Miracle-dz/UniFAR-FTUs huggingface dataset:
Upload Your Data Faster: Driver-Queue based Congestion Control for Wireless Networks

Data upload applications such as streaming of live videos and cloud services bring convenience to our lives. However, the Quality of Experience in wireless networks is often unsatisfactory. One of the reasons is, wireless communication is vulnerable to unpredictable factors such as rapid change of channel and competition of channel resources, leading to hysteresis and inaccuracy when performing a congestion control algorithm. To mitigate this problem, we analyzed the relationship between the real-time length of the NIC driver queue at the sender and the end-to-end transmission performances, and found a strong correlation between them. The reason is, when the wireless link is the first hop of data upload, the bottleneck mostly occurs at this hop, thus causing the accumulation of packets on the NIC driver queue. Based on this observation, we designed QCC, a congestion control algorithm that adjusts the congestion window (cwnd) according to the residual queue length after each round of NIC transmission. Specifically, the cwnd will be quickly reduced when this queue length is large to mitigate congestion, and gradually increased when it is small to increase link utility. By this means, QCC can track the time-varying wireless links quickly and accurately to achieve both high throughput and low latency. We evaluate QCC on both real-world Wi-Fi and cellular network implementations. Our experiment results reveal that QCC can achieve 2.04X lower delays than that of BBR while ensuring the similar link utilization rate as BBR (99% of BBR's throughput).
The chain rule for $\mathcal{F}$-differentiation

Let $X$ be a perfect, compact subset of the complex plane, and let $D^{(1)}(X)$ denote the (complex) algebra of continuously complex-differentiable functions on $X$. Then $D^{(1)}(X)$ is a normed algebra of functions but, in some cases, fails to be a Banach function algebra. Bland and the second author investigated the completion of the algebra $D^{(1)}(X)$, for certain sets $X$ and collections $\mathcal{F}$ of paths in $X$, by considering $\mathcal{F}$-differentiable functions on $X$. In this paper, we investigate composition, the chain rule, and the quotient rule for this notion of differentiability. We give an example where the chain rule fails, and give a number of sufficient conditions for the chain rule to hold. Where the chain rule holds, we observe that the Fa\'a di Bruno formula for higher derivatives is valid, and this allows us to give some results on homomorphisms between certain algebras of $\mathcal{F}$-differentiable functions.
Dextran Nanocapsules with ω-3 in Their Nucleus: An Innovative Nanosystem for Imiquimod Transdermal Delivery

Transdermal administration of molecules across the skin has gained interest because it can be considered a non-invasive route compared with traditional ones. However, going through the skin is challenging due to the presence of the stratum corneum, the main barrier of substances. For this reason, the goal of this research was the combination of omega-3 (ω-3) and a dextran sulfate assembly in a nanostructure form, which allows passage through the skin and improves the bioavailability and the therapeutic profiles of active molecules, such as imiquimod. Here we report a new colloidal system, named dextran nanocapsules, with ω-3 in its nucleus and a coat made of dextran sulfate with a size ~150 nm, monomodal distribution, and negative zeta potential (~−33 mV). This nanosystem encapsulates imiquimod with high efficacy (~86%) and can release it in a controlled fashion following Korsmeyer–Peppas kinetics. This formulation is stable under storage and physiological conditions. Furthermore, a freeze-dried product could be produced with different cryoprotectants and presents a good security profile in the HaCaT cell line. Ex vivo assays with newborn pig skin showed that dextran nanocapsules promote transdermal delivery and retention 10 times higher than non-encapsulated imiquimod. These promising results make this nanosystem an efficient vehicle for imiquimod transdermal delivery.
Two- and three-point Green's functions in two-dimensional Landau-gauge Yang-Mills theory

