Miracle-dz/UniFAR-FTUs huggingface dataset:
Streaming Batch Eigenupdates for Hardware Neural Networks

Neural networks based on nanodevices, such as metal oxide memristors, phase change memories, and flash memory cells, have generated considerable interest for their increased energy efficiency and density in comparison to graphics processing units (GPUs) and central processing units (CPUs). Though immense acceleration of the training process can be achieved by leveraging the fact that the time complexity of training does not scale with the network size, it is limited by the space complexity of stochastic gradient descent, which grows quadratically. The main objective of this work is to reduce this space complexity by using low-rank approximations of stochastic gradient descent. This low spatial complexity combined with streaming methods allows for significant reductions in memory and compute overhead, opening the door for improvements in area, time and energy efficiency of training. We refer to this algorithm and architecture to implement it as the streaming batch eigenupdate (SBE) approach.
Online Data Poisoning Attack

We study data poisoning attacks in the online learning setting where the training items stream in one at a time, and the adversary perturbs the current training item to manipulate present and future learning. In contrast, prior work on data poisoning attacks has focused on either batch learners in the offline setting, or online learners but with full knowledge of the whole training sequence. We show that online poisoning attack can be formulated as stochastic optimal control, and provide several practical attack algorithms based on control and deep reinforcement learning. Extensive experiments demonstrate the effectiveness of the attacks.
Massively Distributed SGD: ImageNet/ResNet-50 Training in a Flash

Scaling the distributed deep learning to a massive GPU cluster level is challenging due to the instability of the large mini-batch training and the overhead of the gradient synchronization. We address the instability of the large mini-batch training with batch-size control and label smoothing. We address the overhead of the gradient synchronization with 2D-Torus all-reduce. Specifically, 2D-Torus all-reduce arranges GPUs in a logical 2D grid and performs a series of collective operation in different orientations. These two techniques are implemented with Neural Network Libraries (NNL). We have successfully trained ImageNet/ResNet-50 in 122 seconds without significant accuracy loss on ABCI cluster.
Closed-Loop Sparse Channel Estimation for Wideband MmWave FD-MIMO Systems

This paper proposes a closed-loop sparse channel estimation (CE) scheme for wideband millimeter-wave hybrid full-dimensional multiple-input multiple-output and time division duplexing based systems, which exploits the channel sparsity in both angle and delay domains. At the downlink CE stage, random transmit precoder is designed at base station (BS) for channel sounding, and receive combiners at user devices (UDs) are designed to visualize hybrid array as a low-dimensional digital array for facilitating the multi-dimensional unitary ESPRIT (MDU-ESPRIT) algorithm to estimate respective angle-of-arrivals (AoAs). At the uplink CE stage, the estimated downlink AoAs, namely, uplink angle-of-departures (AoDs), are exploited to design multi-beam transmit precoder at UDs to enable BS to estimate the uplink AoAs, i.e., the downlink AoDs, and delays of different UDs using the MDU-ESPRIT algorithm based on the designed receive combiners at BS. Furthermore, a maximum likelihood approach is proposed to pair the channel parameters acquired at the two stages, and the path gains are then obtained using least squares estimator. According to spectrum estimation theory, our solution can acquire the super-resolution estimations of the AoAs/AoDs and delays of sparse multipath components with low training overhead. Simulation results verify the better CE performance and lower computational complexity of our solution over existing state-of-the-art approaches.
The Regretful Agent: Heuristic-Aided Navigation Through Progress Estimation

