Miracle-dz/UniFAR-FTUs huggingface dataset:
Study of Distributed Robust Beamforming with Low-Rank and Cross-Correlation Techniques

In this work, we present a novel robust distributed beamforming (RDB) approach based on low-rank and cross-correlation techniques. The proposed RDB approach mitigates the effects of channel errors in wireless networks equipped with relays based on the exploitation of the cross-correlation between the received data from the relays at the destination and the system output and low-rank techniques. The relay nodes are equipped with an amplify-and-forward (AF) protocol and the channel errors are modeled using an additive matrix perturbation, which results in degradation of the system performance. The proposed method, denoted low-rank and cross-correlation RDB (LRCC-RDB), considers a total relay transmit power constraint in the system and the goal of maximizing the output signal-to-interference-plus-noise ratio (SINR). We carry out a performance analysis of the proposed LRCC-RDB technique along with a computational complexity study. The proposed LRCC-RDB does not require any costly online optimization procedure and simulations show an excellent performance as compared to previously reported algorithms.
Reading the Manual: Event Extraction as Definition Comprehension

We ask whether text understanding has progressed to where we may extract event information through incremental refinement of bleached statements derived from annotation manuals. Such a capability would allow for the trivial construction and extension of an extraction framework by intended end-users through declarations such as, “Some person was born in some location at some time. ” We introduce an example of a model that employs such statements, with experiments illustrating we can extract events under closed ontologies and generalize to unseen event types simply by reading new definitions.
Evidence from high-mass X-ray binaries that Galactic WR components of WR+O binaries end their life with a supernova explosion

Context. Theoretical population number studies of binaries with at least one black hole (BH) component obviously depend on whether or not BHs receive a (natal) kick during their formation. Aims. Several observational facts seem to indicate that BHs do indeed receive a kick during their formation. In the present paper, we discuss additional evidence of this. Methods. The progenitors of wind-fed high-mass X-ray binaries (HMXB) with a BH component (BH HMXB) are WR+OB binaries where the Wolf–Rayet (WR) star will finally collapse and form the BH. Starting from the observed population of WR+OB binaries in the solar neighborhood, we predict the population of wind-fed BH HMXBs as a function of the BH-natal kick. Results. The simulations reveal that when WR stars collapse into a BH with a zero or low kick, we should expect 100 or more wind-fed BH HMXBs in the solar neighborhood, whereas only one is observed (Cyg X-1). We consider this as evidence that either WR components in binaries end their life as a neutron star or that they collapse into BHs, both accompanied by a supernova explosion imparting significant (natal) kicks.
Inferring HIV incidence trends and transmission dynamics with a spatio-temporal HIV epidemic model

Reliable estimation of spatio-temporal trends in population-level HIV incidence is becoming an increasingly critical component of HIV prevention policy-making. However, direct measurement is nearly impossible. Current, widely used models infer incidence from survey and surveillance seroprevalence data, but they require unrealistic assumptions about spatial independence across spatial units. In this study, we present an epidemic model of HIV that explicitly simulates the spatial dynamics of HIV over many small, interacting areal units. By integrating all available population-level data, we are able to infer not only spatio-temporally varying incidence, but also ART initiation rates and patient counts. Our study illustrates the feasibility of applying compartmental models to larger inferential problems than those to which they are typically applied, as well as the value of data fusion approaches to infectious disease modeling.
Increasing Traffic Throughput by Controlling Autonomous Vehicles at Low Penetration Rates

Human drivers may behave in an imprecise/unstable manner, leading to traffic oscillations which are harmful to traffic throughput. Recent field experiments have shown that the control of a single autonomous vehicle (AV) can increase traffic throughput on a circular test track, as well as reduce traffic oscillations on straight roads. We consider a mixed traffic environment consisting of humans and autonomous vehicles, where the goal is to find a control policy for the autonomous vehicles which maximizes traffic throughput by preventing oscillations in speed. We formulate this problem as an optimization problem which can be solved using gradient based optimization. Numerical experiments on a circular road show that the optimized control policy improves traffic throughput by 28%.
SAIS: Single-stage Anchor-free Instance Segmentation

