Miracle-dz/UniFAR-FTUs huggingface dataset:
Master Computer Science Esports Match Result Prediction for a Decision Support

The aim of this thesis is to predict the results of matches in the major esports title Counter-Strike: Global Oﬀensive (CS:GO). A CS:GO match can take place on a variety of maps, similarly to how tennis has diﬀerent court materials such as cement, grass, or clay. In CS:GO, the opposing teams undergo a map selection process before a match to determine which map(s) will be played. Thus, the scope of our problem involves predicting the result of a match between two teams on each of the seven maps. After collecting and analyzing CS:GO match data for each map, clustering players based on playstyle, and modelling player and match features, we use machine learning to predict the winner of a CS:GO match based on the map(s) selected by the teams. This provides a decision support system to assist and advise professional teams in their map selection process, and in preparation for speciﬁc opponents. These techniques borrow from traditional sports data science, where modern data analytics techniques have revolutionized player performance and strategy development. Gathering sports data is historically diﬃcult, where the data comes from complicated computer vision models at best, or from humans counting the statistics by hand at worst. Esports is facing the opposite problem-an embarrassment of riches where the data is already digitized and collected to a degree, due to the nature of games themselves being digital environments. However, esports as a whole is so young that data analytics implementations are still in their infancy. The goal of this research is to combine the developments in sports data analytics with the wealth of data inherent to esports in order to predict the result of a CS:GO match per-map.
Reaction hot pressing of {alpha}{prime}- and {beta}{prime}0SiAlON ceramics

Hot pressing kinetics of {alpha}-Si{sub 3}N{sub 4}, AlN, Al{sub 2}O{sub 3}, and Y{sub 2}O{sub 3} powder mixtures forming {alpha}{prime}- and {beta}{prime}-SiAlONs have been studied. Densification proceeds in two steps, first by a small shrinkage upon ternary eutectic oxide melting (SiO{sub 2}-Al{sub 2}O{sub 3}-Y{sub 2}O{sub 3}) at 1340 C, followed by a massive particle rearrangement and further shrinkage at higher temperature when nitride dissolution begins. With better wettability, AlN initially traps the oxide melt and delays densification. In addition, the preferential dissolution of AlN at 1450 C enriches the melt composition in Al, triggering transient precipitation of supersaturated {beta}{prime}-SiAlON. Full densification is readily achieved at 1550 C without complete {alpha}-Si{sub 3}N{sub 4} conversion.
TT-SLAM: Dense Monocular SLAM for Planar Environments

This paper proposes a novel visual SLAM method with dense planar reconstruction using a monocular camera: TT-SLAM. The method exploits planar template-based trackers (TT) to compute camera poses and reconstructs a multi-planar scene representation. Multiple homographies are estimated simultaneously by clustering a set of template trackers supported by superpixelized regions. Compared to RANSAC-based multiple homographies method [1], data association and keyframe selection issues are handled by the continuous nature of template trackers. A non-linear optimization process is applied to all the homographies to improve the precision in pose estimation. Experiments show that the proposed method outperforms RANSAC-based multiple homographies method [1] as well as other dense method SLAM techniques such as LSD-SLAM or DPPTAM, and competes with keypoint-based techniques like ORB-SLAM while providing dense planar reconstructions of the environment.
On the Stability of Nonlinear Receding Horizon Control: A Geometric Perspective

The widespread adoption of nonlinear Receding Horizon Control (RHC) strategies by industry has led to more than 30 years of intense research efforts to provide stability guarantees for these methods. However, current theoretical guarantees require that each (generally nonconvex) planning problem can be solved to (approximate) global optimality, which is an unrealistic requirement for the derivative-based local optimization methods generally used in practical implementations of RHC. This paper takes the first step towards understanding stability guarantees for nonlinear RHC when the inner planning problem is solved to first-order stationary points, but not necessarily global optima. Special attention is given to feedback linearizable systems, and a mixture of positive and negative results are provided. We establish that, under certain strong conditions, first-order solutions to RHC exponentially stabilize linearizable systems. Crucially, this guarantee requires that state costs applied to the planning problems are in a certain sense ‘compatible’ with the global geometry of the system, and a simple counter-example demonstrates the necessity of this condition. These results highlight the need to rethink the role of global geometry in the context of optimization-based control.
A Comprehensive Survey on Knowledge Graph Entity Alignment via Representation Learning

