Miracle-dz/UniFAR-FTUs huggingface dataset:
Realistic River Image Synthesis Using Deep Generative Adversarial Networks

In this paper, we demonstrated a practical application of realistic river image generation using deep learning. Specifically, we explored a generative adversarial network (GAN) model capable of generating high-resolution and realistic river images that can be used to support modeling and analysis in surface water estimation, river meandering, wetland loss, and other hydrological research studies. First, we have created an extensive repository of overhead river images to be used in training. Second, we incorporated the Progressive Growing GAN (PGGAN), a network architecture that iteratively trains smaller-resolution GANs to gradually build up to a very high resolution to generate high quality (i.e., 1,024 × 1,024) synthetic river imagery. With simpler GAN architectures, difficulties arose in terms of exponential increase of training time and vanishing/exploding gradient issues, which the PGGAN implementation seemed to significantly reduce. The results presented in this study show great promise in generating high-quality images and capturing the details of river structure and flow to support hydrological modeling and research.
A novel mutation in the ligand-binding domain of the androgen receptor gene associated with scrotal hypospadias -

Objective: Scrotal hypospadias is the severe form of hypospadias with complex and elusive etiology. The androgens and the androgen receptors play a critical role in the male external genitalia development. The main objective of the study was to evaluate clinical, genetic, biochemical profiles and perform molecular testing of androgen receptor ( AR ) gene in a boy with scrotal hypospadias and micropenis. Methods: Chromosomal analyses is performed by harvesting lymphocytes from peripheral blood sample, hormonal analysis from serum and gene sequencing of AR gene from the DNA. Results: The chromosomal analysis revealed 46,XY karyotype with normal and molecular genetic testing of androgen receptor (AR) gene identified a novel homozygous missense mutation of single nucleotide mutation G to T at position 2702 (GenBank accession number GU784858), resulting in amino acid interchange methionine to isoleucine at codon 780 in exon 6 (Met780Ile) (GenBank Protein_id: ADD 26780). Conclusions: The nature of the novel AR mutation identified in a boy with scrotal hypospadias provides significant information due to importance of missense mutations in the ligand binding domain of the AR, which are able to induce functional abnormalities in the androgen binding capacity, stabilization of active conformation or interaction with coactivators. The novel mutation identified in the study provides additional support to the previously reported genotype-phenotype correlations, and our finding has expanded the spectrum of known mutations of the AR gene.
Transformers for Limit Order Books

We introduce a new deep learning architecture for predicting price movements from limit order books. This architecture uses a causal convolutional network for feature extraction in combination with masked self-attention to update features based on relevant contextual information. This architecture is shown to significantly outperform existing architectures such as those using convolutional networks (CNN) and Long-Short Term Memory (LSTM) establishing a new state-of-the-art benchmark for the FI-2010 dataset.
Dynamic Geometric Set Cover and Hitting Set

We investigate dynamic versions of geometric set cover and hitting set where points and ranges may be inserted or deleted, and we want to efficiently maintain an (approximately) optimal solution for the current problem instance. While their static versions have been extensively studied in the past, surprisingly little is known about dynamic geometric set cover and hitting set. For instance, even for the most basic case of one-dimensional interval set cover and hitting set, no nontrivial results were known. The main contribution of our article are two frameworks that lead to efficient data structures for dynamically maintaining set covers and hitting sets in ℝ1 and ℝ2. The first framework uses bootstrapping and gives a (1 + ε)-approximate data structure for dynamic interval set cover in ℝ1 with O(nα / ε) amortized update time for any constant α > 0; in ℝ2, this method gives O(1)-approximate data structures for unit-square set cover and hitting set with O(n1/2+α) amortized update time. The second framework uses local modification and leads to a (1 + ε)-approximate data structure for dynamic interval hitting set in ℝ1 with Õ(1/ε) amortized update time; in ℝ2, it gives O(1)-approximate data structures for unit-square set cover and hitting set in the partially dynamic settings with Õ(1) amortized update time.
Low field electron mobility in $\alpha-Ga_{2}O_{3}$: An ab-initio approach

