Miracle-dz/UniFAR-FTUs huggingface dataset:
Singular limits of the quasi-linear Kolmogorov-type equation with a source term

Existence, uniqueness and stability of kinetic and entropy solutions to the boundary value problem associated with the Kolmogorov-type, genuinely nonlinear, degenerate hyperbolic–parabolic (ultra-parabolic) equation with a smooth source term is established. In addition, we consider the case when the source term contains a small positive parameter and collapses to the Dirac delta-function, as this parameter tends to zero. In this case, the limiting passage from the original equation with the smooth source to the impulsive ultra-parabolic equation is investigated and the formal limit is rigorously justified. Our proofs rely on the use of kinetic equations and the compensated compactness method for genuinely nonlinear balance laws.
Historical comparison of gender inequality in scientific careers across countries and disciplines

Significance Empirical evidence suggests significant gender differences in the total productivity and impact of academic careers across science, technology, engineering, and mathematics (STEM) fields. Paradoxically, the increase in the number of women academics over the past 60 years has increased these gender differences. Yet, we find that men and women publish a comparable number of papers per year and have equivalent career-wise impact for the same total number of publications. This suggests the productivity and impact of gender differences are explained by different publishing career lengths and dropout rates. This comprehensive picture of gender inequality in academic publishing can help rephrase the conversation around the sustainability of women’s careers in academia, with important consequences for institutions and policy makers. There is extensive, yet fragmented, evidence of gender differences in academia suggesting that women are underrepresented in most scientific disciplines and publish fewer articles throughout a career, and their work acquires fewer citations. Here, we offer a comprehensive picture of longitudinal gender differences in performance through a bibliometric analysis of academic publishing careers by reconstructing the complete publication history of over 1.5 million gender-identified authors whose publishing career ended between 1955 and 2010, covering 83 countries and 13 disciplines. We find that, paradoxically, the increase of participation of women in science over the past 60 years was accompanied by an increase of gender differences in both productivity and impact. Most surprisingly, though, we uncover two gender invariants, finding that men and women publish at a comparable annual rate and have equivalent career-wise impact for the same size body of work. Finally, we demonstrate that differences in publishing career lengths and dropout rates explain a large portion of the reported career-wise differences in productivity and impact, although productivity differences still remain. This comprehensive picture of gender inequality in academia can help rephrase the conversation around the sustainability of women’s careers in academia, with important consequences for institutions and policy makers.
Linux Kernel Networking: Implementation and Theory

Linux Kernel Networking takes you on a guided in-depth tour of the current Linux networking implementation and the theory behind it. Linux kernel networking is a complex topic, so the book won't burden you with topics not directly related to networking. This book will also not overload you with cumbersome line-by-line code walkthroughs not directly related to what you're searching for; you'll find just what you need, with in-depth explanations in each chapter and a quick reference at the end of each chapter. Linux Kernel Networking is the only up-to-date reference guide to understanding how networking is implemented, and it will be indispensable in years to come since so many devices now use Linux or operating systems based on Linux, like Android, and since Linux is so prevalent in the data center arena, including Linux-based virtualization technologies like Xen and KVM. What youll learnKernel networking basics, including socket buffersHow key protocols like ARP, Neighbour Discovery and ICMP are implementedIn-depth looks at both IPv4 and IPv6Everything you need to know about Linux routingHow netfilter and IPsec are implementedLinux wireless networkingAdditional topics like Network Namespaces, NFC, IEEE 802.15. 4, Bluetooth, InfiniBand and more Who this book is for Software developers and architects, project managers, CTOs, network admins and architects, network security professionals, computer science researchers, and Linux kernel hackers. A basic knowledge of C and networking is required; basic knowledge of the Linux kernel is helpful but not mandatory.
Geometric Matrix Midranges

We define geometric matrix midranges for positive definite Hermitian matrices and study the midrange problem from a number of perspectives. Special attention is given to the midrange of two positive definite matrices before considering the extension of the problem to $N > 2$ matrices. We compare matrix midrange statistics with the scalar and vector midrange problem and note the special significance of the matrix problem from a computational standpoint. We also study various aspects of geometric matrix midrange statistics from the viewpoint of linear algebra, differential geometry and convex optimization.
An Attention Mechanism for Musical Instrument Recognition

