Miracle-dz/UniFAR-FTUs huggingface dataset:
Limiting Extrapolation in Linear Approximate Value Iteration

We study linear approximate value iteration (LAVI) with a generative model. While linear models may accurately represent the optimal value function using a few parameters, several empirical and theoretical studies show the combination of least-squares projection with the Bellman operator may be expansive, thus leading LAVI to amplify errors over iterations and eventually diverge. We introduce an algorithm that approximates value functions by combining Q-values estimated at a set of \textit{anchor} states. Our algorithm tries to balance the generalization and compactness of linear methods with the small amplification of errors typical of interpolation methods. We prove that if the features at any state can be represented as a convex combination of features at the anchor points, then errors are propagated linearly over iterations (instead of exponentially) and our method achieves a polynomial sample complexity bound in the horizon and the number of anchor points. These findings are confirmed in preliminary simulations in a number of simple problems where a traditional least-square LAVI method diverges.
Effects of a 4-day low-intensity run after downhill running on recovery of muscle damage and running economy

This study examined whether a short period of either low-intensity running (LIR) exercise or passive rest following downhill running (DHR) would enhance the recovery of muscle damage and running economy (RE). Twenty-four active males participated in the study, and were randomly assigned into LIR (n = 12) and control (CON; n = 12) groups. Both groups performed one bout of DHR for 30 minutes on a treadmill with an incline of −26% and at the intensity of 70% of peak oxygen consumption (V · O2peak). The LIR group performed a 30-minute bout of running at a 0% incline and an intensity of 35% of V · O2peak, 30 minutes after the DHR. The 30-minute bout of LIR was repeated each day for 4 days by the LIR group, while the CON group passively rested during the same time period. RE was measured by rate of V · O2, minute ventilation, respiratory exchange ratio, heart rate, rating of perceived exertion, and stride frequency during a 5-minute bout of level running at 85%of V · O2peak performed before DHR, and at 2, 5 and 7 days thereafter. Blood lactate concentration was measured before and at 3 minutes after a 5-minute bout of level running (performed before DHR, and at 2, 5 and 7 days thereafter). Maximal isometric voluntary strength of the knee extensor, vertical jump, the level of muscle soreness, plasma creatine kinase activity, and myoglobin concentration were assessed before, immediately after, and every day for 7 days after DHR. All criterion measures were significantly changed (p 0.05) from that of the CON group. These results suggest that a 30-minute bout of LIR every day for 4 days following DHR did not improve the recovery of muscle damage or alter RE.
Syntax-Enhanced Self-Attention-Based Semantic Role Labeling

As a fundamental NLP task, semantic role labeling (SRL) aims to discover the semantic roles for each predicate within one sentence. This paper investigates how to incorporate syntactic knowledge into the SRL task effectively. We present different approaches of en- coding the syntactic information derived from dependency trees of different quality and representations; we propose a syntax-enhanced self-attention model and compare it with other two strong baseline methods; and we con- duct experiments with newly published deep contextualized word representations as well. The experiment results demonstrate that with proper incorporation of the high quality syntactic information, our model achieves a new state-of-the-art performance for the Chinese SRL task on the CoNLL-2009 dataset.
Control of Markov Jump Linear Systems with uncertain detections.

This monograph addresses control and filtering problems for systems with sudden changes in their behavior and whose changes are detected and estimated by an imperfect detector. More precisely it considers continuous-time Markov Jump Linear Systems (MJLS) where the current mode of operation is estimated by a detector. This detector is assumed to be imperfect in the sense that it is possible that the detected mode of operation diverges from the real mode of operation. Furthermore the probabilities for these detections are considered to be known. It is assumed that the detector has its own dynamic, which means that the detected mode of information can change independently from the real mode of operation. The novelty of this approach lies in how uncertainties are modeled. A Hidden Markov Model (HMM) is used to model the uncertainties introduced by the detector. For these systems the following problems are addressed: i) Stochastic Stabilizability in mean-square sense, ii) H2 control, iii) H∞ control and iv) the H∞ filtering problem. Solutions based on Linear Matrix Inequalities (LMI) are developed for each of these problems. In case of the H2 control problem, the solution minimizes an upper bound for the H2 norm of the closed-loop control system. For the H∞ control problem a solution is presented that minimizes an upper bound for the H∞ norm of the closed-loop control system. In the case of the H∞ filtering, the solution presented minimizes the H∞ norm of a system representing the estimation error. The solutions for the control problems are illustrated using a numerical example modeling a simple two-tank process.
Investigation into Gust Load Alleviation using Computational Fluid Dynamics

