Miracle-dz/UniFAR-FTUs huggingface dataset:
Unconventional Charge-Density-Wave Gap in Monolayer NbS2

Using scanning tunneling microscopy and spectroscopy, for a monolayer of transition metal dichalcogenide H-NbS2 grown by molecular beam epitaxy on graphene, we provide unambiguous evidence for a charge density wave (CDW) with a 3 × 3 superstructure, which is not present in bulk NbS2. Local spectroscopy displays a pronounced gap on the order of 20 meV at the Fermi level. Within the gap, low-energy features are present. The gap structure with its low-energy features is at variance with the expectation for a gap opening in the electronic band structure due to a CDW. Instead, comparison with ab initio calculations indicates that the observed gap structure must be attributed to combined electron–phonon quasiparticles. The phonons in question are the elusive amplitude and phase collective modes of the CDW transition. Our findings advance the understanding of CDW mechanisms in 2D materials and their spectroscopic signatures.
SelFlow: Self-Supervised Learning of Optical Flow

We present a self-supervised learning approach for optical flow. Our method distills reliable flow estimations from non-occluded pixels, and uses these predictions as ground truth to learn optical flow for hallucinated occlusions. We further design a simple CNN to utilize temporal information from multiple frames for better flow estimation. These two principles lead to an approach that yields the best performance for unsupervised optical flow learning on the challenging benchmarks including MPI Sintel, KITTI 2012 and 2015. More notably, our self-supervised pre-trained model provides an excellent initialization for supervised fine-tuning. Our fine-tuned models achieve state-of-the-art results on all three datasets. At the time of writing, we achieve EPE=4.26 on the Sintel benchmark, outperforming all submitted methods.
Macroscopic Market Making

We propose the macroscopic market making model \`a la Avellaneda-Stoikov, using continuous processes for orders instead of discrete point processes. The model intends to bridge a gap between market making and optimal execution problems, while shedding light on the influence of order flows on the strategy. We demonstrate our model through three problems. The study provides a comprehensive analysis from Markovian to non-Markovian noises and from linear to non-linear intensity functions, encompassing both bounded and unbounded coefficients. Mathematically, the contribution lies in the existence and uniqueness of the optimal control, guaranteed by the well-posedness of the Hamilton-Jacobi-Bellman equation or the (non-)Lipschitz forward-backward stochastic differential equation.
Towards Establishing Systematic Classification Requirements for Automated Driving

Despite the presence of the classification task in many different benchmark datasets for perception in the automotive domain, few efforts have been undertaken to define consistent classification requirements. This work addresses the topic by proposing a structured method to generate a classification structure. First, legal categories are identified based on behavioral requirements for the vehicle. This structure is further substantiated by considering the two aspects of collision safety for objects as well as perceptual categories. A classification hierarchy is obtained by applying the method to an exemplary legal text. A comparison of the results with benchmark dataset categories shows limited agreement. This indicates the necessity for explicit consideration of legal requirements regarding perception.
Convex semi-infinite programming algorithms with inexact separation oracles

Solving convex Semi-Infinite Programming (SIP) problems is challenging when the separation problem, i.e., the problem of finding the most violated constraint, is computationally hard. We propose to tackle this difficulty by solving the separation problem approximately, i.e., by using an inexact oracle. Our focus lies in two algorithms for SIP, namely the Cutting-Planes (CP) and the Inner-Outer Approximation (IOA) algorithms. We prove the CP convergence rate to be in O(1/k), where k is the number of calls to the limited-accuracy oracle, if the objective function is strongly convex. Compared to the CP algorithm, the advantage of the IOA algorithm is the feasibility of its iterates. In the case of a semi-infinite program with Quadratically Constrained Quadratic Programming separation problem, we prove the convergence of the IOA algorithm toward an optimal solution of the SIP problem despite the oracle's inexactness.
Priors in Deep Image Restoration and Enhancement: A Survey

Image restoration and enhancement is a process of improving the image quality by removing degradations, such as noise, blur, and resolution degradation. Deep learning (DL) has recently been applied to image restoration and enhancement. Due to its ill-posed property, plenty of works have been explored priors to facilitate training deep neural networks (DNNs). However, the importance of priors has not been systematically studied and analyzed by far in the research community. Therefore, this paper serves as the first study that provides a comprehensive overview of recent advancements in priors for deep image restoration and enhancement. Our work covers five primary contents: (1) A theoretical analysis of priors for deep image restoration and enhancement; (2) A hierarchical and structural taxonomy of priors commonly used in the DL-based methods; (3) An insightful discussion on each prior regarding its principle, potential, and applications; (4) A summary of crucial problems by highlighting the potential future directions, especially adopting the large-scale foundation models as prior, to spark more research in the community; (5) An open-source repository that provides a taxonomy of all mentioned works and code links.
Direct numerical simulation of heat transfer in a spatially developing turbulent boundary layer

