Miracle-dz/UniFAR-FTUs huggingface dataset:
In-network Congestion-aware Load Balancing at Transport Layer

Load balancing at transport layer is an important function in data centers, content delivery networks, and mobile networks, where per-connection consistency (PCC) has to be met for optimal performance. Cloud-native L4 load balancers are commonly deployed as virtual network functions (VNFs) and are a critical forwarding element in modern cloud infrastructure. We identify load imbalance among service instances as the main cause of additional processing delay caused by transport-layer load balancers. Existing transport-layer load balancers rely on one of two methods: host-level traffic redirection, which may add as much as 12.48% additional traffic to underlying networks, or connection tracking, which consumes a considerable amount of memory in load balancers. Both of these methods result in inefficient usage of network resources. We propose the in-network congestion-aware load Balancer (INCAB) to achieve even load distribution among service instances and optimal network resources usage in addition to meeting the PCC requirement. We show that INCAB is capable of identifying and monitoring each instance’s most-utilized resource and can improve the load distribution among all service instances. INCAB utilizes a Bloom filter and an ultra-compact connection table for in-network flow distribution. Furthermore, it does not rely on end hosts for traffic redirection. Our flow level simulations show that INCAB improves flows’ average completion time by 31.97% compared to stateless solutions.
On the cost of essentially fair clusterings

Clustering is a fundamental tool in data mining. It partitions points into groups (clusters) and may be used to make decisions for each point based on its group. However, this process may harm protected (minority) classes if the clustering algorithm does not adequately represent them in desirable clusters -- especially if the data is already biased. At NIPS 2017, Chierichetti et al. proposed a model for fair clustering requiring the representation in each cluster to (approximately) preserve the global fraction of each protected class. Restricting to two protected classes, they developed both a 4-approximation for the fair $k$-center problem and a $O(t)$-approximation for the fair $k$-median problem, where $t$ is a parameter for the fairness model. For multiple protected classes, the best known result is a 14-approximation for fair $k$-center. We extend and improve the known results. Firstly, we give a 5-approximation for the fair $k$-center problem with multiple protected classes. Secondly, we propose a relaxed fairness notion under which we can give bicriteria constant-factor approximations for all of the classical clustering objectives $k$-center, $k$-supplier, $k$-median, $k$-means and facility location. The latter approximations are achieved by a framework that takes an arbitrary existing unfair (integral) solution and a fair (fractional) LP solution and combines them into an essentially fair clustering with a weakly supervised rounding scheme. In this way, a fair clustering can be established belatedly, in a situation where the centers are already fixed.
Effects of dietary supplemental phytoncide instead of zinc oxide on growth performance, nutrient digestibility, blood profiles, and faecal microflora in growing pigs

This study was aimed to evaluate the effect of phytoncide (PTC) instead of zinc oxide on growth performance, blood profile, nutrient digestibility and faecal microflora in growing pigs. A total of 120 growing pigs [(Landrace × Yorkshire) × Duroc] with initial body weight 24.48 ± 1.62 kg were randomly assigned to four dietary treatments for a 6 weeks feeding trials, the treatments as follow: CON (base diet)，ZO (CON + 0.03% Zinc Oxide), PTC1 (CON + 0.5% PTC), PTC2 (CON + 1.0% PTC). Compared to basal diet, during weeks 1-3, 3-6, and overall experimental period, the ADG of growing pigs fed phytoncide diet trend to be increased, and fed ZO diet was significantly increased (p < 0.05). During weeks 3-6 and overall experiment period, pigs fed the ZO diet showed improvement in feed intake compared to pigs fed basal diet as a trend. Compared with basal diet, the pigs receiving phytoncide diet significantly increased the digestibility of DM and reduced the concentration of aspartate transaminase in pigs receiving 1.0% phytoncide diet. These results suggested that dietary supplement of phytoncide, Korean pine extract, could be used as an alternative to zinc oxide by decreasing detoxify to soil and plants without influencing the performance of growing pigs. Further study is needed to determine the systemic estimation of the dose of phytoncide.
Automatic segmentation of the foveal avascular zone in ophthalmological OCT-A images