In this paper, we propose a simple yet efficientinstance segmentation approach based on the single-stage anchor-free detector, termed SAIS. In our approach, the instancesegmentation task consists of two parallel subtasks which re-spectively predict the mask coefficients and the mask prototypes.Then, instance masks are generated by linearly combining theprototypes with the mask coefficients. To enhance the quality ofinstance mask, the information from regression and classificationis fused to predict the mask coefficients. In addition, center-aware target is designed to preserve the center coordination ofeach instance, which achieves a stable improvement in instancesegmentation. The experiment on MS COCO shows that SAISachieves the performance of the exiting state-of-the-art single-stage methods with a much less memory footpr
Music Style Classification with Compared Methods in XGB and BPNN

Scientists have used many different classification methods to solve the problem of music classification. But the efficiency of each classification is different. In this paper, we propose two compared methods on the task of music style classification. More specifically, feature extraction for representing timbral texture, rhythmic content and pitch content are proposed. Comparative evaluations on performances of two classifiers were conducted for music classification with different composers’ styles. The result shows that XGB (eXtreme Gradient Boosting) is better suited for small datasets than BPNN (Back Propagation Neural Network).
AN In vitro ASSESSMENT OF SUPPLEMENTARY EFFECT OF CONCENTRATES CONTAINING GRADED LEVELS OF GROUND LINSEED (Linum usitatissimum) TO HOUSEHOLD WASTES ON ORGANIC MATTER DEGRADABILITY, SHORT CHAIN FATTY ACIDS, MICROBIAL PROTEIN, METABOLIZABLE ENERGY AND RELATIVE FEED VALUES

With the objective to assess effect of level of linseed inclusion in feeds on nutritive value, an in vitro OM digestibility (IVOMD), total short chain fatty acids (SCFA:mMol l-1), microbial crude protein (MCP:g kg-1 DM), metabolizable energy (MEruminants:MJ kg-1 DM) and relative feed values (RFV) of household wastes=HW (Areke-Atela=AA, carrot peels=CaP, cabbage leaf=Cle, cabbage leftover=Clef, onion leaf=OL, onion peels=OP, potato peels=PP, Tela-Atela=TA) supplemented with linseed (LS) containing concentrates=LSC1…LSC5 (39.32, 37.32, 35.32, 33.32, 31.32% noug cake + 58.99% wheat bran + 1.69% salt + 0, 2, 4, 6, 8% LS, as fed basis) were estimated via an in vitro gas trial. HWs were mixed with LSC (LSC-HW) at 3:1 ratio. Samples were incubated in-vitro with rumen fluid in duplicate and readings recorded at 0, 3, 6, 12, 24, 48, 72 and 96 h of incubation. LSC-HW mixtures had higher CP, IVDMD, IVOMD, ME and SCFA, MCP and RFV than those of HWs alone. IVDMD and IVOMD of the LSC-HW improved with increasing LS levels, mostly at 2 and 4%LSC; but at higher concentrations they declined. AA, TA had high IVOMD. However, IVDMD of AA (with the lowest IVDMD) was much more influenced than TA (with highest IVDMD) by LS levels. Clef had lowest and AA and TA highest ME. SCFA increased over incubation periods and with increasing levels of LS, in Clef improvement (from 0.25 to 0.61 mMol l-1) was significant but in AA (from 0.69 to 0.72 mMol l-1) moderate. Mixing HWs with LSC gave best results at 2%LSC.
The dynamo-wind feedback loop : Assessing their non-linear interplay