Direct numerical simulation has been performed to investigate heat transfer in a zero-pressure-gradient spatially developing turbulent boundary layer with realistic thermal inflow boundary conditions. The temperature is considered as a passive scalar and the molecular Prandtl number is set to be 0.71. The turbulence statistics for both the velocity and temperature fields show good agreement with previous numerical and experimental data in the literature. The present study provides a valuable database for the spatially developing turbulent thermal boundary layer over a wide range of Reynolds numbers from Reθ = 1100 to 1940. The simulation results indicate that both the peak value and peak location of the streamwise velocity fluctuation grow slightly with increasing Reynolds number, same as those of the temperature fluctuation. The relationship between the streamwise velocity and temperature fluctuations has been examined and a strong correlation is observed in the vicinity of the wall. With increasing dist.
Impact of surface physisorption on gas scattering dynamics

Abstract Engineering flow systems operating under low pressures and/or at the micro/nano scale generally include a physically adsorbed gas layer next to the surface. In this paper, we develop a scattering kernel that accounts for the effect of adsorption, arising from van der Waals interactions, on the dynamics of molecules impinging on solid smooth surfaces. In the limit of low bulk density, surface adsorption becomes negligible and the scattering kernel recovers consistently the Cercignani–Lampis model, which best describes molecular collisions with a clean, smooth surface. In the limit of high bulk density, a dense adsorbed molecular layer forms next to the surface and its presence is picked up by the Maxwell model with complete diffuse reflection, which better captures the multiple collisions suffered by molecules. A weight coefficient based on the Langmuir adsorption isotherm is incorporated into the modelling to handle the transition between these two limiting conditions of low and high densities. The proposed model is validated against high-fidelity molecular dynamics simulations that are performed for a variety of gas–surface combinations and adsorbed molecular layers with different densities. It is shown that the proposed model very well captures the scattering patterns of beams of gas molecules at different velocities impinging on surfaces, as well as momentum and energy accommodation coefficients in the entire range of explored conditions.
Heating a Torus with Number Theory

Summary We discuss an amusing application of number theory: suppose you find yourself on the two-dimensional torus equipped with N candles and want to position the candles in such a way that they heat up the room as efficiently as possible. To achieve this goal, we give a construction that uses number theory as the main ingredient and explain related results.
Transitive Associations for Domain Transfer Problem on Opinion Mining

—Classification algorithms need labeled examples to train their model. However there are not enough labeled examples in some domains. There is an approach that training a classifier in one domain and use it to classify examples on different domains. This method is not always successful and this is called domain transfer problem. Spectral Feature Alignment is proposed as a solution to this problem [3]. In this study I investigate this algorithm with a demonstrative example and I do classification experiments on randomly created datasets. Support vector machines are used as a classification tool and the aim of the experiments is to find the impact of the spectral feature alignment on the classification accuracy. Based on the results of these experiments, I will discuss the possible research opportunities on this area.
A Research on the Classification Validity of the Decisions Made According to Norm and Criterion-referenced Assessment Approaches*

Abstract In this study, the criterion-referenced assessment and norm-referenced assessment applications were examined comparatively to see whether the decisions made about the students led to any differences in their classification validity, and the results obtained were analyzed. For this purpose, the evaluation results belonging to the students were transferred into the passed/failed decisions in accordance with the rules in criterion and normreferenced assessment systems, and the obtained findings were analyzed with the discriminant analysis. The study was conducted with 1007 students from six universities who attended courses in the 2011-2012 academic year, and who had previously taken education (pedagogical formation) courses. At the end of the study, the classification validity of the decisions made about the students with the criterion-referenced assessment approach was seen to be higher. Thus, it can be said that making classification decisions about students using the criterion-referenced assessment approach can give more accurate results than the decisions made according to norm-referenced assessment approach.
Mathematical modelling of complex dynamics

