in order to guarantee the desired reconstruction accuracy. Although overcomplete representation can address this dimension issue, the computational complexity will increase with dimension. Inspired by non-parametric methods, here, we propose a metalearning approach to increase the number of basis vectors used in dynamic sparse coding on the fly. An actor-critic algorithm is deployed to automatically choose an appropriate dimension for feature vectors regarding the required level of accuracy. The proposed method benefits from online dictionary learning and fast iterative shrinkage-thresholding algorithm (FISTA) as the optimizer in the inference phase. It aims at choosing the minimum number of bases for the overcomplete representation regarding the reconstruction error threshold. This method allows for online controlling of both the representation dimension and the reconstruction error in a dynamic framework.
A Secure Learning Control Strategy via Dynamic Camouflaging for Unknown Dynamical Systems under Attacks

This paper presents a secure reinforcement learning (RL) based control method for unknown linear time-invariant cyber-physical systems (CPSs) that are subjected to compositional attacks such as eavesdropping and covert attack. We consider the attack scenario where the attacker learns about the dynamic model during the exploration phase of the learning conducted by the designer to learn a linear quadratic regulator (LQR), and thereafter, use such information to conduct a covert attack on the dynamic system, which we refer to as doubly learning-based control and attack (DLCA) framework. We propose a dynamic camouflaging based attack-resilient reinforcement learning (ARRL) algorithm which can learn the desired optimal controller for the dynamic system, and at the same time, can inject sufficient misinformation in the estimation of system dynamics by the attacker. The algorithm is accompanied by theoretical guarantees and extensive numerical experiments on a consensus multi-agent system and on a benchmark power grid model.
High resolution 3D ultrasonic breast imaging by time-domain full waveform inversion

Ultrasound tomography (UST) scanners allow quantitative images of the human breast’s acoustic properties to be derived with potential applications in screening, diagnosis and therapy planning. Time domain full waveform inversion (TD-FWI) is a promising UST image formation technique that fits the parameter fields of a wave physics model by gradient-based optimization. For high resolution 3D UST, it holds three key challenges: firstly, its central building block, the computation of the gradient for a single US measurement, has a restrictively large memory footprint. Secondly, this building block needs to be computed for each of the 103–104 measurements, resulting in a massive parallel computation usually performed on large computational clusters for days. Lastly, the structure of the underlying optimization problem may result in slow progression of the solver and convergence to a local minimum. In this work, we design and evaluate a comprehensive computational strategy to overcome these challenges: firstly, we exploit a gradient computation based on time reversal that dramatically reduces the memory footprint at the expense of one additional wave simulation per source. Secondly, we break the dependence on the number of measurements by using source encoding (SE) to compute stochastic gradient estimates. Also we describe a more accurate, TD-specific SE technique with a finer variance control and use a state-of-the-art stochastic LBFGS method. Lastly, we design an efficient TD multi-grid scheme together with preconditioning to speed up the convergence while avoiding local minima. All components are evaluated in extensive numerical proof-of-concept studies simulating a bowl-shaped 3D UST breast scanner prototype. Finally, we demonstrate that their combination allows us to obtain an accurate 442 × 442 × 222 voxel image with a resolution of 0.5 mm using Matlab on a single GPU within 24 h.
Sequential prediction under log-loss and misspecification

We consider the question of sequential prediction under the log-loss in terms of cumulative regret. Namely, given a hypothesis class of distributions, learner sequentially predicts the (distribution of the) next letter in sequence and its performance is compared to the baseline of the best constant predictor from the hypothesis class. The well-specified case corresponds to an additional assumption that the data-generating distribution belongs to the hypothesis class as well. Here we present results in the more general misspecified case. Due to special properties of the log-loss, the same problem arises in the context of competitive-optimality in density estimation, and model selection. For the $d$-dimensional Gaussian location hypothesis class, we show that cumulative regrets in the well-specified and misspecified cases asymptotically coincide. In other words, we provide an $o(1)$ characterization of the distribution-free (or PAC) regret in this case -- the first such result as far as we know. We recall that the worst-case (or individual-sequence) regret in this case is larger by an additive constant ${d\over 2} + o(1)$. Surprisingly, neither the traditional Bayesian estimators, nor the Shtarkov's normalized maximum likelihood achieve the PAC regret and our estimator requires special"robustification"against heavy-tailed data. In addition, we show two general results for misspecified regret: the existence and uniqueness of the optimal estimator, and the bound sandwiching the misspecified regret between well-specified regrets with (asymptotically) close hypotheses classes.
Spatiotemporal Ground Reaction Force Analysis using Convolutional Neural Networks to Analyze Parkinsonian Gait

