Miracle-dz/UniFAR-FTUs huggingface dataset:
Searching for the closest-pair in a query translate

We consider a range-search variant of the closest-pair problem. Let $\varGamma$ be a fixed shape in the plane. We are interested in storing a given set of $n$ points in the plane in some data structure such that for any specified translate of $\varGamma$, the closest pair of points contained in the translate can be reported efficiently. We present results on this problem for two important settings: when $\varGamma$ is a polygon (possibly with holes) and when $\varGamma$ is a general convex body whose boundary is smooth. When $\varGamma$ is a polygon, we present a data structure using $O(n)$ space and $O(\log n)$ query time, which is asymptotically optimal. When $\varGamma$ is a general convex body with a smooth boundary, we give a near-optimal data structure using $O(n \log n)$ space and $O(\log^2 n)$ query time. Our results settle some open questions posed by Xue et al. [SoCG 2018].
Input-to-State Safety With Control Barrier Functions

This letter presents a new notion of input-to-state safe control barrier functions (ISSf-CBFs), which ensure safety of nonlinear dynamical systems under input disturbances. Similar to how safety conditions are specified in terms of forward invariance of a set, input-to-state safety conditions are specified in terms of forward invariance of a slightly larger set. In this context, invariance of the larger set implies that the states stay either inside or very close to the smaller safe set; and this closeness is bounded by the magnitude of the disturbances. The main contribution of the letter is the methodology used for obtaining a valid ISSf-CBF, given a control barrier function. The associated universal control law will also be provided. Towards the end, we will study unified quadratic programs that combine control Lyapunov functions and ISSf-CBFs in order to obtain a single control law that ensures both safety and stability in systems with input disturbances.
Detecting and Summarizing GUI Changes in Evolving Mobile Apps

Mobile applications have become a popular software development domain in recent years due in part to a large user base, capable hardware, and accessible platforms. However, mobile developers also face unique challenges, including pressure for frequent releases to keep pace with rapid platform evolution, hardware iteration, and user feedback. Due to this rapid pace of evolution, developers need automated support for documenting the changes made to their apps in order to aid in program comprehension. One of the more challenging types of changes to document in mobile apps are those made to the graphical user interface (GUI) due to its abstract, pixel-based representation. In this paper, we present a fully automated approach, called Gcat, for detecting and summarizing GUI changes during the evolution of mobile apps. Gcat leverages computer vision techniques and natural language generation to accurately and concisely summarize changes made to the GUI of a mobile app between successive commits or releases. We evaluate the performance of our approach in terms of its precision and recall in detecting GUI changes compared to developer specified changes, and investigate the utility of the generated change reports in a controlled user study. Our results indicate that Gcat is capable of accurately detecting and classifying GUI changes - outperforming developers - while providing useful documentation.
Efficient feature extraction using hybrid face recognition method

Recognition of faces for human is always a challenging task due to the absence of sufficientfeatures. To solve this problem we propose a novel and efficient face recognition method which is invariant to slight variation in pose, illumination and background. Proposed method uses discrete cosine transform (DCT) and gray-level co-occurrence matrix (GLCM) for the extraction of both visual features and texture features respectively. The Proposed method helps in removing some high frequency details and thus reduce the size of images. These visual features and texture features are fused to form the hybrid feature leading to improve the feature space and nearest neighbor classifier is used for the classification purpose. The proposed method shows better result on variation of background and pose and it also reduces recognition time greatly. This method improves the efficiency and performance of existing method.
Hybrid Beamforming for Broadband Millimeter Wave Massive MIMO Systems