Gust load alleviation has become an integral part of aircraft design to significantly decrease the impact of atmospheric turbulence on aircraft loads, handling qualities and also passenger comfort. During the design of an active control system, the aerodynamic response of the aircraft subjected to gust encounter and control surface deflection effects needs to be modelled. Current industrial practice is based on lowfidelity linear-potential panel methods which are repeatedly evaluated in frequency domain to obtain so-called frequency response functions. Even though rapid turnaround times are possible, important aerodynamic effects such as shock waves and resulting boundary layer separation which define transonic flow conditions are neglected. Typically, robust and adaptive control laws have been designed to account for the shortcomings of the underlying aerodynamic modelling fidelity. In contrast to this, we present initial results of a basic gust controller while using an enhanced aerodynamic modelling by solving the linearised Reynolds-averaged Navier–Stokes equations in frequency domain. Results are presented both for an aerofoil and a large aircraft configuration near transonic cruise conditions. Control laws derived from different levels of the aerodynamic hierarchy are scrutinised during unsteady simulations of realistic gust-encounter scenarios.
PENGARUH PERSEPSI KEBERMANFAATAN DAN PERSEPSI KEMUDAHAN PENGGUNAAN TERHADAP MINAT PENGGUNAAN LAYANAN PEMBAYARAN DIGITAL GO-PAY

One of the phenomena that occur in Indonesia in today’s digital economy era is that people began to implement a payment system that use electronic money. Therefore, the researcher sets out the formulation of the problem as follows: "The Effect of Perceived Usefulness and Perceived Ease of Use on the Interest in Using Digital Payment Services Go-Pay. " The purpose of this research is to examine the factors that influence the interest in using electronic money. The model used in this study is the Technology Acceptance Model (TAM). This research was conducted of GO-PAY digital payment service users who are domiciled in Jakarta. The sampling technique used in this study is a convenience sampling technique. While the obtained data is in the form of primary data because the data collection was done by distributing online questionnaires. A total of 125 questionnaire results were received and can be processed by the researcher. The data analysis was carried out by using structural equation modeling (SEM), also known as Partial Least Squares (PLS). The results of the research obtained by researcher show that the perceived usefulness and perceived ease of use has a positive influence on interest in use, and the perceived ease of use has a positive influence on perceived usefulness. Keywords: Technology Acceptance Model (TAM), Perceived Ease of Use, Perceived Usefulness, Behavioral Intention, Electronic Money
Improving Answer Selection and Answer Triggering using Hard Negatives

In this paper, we establish the effectiveness of using hard negatives, coupled with a siamese network and a suitable loss function, for the tasks of answer selection and answer triggering. We show that the choice of sampling strategy is key for achieving improved performance on these tasks. Evaluating on recent answer selection datasets - InsuranceQA, SelQA, and an internal QA dataset, we show that using hard negatives with relatively simple model architectures (bag of words and LSTM-CNN) drives significant performance gains. On InsuranceQA, this strategy alone improves over previously reported results by a minimum of 1.6 points in P@1. Using hard negatives with a Transformer encoder provides a further improvement of 2.3 points. Further, we propose to use quadruplet loss for answer triggering, with the aim of producing globally meaningful similarity scores. We show that quadruplet loss function coupled with the selection of hard negatives enables bag-of-words models to improve F1 score by 2.3 points over previous baselines, on SelQA answer triggering dataset. Our results provide key insights into answer selection and answer triggering tasks.
A Lightweight Robust Logic Locking Technique to Thwart Sensitization and Cone-Based Attacks

Logic locking is a new technique to prevent an integrated circuit from hardware Trojan, piracy, overbuilding, etc. Most of the well-known logic locking techniques are vulnerable to sensitization and cone based attacks. Though the existing techniques try to thwart these attacks, they are highly circuit topology dependent and require significant overhead. Therefore, this paper presents a new lightweight logic locking technique that effectively neutralizes the sensitization and cone based attacks. We further propose new multi key-input gates and a new replacement-based logic locking algorithm. The replacement of design gates with our multi key-input gates not only effectively prevents the key-sensitization and signal probability skew based removal attacks but also removes rare nets from design. On the other hand, our logic locking algorithm significantly increases the brute-force attempts against cone-based attack with minimum replacements by replacing the nodes which have the maximum number of fan-outs and paths to output nodes. We evaluate our technique using ISCAS benchmarks, and the results demonstrate that the proposed key-gates on average reduce 32 percent area-overhead over the XOR/MUX key-gates, whereas our algorithm on average doubles the effective key size while reducing 50 percent area overhead over the best-known technique.
Muscle strength and jumping distance during 10 years post ACL reconstruction