As deep learning continues to make progress for challenging perception tasks, there is increased interest in combining vision, language, and decision-making. Specifically, the Vision and Language Navigation (VLN) task involves navigating to a goal purely from language instructions and visual information without explicit knowledge of the goal. Recent successful approaches have made in-roads in achieving good success rates for this task but rely on beam search, which thoroughly explores a large number of trajectories and is unrealistic for applications such as robotics. In this paper, inspired by the intuition of viewing the problem as search on a navigation graph, we propose to use a progress monitor developed in prior work as a learnable heuristic for search. We then propose two modules incorporated into an end-to-end architecture: 1) A learned mechanism to perform backtracking, which decides whether to continue moving forward or roll back to a previous state (Regret Module) and 2) A mechanism to help the agent decide which direction to go next by showing directions that are visited and their associated progress estimate (Progress Marker). Combined, the proposed approach significantly outperforms current state-of-the-art methods using greedy action selection, with 5% absolute improvement on the test server in success rates, and more importantly 8% on success rates normalized by the path length.
Explainable Outfit Recommendation with Joint Outfit Matching and Comment Generation

Most previous work on outfit recommendation focuses on designing visual features to enhance recommendations. Existing work neglects user comments of fashion items, which have been proven to be effective in generating explanations along with better recommendation results. We propose a novel neural network framework, neural outfit recommendation (NOR), that simultaneously provides outfit recommendations and generates abstractive comments. Neural outfit recommendation (NOR) consists of two parts: outfit matching and comment generation. For outfit matching, we propose a convolutional neural network with a mutual attention mechanism to extract visual features. The visual features are then decoded into a rating score for the matching prediction. For abstractive comment generation, we propose a gated recurrent neural network with a cross-modality attention mechanism to transform visual features into a concise sentence. The two parts are jointly trained based on a multi-task learning framework in an end-to-end back-propagation paradigm. Extensive experiments conducted on an existing dataset and a collected real-world dataset show NOR achieves significant improvements over state-of-the-art baselines for outfit recommendation. Meanwhile, our generated comments achieve impressive ROUGE and BLEU scores in comparison to human-written comments. The generated comments can be regarded as explanations for the recommendation results. We release the dataset and code to facilitate future research.
The Lottery Ticket Hypothesis at Scale

Recent work on the"lottery ticket hypothesis"proposes that randomly-initialized, dense neural networks contain much smaller, fortuitously initialized subnetworks ("winning tickets") capable of training to similar accuracy as the original network at a similar speed. While strong evidence exists for the hypothesis across many settings, it has not yet been evaluated on large, state-of-the-art networks and there is even evidence against the hypothesis on deeper networks. We modify the lottery ticket pruning procedure to make it possible to identify winning tickets on deeper networks. Rather than set the weights of a winning ticket to their original initializations, we set them to the weights obtained after a small number of training iterations ("late resetting"). Using late resetting, we identify the first winning tickets for Resnet-50 on Imagenet To understand the efficacy of late resetting, we study the"stability"of neural network training to pruning, which we define as the consistency of the optimization trajectories followed by a winning ticket when it is trained in isolation and as part of the larger network. We find that later resetting produces stabler winning tickets and that improved stability correlates with higher winning ticket accuracy. This analysis offers new insights into the lottery ticket hypothesis and the dynamics of neural network learning.
Parameter Space Optimization Towards Constrained Controller Design With Application to Tray Indexing

Tray indexing driven by timing belt actuator is widely used in the industry, where high-precision profile tracking is essential to guarantee good production quality. To avoid humming sound and chip dislodged, an autonomous S-curve trajectory is used as the reference profile and the jerk resulting from the control input chattering needs to be well controlled. To implement the optimal tracking control with respect to an S-curve profile and maintain a guaranteed adequate level of performance towards the perturbation, the composite system is constructed by augmenting the S-curve trajectory with the system model and an <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_2$</tex-math></inline-formula> guaranteed cost optimization problem is formulated. The system performance in terms of tracking accuracy and jerk minimization is quantified as the <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_2$</tex-math></inline-formula>-norm, and it aims to minimize the upper bound to the <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_2$</tex-math></inline-formula>-norm over the uncertain domain. However, due to structural constraints on the composite gain matrix as well as robustness requirement, the algebraic Riccati equations cannot be used. This article presents the theoretical results where all the stabilizing constrained gain matrices can be parameterized over an extended matrical set. Efficient numerical procedures are developed to obtain the <inline-formula><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula>-optimum with guaranteed robustness by linear programming and outer linearization in an iterative framework. Numerical optimization and experiments are conducted to validate the theoretical results and the practical appeal of the proposed approach.
The Optimized Production of 5-(Hydroxymethyl)furfural and Related Products from Spent Coffee Grounds

