Miracle-dz/UniFAR-FTUs huggingface dataset:
An Efficient Hardware Architecture for Activation Function in Deep Learning Processor

In order to explore the efficient design and implementation of activation function in deep learning processor, this paper presents an efficient five-stage pipelined hardware architecture for activation function based on the piecewise linear interpolation, and a novel neuron data-LUT address mapping algorithm. Compared with the previous designs based on serial calculation, the proposed hardware architecture can achieve at least 3 times of acceleration. Four commonly used activation functions are designed based on the proposed hardware architecture, which is implemented on the XC6VLX240T of Xilinx. The LeNet-5 and AlexNet are selected as benchmarks to test the inference accuracy of different activation functions with different piecewise numbers on the MNIST and CIFAR-10 test sets in the deep learning processor prototype system. The experiment results show that the proposed hardware architecture can effectively accomplish the relevant calculation of activation functions in the deep learning processor and the accuracy loss is negligible. The proposed hardware architecture is adaptable for numerous activation functions, which can be widely used in the design of other deep learning processors.
Effect of Magnetic Nanoparticle Additive on Viscoelastic Behaviors of Carbonyl Iron-Based Magnetorheological Suspension

Magnetic Fe3O4/polystyrene (PS) composite particles were synthesized via a Shirasu porous glass (SPG) membrane emulsification technique and introduced to carbonyl iron (CI) particle-based magnetorheological (MR) fluid as an additive to enhance its MR performance and dispersion stability relative to pure CI particle-based MR suspension. The SPG method was adopted for achieving a narrow size distribution of the magnetic composite particles. The morphology and size distribution of Fe3O4/PS composites were examined by transmission electron microscope and dynamic light scattering, respectively. The magnetic characteristic of Fe3O4/PS composites was evaluated using vibrating sample magnetometer. Rheological properties of both pure CI and CI–Fe3O4/PS additive-based MR suspensions were investigated by a rotation rheometer via a steady shear test under different external magnetic field strengths. The dispersion stability of both MR suspensions were also measured through Turbiscan.
Does More Certification Always Benefit a Venture?

An implicit assumption in institutional theory is that more certifications improve a venture’s likelihood for success. However, under certain conditions, we argue more certifications may be detrimental to the venture’s performance. We advance this notion by examining both who is doing the certification and, in turn, what information is revealed to others through the certification. Our study advances two new constructs based on varying instances of follow-on certification: certification broadening, where the initial and follow-on certifiers are different institutions, and certification redundancy, where the initial and follow-on certifiers are the same institution. By studying sequences of certification in the U.S. Small Business Innovation Research federal and state programs, we find that certification broadening generally increases a firm’s ability to acquire private resources, whereas certification redundancy generally decreases a firm’s ability to acquire private resources. This study advances a more d.
Deconstructing the Blockchain to Approach Physical Limits

Transaction throughput, confirmation latency and confirmation reliability are fundamental performance measures of any blockchain system in addition to its security. In a decentralized setting, these measures are limited by two underlying physical network attributes: communication capacity and speed-of-light propagation delay. Existing systems operate far away from these physical limits. In this work we introduce Prism, a new proof-of-work blockchain protocol, which can achieve 1) security against up to 50% adversarial hashing power; 2) optimal throughput up to the capacity C of the network; 3) confirmation latency for honest transactions proportional to the propagation delay D, with confirmation error probability exponentially small in CD ; 4) eventual total ordering of all transactions. Our approach to the design of this protocol is based on deconstructing the blockchain into its basic functionalities and systematically scaling up these functionalities to approach their physical limits.
A Graph-Based Handover Scheduling for Heterogeneous Vehicular Networks