Parkinson’s disease (PD) is a non-curable disease that commonly found among elders that greatly reduce their quality of life. PD primarily affects the gait pattern and slowly changes the walking gait from the normality to disability. The early diagnosing of PD is important for treatments and gait pattern analysis is used as a technique to diagnose PD. The present paper has identified the raw spatiotemporal ground reaction force (GRF) as a key parameter to identify the changes in human gait patterns associated with PD. The changes in GRF are identified using a convolutional neural network through pre-processing, conversion, recognition, and performance evaluation. The proposed algorithm is capable of identifying the severity of the PD and distinguishing the parkinsonian gait from the healthy gait. The technique has shown a 97% of accuracy in automatic decision-making process.
Influence of support acidity on the performance of size-confined Pt nanoparticles in the chemoselective hydrogenation of acetophenone

Size-confined Pt nanoparticles of about 1.5 nm have been introduced into [Al]MCM-41 supports with similar acid strength but various population densities of acid sites by means of wet impregnation. The Pt nanoparticles covered preferentially the surface Bronsted acid sites (BAS) of the supports or were located near acid sites rather than on the bigger free space between acid sites even at a very low acid density (6 BAS per 1000 nm2). The free BAS around the Pt particles did not interact with Pt atoms and the electronic properties of the Pt nanoparticles as probed by DRIFTS combined with CO adsorption were similar for Pt/[Al]MCM-41 with and without nearby free BAS. Ionic effects were generated by the Pt-covered acid sites, whereas the population of BAS did not contribute significantly to the ionic effects induced on the Pt nanoparticles. The coverage of BAS of similar strength by platinum nanoparticles led to similar chemoselectivity and product distribution in acetophenone (Aph) hydrogenation, though the density of BAS on the supports increased by more than 11 times. However, increasing the number of BAS on the supports significantly changed the hydrogenation rate. TOFs continuously increased from 125 h−1 up to 534 h−1, when the population of free BAS increased from 18.2 BAS per 1000 nm2 to 39.9 BAS per 1000 nm2. When the free BAS density was further increased to 70.4 BAS per 1000 nm2, the TOF then dropped to 176 h−1. The hydrogenation pathway is similar for both monofunctional (Pt covering all BAS) and bifunctional catalysts (Pt with free BAS), and the reaction was initiated on the Pt surface. This finding indicates that proper tuning of the population density of acid sites on the support can significantly improve the catalytic performance of the supported metal catalysts while keeping similar product selectivities.
Relaxed Clustered Hawkes Process for Procrastination Modeling in MOOCs

Hawkes processes have been shown to be efficient in modeling bursty sequences in a variety of applications, such as finance and social network activity analysis. Traditionally, these models parameterize each process independently and assume that the history of each point process can be fully observed. Such models could however be inefficient or even prohibited in certain real-world applications, such as in the field of education, where such assumptions are violated. Motivated by the problem of detecting and predicting student procrastination in students Massive Open Online Courses (MOOCs) with missing and partially observed data, in this work, we propose a novel personalized Hawkes process model (RCHawkes-Gamma) that discovers meaningful student behavior clusters by jointly learning all partially observed processes simultaneously, without relying on auxiliary features. Our experiments on both synthetic and real-world education datasets show that RCHawkes-Gamma can effectively recover student clusters and their temporal procrastination dynamics, resulting in better predictive performance of future student activities. Our further analyses of the learned parameters and their association with student delays show that the discovered student clusters unveil meaningful representations of various procrastination behaviors in students.
On the Universal Coefficient Formula and Derivative lim ← − ( i )

From the beginning of 1960, there were many approaches to define exact homology theories using the methods of homological algebra (using an injective resolution) [Bor-Mo], [Mas4], [Mas2], [Kuz], [Skl]. These approaches gave us the unique homology theory on the category of compact Hausdorff spaces [Kuz], [Skl]. Our aim is to develop a method of homological algebra which gives opportunity to define on the category of general topological spaces a unique exact homology theory, generated by the given cochain complex. If H is the cohomology of the cochain complex C = Hom(C∗;G), then the cohomology H is said to be generated by chain complex C∗. If a chain complex C∗ is free, then there is a Universal Coefficient Formula of a cohomology theory [Eil-St], [Mas1], [Sp]. In the paper [Mdz3], using this formula and derivatives of inverse limit, a long exact sequence is written, which shows a relation of a cohomology of direct limit of chain complexes and inverse limit of cohomology groups of 2020 Mathematics Subject Classification: 55N10
Nonrepetitively 3-Colorable Subdivisions of Graphs with a Logarithmic Number of Subdivisions per edge