The aim of this study was to analyze long-term changes in muscle strength and jumping ability and to assess the relationship between these parameters after anterior cruciate ligament (ACL) reconstruction. Sixty-three patients (39 men) with a mean age of 30.3 years (range 19-42) who underwent an ACL reconstruction between 1990-1992 participated in the study. Quadriceps and hamstring isometric and isokinetic strength as well as one-leg hop test for distance were measured preoperatively and six months 1, 2, 3 and 10 years postoperatively. The injured leg was significantly weaker than the non-injured leg in knee extension strength on all test occasions and in knee flexion strength except at ten years. There was a strong correlation between the muscle strength tests at different speeds. The one leg hop for distance showed significant differences between the injured and non-injured leg on all test occasions. A moderate to high correlation was observed between muscle strength and jumping ability with higher values for the non-injured leg. In our study we found a persisting weakness in the quadriceps as well as jumping ability during ten years. This is an indication that it is hard to recover the preoperative functional level after an ACL reconstruction.
Cross-lingual Semantic Specialization via Lexical Relation Induction

Semantic specialization integrates structured linguistic knowledge from external resources (such as lexical relations in WordNet) into pretrained distributional vectors in the form of constraints. However, this technique cannot be leveraged in many languages, because their structured external resources are typically incomplete or non-existent. To bridge this gap, we propose a novel method that transfers specialization from a resource-rich source language (English) to virtually any target language. Our specialization transfer comprises two crucial steps: 1) Inducing noisy constraints in the target language through automatic word translation; and 2) Filtering the noisy constraints via a state-of-the-art relation prediction model trained on the source language constraints. This allows us to specialize any set of distributional vectors in the target language with the refined constraints. We prove the effectiveness of our method through intrinsic word similarity evaluation in 8 languages, and with 3 downstream tasks in 5 languages: lexical simplification, dialog state tracking, and semantic textual similarity. The gains over the previous state-of-art specialization methods are substantial and consistent across languages. Our results also suggest that the transfer method is effective even for lexically distant source-target language pairs. Finally, as a by-product, our method produces lists of WordNet-style lexical relations in resource-poor languages.
On the Achievable Energy Efficiency in Dynamic Licensed Shared Access

The licensed shared access (LSA) promises to be a viable alternative solution to the well-reported spectrum underutilization. The higher priority of the incumbent in the spectrum sharing arrangement implies that the licensee's access to the spectrum can be revoked or restricted at any time. This has been observed to result in degradation of some critical performance metrics of the latter. In this paper, we investigate the effect of this on the energy efficiency (EE) of an LSA sharing between an airport incumbent and a mobile network operator licensee. We formulate expressions for the operating transmit power of the licensee when its spectrum access right is revoked/restricted in both the uplink and downlink transmission directions. We then propose a power allocation scheme that maximizes the EE of the licensee during these time intervals in which the licensee operating transmit power is constrained by the incumbent system's utilization of the spectrum. We further provide analytical discussions on how the achievable EE during this time compares to when the licensee's access to the spectrum is free of any restriction from the incumbent. The results obtained show that while the EE suffers degradation in the uplink when the licensee spectrum access right is restricted, there is no noticeable difference in the achievable EE in the downlink direction. Furthermore, in the uplink, the optimal power allocation provides better EE even than when the spectrum is free especially at lower transmit power and channel number, while in the downlink, the optimal power allocation EE is consistently better than the free spectrum EE.
Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation

Model-agnostic meta-learners aim to acquire meta-learned parameters from similar tasks to adapt to novel tasks from the same distribution with few gradient updates. With the flexibility in the choice of models, those frameworks demonstrate appealing performance on a variety of domains such as few-shot image classification and reinforcement learning. However, one important limitation of such frameworks is that they seek a common initialization shared across the entire task distribution, substantially limiting the diversity of the task distributions that they are able to learn from. In this paper, we augment MAML with the capability to identify the mode of tasks sampled from a multimodal task distribution and adapt quickly through gradient updates. Specifically, we propose a multimodal MAML (MMAML) framework, which is able to modulate its meta-learned prior parameters according to the identified mode, allowing more efficient fast adaptation. We evaluate the proposed model on a diverse set of few-shot learning tasks, including regression, image classification, and reinforcement learning. The results not only demonstrate the effectiveness of our model in modulating the meta-learned prior in response to the characteristics of tasks but also show that training on a multimodal distribution can produce an improvement over unimodal training. The code for this project is publicly available at https://vuoristo.github. io/MMAML.
SF-CNN: A Fast Compression Artifacts Removal via Spatial-To-Frequency Convolutional Neural Networks