The vehicular networks exploiting WiFi have a great advantage in cost and capacity in comparison with the networks using cellular systems. However, to use WiFi in the vehicular environments, the problem of frequent handover (HO) due to the small coverage of access points (APs) should be resolved. In this paper, we propose a new HO technique which is effective in heterogeneous (WiFi/cellular) networks on a real road topology. Specifically, we propose an HO scheduling scheme running on a server, which determines the AP to which a user will be handed over, considering the road topology. Since a user only needs to decide when to initiate the connection to the next AP, a very fast and efficient HO in the vehicular environment can be realized. The design objective of the proposed HO scheduling is to maximize the connection time on WiFi while minimizing the total HO latency and reducing the number of users which contend for the channel within an AP. We show that this objective is well-accomplished with the proposed scheme, by using computer simulation.
Temporal dynamics of spontaneous default-mode network activity mediate the association between reappraisal and depression

Abstract Cognitive reappraisal is associated with major depressive disorder (MDD), while spontaneous activity patterns of the default mode network (DMN) is implicated in reappraisal and MDD. However, neural mechanisms subserving the close association of spontaneous reappraisal and depression are unclear. Spontaneous reappraisal, depression and resting-state functional magnetic resonance imaging (rsfMRI) were measured from 105 healthy subjects. We assessed the temporal complexity (Hurst exponent), Regional Homogeneity (ReHo) and fractional Amplitude of Low Frequency Fluctuation (fALFF) profiles of DMN, a network involved in both reappraisal and depression. Mediation effects of these standard measures on the relationship between reappraisal and depression, and the contributions of each DMN subregion, were assessed. Results indicated that Hurst exponent (H) of DMN, whether extracted by independent component analysis (ICA) or region of interest (ROI), was significantly associated with reappraisal scores. An individual with a higher reappraisal score has a lower Hurst value of DMN. Mediation analyses suggest that H of DMN partially mediates the association between reappraisal and the degree of depression, and this mediation effect arises from the contribution of medial prefrontal cortex. Neither ReHo nor fALFF showed a similar correlation or mediation effect. These findings suggest that temporal dynamics of DMN play an important role in emotion regulation and its association with depression. H of DMN may serve as a neural marker mediating the association between reappraisal and depression.
Diverse Experience of Immigrant Children: How Do Separation and Reunification Shape Their Development?

Although many immigrant children to the United States arrive with their parents, a notable proportion are first separated and later reunited with their parents. How do the experiences of separation and reunification shape the well-being of immigrant children? Data were from a national survey of legal adult immigrants and their families, the New Immigrant Survey from 2003 to 2004 (for academic achievement, age 6-12, N = 876; for psychosocial well-being, age 6-17, N = 1,084). Results indicated that immigrant children who were once separated from their parents exhibited poorer literacy and higher risk of emotional and behavioral problems than those who migrated with parents. A protracted period of separation and previous undocumented status of parents amplified the disadvantages experienced by these children.
Participation of Battery Energy Storage Systems in the Italian Balancing Market: Management Strategies and Economic Results

The possible supply of balancing services by a Battery Energy Storage System (BESS) in the Italian Balancing Market (BM) is considered. A strategy to formulate (downward) bids and (upward) offers is proposed, based on the BM session timetable and on trying to sell the stored energy until the energy left becomes too small; purchase bids are then made to try to recharge the battery. Adopted bid/offer prices are averages of the historical accepted prices or samples from their statistical distribution, and are assumed to be accepted or not based on their comparison to such prices. One year of energy exchanges for the services is simulated and the related costs and revenues are computed. A sensitivity analysis is carried out by varying the adopted bid/offer prices by a fixed percentage. The bidding strategy appears to be promising, but there is room for improvement to reduce the BESS investment payback period and/or to increase the battery expected life against the charge-discharge cycles.
Bisimulations for Logics of Strategies: A Study in Expressiveness and Verification