While the automatic recognition of musical instruments has seen significant progress, the task is still considered hard for music featuring multiple instruments as opposed to single instrument recordings. Datasets for polyphonic instrument recognition can be categorized into roughly two categories. Some, such as MedleyDB, have strong per-frame instrument activity annotations but are usually small in size. Other, larger datasets such as OpenMIC only have weak labels, i.e., instrument presence or absence is annotated only for long snippets of a song. We explore an attention mechanism for handling weakly labeled data for multi-label instrument recognition. Attention has been found to perform well for other tasks with weakly labeled data. We compare the proposed attention model to multiple models which include a baseline binary relevance random forest, recurrent neural network, and fully connected neural networks. Our results show that incorporating attention leads to an overall improvement in classification accuracy metrics across all 20 instruments in the OpenMIC dataset. We find that attention enables models to focus on (or `attend to') specific time segments in the audio relevant to each instrument label leading to interpretable results.
Security for Distributed Deep Neural Networks Towards Data Confidentiality & Intellectual Property Protection

Current developments in Enterprise Systems observe a paradigm shift, moving the needle from the backend to the edge sectors of those; by distributing data, decentralizing applications and integrating novel components seamlessly to the central systems. Distributively deployed AI capabilities will thrust this transition. Several non-functional requirements arise along with these developments, security being at the center of the discussions. Bearing those requirements in mind, hereby we propose an approach to holistically protect distributed Deep Neural Network (DNN) based/enhanced software assets, i.e. confidentiality of their input & output data streams as well as safeguarding their Intellectual Property. Making use of Fully Homomorphic Encryption (FHE), our approach enables the protection of Distributed Neural Networks, while processing encrypted data. On that respect we evaluate the feasibility of this solution on a Convolutional Neuronal Network (CNN) for image classification deployed on distributed infrastructures.
All-optical switch and logic gates based on hybrid silicon-Ge2Sb2Te5 metasurfaces.

We numerically propose a hybrid metasurface (MS) to realize all-optical switch and logic gates in the shortwave infrared (SWIR) band. Such MS consists of one silicon rod and one Ge2Sb2Te5 (GST) rod pair. Utilizing the transition from an amorphous state to a crystalline state of GST, such MS can produce an electromagnetically induced transparency (EIT) analogue with active control. Based on this, we realize all-optical switching at 1770 nm with a modulation depth of 84%. Besides, three different logic gates, NOT, NOR and OR, can also be achieved in this metadevice simultaneously. Thanks to the reversible and fast phase transition process of GST, this device possesses reconfigurable ability as well as fast response time, and has potential applications in future optical networks.
A method for computing the Perron-Frobenius root for primitive matrices

For a nonnegative matrix, the eigenvalue with the maximum magnitude or Perron-Frobenius root exists and is unique if the matrix is primitive. It is shown that for a primitive matrix $A$, there exists a positive rank one matrix $X$ allowing to have the Hadamard product $B = A \circ X$, where the row (column) sums of matrix $B$ are the same and equal to the Perron-Frobenius root. An iterative algorithm is presented to obtain matrix $B$ without an explicit knowledge of $X$. The convergence rate of this algorithm is similar to that of the power method but it uses less computational load.
Faster Deterministic Distributed Coloring Through Recursive List Coloring

We provide novel deterministic distributed vertex coloring algorithms. As our main result, we give a deterministic distributed algorithm to compute a $(\Delta+1)$-coloring of an $n$-node graph with maximum degree $\Delta$ in $2^{O(\sqrt{\log\Delta})}\cdot\log n$ rounds. This improves on the best previously known time complexity for a large range of values of $\Delta$. For graphs with arboricity $a$, we obtain a deterministic distributed algorithm to compute a $(2+o(1))a$-coloring in time $2^{O(\sqrt{\log a})}\cdot\log^2 n$. Further, for graphs with bounded neighborhood independence, we show that a $(\Delta+1)$-coloring can be computed more efficiently in time $2^{O(\sqrt{\log\Delta})} + O(\log^* n)$. This in particular implies that also a $(2\Delta-1)$-edge coloring can be computed deterministically in $2^{O(\sqrt{\log\Delta})} + O(\log^* n)$ rounds, which improves the best known time bound for small values of $\Delta$. All results even hold for the list coloring variants of the problems. As a consequence, we also obtain an improved deterministic $2^{O(\sqrt{\log\Delta})}\cdot\log^3 n$-round algorithm for $\Delta$-coloring non-complete graphs with maximum degree $\Delta\geq 3$. Most of our algorithms only require messages of $O(\log n)$ bits (including the $(\Delta+1)$-vertex coloring algorithms). Our main technical contribution is a recursive deterministic distributed list coloring algorithm to solve list coloring problems with lists of size $\Delta^{1+o(1)}$. Given some list coloring problem and an orientation of the edges, we show how to recursively divide the global color space into smaller subspaces, assign one of the subspaces to each node of the graph, and compute a new edge orientation such that for each node, the list size to out-degree ratio degrades at most by a constant factor on each recursion level.
Fast Visual Object Tracking with Rotated Bounding Boxes