The ghost and gluon propagator and the ghost-gluon and three-gluon vertex of two-dimensional SU(2) Yang-Mills theory in (minimal) Landau gauge are studied using lattice gauge theory. It is found that the results are qualitatively similar to the ones in three and four dimensions. The propagators and the Faddeev-Popov operator behave as expected from the Gribov-Zwanziger scenario. In addition, finite-volume effects affecting these Green's functions are investigated systematically. The critical infrared exponents of the propagators, as proposed in calculations using stochastic quantization and Dyson-Schwinger equations, are confirmed quantitatively. For this purpose lattices of volume up to (42.7 fm){sup 2} have been used.
Supervised Myoelectrical Hand Gesture Recognition in Post-Acute Stroke Patients with Upper Limb Paresis on Affected and Non-Affected Sides

In clinical practice, acute post-stroke paresis of the extremities fundamentally complicates timely rehabilitation of motor functions; however, recently, residual and distorted musculoskeletal signals have been used to initiate feedback-driven solutions for establishing motor rehabilitation. Here, we investigate the possibilities of basic hand gesture recognition in acute stroke patients with hand paresis using a novel, acute stroke, four-component multidomain feature set (ASF-4) with feature vector weight additions (ASF-14NP, ASF-24P) and supervised learning algorithms trained only by surface electromyography (sEMG). A total of 19 (65.9 ± 12.4 years old; 12 men, seven women) acute stroke survivors (12.4 ± 6.3 days since onset) with hand paresis (Brunnstrom stage 4 ± 1/4 ± 1, SIAS 3 ± 1/3 ± 2, FMA-UE 40 ± 20) performed 10 repetitive hand movements reflecting basic activities of daily living (ADLs): rest, fist, pinch, wrist flexion, wrist extension, finger spread, and thumb up. Signals were recorded using an eight-channel, portable sEMG device with electrode placement on the forearms and thenar areas of both limbs (four sensors on each extremity). Using data preprocessing, semi-automatic segmentation, and a set of extracted feature vectors, support vector machine (SVM), linear discriminant analysis (LDA), and k-nearest neighbors (k-NN) classifiers for statistical comparison and validity (paired t-tests, p-value < 0.05), we were able to discriminate myoelectrical patterns for each gesture on both paretic and non-paretic sides. Despite any post-stroke conditions, the evaluated total accuracy rate by the 10-fold cross-validation using SVM among four-, five-, six-, and seven-gesture models were 96.62%, 94.20%, 94.45%, and 95.57% for non-paretic and 90.37%, 88.48%, 88.60%, and 89.75% for paretic limbs, respectively. LDA had competitive results using PCA whereas k-NN was a less efficient classifier in gesture prediction. Thus, we demonstrate partial efficacy of the combination of sEMG and supervised learning for upper-limb rehabilitation procedures for early acute stroke motor recovery and various treatment applications.
Application of Hydrogels as Sustained-Release Drug Carriers in Bone Defect Repair

Large bone defects resulting from trauma, infection and tumors are usually difficult for the body’s repair mechanisms to heal spontaneously. Generally, various types of bones and orthopedic implants are adopted to enhance bone repair and regeneration in the clinic. Due to the limitations of traditional treatments, bone defect repair is still a compelling challenge for orthopedic surgeons. In recent years, bone tissue engineering has become a potential option for bone repair and regeneration. Amidst the various scaffolds for bone tissue engineering applications, hydrogels are considered a new type of non-toxic, non-irritating and biocompatible materials, which are widely used in the biomedicine field currently. Some studies have demonstrated that hydrogels can provide a three-dimensional network structure similar to a natural extracellular matrix for tissue regeneration and can be used to transport cells, biofactors, nutrients and drugs. Therefore, hydrogels may have the potential to be multifunctional sustained-release drug carriers in the treatment of bone defects. The recent applications of different types of hydrogels in bone defect repair were briefly reviewed in this paper.
Endoscopic Ultrasound-Guided Pancreatic Interstitial Laser Ablation Using a Cylindrical Laser Diffuser: A Long-Term Follow-Up Study

