Miracle-dz/UniFAR-FTUs huggingface dataset:
A Measure of Similarity in Textual Data Using Spearman's Rank Correlation Coefficient

In the last decade, many diverse advances have occurred in the field of information extraction from data. Information extraction in its simplest form takes place in computing environments, where structured data can be extracted through a series of queries. The continuous expansion of quantities of data have therefore provided an opportunity for knowledge extraction (KE) from a textual document (TD). A typical problem of this kind is the extraction of common characteristics and knowledge from a group of TDs, with the possibility to group such similar TDs in a process known as clustering. In this paper we present a technique for such KE among a group of TDs related to the common characteristics and meaning of their content. Our technique is based on the Spearman's Rank Correlation Coefficient (SRCC), for which the conducted experiments have proven to be comprehensive measure to achieve a high-quality KE.
G-TAD: Sub-Graph Localization for Temporal Action Detection

Temporal action detection is a fundamental yet challenging task in video understanding. Video context is a critical cue to effectively detect actions, but current works mainly focus on temporal context, while neglecting semantic context as well as other important context properties. In this work, we propose a graph convolutional network (GCN) model to adaptively incorporate multi-level semantic context into video features and cast temporal action detection as a sub-graph localization problem. Specifically, we formulate video snippets as graph nodes, snippet-snippet correlations as edges, and actions associated with context as target sub-graphs. With graph convolution as the basic operation, we design a GCN block called GCNeXt, which learns the features of each node by aggregating its context and dynamically updates the edges in the graph. To localize each sub-graph, we also design an SGAlign layer to embed each sub-graph into the Euclidean space. Extensive experiments show that G-TAD is capable of finding effective video context without extra supervision and achieves state-of-the-art performance on two detection benchmarks. On ActivityNet-1.3 it obtains an average mAP of 34.09%; on THUMOS14 it reaches 51.6% at IoU@0.5 when combined with a proposal processing method. The code has been made available at https://github.com/frostinassiky/gtad.
Shape-aware Feature Extraction for Instance Segmentation

Modern instance segmentation approaches mainly adopt a sequential paradigm - ``detect then segment'', as popularized by Mask R-CNN, which have achieved considerable progress. However, they usually struggle to segment huddled instances, i.e., instances which are crowded together. The essential reason is the detection step is only learned under box-level supervision. Without the guidance from the mask-level supervision, the features extracted from the regions containing huddled instances are noisy and ambiguous, which makes the detection problem ill-posed. To address this issue, we propose a new region-of-interest (RoI) feature extraction strategy, named Shape-aware RoIAlign, which focuses feature extraction within a region aligned well with the shape of the instance-of-interest rather than a rectangular RoI. We instantiate Shape-aware RoIAlign by introducing a novel refining module built upon Mask R-CNN, which takes the mask predicted by Mask R-CNN as the region to guide the computation of Shape-aware RoIAlign. Based on the RoI features re-computed by Shape-aware RoIAlign, the refining module updates the bounding box as well as the mask predicted by Mask R-CNN. Experimental results show that the refining module equipped with Shape-aware RoIAlign achieves consistent and remarkable improvements than Mask R-CNN models with different backbones, respectively, on the challenging COCO dataset. The code will be released.
Capacity Approaching Low Density Spreading in Uplink NOMA via Asymptotic Analysis

Low-density spreading non-orthogonal multiple-access (LDS-NOMA) is considered where <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> single-antenna user-equipments (UEs) communicate with a base-station (BS) over <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula> fading sub-carriers. Each UE <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> spreads its data symbols over <inline-formula> <tex-math notation="LaTeX">$\text {d}_{\text {k}}\ll \text {F}$ </tex-math></inline-formula> sub-carriers. The performance of LDS-NOMA system depends on the allocation of the non-zero elements in the LDS-codes. We aim to identify the LDS resource allocations, based solely on pathlosses, that maximize the ergodic mutual information (EMI). This problem can be solved only via an exhaustive search. Thus, relying on analysis in the regime where <inline-formula> <tex-math notation="LaTeX">$F$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">${{d}_{{k}},\forall {k}}$ </tex-math></inline-formula> converge to <inline-formula> <tex-math notation="LaTeX">$+\infty $ </tex-math></inline-formula> at the same rate, we present EMI as a deterministic equivalent plus a residual term. The deterministic equivalent is a function of pathloss values and LDS-codes, and the small residual term scales as <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}\left({\frac {1}{\min ({d}_{{k}}^{2})}}\right)$ </tex-math></inline-formula>. First, we formulate an optimization problem to identify the resource allocations that maximize the deterministic equivalent of EMI. The Karush-Kuhn-Tucker conditions give a simple resource allocation rule that facilitates the construction of desired LDS-codes via an efficient partitioning algorithm. The finite-regime analysis shows that such sparse solutions additionally harness the small incremental gain inherent in the residual term, and thus, provides a near-optimal performance. The spectral efficiency enhancement relative to regular and random spreading is validated numerically.
The Forgotten Quadrant Survey