Abstract Though generated deep inside the convection zone, the solar magnetic field has a direct impact on the Earth space environment via the Parker spiral. It strongly modulates the solar wind in the whole heliosphere, especially its latitudinal and longitudinal speed distribution over the years. However the wind also influences the topology of the coronal magnetic field by opening the magnetic field lines in the coronal holes, which can affect the inner magnetic field of the star by altering the dynamo boundary conditions. This coupling is especially difficult to model because it covers a large variety of spatio-temporal scales. Quasi-static studies have begun to help us unveil how the dynamo-generated magnetic field shapes the wind, but the full interplay between the solar dynamo and the solar wind still eludes our understanding. We use the compressible magnetohydrodynamical (MHD) code PLUTO to compute simultaneously in 2.5D the generation and evolution of magnetic field inside the star via an α-Ω dynamo process and the corresponding evolution of a polytropic coronal wind over several activity cycles for a young Sun. A multi-layered boundary condition at the surface of the star connects the inner and outer stellar layers, allowing both to adapt dynamically. Our continuously coupled dynamo-wind model allows us to characterize how the solar wind conditions change as a function of the cycle phase, and also to quantify the evolution of integrated quantities such as the Alfvén radius. We further assess the impact of the solar wind on the dynamo itself by comparing our results with and without wind feedback.
Age of Information in Random Access Channels

In applications of remote sensing, estimation, and control, timely communication is critical but not always ensured by high-rate communication. This work proposes decentralized age-efficient transmission policies for random access channels with <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> transmitters. We propose the notion of <italic>age-gain</italic> of a packet to quantify how much the packet will reduce the instantaneous age of information at the receiver side upon successful delivery. We then utilize this notion to propose a transmission policy in which transmitters act in a decentralized manner based on the age-gain of their available packets. In particular, each transmitter sends its latest packet only if its corresponding age-gain is beyond a certain threshold which could be computed adaptively using the collision feedback or found as a fixed value analytically in advance. Both methods improve age of information significantly compared to the state of the art. In the limit of large <inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula>, we prove that when the arrival rate is small (below <inline-formula> <tex-math notation="LaTeX">$\frac {1}{eM}$ </tex-math></inline-formula>), slotted ALOHA-type algorithms are order optimal. As the arrival rate increases beyond <inline-formula> <tex-math notation="LaTeX">$\frac {1}{eM}$ </tex-math></inline-formula>, while age increases under slotted ALOHA, it decreases significantly under the proposed age-based policies. For arrival rates <inline-formula> <tex-math notation="LaTeX">$\theta $ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$\theta =\frac {1}{o(M)}$ </tex-math></inline-formula>, the proposed algorithms provide a multiplicative gain of at least two compared to the minimum age under slotted ALOHA (minimum over all arrival rates). We conclude that it is beneficial to increase the sampling rate (and hence the arrival rate) and transmit packets selectively based on their age-gain. This is surprising and contrary to common practice where the arrival rate is optimized to attain the minimum AoI. We further extend our results to other random access technologies such as Carrier-sense multiple access (CSMA).
Heterogeneous Label Space Transfer Learning for Brain-Computer Interfaces: A Label Alignment Approach

A brain-computer interface (BCI) system usually needs a long calibration session for each new subject/task to adjust its parameters, which impedes its transition from the laboratory to real-world applications. Transfer learning (TL), which leverages labeled data from auxiliary subjects/tasks (source domains), has demonstrated its effectiveness in reducing such calibration effort. Currently, most TL approaches require the source domains to have the same feature space and label space as the target domain, which limits their applications, as the auxiliary data may have different feature spaces and/or different label spaces. This paper considers heterogeneous label spaces transfer learning for BCIs, i.e., the source and target domains have different label spaces. We propose a label alignment (LA) approach to align the source label space to the target label space. It has three desirable properties: 1) LA only needs as few as one labeled sample from each class of the target subject; 2) LA can be used as a preprocessing step before different feature extraction and classification algorithms; and, 3) LA can be integrated with other homogeneous TL approaches to achieve even better performance. Experiments on two motor imagery datasets demonstrated the effectiveness of LA.
Benign endometrial lesions in premenopausal women: three-dimensional power Doppler sonography and cytokines content