We show that for every graph $G$ and every graph $H$ obtained by subdividing each edge of $G$ at least $\Omega(\log |V(G)|)$ times, $H$ is nonrepetitively 3-colorable. In fact, we show that $\Omega(\log \pi'(G))$ subdivisions per edge are enough, where $\pi'(G)$ is the nonrepetitive chromatic index of $G$. This answers a question of Wood and improves a similar result of Pezarski and Zmarz that stated the existence of at least one 3-colorable subdivision with a linear number of subdivision vertices per edge.
New Formulation for Coloring Circle Graphs and its Application to Capacitated Stowage Stack Minimization

A circle graph is a graph in which the adjacency of vertices can be represented as the intersection of chords of a circle. The problem of calculating the chromatic number is known to be NP-complete, even on circle graphs. In this paper, we propose a new integer linear programming formulation for a coloring problem on circle graphs. We also show that the linear relaxation problem of our formulation finds the fractional chromatic number of a given circle graph. As a byproduct, our formulation gives a polynomial-sized linear programming formulation for calculating the fractional chromatic number of a circle graph. We also extend our result to a formulation for a capacitated stowage stack minimization problem.
EmpathBERT: A BERT-based Framework for Demographic-aware Empathy Prediction

Affect preferences vary with user demographics, and tapping into demographic information provides important cues about the users’ language preferences. In this paper, we utilize the user demographics and propose EmpathBERT, a demographic-aware framework for empathy prediction based on BERT. Through several comparative experiments, we show that EmpathBERT surpasses traditional machine learning and deep learning models, and illustrate the importance of user demographics, for predicting empathy and distress in user responses to stimulative news articles. We also highlight the importance of affect information in the responses by developing affect-aware models to predict user demographic attributes.
LPWAN in the TV White Spaces

Low-Power Wide-Area Network (LPWAN) is an enabling Internet-of-Things technology that supports long-range, low-power, and low-cost connectivity to numerous devices. To avoid the crowd in the limited ISM band (where most LPWANs operate) and cost of licensed band, the recently proposed Sensor Network over White Spaces (SNOW) is a promising LPWAN platform that operates over the TV white spaces. As it is a very recent technology and is still in its infancy, the current SNOW implementation uses the Universal Software Radio Peripheral devices as LPWAN nodes, which has high costs (≈$750 USD per device) and large form-factors, hindering its applicability in practical deployment. In this article, we implement SNOW using low-cost, low form-factor, low-power, and widely available commercial off-the-shelf (COTS) devices to enable its practical and large-scale deployment. Our choice of the COTS device (TI CC13x0: CC1310 or CC1350) consequently brings down the cost and form-factor of a SNOW node by 25× and 10×, respectively. Such implementation of SNOW on the CC13x0 devices, however, faces a number of challenges to enable link reliability and communication range. Our implementation addresses these challenges by handling peak-to-average power ratio problem, channel state information estimation, carrier frequency offset estimation, and near-far power problem. Our deployment in the city of Detroit, Michigan, demonstrates that CC13x0-based SNOW can achieve uplink and downlink throughputs of 11.2 and 4.8 kbps per node, respectively, over a distance of 1 km. Also, the overall throughput in the uplink increases linearly with the increase in the number of SNOW nodes.
Extending Neural Keyword Extraction with TF-IDF tagset matching

Keyword extraction is the task of identifying words (or multi-word expressions) that best describe a given document and serve in news portals to link articles of similar topics. In this work, we develop and evaluate our methods on four novel data sets covering less-represented, morphologically-rich languages in European news media industry (Croatian, Estonian, Latvian, and Russian). First, we perform evaluation of two supervised neural transformer-based methods, Transformer-based Neural Tagger for Keyword Identification (TNT-KID) and Bidirectional Encoder Representations from Transformers (BERT) with an additional Bidirectional Long Short-Term Memory Conditional Random Fields (BiLSTM CRF) classification head, and compare them to a baseline Term Frequency - Inverse Document Frequency (TF-IDF) based unsupervised approach. Next, we show that by combining the keywords retrieved by both neural transformer-based methods and extending the final set of keywords with an unsupervised TF-IDF based technique, we can drastically improve the recall of the system, making it appropriate for usage as a recommendation system in the media house environment.