In the last few years, the interest in knowledge bases has grown exponentially in both the research community and the industry due to their essential role in AI applications. Entity alignment is an important task for enriching knowledge bases. This paper provides a comprehensive tutorial-type survey on representative entity alignment techniques that use the new approach of representation learning. We present a framework for capturing the key characteristics of these techniques, propose two datasets to address the limitation of existing benchmark datasets, and conduct extensive experiments using the proposed datasets. The framework gives a clear picture of how the techniques work. The experiments yield important results about the empirical performance of the techniques and how various factors aﬀect the performance. One important observation not stressed by previous work is that techniques making good use of attribute triples and relation predicates as features stand out as winners.
Supporting verification of news articles with automated search for semantically similar articles

Fake information poses one of the major threats for society in the 21st century. Identifying misinformation has become a key challenge due to the amount of fake news that is published daily. Yet, no approach is established that addresses the dynamics and versatility of fake news editorials. Instead of classifying content, we propose an evidence retrieval approach to handle fake news. The learning task is formulated as an unsupervised machine learning problem. For validation purpose, we provide the user with a set of news articles from reliable news sources supporting the hypothesis of the news article in query and the final decision is left to the user. Technically we propose a two-step process: (i) Aggregation-step: With information extracted from the given text we query for similar content from reliable news sources. (ii) Refining-step: We narrow the supporting evidence down by measuring the semantic distance of the text with the collection from step (i). The distance is calculated based on Word2Vec and the Word Mover's Distance. In our experiments, only content that is below a certain distance threshold is considered as supporting evidence. We find that our approach is agnostic to concept drifts, i.e. the machine learning task is independent of the hypotheses in a text. This makes it highly adaptable in times where fake news is as diverse as classical news is. Our pipeline offers the possibility for further analysis in the future, such as investigating bias and differences in news reporting.
The DoF Region of Order-(K - 1) Messages for the K-user MIMO Broadcast Channel with Delayed CSIT

This paper theoretically characterizes the degrees-of-freedom (DoF) region of $\text{order}-(K-1)$ messages for the K-user multiple-input multiple-output (MIMO) broadcast channel with delayed channel state information at the transmitter (CSIT) and arbitrary antenna configurations, where the transmitter has $M$ antennas and the receiver $i=1,2, \cdots, K$ has $N_{i}$ antennas. For the converse, we first derive the DoF region of order $(K-1)$ messages for the $K$ -user MIMO broadcast channel with no CSIT and arbitrary antenna configurations with the aid of the proposed Genie-bound, and then establish the DoF outer region. For the achievability, we first design a 2-phase transmission scheme, and then propose a backward/forward cancellation algorithm for decoding. Specifically, we efficiently derive the achievable DoF region from the designed transmission scheme by transformation approach. The main implication of this paper is that for the order- $(K-1)$ messages of $K$ -user MIMO broadcast channel, the DoF region with delayed CSIT is larger than the DoF region with no CSIT when $N_{2} < M$, where $N_{1}\leq N_{2}\leq\cdots\leq N_{K}$.
Deep Learning Techniques for In-Crop Weed Identification: A Review