________________________________________________________________ Purpose: To investigate the IL-2, IL-4, IL-6, IL-8, IL-10, TNF-, IL-1β, IFN-γ at the local and system levels in endometrial polyps and hyperplasia in premenopausal women; to assess whether threedimensional power Doppler indices can discriminate between hyperplasia and proliferative endometrium. Materials and methods: The prospective analysis included 80 premenopausal women with the suspicion of endometrial hyperplasia and endometrial polyp according to 2D ultrasonography data. Three groups of patients were analyzed according to histological data. There were 25 women with simple endometrial hyperplasia without atypia (the first study group), 15 patients with hyperplastic endometrial polyps (the second study group) and 40 healthy women with endometrium in the early proliferative phase (control group) in premenopausal age. The levels of some cytokines were determined by enzyme linked to immunosorbent assay in serum and in aspirates from the uterus cavity. Three-dimensional power Doppler indexes of the uterus and endometrium were measured in endometrial hyperplasia and proliferative endometrium. Results: Significant dysfunctional changes of local immune system of uterine mucosa in endometrial hyperplasia and endometrial polyp are expressed in activation of pro-inflammatory cytokines, with a deficit of anti-inflammatory cytokines. Endometrial three-dimensional power Doppler indices of uterine and endometrium were significantly higher in endometrial hyperplasia. Conclusions: The local secretion imbalance of proand anti-inflammatory cytokines is present in endometrial hyperplasia and polyps in premenopausal age. Endometrial perfusion increases in endometrial hyperplasia by using threedimensional power Doppler sonography in comparison with healthy women.
Chronic ossified extradural hematoma on the opposite side of the ventriculoperitoneal shunt procedure: A rare case report

Extradural hematoma (EDH) after ventriculoperitoneal shunt procedure is a rare, dangerous but easily manageable complication. It is more common in children and young adults presumably due to relatively lax adhesion of dura to calvarium. We report a case of a 20-year-old male with acqueductal stenosis who underwent ventriculoperitoneal shunt procedure and was discharged after 1 week. A follow-up computed tomographyscan 2 months later showed a chronic ossified EDH in the left frontal reigon. The ventricular catheter was in position and the ventricles were decompressed. Because the patient was asymptomatic, he was managed conservatively. A brief discussion of factors that lead to formation of EDH is included, with stress on techniques to prevent or minimize such complications.
Collaborative study to evaluate the indigestible neutral detergent fiber and indigestible acid detergent fiber contents in feeds by in situ procedure

Objetivou-se conduzir estudo colaborativo para avaliacao dos teores de fibra em detergente neutro indigestivel (FDNi) e fibra em detergente acido indigestivel (FDAi) em alimentos em seis laboratorios de instituicoes integrantes do Instituto Nacional de Ciencia e Tecnologia de Ciencia Animal (INCTCA). Avaliaram-se seis alimentos: feno de capim-braquiaria, cana-de-acucar, silagem de milho, farelo de soja, milho grao e polpa citrica. Os teores estimados de FDNi e FDAi se mostraram dependentes do laboratorio em que a analise foi realizada. Verificou-se que as diferencas entre laboratorios variaram de 2,40 pontos percentuais para o farelo de soja a 8,05 pontos percentuais para a cana-de-acucar para a analise de FDNi e de 1,79 ponto percentual para o milho a 10,06 pontos percentuais para o feno para a analise de FDAi. Observou-se que na avaliacao individual de cada material, a variacao aleatoria total dos resultados entre laboratorios correspondeu de 88,75 a 96,77% e de 88,75 a 98,40% para as analises de FDNi e FDAi, respectivamente. Os teores de FDNi e FDAi sao dependentes do efeito de interacao do material avaliado e do laboratorio e apresentam baixa reprodutibilidade, possivel reflexo das diferencas entre os metodos praticados por cada laboratorio, demonstrando falta de padronizacao dos procedimentos adotados pelos laboratorios.
WaveFlow: A Compact Flow-based Model for Raw Audio