MmWave systems with most prior work focused on its narrowband hybrid analog/digital precoding, however, will likely operate on wideband channels with frequency selectivity. Therefore, in this paper we investigate wideband angular beamforming schemes for mmWave massive MIMO-OFDM systems. First, for the RF analog beamforming, the optimal beamforming of an unconstrained antenna array (UAA) is given as the performance benchmark. Then, the optimal angular beamforming (OAB) and a simple dominant angular beamforming (DAB) of a shared antenna array (SAA) are compared in received SNR and implementation cost. Second, for the baseband digital precoding, the space-frequency vector perturbation (SFVP) precoding is proposed to collect both spatial and multi-path diversity. Finally, analytical and simulation results show that: a) DAB is a cost- effective RF beamforming scheme under LOS channel environment; b) the proposed hybrid DAB-SFVP beamforming scheme achieves the array gain equaling the number of transmit antennas $N_t$ and diversity gain equaling the product of the number of RF chains $K$ and the number of temporal resolvable clusters ℒ.
Modular Mechanistic Networks: On Bridging Mechanistic and Phenomenological Models with Deep Neural Networks in Natural Language Processing

Natural language processing (NLP) can be done using either top-down (theory driven) and bottom-up (data driven) approaches, which we call mechanistic and phenomenological respectively. The approaches are frequently considered to stand in opposition to each other. Examining some recent approaches in deep learning we argue that deep neural networks incorporate both perspectives and, furthermore, that leveraging this aspect of deep learning may help in solving complex problems within language technology, such as modelling language and perception in the domain of spatial cognition.
Thermal Boundary Conductance Mapping at Metal-MoSe2 Interface

Increasing power density in the microelectronic devices has led to thermal management concerns to ensure efficient, reliable, and long-term operation. As device dimensions continue to decrease, the interfaces of dissimilar materials (e.g., metal-semiconductor) may limit heat removal from the device active region. Improving the thermal transport across interfaces is a necessary consideration during the design process and necessitates accurate measurement of the thermal boundary conductance (TBC) and understanding of transport mechanisms. In addition, low-dimensional materials, such as 1D nanotubes and nanowires and 2D sheets, have attracted considerable interest because of their unique physical properties and small footprint. Two-dimensional transition metal dichalcogenides (TMDs) have been studied extensively for their electrical properties, including the metal-TMD electrical contact resistance, but the thermal properties have received much less attention. We measured and analyzed the TBC across Al-Ti-MoSe2-SiO2 interface using time-domain thermoreflectance. The results are an order of magnitude larger than previously reported values at MoSe2-Au interfaces, but still near the low end for typical metal-semiconductor interfaces.
Scheduling Computation Graphs of Deep Learning Models on Manycore CPUs

For a deep learning model, efficient execution of its computation graph is key to achieving high performance. Previous work has focused on improving the performance for individual nodes of the computation graph, while ignoring the parallelization of the graph as a whole. However, we observe that running multiple operations simultaneously without interference is critical to efficiently perform parallelizable small operations. The attempt of executing the computation graph in parallel in deep learning frameworks usually involves much resource contention among concurrent operations, leading to inferior performance on manycore CPUs. To address these issues, in this paper, we propose Graphi, a generic and high-performance execution engine to efficiently execute a computation graph in parallel on manycore CPUs. Specifically, Graphi minimizes the interference on both software/hardware resources, discovers the best parallel setting with a profiler, and further optimizes graph execution with the critical-path first scheduling. Our experiments show that the parallel execution consistently outperforms the sequential one. The training times on four different neural networks with Graphi are 2.1x to 9.5x faster than those with TensorFlow on a 68-core Intel Xeon Phi processor.
A Localization Method Avoiding Flip Ambiguities for Micro-UAVs with Bounded Distance Measurement Errors