In this paper, we demonstrate a novel algorithm that uses ellipse fitting to estimate the bounding box rotation angle and size with the segmentation(mask) on the target for online and real-time visual object tracking. Our method, SiamMask E, improves the bounding box fitting procedure of the state-of-the-art object tracking algorithm SiamMask and still retains a fast-tracking frame rate (80 fps) on a system equipped with GPU (GeForce GTX 1080 Ti or higher). We tested our approach on the visual object tracking datasets (VOT2016, VOT2018, and VOT2019) that were labeled with rotated bounding boxes. By comparing with the original SiamMask, we achieved an improved Accuracy of 0.645 and 0.303 EAO on VOT2019, which is 0.049 and 0.02 higher than the original SiamMask. Our project website is available at this http URL.
Thompson Sampling on Symmetric α-Stable Bandits

Thompson Sampling provides an efficient technique to introduce prior knowledge in the multi-armed bandit problem, along with providing remarkable empirical performance. In this paper, we revisit the Thompson Sampling algorithm under rewards drawn from symmetric $\alpha$-stable distributions, which are a class of heavy-tailed probability distributions utilized in finance and economics, in problems such as modeling stock prices and human behavior. We present an efficient framework for posterior inference, which leads to two algorithms for Thompson Sampling in this setting. We prove finite-time regret bounds for both algorithms, and demonstrate through a series of experiments the stronger performance of Thompson Sampling in this setting. With our results, we provide an exposition of symmetric $\alpha$-stable distributions in sequential decision-making, and enable sequential Bayesian inference in applications from diverse fields in finance and complex systems that operate on heavy-tailed features.
Improving Reverberant Speech Training Using Diffuse Acoustic Simulation

We present an efficient and realistic geometric acoustic simulation approach for generating and augmenting training data in speech-related machine learning tasks. Our physically-based acoustic simulation method is capable of modeling occlusion, specular and diffuse reflections of sound in complicated acoustic environments, whereas the classical image method can only model specular reflections in simple room settings. We show that by using our synthetic training data, the same neural networks gain significant performance improvement on real test sets in far-field speech recognition by 1.58% and keyword spotting by 21%, without fine-tuning using real impulse responses.
Attending to Emotional Narratives

Attention mechanisms in deep neural networks have achieved excellent performance on sequence-prediction tasks. Here, we show that these recently-proposed attention-based mechanisms-in particular, the Transformer with its parallelizable self-attention layers, and the Memory Fusion Network with attention across modalities and time-also generalize well to multimodal time-series emotion recognition. Using a recently-introduced dataset of emotional autobiographical narratives, we adapt and apply these two attention mechanisms to predict emotional valence over time. Our models perform extremely well, in some cases reaching a performance comparable with human raters. We end with a discussion of the implications of attention mechanisms to affective computing.
The Power of Comparisons for Actively Learning Linear Classifiers

In the world of big data, large but costly to label datasets dominate many fields. Active learning, a semi-supervised alternative to the standard PAC-learning model, was introduced to explore whether adaptive labeling could learn concepts with exponentially fewer labeled samples. While previous results show that active learning performs no better than its supervised alternative for important concept classes such as linear separators, we show that by adding weak distributional assumptions and allowing comparison queries, active learning requires exponentially fewer samples. Further, we show that these results hold as well for a stronger model of learning called Reliable and Probably Useful (RPU) learning. In this model, our learner is not allowed to make mistakes, but may instead answer "I don't know. " While previous negative results showed this model to have intractably large sample complexity for label queries, we show that comparison queries make RPU-learning at worst logarithmically more expensive in both the passive and active regimes.
Deep Reinforcement Learning for UAV Navigation Through Massive MIMO Technique

Unmanned aerial vehicles (UAVs) technique has been recognized as a promising solution in future wireless connectivity from the sky, and UAV navigation is one of the most significant open research problems, which has attracted wide interest in the research community. However, the current UAV navigation schemes are unable to capture the UAV motion and select the best UAV-ground links in real-time, and these weaknesses overwhelm the UAV navigation performance. To tackle these fundamental limitations, in this paper, we merge the state-of-the-art deep reinforcement learning with the UAV navigation through massive multiple-input-multiple-output (MIMO) technique. To be specific, we carefully design a deep Q-network (DQN) for optimizing the UAV navigation by selecting the optimal policy, and then we propose a learning mechanism for processing the DQN. The DQN is trained so that the agent is capable of making decisions based on the received signal strengths for navigating the UAVs with the aid of the powerful Q-learning. Simulation results are provided to corroborate the superiority of the proposed schemes in terms of the coverage and convergence compared with those of the other schemes.
Shock-accelerated cosmic rays and streaming instability in the adaptive mesh refinement code Ramses