Weeds are a signiﬁcant threat to the agricultural productivity and the environ-ment. The increasing demand for sustainable agriculture has driven innovations in accurate weed control technologies aimed at reducing the reliance on herbicides. With the great success of deep learning in various vision tasks, many promising image-based weed detection algorithms have been developed. This paper reviews recent developments of deep learning techniques in the ﬁeld of image-based weed detection. The review begins with an introduction to the fundamentals of deep learning related to weed detection. Next, recent progresses on deep weed detection are reviewed with the discussion of the research materials including public weed datasets. Finally, the challenges of developing practically deployable weed detection methods are summarized, together with the discussions of the opportunities for future research. We hope that this review will provide a timely survey of the ﬁeld and attract more researchers to address this inter-disciplinary research problem.
Nonequilibrium Dynamics of the Chiral Quark Condensate under a Strong Magnetic Field

Strong magnetic fields impact quantum-chromodynamics (QCD) properties in several situations; examples include the early universe, magnetars, and heavy-ion collisions. These examples share a common trait—time evolution. A prominent QCD property impacted by a strong magnetic field is the quark condensate, an approximate order parameter of the QCD transition between a high-temperature quark-gluon phase and a low-temperature hadronic phase. We use the linear sigma model with quarks to address the quark condensate time evolution under a strong magnetic field. We use the closed time path formalism of nonequilibrium quantum field theory to integrate out the quarks and obtain a mean-field Langevin equation for the condensate. The Langevin equation features dissipation and noise kernels controlled by a damping coefficient. We compute the damping coefficient for magnetic field and temperature values achieved in peripheral relativistic heavy-ion collisions and solve the Langevin equation for a temperature quench scenario. The magnetic field changes the dissipation and noise pattern by increasing the damping coefficient compared to the zero-field case. An increased damping coefficient increases fluctuations and time scales controlling condensate’s short-time evolution, a feature that can impact hadron formation at the QCD transition. The formalism developed here can be extended to include other order parameters, hydrodynamic modes, and system’s expansion to address magnetic field effects in complex settings as heavy-ion collisions, the early universe, and magnetars.
Implementing G-Machine in HyperLMNtal

Since language processing systems generally allocate/discard memory with complex reference relationships, including circular and indirect references, their implementation is often not trivial. Here, the allocated memory and the references can be abstracted to the labeled vertices and edges of a graph. And there exists a graph rewriting language, a programming language or a calculation model that can handle graph intuitively, safely and efficiently. Therefore, the implementation of a language processing system can be highly expected as an application field of graph rewriting language. To show this, in this research, we implemented G-machine, the virtual machine for lazy evaluation, in hypergraph rewriting language, HyperLMNtal.
Binary black hole mergers from hierarchical triples in open clusters

A promising channel for producing binary black hole mergers is the Lidov-Kozai orbital resonance in hierarchical triple systems. While this mechanism has been studied in isolation, the distribution of such mergers in time and across starforming environments is not well characterized. In this work, we explore Lidov-Kozai-induced black hole mergers in open clusters, combining semi-analytic and Monte Carlo methods to calculate merger rates and delay times for eight different population models. We predict a merger rate density of ∼1–10 Gpc−3 yr−1 for the Lidov-Kozai channel in the local universe, and all models yield delay-time distributions in which a significant fraction of binary black hole mergers (e.g., ∼20%–50% in our baseline model) occur during the open cluster phase. Our findings suggest that a substantial fraction of mergers from hierarchical triples occur within star-forming regions in spiral galaxies.
Frequency-specific segregation and integration of human cerebral cortex: an intrinsic functional atlas

The frequency-specific coupling mechanism of the functional human brain networks underpins its complex cognitive and behavioral functions. Nevertheless, it is not well unveiled what are the frequency-specific subdivisions and network topologies of the human brain. In this study, we estimated functional connectivity of the human cerebral cortex using spectral connection, and conducted frequency-specific parcellation using eigen-clustering and gradient-based methods, and then explored their topological structures. 7T fMRI data of 184 subjects in the HCP dataset were used for parcellation and exploring the topological properties of the functional networks, and 3T fMRI data of another 890 subjects were used to confirm the stability of the frequency-specific topologies. Seven to ten functional networks were stably integrated by two to four dissociable hub categories at specific frequencies, and we proposed an intrinsic functional atlas containing 456 parcels according to the parcellations across frequencies. The results revealed that the functional networks contained stable frequency-specific topologies, which may imply more abundant roles of the functional units and more complex interactions among them.
HumanGPS: Geodesic PreServing Feature for Dense Human Correspondences