In this paper, we propose SF-CNN, a fast convolutional neural network structure for JPEG image compression artifacts removal. Recently, Convolutional Neural Network (CNN)-based image restoration has shown great performance improvement. However, its heavy computational cost makes it difficult to apply to other uses such as high-level vision tasks. Since heavy computation arises from maintaining the spatial resolution of an input image, some works make a structure that is composed of spatial downsampling and upsampling operations. SF-CNN takes Spatial input and predicts residual Frequency using downsampling operations only. Since every 8×8 pixel is grouped and spatially invariant in the JPEG DCT domain, it is possible to downsample the input by a factor of 8 to reduce the computational cost. We show this simple structure is effective for compression artifacts removal. Our scalable baseline networks achieve results comparable to to the reference networks in reduced computations.
Answering Complex Open-domain Questions Through Iterative Query Generation

It is challenging for current one-step retrieve-and-read question answering (QA) systems to answer questions like “Which novel by the author of ‘Armada’ will be adapted as a feature film by Steven Spielberg? ” because the question seldom contains retrievable clues about the missing entity (here, the author). Answering such a question requires multi-hop reasoning where one must gather information about the missing entity (or facts) to proceed with further reasoning. We present GoldEn (Gold Entity) Retriever, which iterates between reading context and retrieving more supporting documents to answer open-domain multi-hop questions. Instead of using opaque and computationally expensive neural retrieval models, GoldEn Retriever generates natural language search queries given the question and available context, and leverages off-the-shelf information retrieval systems to query for missing entities. This allows GoldEn Retriever to scale up efficiently for open-domain multi-hop reasoning while maintaining interpretability. We evaluate GoldEn Retriever on the recently proposed open-domain multi-hop QA dataset, HotpotQA, and demonstrate that it outperforms the best previously published model despite not using pretrained language models such as BERT.
Supplemental Instruction, Calibration, and Self-Efficacy: A Path Model Analysis

SUPPLEMENTAL INSTRUCTION, CALIBRATION, AND SELF-EFFICACY: A PATH MODEL ANALYSIS Jennifer Leigh Grimm Old Dominion University, 2019 Director: Dr. Christopher R. Glass Many students preparing for careers in the fields of science, technology, engineering, and mathematics (STEM) are unable to persist past entry-level courses to complete their college degrees. As a result, many higher education institutions have implemented intervention programs, like Supplemental Instruction (SI), to help students master course content and gain the self-regulated learning (SRL) behaviors necessary for success in challenging STEM courses. Numerous studies have demonstrated that SI attendance is correlated with improved course grades; however, few studies have examined the effect of SI attendance on students’ SRL behaviors, like self-efficacy and calibration, which may explain students’ academic achievement throughout college. The present study examined if students’ pre-existing self-efficacy beliefs and calibration accuracy predicted their decisions to attend SI. In addition, the study explored if SI attendance had a direct effect on students’ final self-efficacy, calibration, and course grades. Students in a fall semester general biology course for science majors were invited to participate in the study, and 320 students completed the preand post-test survey. The surveys measured beginning and final self-efficacy using the Academic Efficacy Scale from the Patterns of Adaptive Learning Scale, and calibration was measured by asking them to predict their first and final exam scores. Running Head: SI, CALIBRATION, & SELF-EFFICACY A path model was analyzed in Mplus via robust maximum likelihood estimations using preand post-test results and students’ total SAT scores, SI attendance, and final course grades. The results indicated that participants with lower self-efficacy were more likely to attend SI; however, students’ beginning calibration accuracy did not predict their SI attendance. Findings also indicated that SI attendance did not predict final self-efficacy or calibration accuracy, but attending SI had a modest, direct effect on participants’ final course grades. Final self-efficacy and calibration accuracy also predicted final course grades. The results of this study demonstrate a need to explore additional SRL variables that may be influenced by SI. In addition, the present study validates the value of SI as an academic support program to raise course grades. Finally, potential course-level instructional strategies are offered for improving students’ self-efficacy and calibration accuracy to support STEM degree persistence. Running Head: SI, CALIBRATION, & SELF-EFFICACY Copyright, 2019, by Jennifer L. Grimm, All Rights Reserved. Running Head: SI, CALIBRATION, & SELF-EFFICACY
Pedestrian forward projection after vehicle collision

This paper is focused in the assessment of the relation between the vehicle collision velocity, the pedestrian throw distance and the pedestrian injury, after vehicle–pedestrian collision with forward projection. A few parameters were considered, in particular the collision velocity (8), the pedestrian mass and height (8), and the mass of the vehicle (3). The results of the throw distance projection were compared using two solution methods (simple and advanced), the results of other authors and with 15 real data accident with forward projections. A total of 192 different conditions were simulated, being the results in good agreement. The injury of the pedestrian was calculated, based on two injury criteria (GSI and FFC), aiming to define a simple relation between pedestrian injury and the vehicle collision velocity. DOI: https:/doi.org/10.24243/JMEB/2.5. 161