In this paper we advance the state of the art on the subject of bisimulations for logics of strategies. Bisimulations are a key notion to study the expressive power of a modal language, as well as for applications to system verification. In this contribution we present novel notions of bisimulation for several significant fragments of Strategy Logic (SL), and prove that they preserve the interpretation of formulas in the corresponding fragments. In selected cases we are able to prove that such bisimulations enjoy the Hennessy-Milner property. Finally, we make use of bisimulations to study the expressiveness of the various fragment of SL, including the complexity of their model checking problems.
Desired Transmission Rate and Coverage Region Analysis for Frequency-Selective Fading Amplify-and-Forward MIMO Relay Channel

The coverage region of wireless networks has an inverse relation with the frequency band of network. Therefore, the coverage region of future wireless networks is less than the current networks, since the next generations of wireless networks have higher frequency bands. Moreover, the cost of new infrastructures for base stations is much than the relay stations. Therefore, wireless relay networks have significant role in the future wireless networks. To this end, in this paper, we study and analyze the desired transmission rate and coverage region in multiple-input multiple-output (MIMO) communication systems for a single-relay network with amplify-and-forward (AF) strategy at the relay. By assuming frequency-selective fading channel, we provide the numerical results for the desired transmission rate.
Energy Exchange Strategy for Local Energy Markets with Heterogenous Renewable Sources

The study, presented in this paper, aims to show the impact of installation of fuel cells in a local energy market with renewables and batteries. The market is in the form of double auction where a prosumer can submit a bid for future timeslot with a given amount and price for electricity, determined by prosumer? s Exchange Strategy, based on the State of Charge (SoC) of the battery. In addition, each prosumer is also equipped with a priority-based agent for internal power management. The proposed simulation is based on the Open Energy Systems (OES), in which 19 autonomous subsystems are interconnected via a DC power bus. Each subsystem is equipped with batteries, solar panels and AC grid connection. For the simulation purposes, fuel cells are added to the configuration. The preliminary numerical evaluation is based on the results from several simulations with various configurations for the subsystems' power sources.
Unsupervised Domain Adaptation for Learning Eye Gaze from a Million Synthetic Images: An Adversarial Approach

With contemporary advancements of graphics engines, recent trend in deep learning community is to train models on automatically annotated simulated examples and apply on real data during test time. This alleviates the burden of manual annotation. However, there is an inherent difference of distributions between images coming from graphics engine and real world. Such domain difference deteriorates test time performances of models trained on synthetic examples. In this paper we address this issue with unsupervised adversarial feature adaptation across synthetic and real domain for the special use case of eye gaze estimation which is an essential component for various downstream HCI tasks. We initially learn a gaze estimator on annotated synthetic samples rendered from a 3D game engine and then adapt the features of unannotated real samples via a zero-sum minmax adversarial game against a domain discriminator following the recent paradigm of generative adversarial networks. Such adversarial adaptation forces features of both domains to be indistinguishable which enables us to use regression models trained on synthetic domain to be used on real samples. On the challenging MPIIGaze real life dataset, we outperform recent fully supervised methods trained on manually annotated real samples by appreciable margins and also achieve 13% more relative gain after adaptation compared to the current benchmark method of SimGAN [31]. Codes available at: https://github.com/abhinavagarwalla/adversarial_da_icvgip18.
Distributionally Robust Reduced Rank Regression and Principal Component Analysis in High Dimensions

We propose robust sparse reduced rank regression and robust sparse principal component analysis for analyzing large and complex high-dimensional data with heavy-tailed random noise. The proposed methods are based on convex relaxations of rank-and sparsity-constrained non-convex optimization problems, which are solved using the alternating direction method of multipliers (ADMM) algorithm. For robust sparse reduced rank regression, we establish non-asymptotic estimation error bounds under both Frobenius and nuclear norms, while existing results focus mostly on rank-selection and prediction consistency. Our theoretical results quantify the tradeoff between heavy-tailedness of the random noise and statistical bias. For random noise with bounded $(1+\delta)$th moment with $\delta \in (0,1)$, the rate of convergence is a function of $\delta$, and is slower than the sub-Gaussian-type deviation bounds; for random noise with bounded second moment, we recover the results obtained under sub-Gaussian noise. Furthermore, the transition between the two regimes is smooth. For robust sparse principal component analysis, we propose to truncate the observed data, and show that this truncation will lead to consistent estimation of the eigenvectors. We then establish theoretical results similar to those of robust sparse reduced rank regression. We illustrate the performance of these methods via extensive numerical studies and two real data applications.
Hierarchical reinforcement learning of multiple grasping strategies with human instructions