Localization is a fundamental function in cooperative control of micro unmanned aerial vehicles (UAVs), but is easily affected by flip ambiguities because of measurement errors and flying motions. This study proposes a localization method that can avoid the occurrence of flip ambiguities in bounded distance measurement errors and constrained flying motions; to demonstrate its efficacy, the method is implemented on bilateration and trilateration. For bilateration, an improved bi-boundary model based on the unit disk graph model is created to compensate for the shortage of distance constraints, and two boundaries are estimated as the communication range constraint. The characteristic of the intersections of the communication range and distance constraints is studied to present a unique localization criterion which can avoid the occurrence of flip ambiguities. Similarly, for trilateration, another unique localization criterion for avoiding flip ambiguities is proposed according to the characteristic of the intersections of three distance constraints. The theoretical proof shows that these proposed criteria are correct. A localization algorithm is constructed based on these two criteria. The algorithm is validated using simulations for different scenarios and parameters, and the proposed method is shown to provide excellent localization performance in terms of average estimated error. Our code can be found at: https://github.com/QingbeiGuo/AFALA.git.
On Kalman-Consensus Filtering With Random Link Failures Over Sensor Networks

This paper is concerned with the distributed state estimation problem over wireless sensor networks. The communication links are unreliable that are subject to random link failures modeled as a set of independent Bernoulli processes. To estimate the plant state collaboratively, a Kalman-consensus filtering approach is developed where the sensors spread the local information obtained from the Kalman filtering algorithm by performing a consensus of the inverse covariance matrices at each time instant. Sufficient conditions for the stochastic boundedness of the Kalman-consensus filter are established. It is shown that the filtering performance is directly influenced by the network connectivity and the collective observability. A numerical example is illustrated to verify the proposed results.
Multi-temporal Sentinel-1 and -2 Data Fusion for Optical Image Simulation

In this paper, we present the optical image simulation from synthetic aperture radar (SAR) data using deep learning based methods. Two models, i.e., optical image simulation directly from the SAR data and from multi-temporal SAR-optical data, are proposed to testify the possibilities. The deep learning based methods that we chose to achieve the models are a convolutional neural network (CNN) with a residual architecture and a conditional generative adversarial network (cGAN). We validate our models using the Sentinel-1 and -2 datasets. The experiments demonstrate that the model with multi-temporal SAR-optical data can successfully simulate the optical image; meanwhile, the state-of-the-art model with simple SAR data as input failed. The optical image simulation results indicate the possibility of SAR-optical information blending for the subsequent applications such as large-scale cloud removal, and optical data temporal super-resolution. We also investigate the sensitivity of the proposed models against the training samples, and reveal possible future directions.
Exploring targets and therapies for amyotrophic lateral sclerosis: current insights into dietary interventions

A growing number of preclinical and human studies demonstrate a disease-modifying effect of nutritional state in amyotrophic lateral sclerosis (ALS). The management of optimal nutrition in ALS is complicated, as physiological, physical, and psychological effects of the disease need to be considered and addressed accordingly. In this regard, multidisciplinary care teams play an integral role in providing dietary guidance to ALS patients and their carers. However, with an increasing research focus on the use of dietary intervention strategies to manage disease symptoms and improve prognosis in ALS, many ALS patients are now seeking or are actively engaged in using complementary and alternative therapies that are dietary in nature. In this article, we review the aspects of appetite control, energy balance, and the physiological effects of ALS relative to their impact on overall nutrition. We then provide current insights into dietary interventions for ALS, considering the mechanisms of action of some of the common dietary interventions used in ALS, discussing their validity in the context of clinical trials.
A low-profile dual-band antenna loaded with the AMC surface

In this paper, a low-profile dual-band antenna with an AMC surface is proposed. The presented antenna is composed of only two parts: the AMC surface and the microstrip-fed printed dipole. There is no inserted spacer between dipole and AMC surface. The height of the antenna is 11mm(0.09λ0, λ0 being the wavelength in free space at 2.5GHz). As a result, the proposed antenna can offer an impedance band from 2.25 GHz to 2.60 GHz and 5.00 GHz to 5.36 when the S11 is larger than 10dB. The antenna also presents great performance in radiation patterns, with the peak gain of 7.2dBi, 7.9dBi at the low and high working frequency, respectively. The proposed antenna can be used for multiband base stations in communication systems.
Large System Analysis of Linear Precoding in Massive MIMO Relay Systems