Background and Aims: Local ablative treatment is another option for improving outcomes and has been evaluated for locally advanced pancreatic cancer. We previously suggested endoscopic ultrasound (EUS)-guided interstitial laser ablation using a cylindrical laser diffuser (CILA) might be a feasible therapeutic option based on experiments performed on pancreatic cancer cell lines and porcine model with a short follow-up (3 days). The aim of this study was to investigate the safety of EUS-CILA performed using optimal settings in porcine pancreas with a long-term follow-up (2 weeks). Methods: EUS-CILA (laser energy of 450 J; 5 W for 90 s) was applied to normal pancreatic tissue in porcine (n = 5) under EUS guidance. Animals were observed clinically for 2 weeks after EUS-CILA to evaluate complications. Computed tomography and laboratory tests were carried out to evaluate safety. Two weeks after EUS-CILA, all pigs were sacrificed, and histopathological safety and efficacy evaluations were conducted. Results: EUS-CILA was technically successful in all five cases. No major complications occurred during the follow-up period. Body weight of porcine did not change during the study period without any significant change in feed intake. Animals remained in excellent condition throughout the experimental period, and laboratory tests and computed tomography (CT) scans provided no evidence of a major complication. Histopathological evaluation showed complete ablation in the ablated area with clear delineation of surrounding normal pancreatic tissue. Mean ablated volume was 55.5 mm2 × 29.0 mm and mean ablated areas in the pancreatic sections of the five pigs were not significantly different (p = 0.368). Conclusions: In conclusion, our experimental study suggests that EUS-CILA is safe and has the potential to be an effective local treatment modality. No major morbidity or mortality occurred during the study period. Further evaluations are warranted before clinical application.
The Influence of Numerical Error on an Inverse Problem Methodology in PDE Models

The inverse problem methodology is a commonly-used framework in the sciences for parameter estimation and inference. It is typically performed by fitting a mathematical model to noisy experimental data. There are two significant sources of error in the process: 1. \ Noise from the measurement and collection of experimental data and 2. \ numerical error in approximating the true solution to the mathematical model. Little attention has been paid to how this second source of error alters the results of an inverse problem. As a first step towards a better understanding of this problem, we present a modeling and simulation study using a simple advection-driven PDE model. We present both analytical and computational results concerning how the different sources of error impact the least squares cost function as well as parameter estimation and uncertainty quantification. We investigate residual patterns to derive an autocorrelative statistical model that can improve parameter estimation and confidence interval computation for first order methods. Building on the results of our investigation, we provide guidelines for practitioners to determine when numerical or experimental error is the main source of error in their inference, along with suggestions of how to efficiently improve their results.
Improving Swimming Performance of Photolithography-Based Microswimmers Using Curvature Structures

The emergence of robotic microswimmers and their huge potential in biomedical applications such as drug delivery, non-invasive surgery, and bio-sensing facilitates studies to improve their effectiveness. Recently, achiral microswimmers that have neither flexible nor helical structures have garnered attention because of their simple structures and fabrication process while preserving adequate swimming velocity and controllability. In this paper, the crescent shape was utilized to create photolithography-fabricated crescent-shaped achiral microswimmers. The microswimmers were actuated using rotating magnetic fields at low Reynolds numbers. Compared with the previously reported achiral microswimmers, the crescent-shaped microswimmers showed significant improvement in forward swimming speed. The effects of different curvatures, arm angles, and procession angles on the velocities of microswimmers were investigated. Moreover, the optimal swimming motion was defined by adjusting the field strength of the magnetic field. Finally, the effect of the thickness of the microswimmers on their swimming velocity was investigated.
Assessment of Predictor-Corrector strategy for the Burridge-Knopoff model

A Predictor-Corrector strategy is employed for the numerical simulation of the one-dimensional Burridge-Knopoff model of earthquakes. This approach is totally explicit and allows to reproduce the main features of the model. The results achieved are compared with those of several previous works available in the literature, in order to state the effectiveness of the novel numerical strategy. Simulations are performed starting from the simplest cases and are aimed at studying the qualitative trends of the phenomena under analysis. By increasing the size of the associated differential system, it is possible to examine data on the basis of the Gutenberg-Richter statistical law. Finally, some tests are conducted to investigate the continuum limit of the discrete Burridge-Knopoff model towards a macroscopic dynamics.
Detecting and Warning Abnormal Transaction of Virtual Cryptocurrency Based on Privacy Protection Framework