The $\alpha$ phase of $Ga_{2}O_{3}$ is an ultra-wideband semiconductor with potential power electronics applications. In this work, we calculate the low field electron mobility in $\alpha-Ga_{2}O_{3}$ from first principles. The 10 atom unit cell contributes to 30 phonon modes and the effect of each mode is taken into account for the transport calculation. The phonon dispersion and the Raman spectrum are calculated under the density functional perturbation theory formalism and compared with experiments. The IR strength is calculated from the dipole moment at the $\Gamma$ point of the Brillouin zone. The electron-phonon interaction elements (EPI) on a dense reciprocal space grid is obtained using the Wannier interpolation technique. The polar nature of the material is accounted for by interpolating the non-polar and polar EPI elements independently as the localized nature of the Wannier functions are not suitable for interpolating the long-range polar interaction elements. For polar interaction the full phonon dispersion is taken into account. The electron mobility is then calculated including the polar, non-polar and ionized impurity scattering.
Multi-Level Optimal Power Flow Solver in Large Distribution Networks

Solving optimal power flow (OPF) problems for large distribution networks incurs high computational complexity. We consider a large multi-phase distribution network of tree topology with a deep penetration of active devices. We divide the network into collaborating areas featuring subtree topology and subareas featuring subsubtree topology. We design a multilevel implementation of the primal-dual gradient algorithm to solve the voltage regulation OPF problems while preserving nodal voltage information and topological information within areas and subareas. Numerical results on a 4,521-node system verify that the proposed algorithm can significantly improve the computational speed without compromising any optimality.
A-TVSNet: Aggregated Two-View Stereo Network for Multi-View Stereo Depth Estimation

We propose a learning-based network for depth map estimation from multi-view stereo (MVS) images. Our proposed network consists of three sub-networks: 1) a base network for initial depth map estimation from an unstructured stereo image pair, 2) a novel refinement network that leverages both photometric and geometric information, and 3) an attentional multi-view aggregation framework that enables efficient information exchange and integration among different stereo image pairs. The proposed network, called A-TVSNet, is evaluated on various MVS datasets and shows the ability to produce high quality depth map that outperforms competing approaches. Our code is available at this https URL.
Non-Coulomb strong electron-hole binding in 
Ta2NiSe5
 revealed by time- and angle-resolved photoemission spectroscopy

We reveal an ultrafast purely electronic phase transition in ${\mathrm{Ta}}_{2}{\mathrm{NiSe}}_{5}$, which is a plausible excitonic insulator, after excited by an ultrafast infrared laser pulse. Specifically, the order parameter of the strong electron-hole binding shrinks with enhancing the pump pulse, and above a critical pump fluence, a photoexcited semimetallic state is experimentally identified with the absence of ultrafast structural transition. In addition, the bare valence and conduction bands and also the effective masses in ${\mathrm{Ta}}_{2}{\mathrm{NiSe}}_{5}$ are determined. These findings and detailed analysis suggest a bare nonequilibrium semimetallic phase in ${\mathrm{Ta}}_{2}{\mathrm{NiSe}}_{5}$ and the strong electron-hole binding cannot be exclusively driven by Coulomb interaction.
Deterministic Intra-Vehicle Communications: Timing and Synchronization

