Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning as a Mixed Convex-Combinatorial Optimization Problem

As neural networks grow deeper and wider, learning networks with hard-threshold activations is becoming increasingly important, both for network quantization, which can drastically reduce time and energy requirements, and for creating large integrated systems of deep networks, which may have non-differentiable components and must avoid vanishing and exploding gradients for effective learning. However, since gradient descent is not applicable to hard-threshold functions, it is not clear how to learn networks of them in a principled way. We address this problem by observing that setting targets for hard-threshold hidden units in order to minimize loss is a discrete optimization problem, and can be solved as such. The discrete optimization goal is to find a set of targets such that each unit, including the output, has a linearly separable problem to solve. Given these targets, the network decomposes into individual perceptrons, which can then be learned with standard convex approaches. Based on this, we develop a recursive mini-batch algorithm for learning deep hard-threshold networks that includes the popular but poorly justified straight-through estimator as a special case. Empirically, we show that our algorithm improves classification accuracy in a number of settings, including for AlexNet and ResNet-18 on ImageNet, when compared to the straight-through estimator.
Femtosecond laser induced structural dynamics and melting of Cu (111) single crystal. An ultrafast time-resolved x-ray diffraction study

Femtosecond, 8.04 keV x-ray pulses are used to probe the lattice dynamics of a 150 nm Cu (111) single crystal on a mica substrate irradiated with 400 nm, 100 fs laser pulses. For pump fluences below the damage and melting thresholds, we observed lattice contraction due to the formation of a blast force and coherent acoustic phonons with a period of ∼69 ps. At larger pump fluence, solid to liquid phase transition, annealing, and recrystallization were measured in real time by monitoring the intensity evolution of the probing fs x-ray rocking curves, which agreed well with theoretical simulation results. The experimental data suggest that the melting process is a purely thermal phase transition. This study provides, in real time, an ultrafast time-resolved detailed description of the significant processes that occur as a result of the interaction of a femtosecond light-pulse with the Cu (111) crystal surface.
Indoor positioning using time difference of arrival between multipath components

Positioning is next to communication the most important field of applications for wireless radio transmissions. This paper considers indoor positioning using wireless signals. Especially in indoor scenarios, multipath reception degrades the accuracy of the positioning device as long as the receiver is based on standard methods. Strategies to mitigate multipath effects on range estimates are in general based on the estimation of the channel impulse response (CIR). All these methods have in common that they determine the CIR in order to remove the influence on the estimate of the line-of-sight path delay. This paper focuses on multipath aided positioning by using the time difference of arrival between multipath components (TDoAbMC). Hence, the paper uses the multipath propagation of the wireless signal to allow positioning in cases of a insufficient number of transmitters or increase the accuracy otherwise. Measurements with a moving receive antenna showed, that multipath components are visible for several meters of receiver movement. To estimate and track the time-variant multipath components of the received signal, the paper uses a Kalman filter which utilizes maximum likelihood estimates as measurements. For positioning, the novel approach treats multipath components as signals from virtual transmitters which are time synchronized to the physical transmitter and fixed in their position. Additionally, using a time difference of arrival approach, the estimation of the user clock bias is not necessary. To use the information of the multipath components, the positioning algorithm has to estimate the user position and the position of the virtual transmitters simultaneously. Furthermore, the new approach does not rely on any prior information such as the room layout or a database for fingerprinting.
Dynamic 3 D Imaging ( Workshop in Conjunction with DAGM ’ 07 ) 1 Spherical Light Field Rendering in Application for Analysis by Synthesis

This paper presents a novel approach for object classification and pose estimation which employs spherical light field rendering to generate virtual views based on synthesis parameters determined and successively refined in a two-stage analysis by synthesis process. Compared to previous object recognition techniques the presented approach provides a significant improvement in terms of object classification quality and computational efficiency. Our GPU based light field renderer exploits per-pixel depth information available with modern time-of-flight sensors such as the PMD camera for high-quality image synthesis in real-time. The renderer uses combined per-pixel RGB and depth values to minimize ghosting artefacts to a non noticeable amount and employs a spherical parameteri-sation to ensure full six degrees of freedom for virtual view synthesis. Synthetic views are used in our two-stage analysis by synthesis technique which implements a pre-classification and pre-pose-estimation to reduce parameter search space, thus providing improved object classification quality at less computation time.
LeadWise: Using Online Bots to Recruite and Guide Expert Volunteers