Aims. We present the Forgotten Quadrant Survey (FQS), an ESO large project that used the 12 m antenna of the Arizona Radio Observatory to map the Galactic plane in the range 220° < l < 240° and −2. °5 < b < 0°, both in 12CO (1–0) and 13CO (1–0), at a spectral resolution of 0.65 km s−1 and 0.26 km s−1. Methods. We used the (1–0) transition of carbon monoxide to trace the molecular component of the interstellar medium. Our data set allows us to easily identify how the molecular dense gas is organised at different spatial scales: from the giant clouds with their denser filamentary networks, down to the clumps and cores that host the new-born stars and to obtain reliable estimates of their key physical parameters such as size and mass. Results. We present the first release of the data of the FQS survey and discuss their quality. Spectra with 0.65 km s−1 velocity channels have noise ranging from 0.8 K to 1.3 K for 12CO (1–0) and from 0.3 K to 0.6 K for 13CO (1–0). In this first paper, we used the 12CO (1–0) spectral cubes to produce a catalogue of 263 molecular clouds. The clouds are grouped in three main structures corresponding to the Local, Perseus, and Outer arms up to a distance of ∼8.6 kpc from the Sun. This is the first self-consistent statistical catalogue of molecular clouds of the outer Galaxy obtained with a subarcminute spatial resolution and therefore able to detect not only the classical giant molecular clouds, but also the small clouds and to resolve the cloud structure at the sub-parsec scale up to a distance of a few kiloparsec. We found two classes of objects: structures with sizes above a few parsecs that are typical molecular clouds and may be self-gravitating, and subparsec structures that cannot be in gravitational equilibrium and are likely transient or confined by external pressure. We used the ratio between the Herschel H2 column density and the integrated intensity of the CO lines to calculate the CO conversion factor and we found mean values of (3.3 ± 1.4) × 1020 cm−2 (K km s−1)−1 and (1.2 ± 0.4) × 1021 cm−2 (K km s−1)−1, for 12CO (1–0) and 13CO (1–0), respectively. Conclusions. The FQS contributes to the general effort in producing a new generation of high-quality spectroscopic data for the Galactic plane in the less-studied third Galactic quadrant toward the outer Galaxy. The FQS has produced a data set of great legacy value, largely improving the data quality both in terms of sensitivity and spatial resolution over previous data sets.
From Persistent Homology to Reinforcement Learning with Applications for Retail Banking

The retail banking services are one of the pillars of the modern economic growth. However, the evolution of the client's habits in modern societies and the recent European regulations promoting more competition mean the retail banks will encounter serious challenges for the next few years, endangering their activities. They now face an impossible compromise: maximizing the satisfaction of their hyper-connected clients while avoiding any risk of default and being regulatory compliant. Therefore, advanced and novel research concepts are a serious game-changer to gain a competitive advantage. In this context, we investigate in this thesis different concepts bridging the gap between persistent homology, neural networks, recommender engines and reinforcement learning with the aim of improving the quality of the retail banking services. Our contribution is threefold. First, we highlight how to overcome insufficient financial data by generating artificial data using generative models and persistent homology. Then, we present how to perform accurate financial recommendations in multi-dimensions. Finally, we underline a reinforcement learning model-free approach to determine the optimal policy of money management based on the aggregated financial transactions of the clients. Our experimental data sets, extracted from well-known institutions where the privacy and the confidentiality of the clients were not put at risk, support our contributions. In this work, we provide the motivations of our retail banking research project, describe the theory employed to improve the financial services quality and evaluate quantitatively and qualitatively our methodologies for each of the proposed research scenarios.
Host-based anomaly detection using Eigentraces feature extraction and one-class classification on system call trace data

This paper proposes a methodology for host-based anomaly detection using a semi-supervised algorithm namely one-class classifier combined with a PCA-based feature extraction technique called Eigentraces on system call trace data. The one-class classification is based on generating a set of artificial data using a reference distribution and combining the target class probability function with artificial class density function to estimate the target class density function through the Bayes formulation. The benchmark dataset, ADFA-LD, is employed for the simulation study. ADFA-LD dataset contains thousands of system call traces collected during various normal and attack processes for the Linux operating system environment. In order to pre-process and to extract features, windowing on the system call trace data followed by the principal component analysis which is named as Eigentraces is implemented. The target class probability function is modeled separately by Radial Basis Function neural network and Random Forest machine learners for performance comparison purposes. The simulation study showed that the proposed intrusion detection system offers high performance for detecting anomalies and normal activities with respect to a set of well-accepted metrics including detection rate, accuracy, and missed and false alarm rates.
The Effect of Adverse Childhood Experiences on Adult Health: 2012 North Carolina Behavioral Risk Factor Surveillance System Survey