As we power through to the future, in-vehicle communications’ reliance on speed is becoming a challenging predicament. This is mainly due to the ever-increasing number of electronic control units (ECUs), which will continue to drain network capacity, hence further increasing bandwidth demand. For a wired network, a tradeoff between bandwidth requirement, reliability, and cost-effectiveness has been our main motivation in developing a high-speed network architecture that is based on the integration of two time-triggered protocols namely; Time Triggered Ethernet (TT-E) and Time Triggered Controller Area Network (TT-CAN). Therefore, as a visible example of an Internet of Vehicles technology, we present a time triggered communication-based network architecture. The new architecture can provide scalable integration of advanced functionalities, while maintaining safety and high reliability. To comply with the bandwidth requirement, we consider high-speed TT-Ethernet as the main bus (i.e., backbone network) where sub-networks can use more cost-effective and lower bandwidth TT-CAN to communicate with other entities in the network via a gateway. The main challenge in the proposed network architecture has been to resolve interoperability between two entirely different time-triggered protocols, especially in terms of timing and synchronization. In this paper, we first explore the main key drivers of the proposed architecture, which are bandwidth, reliability, and timeliness. We then demonstrate the effectiveness of our gateway design in providing full interoperability between the two time-triggered protocols.
Determination of latent dimensionality in international trade flow

Currently, high-dimensional data is ubiquitous in data science, which necessitates the development of techniques to decompose and interpret such multidimensional (aka tensor) datasets. Finding a low dimensional representation of the data, that is, its inherent structure, is one of the approaches that can serve to understand the dynamics of low dimensional latent features hidden in the data. Moreover, decomposition methods with non-negative constraints are shown to extract more insightful factors. Nonnegative RESCAL is one such technique, particularly well suited to analyze self-relational data, such as dynamic networks found in international trade flows. Particularly, non-negative RESCAL computes a low dimensional tensor representation by finding the latent space containing multiple modalities. Furthermore, estimating the dimensionality of this latent space is crucial for extracting meaningful latent features. Here, to determine the dimensionality of the latent space with non-negative RESCAL, we propose a latent dimension determination method which is based on clustering of the solutions of multiple realizations of non-negative RESCAL decompositions. We demonstrate the performance of our model selection method on synthetic data. We then apply our method to decompose a network of international trade flows data from International Monetary Fund and shows that with a correct latent dimension determination, the resulting features are able to capture relevant empirical facts from economic literature.
First-principles Hubbard 
U
 and Hund's 
J
 corrected approximate density functional theory predicts an accurate fundamental gap in rutile and anatase 
TiO2

Titanium dioxide (${\mathrm{TiO}}_{2}$) presents a long-standing challenge for approximate Kohn-Sham density functional theory (KS-DFT), as well as to its Hubbard-corrected extension, $\text{DFT+}\mathit{\text{U}}$. We find that a previously proposed extension of first-principles $\text{DFT+}\mathit{\text{U}}$ to incorporate a Hund's $J$ correction, termed $\text{DFT+}\mathit{\text{U}}\text{+}\mathit{\text{J}}$, in combination with parameters calculated using a recently proposed linear-response theory, predicts fundamental band gaps that are accurate to well within the experimental uncertainty in rutile and anatase ${\mathrm{TiO}}_{2}$. Our approach builds upon established findings that Hubbard correction of both the titanium $3d$ and oxygen $2p$ subspaces in ${\mathrm{TiO}}_{2}$, symbolically giving $\text{DFT+}{\mathit{\text{U}}}^{d,p}$, is necessary to achieve acceptable band gaps using $\text{DFT+}\mathit{\text{U}}$. This requirement remains when the first-principles Hund's $J$ is included. We also find that the calculated gap depends on the correlated subspace definition even when using subspace-specific first-principles $U$ and $J$ parameters. Using the simplest reasonable correlated subspace definition and underlying functional, the local density approximation, we show that high accuracy results from using a relatively uncomplicated form of the $\text{DFT+}\mathit{\text{U}}\text{+}\mathit{\text{J}}$ functional. For closed-shell systems such as ${\mathrm{TiO}}_{2}$, we describe how various $\text{DFT+}\mathit{\text{U}}\text{+}\mathit{\text{J}}$ functionals reduce to $\text{DFT+}\mathit{\text{U}}$ with suitably modified parameters, so that reliable band gaps can be calculated for rutile and anatase with no modifications to a conventional $\text{DFT+}\mathit{\text{U}}$ code.
Adaptive Federated Optimization

