Miracle-dz/UniFAR-FTUs huggingface dataset:
Local number variances and hyperuniformity of the Heisenberg family of determinantal point processes

The bulk scaling limit of eigenvalue distribution on the complex plane C of the complex Ginibre random matrices provides a determinantal point process (DPP). This point process is a typical example of disordered hyperuniform system characterized by an anomalous suppression of large-scale density fluctuations. As extensions of the Ginibre DPP, we consider a family of DPPs defined on the D-dimensional complex spaces C , D∈N , in which the Ginibre DPP is realized when D = 1. This one-parameter family ( D∈N ) of DPPs is called the Heisenberg family, since the correlation kernels are identified with the Szegő kernels for the reduced Heisenberg group. For each D, using the modified Bessel functions, an exact and useful expression is shown for the local number variance of points included in a ball with radius R in R2D≃CD . We prove that any DPP in the Heisenberg family is in the hyperuniform state of class I, in the sense that the number variance behaves as R 2D−1 as R → ∞. Our exact results provide asymptotic expansions of the number variances in large R.
Work Extraction and Performance of Colloidal Heat Engines in Viscoelastic Baths

A colloidal particle embedded in a fluid can be used as a microscopic heat engine by means of a sequence of cyclic transformations imposed by an optical trap. We investigate a model for the operation of such kind of Brownian engines when the surrounding medium is viscoelastic, which endows the particle dynamics with memory friction. We analyze the effect of the relaxation time of the fluid on the performance of the colloidal engine under finite-time Stirling cycles. We find that, due to the frequency-dependence of the friction in viscoelastic fluids, the mean power delivered by the engine and its efficiency can be highly enhanced as compared to those in a viscous environment with the same zero-shear viscosity. In addition, with increasing fluid relaxation time the interval of cycle times at which positive power output can be delivered by the engine broadens. Our results reveal the importance of the transient behavior of the friction experienced by a Brownian heat engine in a complex fluid, which cannot be neglected when driven by thermodynamic cycles of finite duration.
Self-attention Comparison Module for Boosting Performance on Retrieval-based Open-Domain Dialog Systems

Since the pre-trained language models are widely used, retrieval-based open-domain dialog systems, have attracted considerable attention from researchers recently. Most of the previous works select a suitable response only according to the matching degree between the query and each individual candidate response. Although good performance has been achieved, these recent works ignore the comparison among the candidate responses, which could provide rich information for selecting the most appropriate response. Intuitively, better decisions could be made when the models can get access to the comparison information among all the candidate responses. In order to leverage the comparison information among the candidate responses, in this paper, we propose a novel and plug-in Self-attention Comparison Module for retrieval-based open-domain dialog systems, called SCM. Extensive experiment results demonstrate that our proposed self-attention comparison module effectively boosts the performance of the existing retrieval-based open-domain dialog systems. Besides, we have publicly released our source codes for future research.
Quantum Criticality in Topological Superfluids/Superconductors: Surface criticality, Thermal properties, and Lifshitz Majorana fields

Time reversal invariant (TRI) topological superfluids (TSFs) and topological superconductors (TSCs) are robust symmetry protected gapped topological states. In this article, we study the evolution of these topological states in the presence of time reversal symmetry breaking (TRB) fields and/or sufficiently large TRI fields. Physically, one of the realizations of TRB fields can be internal spin exchange fields due to background magnetic ordering. We find that the fully gapped TSFs and TSCs are generically separated from other nodal states by various zero temperature quantum critical points that are characterized by generalized quantum Lifshitz fields with distinct scaling properties. These emergent Lifshitz fields also define finite temperature properties in quantum critical regimes. Moreover, for a certain subset of TRB fields, there exist a precursor to bulk transitions, where surface states can also exhibit quantum critical behavior near zero fields. The possibility for a fully gapped TSF/TSC to smoothly crossover into a topologically trivial state without a phase transition is also examined.
Neural Joint Entropy Estimation

Estimating the entropy of a discrete random variable is a fundamental problem in information theory and related fields. This problem has many applications in various domains, including machine learning, statistics, and data compression. Over the years, a variety of estimation schemes have been suggested. However, despite significant progress, most methods still struggle when the sample is small, compared to the variable's alphabet size. In this work, we introduce a practical solution to this problem, which extends the work of McAllester and Statos. The proposed scheme uses the generalization abilities of cross-entropy estimation in deep neural networks (DNNs) to introduce improved entropy estimation accuracy. Furthermore, we introduce a family of estimators for related information-theoretic measures, such as conditional entropy and mutual information (MI). We show that these estimators are strongly consistent and demonstrate their performance in a variety of use cases. First, we consider large alphabet entropy estimation. Then, we extend the scope to MI estimation. Next, we apply the proposed scheme to conditional MI estimation, as we focus on independence testing tasks. Finally, we study a transfer entropy (TE) estimation problem. The proposed estimators demonstrate improved performance compared to existing methods in all of these setups.
Lipid production by microalgae Chlorella pyrenoidosa cultivated in palm oil mill effluent (POME) using hybrid photo bioreactor (HPBR).