In this paper we study on a massive MIMO relay system with linear precoding under the conditions of imperfect channel state information at the transmitter (CSIT) and per-user channel transmit correlation. In our system the source-relay channels are massive multiple-input multiple-output (MIMO) ones and the relay-destination channels are massive multiple-input single-output (MISO) ones. Large random matrix theory (RMT) is used to derive a deterministic equivalent of the signalto- interference-plus-noise ratio (SINR) at each user in massive MIMO amplify-forward and decode-forward (M-MIMO-ADF) relaying with regularized zero-forcing (RZF) precoding, as the number of transmit antennas and users M,K→∞ and M≫K. Simulation results show that the deterministic equivalent of the SINR at each user in M-MIMO-ADF relaying and the results of Theorem 1, Theorem 2 are accurate.
Assessment of the effect of direct oral anticoagulants dabigatran, rivaroxaban, and apixaban in healthy male volunteers using a thrombin generation assay

Essentials The value of thrombin generation assay (TGA) in monitoring direct oral anticoagulants (DOAC) is not well defined. TGA parameters were measured and correlated to DOAC levels in 10 healthy volunteers after oral intake of DOACs. Lag time is the only sensitive TGA parameter across different DOACs, dabigatran, rivaroxaban and apixaban. Endogenous thrombin potential had weak correlation with DOAC levels and not suitable as stand‐alone parameter.
Guazuma ulmifolia Lam. Decreases Oxidative Stress in Blood Cells and Prevents Doxorubicin-Induced Cardiotoxicity

Doxorubicin (DOX) is an efficient chemotherapeutic agent, but its clinical application is limited by its cardiotoxicity associated with increased oxidative stress. Thus, the combination of DOX and antioxidants has been encouraged. In this study, we evaluated (I) the chemical composition and antioxidant capacity of aqueous extracts from Guazuma ulmifolia stem bark (GUEsb) and leaves (GUEl) in 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging, 2,2′-azobis(2-amidinopropane) dihydrochloride- (AAPH-) or DOX-induced lipid peroxidation inhibition in human blood cells, and intracellular reactive oxygen species (ROS) quantification using the fluorescent probe dichloro-dihydro-fluorescein diacetate (DCFH-DA) in K562 erythroleukemia cells incubated with GUEsb and stimulated with hydrogen peroxide; (II) the viability of K562 cells and human leukocytes treated with GUEsb in the absence or presence of DOX using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay; (III) the acute toxicity of GUEsb; and (IV) the cardioprotective effect of GUEsb in C57Bl/6 mice treated with DOX. The chemical composition indicated the presence of flavan-3-ol derivatives and condensed tannins in GUEsb and glycosylated flavonoids in GUEl. GUEsb and GUEl showed free-radical scavenging antioxidant activity, antihemolytic activity, and AAPH- as well as DOX-induced malondialdehyde content reduction in human erythrocytes. Based on its higher antioxidant potential, GUEsb was selected and subsequently showed intracellular ROS reduction without impairing the chemotherapeutic activity of DOX in K562 cells or inducing leukocyte cell death, but protected them against DOX-induced cell death. Yet, GUEsb did not show in vivo acute toxicity, and it prevented MDA generation in the cardiac tissue of DOX-treated mice, thus demonstrating its cardioprotective effect. Taken together, the results show that GUEsb and GUEl are natural alternatives to treat diseases associated with oxidative stress and that, in particular, GUEsb may play an adjuvant role in DOX chemotherapy.
Mechanical Characterization of Intermetallic Compounds in SAC Solder Joints at Elevated Temperatures