Federated learning is a distributed machine learning paradigm in which a large number of clients coordinate with a central server to learn a model without sharing their own training data. Due to the heterogeneity of the client datasets, standard federated optimization methods such as Federated Averaging (FedAvg) are often difficult to tune and exhibit unfavorable convergence behavior. In non-federated settings, adaptive optimization methods have had notable success in combating such issues. In this work, we propose federated versions of adaptive optimizers, including Adagrad, Adam, and Yogi, and analyze their convergence in the presence of heterogeneous data for general nonconvex settings. Our results highlight the interplay between client heterogeneity and communication efficiency. We also perform extensive experiments on these methods and show that the use of adaptive optimizers can significantly improve the performance of federated learning.
A general framework for scientifically inspired explanations in AI

Explainability in AI is gaining attention in the computer science community in response to the increasing success of deep learning and the important need of justifying how such systems make predictions in life-critical applications. The focus of explainability in AI has predominantly been on trying to gain insights into how machine learning systems function by exploring relationships between input data and predicted outcomes or by extracting simpler interpretable models. Through literature surveys of philosophy and social science, authors have highlighted the sharp difference between these generated explanations and human-made explanations and claimed that current explanations in AI do not take into account the complexity of human interaction to allow for effective information passing to not-expert users. In this paper we instantiate the concept of structure of scientific explanation as the theoretical underpinning for a general framework in which explanations for AI systems can be implemented. This framework aims to provide the tools to build a "mental-model" of any AI system so that the interaction with the user can provide information on demand and be closer to the nature of human-made explanations. We illustrate how we can utilize this framework through two very different examples: an artificial neural network and a Prolog solver and we provide a possible implementation for both examples.
Stochastic Calibration of Radio Interferometers

With ever increasing data rates produced by modern radio telescopes like LOFAR and future telescopes like the SKA, many data processing steps are overwhelmed by the amount of data that needs to be handled using limited compute resources. Calibration is one such operation that dominates the overall data processing computational cost, nonetheless, it is an essential operation to reach many science goals. Calibration algorithms do exist that scale well with the number of stations of an array and the number of directions being calibrated. However, the remaining bottleneck is the raw data volume, which scales with the number of baselines, and which is proportional to the square of the number of stations. We propose a 'stochastic' calibration strategy where we only read in a mini-batch of data for obtaining calibration solutions, as opposed to reading the full batch of data being calibrated. Nonetheless, we obtain solutions that are valid for the full batch of data. Normally, data need to be averaged before calibration is performed to accommodate the data in size-limited compute memory. Stochastic calibration overcomes the need for data averaging before any calibration can be performed, and offers many advantages including: enabling the mitigation of faint radio frequency interference; better removal of strong celestial sources from the data; and better detection and spatial localization of fast radio transients.
A Convolutional Baseline for Person Re-Identification Using Vision and Language Descriptions

Classical person re-identification approaches assume that a person of interest has appeared across different cameras and can be queried by one of the existing images. However, in real-world surveillance scenarios, frequently no visual information will be available about the queried person. In such scenarios, a natural language description of the person by a witness will provide the only source of information for retrieval. In this work, person re-identification using both vision and language information is addressed under all possible gallery and query scenarios. A two stream deep convolutional neural network framework supervised by cross entropy loss is presented. The weights connecting the second last layer to the last layer with class probabilities, i.e., logits of softmax layer are shared in both networks. Canonical Correlation Analysis is performed to enhance the correlation between the two modalities in a joint latent embedding space. To investigate the benefits of the proposed approach, a new testing protocol under a multi modal ReID setting is proposed for the test split of the CUHK-PEDES and CUHK-SYSU benchmarks. The experimental results verify the merits of the proposed system. The learnt visual representations are more robust and perform 22\% better during retrieval as compared to a single modality system. The retrieval with a multi modal query greatly enhances the re-identification capability of the system quantitatively as well as qualitatively.
A Machine Learning Framework for Data Ingestion in Document Images