In this paper, we address the problem of building dense correspondences between human images under arbitrary camera viewpoints and body poses. Prior art either assumes small motion between frames or relies on local descriptors, which cannot handle large motion or visually ambiguous body parts, e.g., left vs. right hand. In contrast, we propose a deep learning framework that maps each pixel to a feature space, where the feature distances reflect the geodesic distances among pixels as if they were projected onto the surface of a 3D human scan. To this end, we introduce novel loss functions to push features apart according to their geodesic distances on the surface. Without any semantic annotation, the proposed embeddings automatically learn to differentiate visually similar parts and align different subjects into an unified feature space. Extensive experiments show that the learned embeddings can produce accurate correspondences between images with remarkable generalization capabilities on both intra and inter subjects.1
Adsorption isotherms of copper and zinc in clay minerals of calcareous soils and their effects on X-ray diffraction

Reactions of elements with the water mineral interface are important and affect their bioavailability and transportation within soil. Effects of metal sorption on X-ray-diffraction (XRD-photographs) of clay minerals have been not studied. Therefore, sorption experiments were done on clay fractions of two calcareous soils using 12 concentrations of 0–2000 mg L−1 Zn(NO3)2 and Cu(NO3)2. Langmuir and Freundlich isotherms’ coefficients were determined. After sorption, XRD-diffraction were prepared and compared with those of initial samples. Langmuir (R2 = 0.996–0.999 and SE = 0.001–0.002) and Freundlich equation were the best-model for Zn and Cu-sorption, respectively. Sorption energy was higher for Zn than Cu, whereas the maximum concentration of sorbed-Cu was higher than that of Zn. Distribution coefficient (Kd) of Cu were more (threefold) than that of Zn. The Kd values representing the slope of Freundlich isotherms decreased according to linear regression equations (R2 = 0.72–0.91) as the equilibrium concentrations of metals increased. No significant differences were observed among XRD-photographs of applied concentrations (some negligible differences were found in position/sharpness of peaks). Dry-XRD-method resulted in omission of intensity peaks at 2θ which may interfere in recognition of clays that show a maximum intensity >1.4 nm in the mentioned 2θ. Zinc can become more leachable especially in Shekarbani-soil-series, whereas, Cu highly adsorb on clay minerals and can show less tendency to transportation.
Modeling the fast optical transient SN 2019bkc/ATLAS19dqr with a central engine and implication for its origin

Modern wide-field high-cadence surveys have revealed the significant diversity of optical transient phenomena in their luminosity and timescale distributions, which led to the discovery of some mysterious fast optical transients (FOTs). These FOTs can usually rise and decline remarkably in a timescale of a few days to weeks, which are obviously much rapider than ordinary supernovae. SN 2019bkc/ATLAS19dqr is one of the fastest detected FOTs so far and, meanwhile, it was found to be un-associated with a host galaxy. These discoveries provide a good chance to explore the possible origins of FOTs. So, we model the light curves of SN 2019bkc in details. It is found that SN 2019bkc can be well explained by the thermal emission of an explosion ejecta that is powered by a long-lasting central engine. The engine could be a spinning-down millisecond magnetar or a fallback accretion onto a compact object. Combining the engine property, the mass of the ejecta, and the hostlessness of SN 2019bkc, we suggest that this FOT is likely to originate from a merger of a white dwarf and a neutron star.
Broadband optical conductivity of the chiral multifold semimetal PdGa