Intermetallics (IMCs) in the solder joints have received a great deal of attention due to their increasing volume fraction with continuously decreasing package size. They possess very different mechanical behavior than those of the Sn rich dendrites and Cu pads in the solder joint. Understanding the behavior of interfacial IMCs has become a major concern for the researchers as fracturing near the interfacial IMC layers is often found to be the primary reason for failures caused by drop impacts. While subjected to Isothermal aging, Pb-free Sn–Ag–Cu (SAC) solder joints lead to growth of intermetallic (IMC) particles in the solder bulk as well as growth of intermetallic layers at the joint interfaces with copper bond pads. The most common IMCs found in SAC joints are Ag3Sn and Cu6Sn5 binary compounds. Ternary IMCs like Cu-Ni-Sn can also form at the interface of Ni containing surface finish (i.e. ENIG) and SAC solder. The continuous performance demand and improvement in 3D packaging has led to increased joule heating and higher operating temperatures, which necessitate measuring the mechanical properties and time dependent deformation (creep) behaviors of these IMCs at elevated temperatures. In this study, the mechanical behaviors of IMC particles and layers in SAC solder joints have been characterized using nanoindentation. SAC solder joints extracted from BGA packages were first aged for 6 months at T = 125 °C. Nanoindentation test samples were subsequently prepared by cross-sectioning the aged solder joints, and then mounting them in epoxy mold and polishing them to prepare the joint surfaces for nanoindentation. Intermetallics formed in the bulk solder region, copper pad /SAC solder and ENIG /SAC solder interfaces were observed and detected using SEM and energy-dispersive x-ray spectroscopy (EDS) technique. They were then indented to measure their mechanical properties which includes the elastic modulus and hardness at room temperature and at elevated temperatures (50, 75, 100 and 125 °C using a heating stage. Time dependent deformation (creep) behaviors were also evaluated at room temperature and 100 °C to see the effect of high temperature on the creep rate of IMCs. The measured properties of the IMCs were found to be significantly higher than the Sn-matrix forming the solder joints. However, with the increased temperature, the elastic modulus and hardness value gradually decreased and significantly dropped at 125 °C compared to their room temperature measurements. The creep strain rate was found to increase as the temperature increases to 100 °C to for all the IMCs.
A novel wide-angle scanning pattern reconfigurable antenna

A pattern reconfigurable planar quasi-Yagi antenna with switched connections is proposed. The antenna consists of four elements and using PIN diodes to control states. By switching between the four different states of the PIN diodes, the proposed antenna can operate in different states in similar frequency ranges. The simulated impedance bandwidth (|S11|<-10dB) of the four states is 18.9% with a center frequency around of 5.8 GHz. The maximum gain of four states is all more than 5.3dBi. And the main beam direction is 80°, 179°, 262° and 353° for the four states. The proposed antenna provides a potential application in multifunctional, conformal, morphing and integrated structures.
Ray-Based Evaluation of Dual-Polarized MIMO in (Ultra-)Dense Millimeter-Wave Urban Deployments

Dense deployments of millimeter-wave (mmWave) base stations (BSs) are being considered as the most feasible solution to meet the steadily growing data rate demands of mobile users. Accordingly, the achievable performance gains of mmWave-based dense networks in real deployments have to be studied carefully, since mmWave radio technology features specific transceiver, antenna, and propagation properties. In this paper, we contribute an accurate performance evaluation of single- versus dual-polarized MIMO systems operating over the mmWave channel in typical urban scenarios as well as address the impact of device- and network-centric parameters on the performance gains enabled by MIMO in dense to ultra-dense BS deployments. This study relies on our in-house ray-based modeler and takes into account the key mmWave system effects, such as multi-path propagation, utilization of dual-polarized antennas, and characteristic interference models. Our results show that the benefit of using mmWave- MIMO grows with increasing BS density, thus encouraging a further study of this technology especially for (ultra-)dense setups. We also demonstrate that non-coherent non-polarized diffuse scattering component may reduce the capacity gain of dual-polarized vs. single- polarized MIMO