Angiography by Optical Coherence Tomography (OCT-A) is a non-invasive retinal imaging modality of recent appearance that allows the visualization of the vascular structure at predefined depths based on the detection of the blood movement through the retinal vasculature. In this way, OCT-A images constitute a suitable scenario to analyze the retinal vascular properties of regions of interest as is the case of the macular area, measuring the characteristics of the foveal vascular and avascular zones. Extracted parameters of this region can be used as prognostic factors that determine if the patient suffers from certain pathologies (such as diabetic retinopathy or retinal vein occlusion, among others), indicating the associated pathological degree. The manual extraction of these biomedical parameters is a long, tedious and subjective process, introducing a significant intra and inter-expert variability, which penalizes the utility of the measurements. In addition, the absence of tools that automatically facilitate these calculations encourages the creation of computer-aided diagnosis frameworks that ease the doctor’s work, increasing their productivity and making viable the use of this type of vascular biomarkers. In this work we propose a fully automatic system that identifies and precisely segments the region of the foveal avascular zone (FAZ) using a novel ophthalmological image modality as is OCT-A. The system combines different image processing techniques to firstly identify the region where the FAZ is contained and, secondly, proceed with the extraction of its precise contour. The system was validated using a representative set of 213 healthy and diabetic OCT-A images, providing accurate results with the best correlation with the manual measurements of two experts clinician of 0.93 as well as a Jaccard’s index of 0.82 of the best experimental case in the experiments with healthy OCT-A images. The method also provided satisfactory results in diabetic OCT-A images, with a best correlation coefficient with the manual labeling of an expert clinician of 0.93 and a Jaccard’s index of 0.83. This tool provides an accurate FAZ measurement with the desired objectivity and reproducibility, being very useful for the analysis of relevant vascular diseases through the study of the retinal micro-circulation.
Landscape Level Effects of Lion Presence (Panthera leo) on Two Contrasting Prey Species

Due to the strong individual cost of being predated, potential prey species alter their behavior and physiology in response to predation risk. Such alterations may cause major indirect consequences on prey populations that are additive to the direct demographic effects caused by prey being killed. However, although earlier studies showed strong general effects of the presence of apex predators, recent data suggest that indirect effects may be highly context dependent and not consistently present. We combined behavioral data with data on endocrine stress and stable isotopes to assess landscape level effects of lion (Panthera leo) presence on two prey species in South Africa, impala (Aepyceros melampus) and blue wildebeest (Connochaetes taurinus). We also evaluated if there was any seasonal variation in such effects. In addition, we provide results from a physiological validation for an enzyme-linked immunoassay (EIA) that can be used for non-invasive monitoring of glucocorticoid stress metabolite concentrations in impala from fecal pellets. We did not find any significant differences in vigilance behavior, fecal glucocorticoid metabolite concentrations, 13C values or isotope niche breadth between animals living with and without lions for either species. However, wildebeest living in a reserve with lions spent more time foraging compared to wildebeest in a lion-free environment, but only during the wet season. Values of fecal 15N suggest a shift in habitat use, with impala and wildebeest living with lions potentially feeding in less productive areas compared to animals living without lions. For both species, characteristics of the social groups appeared to be more important than individual characteristics for both foraging and vigilance behavior. Our results highlight that antipredator responses may be highly dynamic and scale-dependent. We urge for further studies that quantify at what temporal and spatial scales predation risk is causing indirect effects on prey populations.
Design of a four degree-of-freedom force-reflecting manipulandum with a specified force/torque workspace

It is noted that the performance of force-reflecting hand controllers, also called manual interfaces or manipulanda, is at present limited more by mechanical design than by computing hardware. Some of the important mechanical features of a high-performance manual interface are low inertia, low friction, high stiffness, backdriveability, high output force capability, multiple degrees-of-freedom, and sizable range of motion. A four-degree-of-freedom manipulandum that utilizes a three-degree-of-freedom parallel-link mechanism was designed to incorporate these features. The kinematic design of the manipulator prompted the definition of a force/torque workspace as the volume of operation within which certain maximum desired endpoint forces and torques can be achieved, given actuators of limited output. The design requires low actuator torques, is small in size, and does not approach joint limits in the workspace. <<ETX>>
FFT processor using field programmable gate arrays

The design and construction of a hardware fast Fourier transform processor are presented. The hardware relies principally on field programmable gate arrays (FPGAs). The hardware architecture is based on Tukey-Cooley butterfly algorithms. It uses 218 configurable logic blocks (CLBs) and 42 input-output processors (IOBs) and implements a simple parallel processing architecture. The input of the processor is 16 real-value points and the output is complex. The execution time of the transform is estimated to be 0.736 ms.<<ETX>>
Neural Networks with Activation Networks

This work presents an adaptive activation method for neural networks that exploits the interdependency of features. Each pixel, node, and layer is assigned with a polynomial activation function, whose coefficients are provided by an auxiliary activation network. The activation of a feature depends on the features of neighboring pixels in a convolutional layer and other nodes in a dense layer. The dependency is learned from data by the activation networks. In our experiments, networks with activation networks provide significant performance improvement compared to the baseline networks on which they are built. The proposed method can be used to improve the network performance as an alternative to increasing the number of nodes and layers.
A Survey of 5G Channel Measurements and Models