For detecting and warning abnormal transaction of virtual cryptocurrency: we proposed PROTECTION (PRivacy-preserving suspiciOus Transaction detECTION), and proposed big matrix inversion algorithm to solve the problem that the physics of TEE is easily limited by memory size. Based on the privacy protection framework, we proposed three supervised learning algorithms to detect and warn abnormal transactions, they respectively are the federated logistic regression model(VERTIGO) over vertically partitioned data, the federated random forest model over vertically partitioned data, and the federated multilayer perceptron model over vertically partitioned data. According to the experimental results, we found that among the three algorithms, the federated logistic regression model(VERTIGO) over vertically partitioned data is ahead of the federated random forest model over vertically partitioned data, and the federated multilayer perceptron model over vertically partitioned data in all indicators, it has a good effect on detecting abnormal transaction of virtual cryptocurrency.
The cycline subalgebra of a Kumjian-Pask algebra

Let $\Lambda$ be a row-finite higher-rank graph with no sources. We identify a maximal commutative subalgebra $\mathcal{M}$ inside the Kumjian-Pask algebra ${\rm KP}_R(\Lambda)$. We also prove a generalized Cuntz-Krieger uniqueness theorem for Kumjian-Pask algebras which says that a representation of ${\rm KP}_R(\Lambda)$ is injective if and only if it is injective on $\mathcal{M}$.
An optimal result for global classical and bounded solutions in a two-dimensional Keller-Segel-Navier-Stokes system with sensitivity

This paper deals with a boundary-value problem for a coupled chemotaxis-Navier-Stokes system involving tensor-valued sensitivity with saturation $$\left\{ \begin{array}{l} n_t+u\cdot\nabla n=\Delta n-\nabla\cdot(nS(x,n,c)\nabla c),\quad x\in \Omega, t>0, c_t+u\cdot\nabla c=\Delta c-c+n,\quad x\in \Omega, t>0,\\ u_t+\kappa(u \cdot \nabla)u+\nabla P=\Delta u+n\nabla \phi,\quad x\in \Omega, t>0,\\ \nabla\cdot u=0,\quad x\in \Omega, t>0, \end{array}\right. $$ which describes chemotaxis-fluid interaction in cases when the evolution of the chemoattractant is essentially dominated by production through cells, where $\kappa\in \mathbb{R},\phi\in W^{2,\infty}(\Omega)$ and $S$ is a given function with values in $\mathbb{R}^{2\times2}$ which fulfills $$|S(x,n,c)| \leq C_S (1 + n)^{-\alpha}$$ with some $C _S > 0$ and $\alpha \geq 0. $ If $\alpha>0$ and $\Omega\subseteq \mathbb{R}^2$ is a {\bf bounded} domain with smooth boundary, then for all reasonably regular initial data, a corresponding initial-boundary value problem for $(KSNF)$ possesses a global classical solution which is bounded on $\Omega\times(0,\infty)$. This extends a recent result by Wang-Winkler-Xiang (Annali della Scuola Normale Superiore di Pisa-Classe di Scienze. XVIII, (2018), 2036--2145) which asserts global existence of bounded solutions under the constraint $\Omega\subseteq \mathbb{R}^2$ is a bounded {\bf convex domain} with smooth boundary. Moreover, we shall improve the result of Wang-Xiang (J. Diff. Eqns., 259(2015), 7578--7609), who proved the possibility of global and bounded, in the case that ${\bf\kappa\equiv0}$ and $\alpha>0$. In comparison to the result for the corresponding fluid-free system, the {\bf optimal condition} on the parameter $\alpha$ for both {\bf global existence} and {\bf boundedness} are obtained.
Controlling a resonant transmission across the δ′-potential: the inverse problem