Soft materials have a wide range of applications, which include the production of masks for nano–lithography, the separation of membranes with nano–pores, and the preparation of nano–size structures for electronic devices. Self–organization in soft matter is a primary mechanism for the formation of structure. Block copolymers are long chain molecules composed of several different polymer blocks covalently bonded into a single macromolecule, which belong to an important class of soft materials which can self–assemble into different nano–structures due to their natural ability to microphase separate. Experimental and theoretical studies of block copolymers are quite challenging and, without computer simulations, it is difficult and problematic to analyse modern experiments. The Cell Dynamics Simulation (CDS) technique is a fast and accurate computational technique, which has been used to investigate block copolymers. The stability has been analysed by making use of different discrete Laplacian operators using well–chosen time steps in CDS. This analysis offers stability conditions for phase–field, based on the Cahn–Hilliard Cook (CHC) equations of which CDS is the finite difference approximation. To overcome grid related artefacts (discretization errors) in the computational grid, the study has been done for employing an isotropic Laplacian operator in the CDS framework. Several 2D and 3D discrete Laplacians have been quantitatively compared for their isotropy. The novel 2D 9–point BV(D2Q9) isotropic stencil operators have been derived from the B.A.C. van Vlimmeren method and their isotropy measure has been determined optimally better than other exiting 2D 9–point discrete Laplacian operators. Overall, the stencils in 9–point family Laplacians in 2D and the 19–point stencil operators in 3D have been found to be optimal in terms of isotropy and time step stability. Considerable implementation of Laplacians with good isotropy has played an important role in achieving a proper structure factor in modelling methods of block copolymers. The novel models have been developed by implementing CDS via more stable implicit methods, including backward Euler, Crank–Nicolson (CN) and Alternating Direction Implicit (ADI) methods. The CN scheme were implemented for both one order and two order parameter systems in CDS and successful results were obtained compared to forward Euler method. Due to the implementation of implicit methods, the CDS has achieved second–order accuracy both in time and space and it has become stronger, robust and more stable technique for simulation of the phase–separation phenomena in soft materials.
AMNet: Deep Atrous Multiscale Stereo Disparity Estimation Networks

In this paper, a new deep learning architecture for stereo disparity estimation is proposed. The proposed atrous multiscale network (AMNet) adopts an efficient feature extractor with depthwise-separable convolutions and an extended cost volume that deploys novel stereo matching costs on the deep features. A stacked atrous multiscale network is proposed to aggregate rich multiscale contextual information from the cost volume which allows for estimating the disparity with high accuracy at multiple scales. AMNet can be further modified to be a foreground-background aware network, FBA-AMNet, which is capable of discriminating between the foreground and the background objects in the scene at multiple scales. An iterative multitask learning method is proposed to train FBA-AMNet end-to-end. The proposed disparity estimation networks, AMNet and FBA-AMNet, show accurate disparity estimates and advance the state of the art on the challenging Middlebury, KITTI 2012, KITTI 2015, and Sceneflow stereo disparity estimation benchmarks.
Supervised descriptive local pattern mining with complex target concepts

Findingsubsetsofadatasetthatsomehowdeviatefromthenorm,i.e.where something interesting is going on, is a classical Data Mining task. In traditional local pattern mining methods, such deviations are measured in terms of a relatively high occurrence (frequent itemset mining), or an unusual distribution for one designated target attribute (common use of subgroup discovery). These, however, do not encom- pass all forms of "interesting". To capture a more general notion of interestingness in subsets of a dataset, we develop Exceptional Model Mining (EMM). This is a super- vised local pattern mining framework, where several target attributes are selected, and a model over these targets is chosen to be the target concept. Then, we strive to find subgroups: subsets of the dataset that can be described by a few conditions on single attributes. Such subgroups are deemed interesting when the model over the targets on the subgroup is substantially different from the model on the whole dataset. For instance, we can find subgroups where two target attributes have an unusual cor- relation, a classifier has a deviating predictive performance, or a Bayesian network fitted on several target attributes has an exceptional structure. We give an algorithmic
Participation in post-compulsory education in Scotland

This paper uses data from the Scottish Young People's Cohort Study to investigate the factors affecting participation in post-compulsory education in Scotland. These surveys are a unique source of information on routes through school and on entry to higher education. The major aim of the paper is to estimate the effects of the structure of post-compulsory schooling and certification on participation rates. The main statistical technique is multilevel logistic regression. Another aim is the investigation of regional differences in participation in Scotland.
Concurrent Endurance and Explosive Type Strength Training Improves Neuromuscular and Anaerobic Characteristics in Young Distance Runners

endurance athletes. However, it has been suggested that concurrent endurance and strength training Abstract ! To study effects of concurrent explosive strength and endurance training on aerobic and anaerobic performance and neuromuscular characteristics, 13 experimental (E) and 12 control (C) young (16–18 years) distance runners trained for eight weeks with the same total training volume but 19% of the endurance training in E was replaced by explosive training. Maximal speed of maximal anaerobic running test and 30-m speed improved in E by 3.0 ± 2.0% (p < 0.01) and by 1.1 ± 1.3% (p < 0.05), respectively. Maximal speed of aerobic running test, maximal oxygen uptake and running economy remained unchanged in both groups. Concentric and isometric leg extension forces increased in E but not in C. E also improved (p < 0.05) force-time characteristics accompanied by increased (p < 0.05) rapid neural activation of the muscles. The thickness of quadriceps femoris increased in E by 3.9 ± 4.7% (p < 0.01) and in C by 1.9 ± 2.0% (p < 0.05). The concurrent explosive strength and endurance training improved anaerobic and selective neuromuscular performance characteristics in young distance runners without decreases in aerobic capacity, although almost 20% of the total training volume was replaced by explosive strength training for eight weeks. The neuromuscular improvements could be explained primarily by neural adaptations.
Determining the locations of faults in distribution systems