The increasing consumption of coffee worldwide has led to higher amounts of spent coffee grounds (SCG) being produced which are generally disposed of in landfill or used as compost. However, the wide range of molecules present in SCG such as saccharides, lignin, lipids and proteins give this biomass source a large chemical functionality. In this work, SCG were fractionated to separate the components into three separate portions for further valorization; these were hemicellulose-enriched fractions (HEF), lignin-enriched fraction (LEF) and cellulose-enriched fraction (CEF). HEF was effectively used in the growth of the oleaginous yeast Metschnikowia pulcherrima, additionally, the C6 sugars present in this fraction suggests that it can be used in the production of 5-hydroxymethylfurfural (HMF). The LEF had a considerable high heating value (HHV) and would be suitable as a biofuel component for combustion. CEF was efficiently used in the production of HMF as 0.35 g of this product were obtained from 10 g of SCG. Such results demonstrate that SCG can be effectively used in the production of HMF within a biorefinery concept.
Decreasing the Single-Phase Inverter Output Voltage Distortions Caused by Impedance Networks

This article presents the possibility of decreasing the inverter output voltage distortions that are caused by the typical distortions in the output voltage pulses of an impedance network using the qZ-source as an example. DC sources, such as photovoltaic cells with the qZ-source, increase the dc voltage; an impedance network operates in the continuous current mode and the discontinuous current mode (DCM). Both of these, but particularly DCM, implement the characteristic distortions to the impedance network output pulses, which results in the distortions in the inverter output voltage. It is possible to decrease these distortions in a passive way (increasing the capacitors of the network) or by using an efficient instantaneous controller of the inverter output voltage. Two controllers—a single-input single-output PID controller and a multi-input single-output improved passivity-based controller—were compared using the experimental model voltage source inverter with the input qZ-source.
Oblique Wide-Angle Multi-Sector Metamaterial Absorber for Space Applications

This article presents the design, realization and measurement of lightweight absorbing material for space applications. The electromagnetic absorber, operating on the [2 GHz, 2.3 GHz] frequency band, is designed for oblique incidence ranging from 35° to 65°. Wide-angle designs are demonstrated to be particularly challenging at oblique incidence and an approach consisting in dividing the surface in two different sectors with respect to the incoming angle is proposed. A specific measurement setup is presented in order to characterize this new kind of evolutive absorber. The measurement results show that the sectorial absorber achieves a reflection coefficient inferior to −11.5 dB, corresponding to an absorptivity above 0.965 on the frequency band [2 GHz, 2.3 GHz] for both TE and TM polarizations for angles of incidence varying from 35° to 65°.
The Association of Bone Mineral Density with Cardiovascular Disease