Paper documents are widely used as an irreplaceable channel of information in many fields, especially in financial industry, fostering a great amount of demand for systems which can convert document images into structured data representations. In this paper, we present a machine learning framework for data ingestion in document images, which processes the images uploaded by users and return fine-grained data in JSON format. Details of model architectures, design strategies, distinctions with existing solutions and lessons learned during development are elaborated. We conduct abundant experiments on both synthetic and real-world data in State Street. The experimental results indicate the effectiveness and efficiency of our methods.
Adversarial Attacks and Defenses on Graphs: A Review and Empirical Study

Deep neural networks (DNNs) have achieved significant performance in various tasks. However, recent studies have shown that DNNs can be easily fooled by small perturbation on the input, called adversarial attacks. As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability. Adversary can mislead GNNs to give wrong predictions by modifying the graph structure such as manipulating a few edges. This vulnerability has arisen tremendous concerns for adapting GNNs in safety-critical applications and has attracted increasing research attention in recent years. Thus, it is necessary and timely to provide a comprehensive overview of existing graph adversarial attacks and the countermeasures. In this survey, we categorize existing attacks and defenses, and review the corresponding state-of-the-art methods. Furthermore, we have developed a repository with representative algorithms (this https URL). The repository enables us to conduct empirical studies to deepen our understandings on attacks and defenses on graphs.
TAdam: A Robust Stochastic Gradient Optimizer

Machine learning algorithms aim to find patterns from observations, which may include some noise, especially in robotics domain. To perform well even with such noise, we expect them to be able to detect outliers and discard them when needed. We therefore propose a new stochastic gradient optimization method, whose robustness is directly built in the algorithm, using the robust student-t distribution as its core idea. Adam, the popular optimization method, is modified with our method and the resultant optimizer, so-called TAdam, is shown to effectively outperform Adam in terms of robustness against noise on diverse task, ranging from regression and classification to reinforcement learning problems. The implementation of our algorithm can be found at this https URL
DangKiller: Eliminating Dangling Pointers Efficiently via Implicit Identifier

Use-After-Free vulnerabilities, allowing the attacker to access unintended memory via dangling pointers, are more threatening. However, most detection schemes can only detect dangling pointers and invalid them, but not provide a tolerance mechanism to repair the errors at runtime. Also, these techniques obtain and manage the metadata inefficiently with complex structures and too much scan (sweep). The goal of this paper is to use compiler instrumentation to eliminate dangling pointers automatically and efficiently. In this paper, we observe that most techniques lack accurate efficient pointer graph metadata maintaining methods, so they need to scan the log to reduce the redundancy and sweep the whole address space to find dangling pointers. Also, they lack a direct, efficiently obtaining metadata approach. The key insight of this paper is that a unique identifier can be used as a key to a hash or direct-map algorithm. Thus, this paper maintains the same implicit identifier with each memory object and its corresponding referent. Associating the unique ID with metadata for memory objects, obtaining and managing the pointer graph metadata can be efficiently. Therefore, with the delayed free technique adopted into C/C++, we present the DangKiller as a novel and lightweight dangling pointer elimination solution. We first demonstrate the MinFat Pointer, which can calculate unique implicit ID for each object and pointer quickly, and use hash algorithm to obtain metadata. Secondly, we propose the Log Cache and Log Compression mechanism based on the ID to decrease the redundancy of dangling pointer candidates. Coupled with the Address Tagging architecture on an ARM64 system, our experiments show that the DangKiller can eliminate use-after-free vulnerabilities at only 11% and 3% runtime overheads for the SPEC CPU2006 and 2017 benchmarks respectively, except for unique cases.