Palm oil mill effluent (POME) as high organic wastewater is a promising substrate in the scenario of algae bloom, by enhancing its lipid production to be further used in biofuel manufacturing. In this research, effect of POME as high nutritional substrate, different cultivation scales such as flask or hybrid photo bioreactor (HPBR), carbon-to-total nitrogen (C:TN) ratio, various light and dark cycles, and diverse organic loading rates (OLR) on the lipid productivity of microalgae Chlorella pyrenoidosa was assessed. Results demonstrated high microalgae growth rate (1.80 d �1 ) at 250 mg COD/L of substrate, while moderate increase (1.37 d �1 ) and growth inhibition (0.80 d �1 ) were recorded at 500 mg COD/L and 1,000 mg COD/L of substrate concentration, respectively. Furthermore, a result proved that low-volume cultivation of microalgae in a flask with lipid productivity at 1.78 mg/L d significantly restricted microalgae production compared with larger scale such as HPBR with lipid productivity at 230 mg/L d. Moreover, highest lipid production at 44.5, 114.9, and 100.5 mg/L d, C:TN ratio at 100:6 and OLR at 36 kg COD/m 3 d, respectively, were documented for continuous illuminaion (24 h). The combination of above conditions can be optimal setting to reach the highest lipid productivity by microalgae C. pyrenoidosa. In addition, the results of this study can be further considered in microalgae lipid production using other wastewaters in order to enhance the lipid production as well as wastewater treating functions.
Understanding Infection Progression under Strong Control Measures through Universal COVID‐19 Growth Signatures

Widespread growth signatures in COVID‐19 confirmed case counts are reported, with sharp transitions between three distinct dynamical regimes (exponential, superlinear, and sublinear). Through analytical and numerical analysis, a novel framework is developed that exploits information in these signatures. An approach well known to physics is applied, where one looks for common dynamical features, independently from differences in other factors. These features and associated scaling laws are used as a powerful tool to pinpoint regions where analytical derivations are effective, get an insight into qualitative changes of the disease progression, and infer the key infection parameters. The developed framework for joint analytical and numerical analysis of empirically observed COVID‐19 growth patterns can lead to a fundamental understanding of infection progression under strong control measures, applicable to outbursts of both COVID‐19 and other infectious diseases.
Intermediate-mass Black Holes from High Massive-star Binary Fractions in Young Star Clusters

Black holes formed in dense star clusters, where dynamical interactions are frequent, may have fundamentally different properties than those formed through isolated stellar evolution. Theoretical models for single-star evolution predict a gap in the black hole mass spectrum from roughly 40–120 M⊙ caused by (pulsational) pair-instability supernovae. Motivated by the recent LIGO/Virgo event GW190521, we investigate whether black holes with masses within or in excess of this “upper-mass gap” can be formed dynamically in young star clusters through strong interactions of massive stars in binaries. We perform a set of N-body simulations using the CMC cluster-dynamics code to study the effects of the high-mass binary fraction on the formation and collision histories of the most massive stars and their remnants. We find that typical young star clusters with low metallicities and high binary fractions in massive stars can form several black holes in the upper-mass gap and often form at least one intermediate-mass black hole. These results provide strong evidence that dynamical interactions in young star clusters naturally lead to the formation of more massive black hole remnants.
Is it feasible to detect FLOSS version release events from textual messages? A case study on Stack Overflow

Topic Detection and Tracking (TDT) is a very active research question within the area of text mining, generally applied to news feeds and Twitter datasets, where topics and events are detected. The notion of “event” is broad, but typically it applies to occurrences that can be detected from a single post or a message. Little attention has been drawn to what we call “micro-events”, which, due to their nature, cannot be detected from a single piece of textual information. The study investigates the feasibility of micro-event detection on textual data using a sample of messages from the Stack Overflow Q&A platform and Free/Libre Open Source Software (FLOSS) version releases from Libraries.io dataset. We build pipelines for detection of micro-events using three different estimators whose parameters are optimized using a grid search approach. We consider two feature spaces: LDA topic modeling with sentiment analysis, and hSBM topics with sentiment analysis. The feature spaces are optimized using the recursive feature elimination with cross validation (RFECV) strategy. In our experiments we investigate whether there is a characteristic change in the topics distribution or sentiment features before or after micro-events take place and we thoroughly evaluate the capacity of each variant of our analysis pipeline to detect micro-events. Additionally, we perform a detailed statistical analysis of the models, including influential cases, variance inflation factors, validation of the linearity assumption, pseudo R2 measures and no-information rate. Finally, in order to study limits of micro-event detection, we design a method for generating micro-event synthetic datasets with similar properties to the real-world data, and use them to identify the micro-event detectability threshold for each of the evaluated classifiers.
Perturbed Adaptive Belief Propagation Decoding for High-Density Parity-Check Codes