Objective: Adverse childhood experiences (ACEs) include emotional, physical and sexual abuse as well as household dysfunction experienced before age 18. Research has demonstrated that ACEs put individuals at increased risk for a wide variety of chronic mental, physical and emotional health problems in adulthood. Our objective was to estimate the prevalence of ACEs and examine their association with perceived health, health risks and chronic conditions among North Carolina adults. Methods: In 2012, 10,383 adults responded to the ACE module included in the N.C. Behavioral Risk Factor Surveillance System (BRFSS) survey. These population-based data were used to calculate the prevalence of ACEs among North Carolina adults and to compare the risk for perceived poor health, health risks and chronic conditions in adulthood among individuals who experienced no ACEs, one to two ACEs and three to eight ACEs. Results: More than half of respondents (57.6%) reported having experienced at least one ACE. Exposure to ACEs varied by age, sex, race, income and education. Multivariate analysis indicated a statistically significant increase in risk for current smoking, binge drinking, obesity, HIV risk behaviors, poor physical and mental health, activity limitation, chronic obstructive pulmonary disease, cardiovascular disease, arthritis, depression and disability with increasing ACE exposure, even after controlling for sex, age, race and education. An increased risk was also found for perceived poor health and chronic condition outcomes after additional adjustment for current smoking and obesity. Conclusion: ACEs are prevalent among the general population of North Carolina and are significantly related to development of health risk behaviors and poor health outcomes in adulthood. Examining the epidemiology of ACEs and associated outcomes in North Carolina emphasizes the importance of a life course perspective and strengthens the argument for focusing on the prevention of ACEs as an effective long-term strategy for improving population health outcomes.
Finding Route Hotspots in Large Labeled Networks

In many advanced network analysis applications, like social networks, e-commerce, and network security, hotspots are generally considered as a group of vertices that are tightly connected owing to the similar characteristics, such as common habits and location proximity. In this article, we investigate the formation of hotspots from an alternative perspective that considers the routes along the network paths as the auxiliary information, and attempt to find the route hotspots in large labeled networks. A route hotspot is a cohesive subgraph that is covered by a set of routes, and these routes correspond to the same sequential pattern consisting of vertices’ labels. To the best of our knowledge, the problem of Finding Route Hotspots in Large Labeled Networks has not been tackled in the literature. However, it is challenging as counting the number of hotspots in a network is #P-hard. Inspired by the observation that the sizes of hotspots decrease with the increasing lengths of patterns, we prove several anti-monotonicity properties of hotspots, and then develop a scalable algorithm called FastRH that can use these properties to effectively prune the patterns that cannot form any hotspots. In addition, to avoid the duplicate computation overhead, we judiciously design an effective index structure called RH-Index for storing the hotspot and pattern information collectively, which also enables incremental updating and efficient query processing. Our experimental results on real-world datasets clearly demonstrate the effectiveness and scalability of our proposed methods.
The Early Roots of Statistical Learning in the Psychometric Literature: A review and two new results

Machine and Statistical learning techniques become more and more important for the analysis of psychological data. Four core concepts of machine learning are the bias variance trade-off, cross-validation, regularization, and basis expansion. We present some early psychometric papers, from almost a century ago, that dealt with cross-validation and regularization. From this review it is safe to conclude that the origins of these lie partly in the field of psychometrics. From our historical review, two new ideas arose which we investigated further: The first is about the relationship between reliability and predictive validity; the second is whether optimal regression weights should be estimated by regularizing their values towards equality or shrinking their values towards zero. In a simulation study we show that the reliability of a test score does not influence the predictive validity as much as is usually written in psychometric textbooks. Using an empirical example we show that regularization towards equal regression coefficients is beneficial in terms of prediction error.
Nonadiabatic single-qubit quantum Otto engine