In this work, we propose WaveFlow, a small-footprint generative flow for raw audio, which is directly trained with maximum likelihood. It handles the long-range structure of 1-D waveform with a dilated 2-D convolutional architecture, while modeling the local variations using expressive autoregressive functions. WaveFlow provides a unified view of likelihood-based models for 1-D data, including WaveNet and WaveGlow as special cases. It generates high-fidelity speech as WaveNet, while synthesizing several orders of magnitude faster as it only requires a few sequential steps to generate very long waveforms with hundreds of thousands of time-steps. Furthermore, it can significantly reduce the likelihood gap that has existed between autoregressive models and flow-based models for efficient synthesis. Finally, our small-footprint WaveFlow has only 5.91M parameters, which is 15$\times$ smaller than WaveGlow. It can generate 22.05 kHz high-fidelity audio 42.6$\times$ faster than real-time (at a rate of 939.3 kHz) on a V100 GPU without engineered inference kernels.
Simulation of neural function in an artificial Hebbian network

Artificial neural networks have diverged far from their early inspiration in neurology. In spite of their technological and commercial success, they have several shortcomings, most notably the need for a large number of training examples and the resulting computation resources required for iterative learning. Here we describe an approach to neurological network simulation, both architectural and algorithmic, that adheres more closely to established biological principles and overcomes some of the shortcomings of conventional networks.
Object Pose Estimation in Robotics Revisited.

Vision-based object grasping and manipulation in robotics require accurate estimation of the object 6D pose. Therefore pose estimation has received significant attention and multiple datasets and evaluation metrics have been proposed. Most of the existing evaluation metrics rank the estimated poses solely based on the visual perspective i.e. how well two geometrical surfaces are aligned, which does not directly indicate the goodness of the pose for a robot manipulation. In robotic manipulation the optimal grasp pose depends on many factors such as target object weight and material, robot, gripper, and the task itself. In this work we address these factors by proposing a probabilistic evaluation metric that ranks an estimated object pose based on the conditional probability of completing a task given this estimated pose. The evaluation metric is validated in controlled experiments and a number of baseline and recent pose estimation methods are compared on a dataset of industrial parts for assembly tasks. The experimental results confirm that the proposed evaluation metric measures the fitness of an estimated pose more accurately for a robotic task compared to prior metrics.
Real-Time Panoptic Segmentation From Dense Detections

Panoptic segmentation is a complex full scene parsing task requiring simultaneous instance and semantic segmentation at high resolution. Current state-of-the-art approaches cannot run in real-time, and simplifying these architectures to improve efficiency severely degrades their accuracy. In this paper, we propose a new single-shot panoptic segmentation network that leverages dense detections and a global self-attention mechanism to operate in real-time with performance approaching the state of the art. We introduce a novel parameter-free mask construction method that substantially reduces computational complexity by efficiently reusing information from the object detection and semantic segmentation sub-tasks. The resulting network has a simple data flow that requires no feature map re-sampling, enabling significant hardware acceleration. Our experiments on the Cityscapes and COCO benchmarks show that our network works at 30 FPS on 1024x2048 resolution, trading a 3% relative performance degradation from the current state of the art for up to 440% faster inference.
Using the Web as an Implicit Training Set: Application to Noun Compound Syntax and Semantics

An important characteristic of English written text is the abundance of noun compounds - sequences of nouns acting as a single noun, e.g., colon cancer tumor suppressor protein. While eventually mastered by domain experts, their interpretation poses a major challenge for automated analysis. Understanding noun compounds' syntax and semantics is important for many natural language applications, including question answering, machine translation, information retrieval, and information extraction. I address the problem of noun compounds syntax by means of novel, highly accurate unsupervised and lightly supervised algorithms using the Web as a corpus and search engines as interfaces to that corpus. Traditionally the Web has been viewed as a source of page hit counts, used as an estimate for n-gram word frequencies. I extend this approach by introducing novel surface features and paraphrases, which yield state-of-the-art results for the task of noun compound bracketing. I also show how these kinds of features can be applied to other structural ambiguity problems, like prepositional phrase attachment and noun phrase coordination. I address noun compound semantics by automatically generating paraphrasing verbs and prepositions that make explicit the hidden semantic relations between the nouns in a noun compound. I also demonstrate how these paraphrasing verbs can be used to solve various relational similarity problems, and how paraphrasing noun compounds can improve machine translation.