Algebraic codes such as BCH code are receiving renewed interest as their short block lengths and low/no error floors make them attractive for ultra-reliable low-latency communications (URLLC) in 5G wireless networks. This article aims at enhancing the traditional adaptive belief propagation (ABP) decoding, which is a soft-in-soft-out (SISO) decoding for high-density parity-check (HDPC) algebraic codes, such as Reed-Solomon (RS) codes, Bose-Chaudhuri-Hocquenghem (BCH) codes, and product codes. The key idea of traditional ABP is to sparsify certain columns of the parity-check matrix corresponding to the least reliable bits with small log-likelihood-ratio (LLR) values. This sparsification strategy may not be optimal when some bits have large LLR magnitudes but wrong signs. Motivated by this observation, we propose a Perturbed ABP (P-ABP) to incorporate a small number of unstable bits with large LLRs into the sparsification operation of the parity-check matrix. In addition, we propose to apply partial layered scheduling or hybrid dynamic scheduling to further enhance the performance of P-ABP. Simulation results show that our proposed decoding algorithms lead to improved error correction performances and faster convergence rates than the prior-art ABP variants.
Image Translation via Fine-grained Knowledge Transfer

Prevailing image-translation frameworks mostly seek to process images via the end-to-end style, which has achieved convincing results. Nonetheless, these methods lack interpretability and are not scalable on different imagetranslation tasks (e.g., style transfer, HDR, etc.). In this paper, we propose an interpretable knowledge-based imagetranslation framework, which realizes the image-translation through knowledge retrieval and transfer. In details, the framework constructs a plug-and-play and model-agnostic general purpose knowledge library, remembering taskspecific styles, tones, texture patterns, etc. Furthermore, we present a fast ANN searching approach, Bandpass Hierarchical K-Means (BHKM), to cope with the difficulty of searching in the enormous knowledge library. Extensive experiments well demonstrate the effectiveness and feasibility of our framework in different image-translation tasks. In particular, backtracking experiments verify the interpretability of our method. Our code soon will be available at https://github.com/AceSix/Knowledge_ Transfer.
On the graphs having at most one positive eccentricity eigenvalue

The eccentricity (anti-adjacency) matrix ε(G) of a graph G is obtained from the distance matrix by retaining the eccentricities in each row and each column. This matrix is first defined in 2018 by Wang et al. [1]. In this paper we have characterized the graphs which have at most one (hence exactly) positive eigenvalue of ε(G).
Unsupervised learning the phase diagram of frustrated Ising models

Danilo Rodrigues de Assis Elias, Enzo Granato, and Maurice de Koning 3 Instituto de F́ısica “Gleb Wataghin”, Universidade Estadual de Campinas, UNICAMP, 13083-859, Campinas, São Paulo, Brazil∗ Laboratório Associado de Sensores e Materiais, Instituto Nacional de Pesquisas Espaciais, 12227-010 São José dos Campos, SP, Brazil Center for Computing in Engineering & Sciences, Universidade Estadual de Campinas, UNICAMP, 13083-861, Campinas, São Paulo, Brazil† (Dated: December 22, 2020)
Human 3D keypoints via spatial uncertainty modeling

We introduce a technique for 3D human keypoint estimation that directly models the notion of spatial uncertainty of a keypoint. Our technique employs a principled approach to modelling spatial uncertainty inspired from techniques in robust statistics. Furthermore, our pipeline requires no 3D ground truth labels, relying instead on (possibly noisy) 2D image-level keypoints. Our method achieves near state-ofthe-art performance on Human3.6m while being efficient to evaluate and straightforward to implement.
Narrative Incoherence Detection

Motivated by the increasing popularity of intelligent editing assistant, we introduce and investigate the task of narrative incoherence detection: Given a (corrupted) long-form narrative, decide whether there exists some semantic discrepancy in the narrative flow. Specifically, we focus on the missing sentence and incoherent sentence detection. Despite its simple setup, this task is challenging as the model needs to understand and analyze a multi-sentence narrative text, and make decisions at the sentence level. As an initial step towards this task, we implement several baselines either directly analyzing the raw text (\textit{token-level}) or analyzing learned sentence representations (\textit{sentence-level}). We observe that while token-level modeling enjoys greater expressive power and hence better performance, sentence-level modeling possesses an advantage in efficiency and flexibility. With pre-training on large-scale data and cycle-consistent sentence embedding, our extended sentence-level model can achieve comparable detection accuracy to the token-level model. As a by-product, such a strategy enables simultaneous incoherence detection and infilling/modification suggestions.
Neutrino parameters in the Planck-scale lepton number breaking scenario with extended scalar sectors