According to Clausius formulation of the second law of thermodynamics, for any thermal machine withdrawing heats ${Q}_{1,2}$ from two heat reservoirs at temperatures ${T}_{1,2}$, it holds that ${Q}_{1}/{T}_{1}+{Q}_{2}/{T}_{2}\ensuremath{\le}0$. Combined with the observation that the quantity ${Q}_{1}+{Q}_{2}$ is the work $W$ done by the system, that inequality tells us that only four operation modes are possible for the thermal machine, namely, heat engine, refrigerator, thermal accelerator, and heater. We illustrate their emergence in the finite time operation of a quantum Otto engine realized with a single qubit. We first focus on the ideal case when isochoric and thermally insulated strokes are well separated and give general results as well as results pertaining to the specific finite-time Landau-Zener dynamics. We then present realistic results pertaining to the solid-state experimental implementation proposed by Karimi and Pekola [Phys. Rev. B 94, 184503 (2016)]. That device is nonadiabatic both in the quantum mechanical sense and in the thermodynamical sense. Oscillations in the power extracted from the baths due to coherent LZ tunneling at too low of temperatures are observed that might hinder the robustness of the operation of the device against experimental noise on the control parameters.
Closure coefficients in scale-free complex networks

The formation of triangles in complex networks is an important network property that has received tremendous attention. The formation of triangles is often studied through the clustering coefficient. The closure coefficient or transitivity is another method to measure triadic closure. This statistic measures clustering from the head node of a triangle (instead of from the center node, as in the often studied clustering coefficient). We perform a first exploratory analysis of the behavior of the local closure coefficient in two random graph models that create simple networks with power-law degrees: the hidden-variable model and the hyperbolic random graph. We show that the closure coefficient behaves significantly different in these simple random graph models than in the previously studied multigraph models. We also relate the closure coefficient of high-degree vertices to the clustering coefficient and the average nearest neighbor degree.
A New Probe of Gaussianity and Isotropy for CMB Maps

We introduce a new mathematical tool (a direction-dependent probe) to analyse the randomness of purported isotropic Gaussian random fields on the sphere. We apply the probe to assess the full-sky cosmic microwave background (CMB) temperature maps produced by the {\it Planck} collaboration (PR2 2015 and PR3 2018), with special attention to the inpainted maps. To study the randomness of the fields represented by each map we use the autocorrelation of the sequence of probe coefficients (which are just the full-sky Fourier coefficients $a_{\ell,0}$ if the $z$ axis is taken in the probe direction). If the field is {isotropic and Gaussian} then the probe coefficients for a given direction should be realisations of uncorrelated scalar Gaussian random variables. We find that for most of the maps there are many directions for which this is not the case. We make a first attempt at justifying the features of the temperature maps that contribute to the apparent lack of randomness. In the case of \texttt{Commander} 2015 we mimic an aspect of the observed behaviour with a model field that is Gaussian but not isotropic. In contrast, the non-inpainted 2018 \texttt{SEVEM} map (which has visible equatorial pollution) is modelled by an isotropic Gaussian random field plus a non-random needlet-like structure located near the galactic centre.
Nematicity with a twist: Rotational symmetry breaking in a moiré superlattice

Twisted 2D materials realize a unique electronic nematic state, quite different from its counterpart in bulk quantum materials. Motivated by recent reports of nematic order in twisted bilayer graphene (TBG), we investigate the impact of the triangular moiré superlattice degrees of freedom on nematicity. In TBG, the nematic order parameter is not Ising like, as in tetragonal crystals, but has a three-state Potts character related to the threefold rotational symmetry (C3z) of the moiré superlattice. We find that, even in the presence of static strain that explicitly breaks the C3z symmetry, the system can still undergo a nematic-flop phase transition that spontaneously breaks in-plane twofold rotations. Moreover, elastic fluctuations, manifested as acoustic phonons, mediate a nemato-orbital coupling that ties the nematic director orientation to certain soft directions in momentum space, rendering the Potts-nematic transition mean field and first order. In contrast to the case of rigid crystals, the Fermi surface hot spots associated with these soft directions are maximally coupled to low-energy nematic fluctuations in the moiré superlattice case.
Methods for Predicting Behavior of Elephant Flows in Data Center Networks

Several Traffic Engineering (TE) techniques based on SDN (Software-defined networking) proposed to resolve flow competitions for network resources. However, there is no comprehensive study on the probability distribution of their throughput. Moreover, there is no study on predicting the future of elephant flows. To address these issues, we propose a new stochastic performance evaluation model to estimate the loss rate of two state-of-art flow scheduling algorithms including Equalcost multi-path routing (ECMP), Hedera besides a flow congestion control algorithm which is Data Center TCP (DCTCP). Although these algorithms have theoretical and practical benefits, their effectiveness has not been statistically investigated and analyzed in conserving the elephant flows. Therefore, we conducted extensive experiments on the fat-tree data center network to examine the efficiency of the algorithms under different network circumstances based on Monte Carlo risk analysis. The results show that Hedera is still risky to be used to handle the elephant flows due to its unstable throughput achieved under stochastic network congestion. On the other hand, DCTCP found suffering under high load scenarios. These outcomes might apply to all data center applications, in particular, the applications that demand high stability and productivity.