Cardiovascular disease (CVD) and osteoporosis are common age-related conditions with major public health impact. Mounting evidence suggests a link between the two diseases. The purpose of this research was to investigate the association of BMD measures (areal and volumetric) with prevalent CVD, incident CVD, and subclinical measures of atherosclerosis.We utilized data from two prospective epidemiological studies: a) the Health, Aging, and Body Composition (Health ABC) study that enrolled a population of older men and women (age 68-80 years, 51% women, 42% black), and b) the Study of Women's Health Across the Nation (SWAN) that followed a cohort of women through the menopause transition (age 45-58 years, 61% white, 64% peri-menopausal). In the cross-sectional Health ABC analysis, lower volumetric BMD (vBMD) measures of the spine were associated with higher CVD prevalence in men and women, and areal BMD (aBMD) of the trochanter was related to CVD in women. Additionally, aBMD of the total hip was related to subclinical peripheral arterial disease in men.In the SWAN analysis, we observed an inverse cross-sectional association between trabecular vBMD of the spine and aortic calcification. Meanwhile, no associations with coronary artery calcification were noted after adjusting for age.In the longitudinal Health ABC analysis, lower vBMD measures of the spine were associated with higher CVD incidence in white men, but not in blacks. In women, aBMD of the femoral neck was associated with incident CVD in the full cohort. In race-specific analyses, aBMD measures of the total hip, femoral neck, and trochanter exhibited significant relationships with incident CVD in black women, but not in whites.These relationships were independent of age and shared risk factors between osteoporosis and CVD. The inflammatory cytokines interleukin-6 and tumor necrosis factor-alpha, oxidized LDL, and endogenous estradiol did not explain these associations. Overall, our findings provide epidemiological evidence for the presence of an inverse association between BMD and CVD. An understanding of the common mechanisms underlying bone loss and atherogenesis has significant public health implications as it may set the stage for dual-purpose preventive and therapeutic interventions that target both osteoporosis and CVD.
Maternal age and parenting strategies

In recent decades, the age that women give birth to their children has been gradually increasing in many developed countries. This trend may have important consequences for parenting. The present study aims to analyze whether and how maternal age influences parents' strategies with respect to the presence of parents in their children’s daily life. Using data from the 2005 Italian Birth Sample Survey, we focus on the early infancy period and consider three domains of parenting: a) support for the baby’s care (grandparents, babysitter, or creche), b) the mothers’ return to work after childbirth, and c) the father’s time spent with the child. Results show that maternal age has mixed effects depending on the employment status of the mothers. Older unemployed mothers are more prone to adopt traditional parenting models than their younger counterparts. On the contrary, employed older mothers are more likely than younger ones to have less traditional parenting models. Silvia MEGGIOLARO, Researcher, Department of Statistical Sciences, Universityof Padua, Via Cesare Battisti, 241, 35121-Padova, Italy. E-mail: meg@stat.unipd. it . Fausta ONGARO, Professor of Demography, Department of Statistical Sciences, Universityof Padua, Via Cesare Battisti, 241, 35121-Padova, Italy. E-mail: ongaro@stat.unipd. it .
MULTIVARIATE ANALYSIS OF CHEMICAL AND PHYSICAL ATTRIBUTES OF QUARTZIPSAMMENTS UNDER DIFFERENT AGRICULTURAL USES

The inadequate agricultural management in the semiarid region of northeastern Brazil, associated with the fragility of most soils, has caused degradation of the quality of this resource. This study aimed to evaluate the quality of Quartzipsamments under different uses through the multivariate analysis of chemical and physical attributes. Five areas were selected: four of them with agricultural use (onion, banana, cassava, and corn) and the other with Caatinga. Soil samples were collected at depths of 0.00–0.10, 0.10–0.20, and 0.20–0.40 m for chemical and physical analyses. The data were submitted to descriptive analysis and then to the multivariate analysis using the principal component analysis (PCA) technique and Ward’s clustering method. PCA and clustering techniques allowed the identification of different production systems in subsurface layers, suggesting the effect of fertility on the differentiation of areas with onion and banana, as well as the clustering of cassava and corn cultivations. Physical attributes and sodium content of the surface layer of the area with onion showed a tendency of compaction and salinization process of this environment. In general, fertility was the main responsible for the improvement of soil quality with agricultural use when compared to the Caatinga.
ECG Noise Reduction with the Use of the Ant Lion Optimizer Algorithm