In order to help non-profits recruit volunteers with special-ized knowledge we propose LeadWise, a system that uses social media bots to recruit and guide contributions from ex-perts to assist non-profits in reaching their goals. We test the feasibility of using the system to recruit specialized vol-unteers for Wikipedia. We focus in particular on experts who can help Wikipedia in its objective of reducing the gen-der gap by covering more women in its articles. Results from our first pilot show that LeadWise was able to obtain a noteworthy number of expert participants in a two week period with limited requests to targeted specialists.
Effect of Supplementary Cementitious Material Content and Binder Dispersion on Packing Density and Compressive Strength of Sustainable Cement Paste

The reduction of cement content and incorporation of high volume of supplementary cementitious materials (SCMs) are key factors for the design of environmentally friendly concrete (Eco-Crete). The effectiveness of incorporating an SCM to enhance rheological and mechanical properties and durability of concrete depends on the type and content of SCM, as well as the degree of dispersion of the binder resulting from the use of high-range water reducer (HRWR). An experimental investigation was undertaken to evaluate the effect of binary and ternary cementitious materials on the packing density of the binder and compressive strength at 7 and 56 days of cement paste. The influence of the dispersion state of the binder on packing density was evaluated using the wet packing density approach to determine the optimum water demand (OWD) needed to achieve maximum wet density. The effectiveness of incorporating a high volume of SCM to enhance packing density is shown to increase with an increase of HRWR dosage, resulting from a greater degree of dispersion of the binder. The incorporation of sufficient dosage of HRWR led to lower OWD needed to achieve maximum density and higher packing density. The coupled effect of these changes results in higher compressive strength. Compared to the packing density of 0.58 for binder with 100% cement, the use of SCMs in a well-dispersed system is shown to secure packing density of 0.60 to 0.73. The ternary binders containing 40 or 50% slag and 5 or 10% silica fume, by volume of total binder, can ensure lower OWD, greater packing density, lower CO₂ emission, and higher compressive strength. Such binder systems can be adopted for use in sustainable cement-based materials. © 2016, American Concrete.
In Silico Approach to Finding New Active Compounds from Histone Deacetylase (HDAC) Family.

BACKGROUND Histone Deacetylase (HDAC) enzymes in the human body play an important role in the transcriptional regulation of gene expression. In the last decade, HDAC inhibitors and activators have been explored and have become known as therapeutic agents for many diseases such as osteodystrophy, neurogenerative disorders, cardiomyopathy, cancer, and diabetes. In recent years, the development of HDAC inhibitors or activators to obtain new potent lead compounds has been conducted using in vitro, in vivo, and in silico methods. Some HDAC family inhibitors and activators have been discovered. But some compounds have limitations such as not selectively binding to one of the HDAC variants. METHODS At present, through bioinformation, HDAC family sequences have been revealed, and some in silico methods such as molecular modelling (homology modelling and pharmacophore modelling), virtual screening, and molecular dynamics are widely used to find and develop new potent and selective compounds. RESULTS The main utilization of molecular modelling in this work is intended to complete the HDAC structure that partially lacks data regarding its amino acid monomer. Virtual screening methods are helpful in finding the best binding affinity of the test compounds. By molecular dynamic simulation, the temperature, time, and pressure can be adjusted to analyze the hydrogen bond. CONCLUSION Combining these in silico approaches will be a more effective and efficient solution in finding new lead compounds for HDAC drug discovery research in the future.
High-Throughput Lossless Compression on Tightly Coupled CPU-FPGA Platforms