ABSTRACT Grasping is an essential component for robotic manipulation and has been investigated for decades. Prior work on grasping often assumes that a sufficient amount of training data is available for learning and planning robotic grasps. However, constructing such an exhaustive training dataset is very challenging in practice, and it is desirable that a robotic system can autonomously learn and improves its grasping strategy. Although recent work has presented autonomous data collection through trial and error, such methods are often limited to a single grasp type, e.g. vertical pinch grasp. To address these issues, we present a hierarchical policy search approach for learning multiple grasping strategies. To leverage human knowledge, multiple grasping strategies are initialized with human demonstrations. In addition, a database of grasping motions and point clouds of objects is also autonomously built upon a set of grasps given by a user. The problem of selecting the grasp location and grasp policy is formulated as a bandit problem in our framework. We applied our reinforcement learning to grasping both rigid and deformable objects. The experimental results show that our framework autonomously learns and improves its performance through trial and error and can grasp previously unseen objects with a high accuracy. GRAPHICAL ABSTRACT
Architecture Considerations for Stochastic Computing Accelerators

Stochastic computing (SC) is an alternative computing technique for embedded systems which offers lower area and power, and better error resilience compared to binary-encoded (BE) computation. However, the potential of and general design methodologies for SC in accelerator architectures are not well-understood. In this paper, we evaluate individual SC operations, and end-to-end accelerator architectures to understand when and why SC accelerators can achieve compelling energy efficiency gains. Based on these results, we present general design guidelines that should be considered when building energy-optimal SC accelerator architectures. We also evaluate a fully fabricated ASIC prototype—the first of its kind—to empirically evaluate the error tolerance limits of voltage overscaling (VOS) in SC. Our results show that energy efficiency gains from SC primarily stem from SC’s simpler datapaths which require fewer sequential elements compared to BE equivalents. This allows them to achieve energy efficiency gains as high as <inline-formula> <tex-math notation="LaTeX">$2.4 \times $ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$30 \times $ </tex-math></inline-formula> at 8-bit and 4-bit fixed-point precision, respectively. We also find that VOS can improve the energy efficiency further by up to <inline-formula> <tex-math notation="LaTeX">$1.9\times $ </tex-math></inline-formula> by exploiting SC’s error tolerant encoding.
University spin-off firms’ struggle with openness in early knowledge relationships: in search of antecedents and outcomes

ABSTRACT Little is known about how young high-tech ventures create openness in their knowledge networks. This paper explores the influence of antecedent resources on openness in knowledge networks, seen as diversity in knowledge partners, and explores the impact of openness on growth. The results from 105 university spin-off firms suggest that three antecedents positively influence openness, namely, founders’ prestart experience, education and innovation experience, and one negatively, namely, size of the founding team. Regarding non-linearity, there are signs of cubic influences, potentially in line with passing critical junctures. In addition, external factors tend to have no influence on openness, except for region of location. Further, shaping the right amount of openness and benefitting from it seem a struggle, as an increasing openness tends to influence growth with decreasing returns.
Internal Model-Based Active Resonance Damping Current Control of a Grid-Connected Voltage-Sourced Converter With an LCL Filter