Cosmic rays (CRs) are thought to play a dynamically important role in several key aspects of galaxy evolution, including the structure of the interstellar medium, the formation of galactic winds, and the non-thermal pressure support of halos. We introduce a numerical model solving for the CR streaming instability and acceleration of CRs at shocks with a fluid approach in the adaptive mesh refinement code RAMSES. CR streaming is solved with a diffusion approach and its anisotropic nature is naturally captured. We introduce a shock finder for the RAMSES code that automatically detects shock discontinuities in the flow. Shocks are the loci for CR injection, and their efficiency of CR acceleration is made dependent on the upstream magnetic obliquity according to the diffuse shock acceleration mechanism. We show that the shock finder accurately captures shock locations and estimates the shock Mach number for several problems. The obliquity-dependent injection of CRs in the Sedov solution leads to situations where the supernova bubble exhibits large polar caps (homogeneous background magnetic field), or a patchy structure of the CR distribution (inhomogeneous background magnetic field). Finally, we combine both accelerated CRs with streaming in a simple turbulent interstellar medium box, and show that the presence of CRs significantly modifies the structure of the gas.
PTAS and Exact Algorithms for r-Gathering Problems on Tree

r-gathering problem is a variant of facility location problems. In this problem, we are given a set of users and a set of facilities on same metric space. We open some of the facilities and assign each user to an open facility, so that at least r users are assigned to every open facility. We aim to minimize the maximum distance between user and assigned facility. In general, this problem is NP-hard and admit an approximation algorithm with factor 3. It is known that the problem does not admit any approximation algorithm within a factor less than 3. In our another paper, we proved that this problem is NP-hard even on spider, which is a special case of tree metric. In this paper, we concentrate on the problems on a tree. First, we give a PTAS for r-gathering problem on a tree. Furthermore, we give PTAS for some variants of the problems on a tree, and also give exact polynomial-time algorithms for another variants of r-gathering problem on a tree.
Biomechanical Models for Modified Pendulum Test of the Upper Limb

The main goal of this study was to develop and evaluate biomechanical models for interpreting the results of pendulum test of the elbow. Three biomechanical models with different degrees of complexity were proposed. Model 1 consisted of simple spring and damping elements. In model 2, non-linear damping element was added. In model 3, a structure of thixotropic effect was added. By using optimization techniques, we estimated parameters from these models as the candidate indicators of spasticity. In model 1, the estimated stiffness constant and damping coefficient were larger in stroke patients with spasticity. In model 2, though adding a non-linear damping term marginally improved the optimization, the added non-linear damping effect was not more prominent in the stroke patients. In model 3, no unique solution could be obtained. We concluded that, for analyzing the results of upper limb pendulum test, increasing the complexity of models did not increase the capability to differentiate spasticity from normotonus. From the simple linear model, both stiffness constant and damping coefficient were increased in the stroke patients with spasticity.
Toward Orientation Learning and Adaptation in Cartesian Space

As a promising branch of robotics, imitation learning emerges as an important way to transfer human skills to robots, where human demonstrations represented in Cartesian or joint spaces are utilized to estimate task/skill models that can be subsequently generalized to new situations. While learning Cartesian positions suffices for many applications, the end-effector orientation is required in many others. Despite recent advances in learning orientations from demonstrations, several crucial issues have not been adequately addressed yet. For instance, how can demonstrated orientations be adapted to pass through arbitrary desired points that comprise orientations and angular velocities? In this article, we propose an approach that is capable of learning multiple orientation trajectories and adapting learned orientation skills to new situations (e.g., via-points and end-points), where both orientation and angular velocity are considered. Specifically, we introduce a kernelized treatment to alleviate explicit basis functions when learning orientations, which allows for learning orientation trajectories associated with high-dimensional inputs. In addition, we extend our approach to the learning of quaternions with angular acceleration or jerk constraints, which allows for generating smoother orientation profiles for robots. Several examples including experiments with real 7-DoF robot arms are provided to verify the effectiveness of our method.
Characterizing Bitcoin donations to open source software on GitHub

Web-based hosting services for version control, such as GitHub, have made it easier for people to develop, share, and donate money to software repositories. In this paper, we study the use of Bitcoin to make donations to open source repositories on GitHub. In particular, we analyze the amount and volume of donations over time, in addition to its relationship to the age and popularity of a repository. We scanned over three million repositories looking for donation addresses. We then extracted and analyzed their transactions from Bitcoin's public blockchain. Overall, we found a limited adoption of Bitcoin as a payment method for receiving donations, with nearly 44 thousand deposits adding up to only 8.3 million dollars in the last 10 years. We also found weak positive correlation between the amount of donations in dollars and the popularity of a repository, with highest correlation (r=0.013) associated with number of forks.