Recently, the non-zero transmission of a quantum particle through the one-dimensional singular potential given in the form of the derivative of Dirac’s delta function, λδ′(x), with , being a potential strength constant, has been discussed by several authors. The transmission occurs at certain discrete values of λ forming a resonance set {λn}∞n = 1. For λ∉{λn}∞n = 1 this potential has been shown to be a perfectly reflecting wall. However, this resonant transmission takes place only in the case when the regularization of the distribution δ′(x) is constructed in a specific way. Otherwise, the δ′-potential is fully non-transparent. Moreover, when the transmission is non-zero, the structure of a resonant set depends on a regularizing sequence Δ′ε(x) that tends to δ′(x) in the sense of distributions as ε → 0. Therefore, from a practical point of view, it would be interesting to have an inverse solution, i.e. for a given , to construct such a regularizing sequence Δ′ε(x) that the δ′-potential at this value is transparent. If such a procedure is possible, then this value has to belong to a corresponding resonance set. This paper is devoted to solving this problem and, as a result, the family of regularizing sequences is constructed by tuning adjustable parameters in the equations that provide a resonance transmission across the δ′-potential. This construction can be realized if each regularizing sequence Δ′ε(x) depends on and this is a key point of our approach. Next, we can solve the inverse problem if the regularization is constructed from rectangles. Since in some cases the renormalization procedure Δ′ε(x) → δ′(x) leads to the existence of an effective δ-interaction, it is reasonable from the beginning to consider the linear combination V(x) = ηδ(x) + λδ′(x) with .
A Class of Infinite-dimensional Frobenius Manifolds and Their Submanifolds

We construct a class of infinite-dimensional Frobenius manifolds on the space of pairs of certain even functions meromorphic inside or outside the unit circle. Via a bi-Hamiltonian recursion relation, the principal hierarchies associated to such Frobenius manifolds are found to be certain extensions of the dispersionless two-component BKP hierarchy. Moreover, we show that these infinite-dimensional Frobenius manifolds contain finite-dimensional Frobenius submanifolds as defined on the orbit space of Coxeter groups of types B and D.
Image Processing for Detecting Botnet Attacks: A Novel Approach for Flexibility and Scalability

Continued adoption of the Internet of Things (IoT) redefines the paradigm of network architectures. Historically, network architectures relied on centralized resources and data centers. The introduction of the IoT challenges this notion by placing computing resources and observation at the edge of the network. As a result, decentralized approaches for information processing and gathering can be adopted and explored. However, this shift greatly expands the network footprint and shifts traffic away from the center of the network, where observation and cybersecurity monitoring tools are frequently located. Further, IoT devices are often computationally constrained, limiting their readiness to deal with cyber-threats. These security vulnerabilities make the IoT an easy target for hacking groups and lead to the proliferation of zombie networks of compromised devices. Frequently, zombie networks, otherwise known as botnets, are coordinated to attack targets and overload network resources through a Distributed Denial of Service (DDoS) attack. In order to crack down on these botnets, it is essential to develop new methods for quickly and efficiently detecting botnet activity. This study proposes a novel botnet detection technique that first pre-processes network data through computer vision and image processing. The processed dataset is then sent to a neural network for final classification. Two neural networks will be explored, a sequential model and an auto-encoder model. The application of image processing has two advantages over current methods. First, the image processing is simple enough to be completed at the edge of the network by the IoT devices. Second, preprocessing the data allows us to use a shallower network, decreasing detection time further. We will utilize the N-BaIoT dataset and compare our findings to their results.
Hybrid Control of Orientation and Position for Redundant Manipulators Using Neural Network

Position and orientation of the end-effector of redundant manipulators perform a core role in various complex tasks. However, most quadratic programming (QP)-based robot control approaches merely take the position of the end-effector into account, which is relatively inadequate and impractical. Driven by this significant deficiency, this article develops a control method for end-effector orientation representations by analyzing a rotation matrix. Specifically, it is formulated as an equality constraint and applied to control issues of Euler angles and axis-angle representation. On this basis, a QP-based position and orientation control (POC) scheme is proposed for the kinematic control of redundant manipulators. To handle such a POC problem, a dynamic neural network (DNN) is designed with rigorous theoretical analyses. Simulation results show that the POC scheme can accurately control the orientation representations and position of the end-effector. Experimental results and comparisons with state-of-the-art approaches highlight the feasibility and superiority of the proposed method.