The electrocardiogram (ECG) signal is susceptible to noise and artifacts and it is essential to remove that noise in order to support any decision making for automatic heart disorder diagnosis systems. In this paper, the use of Ant Lion Optimizer (ALO) for optimizing and identifying the cutoff frequency of the ECG signal for low-pass filtering is investigated. Generally, the spectrums of the ECG signal are extracted from two classes: arrhythmia and supraventricular. Baseline wander is removed by a moving median filter. A dataset of the extracted features of the ECG spectrums is used to train the ALO. The performance of the ALO is investigated. The ALO-identified cutoff frequency is applied to a Finite Impulse Response (FIR) filter and the resulting signal is evaluated against the original clean and conventional filtered ECG signals. The results show that the intelligent ALO-based system successfully denoised the ECG signals more effectively than the conventional method. The accuracy percentage increased by 2%.
PEDOFUNCTIONS APPLIED TO THE LEAST LIMITING WATER RANGE TO ESTIMATE SOIL WATER CONTENT AT SPECIFIC POTENTIALS

2 Universidade Federal de Mato Grosso UFMT/ Sinop MT, Brasil. 3 Universidade Estadual Paulista "Júlio de Mesquita Filho" UNESP/ Ilha Solteira SP, Brasil. 4 Universidade Federal de Mato Grosso UFMT/ Cuiabá MT, Brasil. Received in: 1-19-2018 Accepted in: 5-29-2019 Engenharia Agrícola, Jaboticabal, v.39, n.4, p.444-456, jul./aug. 2019 Doi: http://dx.doi. org/10.1590/1809-4430-Eng.Agric. v39n4p444-456/2019
Information-theoretic investigation of impact of huggable communication medium on prefrontal brain activation*

ABSTRACT This paper examines the effect of mediated hugs that are achieved with a huggable communication medium on the brain activities of users during conversations. We measured their brain activities with functional near-infrared spectroscopy (NIRS) and evaluated them with two information theoretic measures: permutation entropy, an indicator of relaxation, and multiscale entropy, which captures complexity in brain activation at multiple time scales. We first verify the influence of lip movements on brain activities during conversation and then compare brain activities during tele-conversation through a huggable communication medium with a mobile phone. Our analysis of NIRS signals shows that mediated hugs decrease permutation entropy and increase multiscale entropy. These results suggest that touch interaction through a mediated hug induces a relaxed state in our brain but increases complex patterns of brain activation. GRAPHICAL ABSTRACT
Towards Reconfigurable Electronics: Silicidation of Top-Down Fabricated Silicon Nanowires

We present results of our investigations on nickel silicidation of top-down fabricated silicon nanowires (SiNWs). Control over the silicidation process is important for the application of SiNWs in reconfigurable field-effect transistors. Silicidation is performed using a rapid thermal annealing process on the SiNWs fabricated by electron beam lithography and inductively-coupled plasma etching. The effects of variations in crystallographic orientations of SiNWs and different NW designs on the silicidation process are studied. Scanning electron microscopy and transmission electron microscopy are performed to study Ni diffusion, silicide phases, and silicide–silicon interfaces. Control over the silicide phase is achieved together with atomically sharp silicide–silicon interfaces. We find that {111} interfaces are predominantly formed, which are energetically most favorable according to density functional theory calculations. However, control over the silicide length remains a challenge.
The impact of human consideration, schedule types and product mix on scheduling objectives for unpaced mixed-model assembly lines

There is an increasing awareness in scheduling research that human behaviour needs to be considered explicitly in scheduling models. Although most scheduling literature ignores human behaviour, especially sequence-dependent processing times form a good basis for explicit consideration. Hence, a processing time function is derived that considers the effects of learning, forgetting, fatigue and recovery. The necessity for explicit human consideration can be regarded as most urgent for unpaced highly-manual mixed-model assembly lines. Based on real data a simulation study is conducted to determine the effect of explicit human consideration while also taking into account the effects of different idealised schedule types and the product mix. The results strongly indicate that the product mix has a consistently high impact on scheduling objectives, the schedule type affects lower-level objectives like starving and blocking times to a greater extent than higher-level objectives like makespan and flow time, and that for certain objectives the height of the objective values and the relative favourability of schedule types depends on human consideration.