Data compression techniques have been widely used to reduce data storage and movement overhead, especially in the big data era. While FPGAs are well suited to accelerate the computation-intensive lossless compression algorithms, big data compression with parallel requests intrinsically poses two challenges to the overall system throughput. First, scaling existing single-engine FPGA compression accelerator designs already encounters bottlenecks which will result in lower clock frequency, saturated throughput and lower area efficiency. Second, when such FPGA compression accelerators are integrated with the processors, the overall system throughput is typically limited by the communication between a CPU and an FPGA. We propose a novel multi-way parallel and fully pipelined architecture to achieve high-throughput lossless compression on modern Intel-Altera HARPv2 platforms. To compensate for the compression ratio loss in a multi-way design, we implement novel techniques, such as a better data feeding method and a hash chain to increase the hash dictionary history. Our accelerator kernel itself can achieve a compression throughput of 12.8 GB/s (2.3x better than the current record throughput) and a comparable compression ratio of 2.03 for standard benchmark data. Our approach enables design scalability without a reduction in clock frequency and also improves the performance per area efficiency (up to 1.5x). Moreover, we exploit the high CPU-FPGA communication bandwidth of HARPv2 platforms to improve the compression throughput of the overall system, which can achieve an average practical end-to-end throughput of 10.0 GB/s (up to 12 GB/s for larger input files) on HARPv2.
Next generation biosensor systems for environmental water quality monitoring

The recognized and unknown health risks and the harmful environmental impacts associated with the ever-increasing number of emerging pollutants in our water present a serious threat to us all. This poses a pressing need for sensitive, feasible and effective water pollutants monitoring methods, which are required for understanding the fate and transport of these pollutants, for exposure assessment and for regulatory decision making to eliminate these threats. Current water pollutants detection mostly rely on chemical analysis (i.e., GC-MS, HPLC-MS, etc.) that are often time consuming, expensive and require highly trained personnel and therefore do not allow sufficiently frequent monitoring of contaminants in the environment. There is a great need for sensitive, simple, rapid, cost-effective and portable detection methods to allow more comprehensive, on-site monitoring with special and temporal resolutions. Sensing technologies such as biosensors have been shown to serve as a suitable alternative or as complementary analytical methods for environmental monitoring. In general, biosensors are defined as analytical sensing devices incorporating a biological material (i.e., enzymes, antibodies, nucleic acids, natural products, etc.), or a biomimic (i.e., synthetic receptors, imprinted polymers, etc.) intimately integrated within a physicochemical transducer or transducing microsystem, which may be optical, electrochemical, thermometric, piezoelectric, magnetic or micromechanical. The main advantages of biosensors, over traditional analytical techniques for the detection of environmental contaminants, are the possibility of portability, miniaturization and work on-site, easier to use, less costly and the ability to measure pollutants in complex matrices with minimal sample preparation. ; The objectives of this study are to develop and demonstrate the application of improved potentially portable biosensor systems for rapid, reusable, easy-to-use, specific, real time and on-site detection for various environmental pollutants with either enhanced performance or novel sensing mechanisms. We have developed a number of biosensors using two separate sensing systems; one is evanescent wave optical fiber system and another is SWCNT based nanobiosensor system. For each of the sensing systems, we explored the employment of various biorecognition elements including conventional element such as antibody and newer generation biorecognition elements such as aptamer, DNAzyme and G-quadruplex. We have demonstrated that with these two sensing systems and with various biorecognitions comments, we can develop biosensors for different classes of environmental pollutants including metals, organic pollutants (EDCs), antibiotics, virus and bacteria. ; First, we developed five biosensors on a portable optical fiber sensing system for real-time detection of Lead ion (Pb 2+ ), 17-β Estradiol, Bisphenol A, E coli 0157H:7 and adenovirus, using aptamers, DNAzyme and antibody as biorecognition elements, respectively. Because of the optical…
Switch short-circuit fault detection algorithm based on drain-to-source voltage monitoring for a fault tolerant DC/DC converter