The fifth generation (5G) mobile communication systems will be in use around 2020. The aim of 5G systems is to provide anywhere and anytime connectivity for anyone and anything. Several new technologies are being researched for 5G systems, such as massive multiple-input multiple-output communications, vehicle-to-vehicle communications, high-speed train communications, and millimeter wave communications. Each of these technologies introduces new propagation properties and sets specific requirements on 5G channel modeling. Considering the fact that channel models are indispensable for system design and performance evaluation, accurate and efficient channel models covering various 5G technologies and scenarios are urgently needed. This paper first summarizes the requirements of the 5G channel modeling, and then provides an extensive review of the recent channel measurements and models. Finally, future research directions for channel measurements and modeling are provided.
MAINTAINABILITY ASSESSMENT OF WEB BASED APPLICATION

Many World Wide Web (www) application incorporate important business assets and after a convenient way for business to promote their services through internet. Many web applications have evolved from simple HTML page to complex application [1], some are evolved from different web technologies that have a high maintenances cost. As during evolution of web application well-defined software process and methodologies are used for development of application. Several maintainability models have been defined for measuring the maintainability of web application. It is very difficult to choose maintainability model. So a strong need for methods and models which have some common criteria to assess the maintainability model of existing web applications.
Biomarkers for bipolar disorder: current status and challenges ahead

ABSTRACT Introduction: Bipolar disorder (BD) is a chronic psychiatric disorder marked by clinical and pathophysiological heterogeneity. There is a high expectation that personalized approaches can improve the management of patients with BD. For that, identification and validation of potential biomarkers are fundamental. Areas covered: This manuscript will critically review the current status of different biomarkers for BD, including peripheral, genetic, neuroimaging, and neurophysiological candidates, discussing the challenges to move the field forward. Expert commentary: There are no lab or complementary tests currently recommended for the diagnosis or management of patients with BD. Panels composed by multiple biomarkers will probably contribute to stratifying patients according to their clinical stage, therapeutic response, and prognosis.
Unsupervised Learning of Shape Concepts - From Real-World Objects to Mental Simulation

An unsupervised shape analysis is proposed to learn concepts reflecting shape commonalities. Our approach is two-fold: i) a spatial topology analysis of point cloud segment constellations within objects is used in which constellations are decomposed and described in a hierarchical and symbolic manner. ii) A topology analysis of the description space is used in which segment decompositions are exposed in. Inspired by Persistent Homology, groups of shape commonality are revealed. Experiments show that extracted persistent commonality groups can feature semantically meaningful shape concepts; the generalization of the proposed approach is evaluated by different real-world datasets. We extend this by not only learning shape concepts using real-world data, but by also using mental simulation of artificial abstract objects for training purposes. This extended approach is unsupervised in two respects: label-agnostic (no label information is used) and instance-agnostic (no instances preselected by human supervision are used for training). Experiments show that concepts generated with mental simulation, generalize and discriminate real object observations. Consequently, a robot may train and learn its own internal representation of concepts regarding shape appearance in a self-driven and machine-centric manner while omitting the tedious process of supervised dataset generation including the ambiguity in instance labeling and selection.
A Novel Power Management Save-Restore Flow Architecture using Calibrated Ring Oscillator Clock Generation

This paper deals with the issue of system wake up latency as a consequence of power gating techniques. There are different architectures proposed to reduce the power inside the IPs (Intellectual property) like clock gating and power gating, Still system level IDLE power gating is a bigger concern. According to the nature of the system, multiple system states are created based on power and IDLE conditions. Another drawback of the power gating is system wake up latency which will be impacting the system performance. Here in this paper we have introduced an innovative way to reduce the system latency by a new power architecture flow with a calibrated ring oscillator clock and ran all power management operations on the newly generated clock. The overall impact in performance, power and area will also be discussed in this paper.
Pyramid Embedded Generative Adversarial Network for Automated Font Generation

In this paper, we investigate the Chinese font synthesis problem and propose a Pyramid Embedded Generative Adversarial Network (PEGAN) to automatically generate Chinese character images. The PEGAN consists of one generator and one discriminator. The generator is built using one encoder-decoder structure with cascaded refinement connections and mirror skip connections. The cascaded refinement connections embed a multiscale pyramid of down-sampled original input into the encoder feature maps of different layers, and multi-scale feature maps from the encoder are connected to the corresponding feature maps in the decoder to make the mirror skip connections. Through combining the generative adversarial loss, pixel-wise loss, category loss and perceptual loss, the generator and discriminator can be trained alternately to synthesize character images. In order to verify the effectiveness of our proposed PEGAN, we first build one evaluation set, in which the characters are selected according to their stroke number and frequency of use, and then use both qualitative and quantitative metrics to measure the performance of our model comparing with the baseline method. The experimental results demonstrate the effectiveness of our proposed model, it shows the potential to automatically extend small font banks into complete ones.
Privacy Issues and Data Protection in Big Data: A Case Study Analysis under GDPR