Three-phase passive harmonic filters (e.g., LCL filters) are often utilized to attenuate harmonic currents caused by pulsewidth modulation of voltage-sourced converters (VSC) to achieve high power quality. However, LCL filters may jeopardize the system stability due to their inherent susceptibility to resonance. This paper proposes an active damping current controller based on the internal model principle for a grid-connected VSC equipped with an LCL filter. This controller can be considered a high-order multi-input multi-output transfer function, implemented in the $dq$ reference frame, including decoupling terms for improved transient behavior. Simulation case studies are performed in the PSCAD/EMTDC environment to evaluate the transient behavior of the controller and test its robustness against parameter variations and unbalanced and distorted grid conditions. Experimental results verify the performance of the proposed controller and confirm its ability to operate at low switching frequencies. The results are compared with two existing active damping strategies (virtual RC and notch filter methods), confirming that the proposed controller shows significantly improved robustness with no degradation of transient behavior, has the ability to operate at low switching frequencies, and does not need additional sensors.
Sotatercept, a novel transforming growth factor β ligand trap, improves anemia in β-thalassemia: a phase II, open-label, dose-finding study

β-thalassemia, a hereditary blood disorder caused by defective synthesis of hemoglobin β globin chains, leads to ineffective erythropoiesis and chronic anemia that may require blood transfusions. Sotatercept (ACE-011) acts as a ligand trap to inhibit negative regulators of late-stage erythropoiesis in the transforming growth factor β superfamily, correcting ineffective erythropoiesis. In this phase II, open-label, dose-finding study, 16 patients with transfusion-dependent β -thalassemia and 30 patients with non-transfusion-dependent β-thalassemia were enrolled at seven centers in four countries between November 2012 and November 2014. Patients were treated with sotatercept at doses of 0.1, 0.3, 0.5, 0.75, or 1.0 mg/kg to determine a safe and effective dose. Doses were administered by subcutaneous injection every 3 weeks. Patients were treated for ≤22 months. Response was assessed as a ≥20% reduction in transfusion burden sustained for 24 weeks in transfusion-dependent β-thalassemia patients, and an increase in hemoglobin level of ≥1.0 g/dL sustained for 12 weeks in non-transfusion-dependent β-thalassemia patients. Sotatercept was well tolerated. After a median treatment duration of 14.4 months (range 0.6-35.9), no severe life-threatening adverse events were observed. Thirteen percent of patients reported serious but manageable adverse events. The active dose of sotatercept was ≥0.3 mg/kg for patients with non-transfusion-dependent β-thalassemia and ≥0.5 mg/kg for those with transfusion-dependent β-thalassemia. Of 30 non-transfusion-dependent β-thalassemia patients treated with ≥0.1 mg/kg sotatercept, 18 (60%) achieved a mean hemoglobin increase ≥1.0 g/dL, and 11 (37%) an increase ≥1.5 g/dL, sustained for ≥12 weeks. Four (100%) transfusion-dependent β-thalassemia patients treated with 1.0 mg/kg sotatercept achieved a transfusion-burden reduction of ≥20%. Sotatercept was effective and well tolerated in patients with β-thalassemia. Most patients with non-transfusion-dependent β-thalassemia treated with higher doses achieved sustained increases in hemoglobin level. Transfusion-dependent β-thalassemia patients treated with higher doses of sotatercept achieved notable reductions in transfusion requirements. This trial was registered at ClinicalTrials.gov with the number NCT01571635.
Message-Passing Based OFDM Receiver for Time-Varying Sparse Multipath Channels

In this paper, we propose a novel receiver design for orthogonal frequency division multiplexing (OFDM) transmission over time-varying channels, where the receiver employs a turbo message passing (TMP) framework for a joint channel estimation and the data detection. We show that the proposed TMP-based receiver is able to make more efficient use of the prior information about the channel and the data for intercarrier interference cancellation than the existing iterative receivers. The proposed TMP algorithm can be further combined with a soft-input soft-output decoder for the joint channel estimation, data detection, and decoding. Numerical results are presented to demonstrate the performance superiority of our proposed TMP-based OFDM receiver.