Switch Short-Circuit Fault (SSCF) is one of the most harmful failure mode in a DC-DC Converter. As a consequence, the earliest identification should be ensured in order to avoid the shutdown of the whole system. In this paper, a fast and simple method is introduced for detecting and identifying the faulty leg caused by a SSCF in a six-phase Interleaved Boost Converter (IBC) for Fuel Cell Vehicle application. The proposed detection technique is based on a comparison between the power MOSFET Drain-to-Source Voltage VDS in ON state with an adjustable threshold voltage VDS-ON-TH by using only control pulses and driver information. After the fault detection, and its isolation, remedial actions by control reconfiguration methodology are applied to allow the DC-DC converter to operate in pre-fault conditions. To confirm the effectiveness of the suggested approach, simulation tests are reported using Matlab/Simulink© and ANSYS/Simplorer©. Also, comparative study between the developed voltage-based method and current-based method is provided by numerical simulations. The results obtained by the proposed method indicate that the SSCF can be detected not only in switch ON state but also in OFF state by adding some modifications to the proposed algorithm. In switch OFF state, the results prove similar behavior detection time for both the methods which detects fault occurrence in less than one switching period. By contrast, for the ON state, the proposed method detects the failure for a 100 kHz frequency using the voltage information provided by the driver. The advantage of this method is that the detections are valid in both states (ON and OFF), the suggested algorithm is simple, and easily configurable.
Low-rate turbo-Hadamard codes

This paper is concerned with a class of low-rate codes constructed from Hadamard code arrays. A recursive encoding principle is employed to introduce an interleaving gain. Very simple trellis codes with only two or four states are sufficient for this purpose, and the decoding cost involved in the trellis part is typically negligible. Both simulation and analytical results are provided to demonstrate the advantages of the proposed scheme. The proposed scheme is of theoretical interest as it can achieve performance of BER=10/sup -5/ at E/sub b//N/sub 0//spl ap/-1.2dB (only about 0.4 dB away from the ultimate low-rate Shannon limit) with an information block size of 65534. To the authors' knowledge, this is the best result achieved to date with respect to the ultimate Shannon limit. With regard to practical issues, the decoding complexity of the proposed code is considerably lower than that of existing low-rate turbo-type codes with comparable performance.
Lagrangian Formulation of a Consistent Relativistic Guiding Center Theory

A new relativistic guiding center mechanics is presented that conserves energy (in timeindpendent Fields) and satisfies a Liouville's theorem. The theory reduces to Littlejohn's theory in the non-relativistic limit and agrees to leading orders in ε ≡ rg/L with the relativistic theory by Morozov and Solov'ev (which generally lacks a Liouville's theorem). The new theory is developed from an appropriate Lagrangian and is supplemented by a collisionless relativistic kinetic equation for the guiding centers. Moment equations for guiding center density and energy density are also derived
Emergence of Selective Invariance in Hierarchical Feed Forward Networks

Many theories have emerged which investigate how in- variance is generated in hierarchical networks through sim- ple schemes such as max and mean pooling. The restriction to max/mean pooling in theoretical and empirical studies has diverted attention away from a more general way of generating invariance to nuisance transformations. We con- jecture that hierarchically building selective invariance (i.e. carefully choosing the range of the transformation to be in- variant to at each layer of a hierarchical network) is im- portant for pattern recognition. We utilize a novel pooling layer called adaptive pooling to find linear pooling weights within networks. These networks with the learnt pooling weights have performances on object categorization tasks that are comparable to max/mean pooling networks. In- terestingly, adaptive pooling can converge to mean pooling (when initialized with random pooling weights), find more general linear pooling schemes or even decide not to pool at all. We illustrate the general notion of selective invari- ance through object categorization experiments on large- scale datasets such as SVHN and ILSVRC 2012.
Queue-Aware Distributive Resource Control for Delay-Sensitive Two-Hop MIMO Cooperative Systems