Two-loop effects on the right-handed neutrino masses can have an impact on the low-energy phenomenology, especially when the right-handed neutrino mass spectrum is very hierarchical at the cut-off scale. In this case, the physical masses of the lighter right-handed neutrinos can be dominated by quantum effects induced by the heavier ones. Further, if the heaviest right-handed neutrino mass is at around the Planck scale, two-loop effects on the right-handed neutrino masses generate, through the seesaw mechanism, an active neutrino mass which is in the ballpark of the experimental values. In this paper we investigate extensions of the Planck-scale lepton number breaking scenario by additional Higgs doublets (inert or not). We find that under reasonable assumptions these models lead simultaneously to an overall neutrino mass scale and to a neutrino mass hierarchy in qualitative agreement with observations.
PPGN: Phrase-Guided Proposal Generation Network For Referring Expression Comprehension

Reference expression comprehension (REC) aims to find the location that the phrase refer to in a given image. Proposal generation and proposal representation are two effective techniques in many two-stage REC methods. However, most of the existing works only focus on proposal representation and neglect the importance of proposal generation. As a result, the low-quality proposals generated by these methods become the performance bottleneck in REC tasks. In this paper, we reconsider the problem of proposal generation, and propose a novel phrase-guided proposal generation network (PPGN). The main implementation principle of PPGN is refining visual features with text and generate proposals through regression. Experiments show that our method is effective and achieve SOTA performance in benchmark datasets.
Rethinking Road Surface 3-D Reconstruction and Pothole Detection: From Perspective Transformation to Disparity Map Segmentation

Potholes are one of the most common forms of road damage, which can severely affect driving comfort, road safety, and vehicle condition. Pothole detection is typically performed by either structural engineers or certified inspectors. However, this task is not only hazardous for the personnel but also extremely time consuming. This article presents an efficient pothole detection algorithm based on road disparity map estimation and segmentation. We first incorporate the stereo rig roll angle into shifting distance calculation to generalize perspective transformation. The road disparities are then efficiently estimated using semiglobal matching. A disparity map transformation algorithm is then performed to better distinguish the damaged road areas. Subsequently, we utilize simple linear iterative clustering to group the transformed disparities into a collection of superpixels. The potholes are finally detected by finding the superpixels, whose intensities are lower than an adaptively determined threshold. The proposed algorithm is implemented on an NVIDIA RTX 2080 Ti GPU in CUDA. The experimental results demonstrate that our proposed road pothole detection algorithm achieves state-of-the-art accuracy and efficiency.
Sensor-Based Temporal Logic Planning in Uncertain Semantic Maps

—This paper addresses a multi-robot mission planning problem in uncertain semantic environments. The environment is modeled by static labeled landmarks with uncertain positions and classes giving rise to an uncertain semantic map generated by semantic SLAM algorithms. Our goal is to design control policies for sensing robots so that they can accomplish complex collaborative high level tasks captured by global temporal logic speciﬁcations. To account for environmental and sensing uncertainty, we extend Linear Temporal Logic (LTL) by including sensor-based predicates allowing us to incorporate uncertainty and probabilistic satisfaction requirements directly into the task speciﬁcation. The sensor-based LTL planning problem gives rise to an optimal control problem, solved by a novel sampling-based algorithm, that generates open-loop control policies that can be updated online to adapt to the map that is continuously learned by existing semantic SLAM methods. We provide extensive experiments that corroborate the theoretical analysis and show that the proposed algorithm can address large-scale planning tasks in the presence of uncertainty.
Semi-supervised Hyperspectral Image Classification with Graph Clustering Convolutional Networks

Hyperspectral image classification (HIC) is an important but challenging task, and a problem that limits the algorithmic development in this field is that the ground truths of hyperspectral images (HSIs) are extremely hard to obtain. Recently a handful of HIC methods are developed based on the graph convolution networks (GCNs), which effectively relieves the scarcity of labeled data for deep learning based HIC methods. To further lift the classification performance, in this work we propose a graph convolution network (GCN) based framework for HSI classification that uses two clustering operations to better exploit multi-hop node correlations and also effectively reduce graph size. In particular, we first cluster the pixels with similar spectral features into a superpixel and build the graph based on the superpixels of the input HSI. Then instead of performing convolution over this superpixel graph, we further partition it into several sub-graphs by pruning the edges with weak weights, so as to strengthen the correlations of nodes with high similarity. This second round of clustering also further reduces the graph size, thus reducing the computation burden of graph convolution. Experimental results on three widely used benchmark datasets well prove the effectiveness of our proposed framework.