We present an optical conductivity study of the multifold semimetal PdGa, performed in a broad spectral range ($100--20 000 {\mathrm{cm}}^{\ensuremath{-}1}$; 12 meV--2.5 eV) down to $T=10$ K. The conductivity at frequencies below $4000 {\mathrm{cm}}^{\ensuremath{-}1}$ is dominated by free carriers while at higher frequencies interband transitions provide the major contribution. The spectra do not demonstrate a significant temperature evolution: Only the intraband part changes as a function of temperature with the plasma frequency remaining constant. The interband contribution to the conductivity exhibits a broad peak at around $5500 {\mathrm{cm}}^{\ensuremath{-}1}$ and increases basically monotonously at frequencies above $9000 {\mathrm{cm}}^{\ensuremath{-}1}$. The band-structure-based computations reproduce these features of the interband conductivity and predict its linear-in-frequency behavior as frequency diminishes.
Random line graphs and edge-attributed network inference

We extend the latent position random graph model to the line graph of a random graph, which is formed by creating a vertex for each edge in the original random graph, and connecting each pair of edges incident to a common vertex in the original graph. We prove concentration inequalities for the spectrum of a line graph, as well as limiting distribution results for the largest eigenvalue and the empirical spectral distribution in certain settings. For the stochastic blockmodel, we establish that although naive spectral decompositions can fail to extract necessary signal for edge clustering, there exist signal-preserving singular subspaces of the line graph that can be recovered through a carefully-chosen projection. Moreover, we can consistently estimate edge latent positions in a random line graph, even though such graphs are of a random size, typically have high rank, and possess no spectral gap. Our results demonstrate that the line graph of a stochastic block model exhibits underlying block structure, and in simulations, we synthesize and test our methods against several commonly-used techniques, including tensor decompositions, for cluster recovery and edge covariate inference. By naturally incorporating information encoded in both vertices and edges, the random line graph improves network inference.
Context-aware short-term interest first model for session-based recommendation

In the case that user profiles are not available, the recommendation based on anonymous session is particularly important, which aims to predict the items that the user may click at the next moment based on the user's access sequence over a while. In recent years, with the development of recurrent neural network, attention mechanism, and graph neural network, the performance of session-based recommendation has been greatly improved. However, the previous methods did not comprehensively consider the context dependencies and short-term interest first of the session. Therefore, we propose a context-aware short-term interest first model (CASIF). The aim of this paper is improve the accuracy of recommendations by combining context and short-term interest. In CASIF, we dynamically construct a graph structure for session sequences and capture rich context dependencies via graph neural network (GNN), latent feature vectors are captured as inputs of the next step. Then we build the short-term interest first module, which can to capture the user's general interest from the session in the context of long-term memory, at the same time get the user's current interest from the item of the last click. In the end, the short-term and long-term interest are combined as the final interest and multiplied by the candidate vector to obtain the recommendation probability. Finally, a large number of experiments on two real-world datasets demonstrate the effectiveness of our proposed method.
Realistic face animation generation from videos

3D face reconstruction and face alignment are two fundamental and highly related topics in computer vision. Recently, some works start to use deep learning models to estimate the 3DMM coefficients to reconstruct 3D face geometry. However, the performance is restricted due to the limitation of the pre-defined face templates. To address this problem, some end-to-end methods, which can completely bypass the calculation of 3DMM coefficients, are proposed and attract much attention. In this report, we introduce and analyse three state-of-the-art methods in 3D face reconstruction and face alignment. Some potential improvement on PRN are proposed to further enhance its accuracy and speed.
Tracking 6-DoF Object Motion from Events and Frames

Event cameras are promising devices for low latency tracking and high-dynamic range imaging. In this paper, we propose a novel approach for 6 degree-of-freedom (6-DoF) object motion tracking that combines measurements of event and frame-based cameras. We formulate tracking from high rate events with a probabilistic generative model of the event measurement process of the object. On a second layer, we refine the object trajectory in slower rate image frames through direct image alignment. We evaluate the accuracy of our approach in several object tracking scenarios with synthetic data, and also perform experiments with real data.