Big data has become a great asset for many organizations, promising improved operations and new business opportunities. However, big data has increased access to sensitive information that when processed can directly jeopardize the privacy of individuals and violate data protection laws. As a consequence, data controllers and data processors may be imposed tough penalties for non-compliance that can result even to bankruptcy. In this paper, we discuss the current state of the legal regulations and analyse different data protection and privacy-preserving techniques in the context of big data analysis. In addition, we present and analyse two real-life research projects as case studies dealing with sensitive data and actions for complying with the data regulation laws. We show which types of information might become a privacy risk, the employed privacy-preserving techniques in accordance with the legal requirements, and the influence of these techniques on the data processing phase and the research results.
Linguistic analysis of the autobiographical memories of individuals with major depressive disorder

Background Major depressive disorder (MDD) is characterized by biases in memory, attention, and cognition. The present study utilized the Linguistic Inquiry and Word Count (LIWC) to examine the content of specific autobiographical memories (AMs) recalled by individuals with MDD during an autobiographical memory task. Methods We examined various features of the text (including use of affective, cognitive, and self-referential terms), as well as their associations with clinical and cognitive features of MDD (depression severity, autobiographical memory specificity, amygdala activity), in 45 unmedicated adults with MDD compared to 61 healthy controls. Results When recalling positive memories MDD individuals used the word “I” less, fewer positive words, more words indicating present focus (present tense verbs), and fewer words overall to describe memories compared to controls. When recalling negative memories, MDD individuals used “I” more, more words indicating present focus, and more words overall to describe memories relative to controls. Depression severity was correlated with word count, the use of “I”, and words indicating present focus in negative memories and inversely correlated with word count and the use of “I” in positive memories. Autobiographical memory specificity was correlated with word count, the use of “I”, and words indicating present focus for positive memories and inversely correlated with the use of “I” and words indicating present focus for negative memories. Limitations Due to the nature of AM recall, we could not control for the number of memories which participants recalled in each mnemonic category. Conclusions Results align with literature implicating rumination and intensive self-focus in depression and suggest that interventions targeting specific word use may be therapeutically beneficial in the treatment of MDD.
Real-Time High Speed Motion Prediction Using Fast Aperture-Robust Event-Driven Visual Flow

Optical flow is a crucial component of the feature space for early visual processing of dynamic scenes especially in new applications such as self-driving vehicles, drones and autonomous robots. The dynamic vision sensors are well suited for such applications because of their asynchronous, sparse and temporally precise representation of the visual dynamics. Many algorithms proposed for computing visual flow for these sensors suffer from the aperture problem as the direction of the estimated flow is governed by the curvature of the object rather than the true motion direction. Some methods that do overcome this problem by temporal windowing under-utilize the true precise temporal nature of the dynamic sensors. In this paper, we propose a novel multi-scale plane fitting based visual flow algorithm that is robust to the aperture problem and also computationally fast and efficient. Our algorithm performs well in many scenarios ranging from fixed camera recording simple geometric shapes to real world scenarios such as camera mounted on a moving car and can successfully perform event-by-event motion estimation of objects in the scene to allow for predictions of upto 500 ms i.e., equivalent to 10 to 25 frames with traditional cameras.
Support vector machines for classification of hyperspectral remote-sensing images

In this paper, we address the problem of classification of hyperspectral remote-sensing images (in the original hyperdimensional feature space) by Support Vector Machines (SVMs). In particular, we investigate the effectiveness of SVMs in terms of classification accuracy, computational time and stability to parameter setting. Experiments, carried out on a standard AVIRIS hyperspectral data set, include a comparison with two other widely used nonparametric approaches, i.e., the K-nn and the Radial Basis Function (RBF) neural networks classifiers. The obtained results point out interesting properties of SVMs in hyperdimensional feature spaces and suggest them as a promising tool to classify hyperspectral remote-sensing images.
Analytic Network Learning

Based on the property that solving the system of linear matrix equations via the column space and the row space projections boils down to an approximation in the least squares error sense, a formulation for learning the weight matrices of the multilayer network can be derived. By exploiting into the vast number of feasible solutions of these interdependent weight matrices, the learning can be performed analytically layer by layer without needing of gradient computation after an initialization. Possible initialization schemes include utilizing the data matrix as initial weights and random initialization. The study is followed by an investigation into the representation capability and the output variance of the learning scheme. An extensive experimentation on synthetic and real-world data sets validates its numerical feasibility.