The conventional approach for estimating the locations of transmission line faults has been to measure the apparent impedance from a line terminal to the fault and to convert the reactive component of the impedance to line length. Several methods, that use voltages and currents measured at one or both line terminals, have been proposed in the past. Methods for locating faults on radial transmission lines and rural distribution feeders have also been suggested. These methods do not adequately address the problems associated with fault location on distribution systems that have single or multiphase laterals and/or tapped loads. A technique that estimates the location of a shunt fault on a radial distribution system that has several single and/or multiphase laterals has been developed. Load taps and non-homogeneity of the system are taken into account. The apparent location of a fault is first estimated by computing the impedance from the fundamental frequency voltage and current phasors, and converting the reactive component of the impedance to line length. The sequence voltages and currents at the fault are expressed as functions of the distance to the fault as well as the impedances of loads beyond the fault. The expression for the imaginary component of the fault impedance is equated to zero and the resulting equation is solved using an iterative approach. Multiple estimates may be obtained for a fault in a distribution system that has laterals. One of the estimates is identified as the most likely fault location by using information from fault indicators which are strategically placed on the laterals. The developed technique, which can handle single-phase-to-ground, two-phase-to-ground, phase-to-phase and balanced three-phase faults was tested to evaluate its suitability. Results from computer simulations of faults indicate that the proposed technique is more accurate than the reactive component method. Studies also demonstrate that the sensitivity of the proposed technique is comparable to that of the reactive component method. A prototype fault location system was also developed. The system was tested using simulated voltage and current waveforms. Results show a close agreement with those obtained from the non-real time tests.
Restless Bandits for Dynamic Sourcing of Resources in Social and Information Networks

We study a problem of information gathering in a social network with dynamically available sources and time varying quality of information. We formulate this problem as a restless multi-armed bandit (RMAB). In this problem, information quality of a source corresponds to the state of an arm in RMAB. The decision making agent does not know the quality of information from sources a priori. But the agent maintains a belief about the quality of information from each source. This is a problem of RMAB with partially observable states. The objective of the agent is to gather relevant information efficiently from sources by contacting them. We formulate this as a infinite horizon discounted reward problem, where reward depends on quality of information. We study Whittle index policy which determines the sequence of play of arms that maximizes long term cumulative reward. We illustrate the performance of index policy, myopic policy and compare with uniform random policy through numerical examples.
Invariant preservation in geo-replicated data stores

The Internet has enabled people from all around the globe to communicate with each other in a matter of milliseconds. This possibility has a great impact in the way we work, behave and communicate, while the full extent of possibilities are yet to be known. As we become more dependent of Internet services, the more important is to ensure that these systems operate correctly, with low latency and high availability for millions of clients scattered all around the globe. To be able to provide service to a large number of clients, and low access latency for clients in different geographical locations, Internet services typically rely on georeplicated storage systems. Replication comes with costs that may affect service quality. To propagate updates between replicas, systems either choose to lose consistency in favor of better availability and latency (weak consistency), or maintain consistency, but the system might become unavailable during partitioning (strong consistency). In practice, many production systems rely on weak consistency storage systems to enhance user experience, overlooking that applications can become incorrect due to the weaker consistency assumptions. In this thesis, we study how to exploit application’s semantics to build correct applications without affecting the availability and latency of operations. We propose a new consistency model that breaks apart from traditional knowledge that applications consistency is dependent on coordinating the execution of operations across replicas. We show that it is possible to execute most operations with low latency and in an highly available way, while preserving application’s correctness. Our approach consists in specifying the fundamental properties that define the correctness of applications, i.e. the application invariants, and identify and prevent concurrent executions that potentially can make the state of the database inconsistent, i.e. that may violate some invariant. We explore different, complementary, approaches to implement this model. The Indigo approach consists in preventing conflicting operations from executing concurrently, by restricting the operations that each replica can execute at each moment to maintain application’s correctness. The IPA approach does not preclude the execution of any operation, ensuring high availability. To maintain application correctness, operations are modified to prevent invariant violations during replica reconciliation, or, if modifying operations provides an