In this paper, we consider a queue-aware distributive resource control algorithm for two-hop MIMO cooperative systems. We shall illustrate that relay buffering is an effective way to reduce the intrinsic half-duplex penalty in cooperative systems. The complex interactions of the queues at the source node and the relays are modeled as an average-cost infinite horizon Markov decision process (MDP). The traditional approach solving this MDP problem involves centralized control with huge complexity. To obtain a distributive and low complexity solution, we introduce a linear structure which approximates the value function of the associated Bellman equation by the sum of per-node value functions. We derive a distributive two-stage two-winner auction-based control policy which is a function of the local CSI and local QSI only. Furthermore, to estimate the best fit approximation parameter, we propose a distributive online stochastic learning algorithm using stochastic approximation theory. Finally, we establish technical conditions for almost-sure convergence and show that under heavy traffic, the proposed low complexity distributive control is global optimal.
Tolerance of KS-4202 Soybean to the Attack of Bemisia tabaci Biotype B (Hemiptera: Aleyrodidae)

Abstract Bemisia tabaci (Gennadius) biotype B (Hemiptera: Aleyrodidae) is considered one of the most important pests of soybean, Glycine max L. (Merrill) (Fabaceae), in Brazil and worldwide. Although chemical control still represents the principal strategy used to control this insect, less aggressive strategies such as the use of resistant genotypes stand out as potentially efficient alternatives for integrated pest management programs. This study aimed to evaluate the possible occurrence of tolerance to B. tabaci biotype B in the ‘KS-4202’ soybean genotype, which is already recognized as tolerant to Aphis glycines Matsumura (Hemiptera: Aphididae) in the United States. The ‘Conquista’ Brazilian cultivar was used as a susceptible control. In a greenhouse, plants (stages V3–V4) of both genotypes were individualized and subjected to 6 patterns of infestation: 1) uninfested and without chemical control; 2) infested and without chemical control; 3) infested and sprayed at 15 d after infestation (DAI); 4) infested and sprayed at 30 DAI; 5) infested and sprayed at 45 DAI; and 6) infested and sprayed at 60 DAI. The study was performed in a completely randomized design with 6 replications for each pattern of infestation. We evaluated the following parameters of productivity: number of pods per plant, dry weight of pods per plant, number of seeds per plant, dry weight of seeds per plant, and dry weight of biomass per plant. A 2-by-2 factorial bioassay was carried out to evaluate the plant responses to whitefly feeding, with 5 replications for each combination. The factors were 2 soybean genotypes (‘KS-4202’ and ‘Conquista’) and 2 levels of infestation (0 and 25 pairs), with 4 collection dates of leaflets (7, 14, 21, and 28 DAI). The protein contents and enzyme activities (dismutase superoxide, peroxidase, and polyphenoloxidase) were also determined for each collection date. Whitefly infestation had a negative effect on the weight of seeds and dry weight of biomass of ‘Conquista’ plants for even the shortest period of infestation (15 d). In contrast, for ‘KS-4202’, there was no difference in the number of pods per plant, number of seeds per plant, or dry weight of biomass between infested (15, 30, 45, and 60 d) and uninfested plants. Our results demonstrated that the ‘KS-4202’ genotype is tolerant to B. tabaci biotype B feeding. However, studies are still necessary to better understand the causes of this tolerance because the main factors of tolerance found in this genotype are not the oxidative enzymes studied here.
Improved Use of Foot Force Sensors and Mobile Phone GPS for Mobility Activity Recognition

Recent advances in the development of multimodal wearable sensors enable us to gather richer contexts of mobile user activities. The combination of foot force sensor (FF) and GPS is able to afford fine-grained mobility activity recognition. We derive and identify 12 (out of 31) maximally informative FF features, and the minimal most effective insole positions (two per foot) for sensing, to improve the use of FF + GPS methods for mobility activity recognition. We tested the improved FF + GPS method using over 7000 samples collected from ten volunteers in a natural, unconstrained, environment. The results show that the improved FF + GPS can achieve an average accuracy of over 90% when detecting five different mobility activities, including walking, cycling, bus-passenger, car-passenger, and car-driver.