Miracle-dz/UniFAR-FTUs huggingface dataset:
The Sufﬁciency of Off-policyness: PPO is insufﬁcient according an Off-policy Measure

One of the major difﬁculties of reinforcement learning is learning from off-policy samples, which are collected by a different policy (behavior policy) from what the algorithm evaluates (the target policy). Off-policy learning needs to correct the distribution of the samples from the behavior policy towards that of the target policy. Unfortunately, important sampling has an inherent high variance issue which leads to poor gradient estimation in policy gradient methods. We focus on an off-policy Actor-Critic architecture, and propose a novel method, called Preconditioned Proximal Policy Optimization (P3O), which can control the high variance of importance sampling by applying a preconditioner to the Conservative Policy Iteration (CPI) objective. This preconditioning uses the sigmoid function in a special way that when there is no policy change, the gradient is maximal and hence policy gradient will drive a big parameter update for an efﬁcient exploration of the parameter space . This is a novel exploration method that has not been studied before given that existing exploration methods are based on the novelty of states and actions. We compare with several best-performing algorithms on both discrete and continuous tasks and the results showed that PPO is insufﬁcient in off-policyness , and our P3O is more off-policy than PPO according to the “off-policyness” measured by the DEON metric, and P3O explores in a larger policy space than PPO. Results also show that our P3O maximizes the CPI objective better than PPO during the training process.
Robust lane detection and tracking using improved Hough transform and Gaussian Mixture Model

Robust lane detection and tracking approach using improved Hough transform and Gaussian Mixture Model is proposed in this paper. The approach consists of three parts: lane markings detection, lane parameters estimation and lane position tracking. Firstly, lane marking pixels are extracted using edge and color features. Then, these pixels are used to estimate the lane boundaries. After the vanishing point has been predicted by a RANSAC algorithm, we use an improved Hough transform to detect the straight lane boundaries in the near field, and apply a parabolic model to represent curved lanes probably existed in the far field. Finally, a novel lane parameters determination method, which uses Gaussian Mixture Model to represent and update the parameters of lane boundaries, is proposed to ensure the stability of the lane tracking system. The proposed approach is tested with some real videos captured on a highway with challenging road environments, and the results demonstrate that our system is very reliable and can also be implemented in real-time.
Substrate integrated waveguide based bandpass filter for X and K u -band application

A dual and triple band bandpass filter is designed operating at X and K u -band. Based on pure geometric analysis of width to length ratio's relationship with modes generated in substrate integrated waveguide cavity, a square substrate integrated waveguide cavity is designed to operate at TE 101 , TE 201 , TE 102 modes. Coupling matrix for passband is derived based on coupling topology from the resonating nodes, with the center frequency of 10.5GHz, 15.5GHz, 17.5GHz for the first, second and third passband. The modes excited in a cavity with variation of different feeding positions is analyzed. Two identical square cavities are magnetically coupled together to get triple passband response and it was found that theoretical and simulated results are in good agreement with each other.
Attribution of predictive uncertainties in classification models

Predictive uncertainties in classification tasks are often a consequence of model inadequacy or insufficient training data. In popular applications, such as image processing, we are often required to scrutinise these uncertainties by meaningfully attributing them to input features. This helps to improve interpretability assessments. However, there exist few effective frameworks for this purpose. Vanilla forms of popular methods for the provision of saliency masks, such as SHAP or integrated gradients, adapt poorly to target measures of uncertainty. Thus, state-of-the-art tools instead proceed by creating counterfactual or adversarial feature vectors, and assign attributions by direct comparison to original images. In this paper, we present a novel framework that combines path integrals, counterfactual explanations and generative models, in order to procure attributions that contain few observable artefacts or noise. We evidence that this outperforms existing alternatives through quantitative evaluations with popular benchmarking methods and data sets of varying complexity.
A Primal-Dual Approach to Bilevel Optimization with Multiple Inner Minima

Bilevel optimization has found extensive applications in modern machine learning problems such as hyperparameter optimization, neural architecture search, meta-learning, etc. While bilevel problems with a unique inner minimal point (e.g., where the inner function is strongly convex) are well understood, such a problem with multiple inner minimal points remains to be challenging and open. Existing algorithms designed for such a problem were applicable to restricted situations and do not come with a full guarantee of convergence. In this paper, we adopt a reformulation of bilevel optimization to constrained optimization, and solve the problem via a primal-dual bilevel optimization (PDBO) algorithm. PDBO not only addresses the multiple inner minima challenge, but also features fully first-order efficiency without involving second-order Hessian and Jacobian computations, as opposed to most existing gradient-based bilevel algorithms. We further characterize the convergence rate of PDBO, which serves as the first known non-asymptotic convergence guarantee for bilevel optimization with multiple inner minima. Our experiments demonstrate desired performance of the proposed approach.
Influence of graded level of salt and maturation times on quality traits of beef and pork sun-dried meat: A test pilot

The goal of this study was to assess the qualitative and sensorial characteristics of beef and pork sun-dried meat (SDM) with different salt (NaCl) levels (6%, 8% and 10%) and curing times (30 and 40 h). Samples from the beef strip loin (Gluteus medius) and pork loin (Longissimus thoracic) were cut to a thickness of 5 cm. Three levels of salt were applied, and the meat was allowed to cure for either 30 or 40 hours with three replications per treatment. The pH, shear force (SF), luminosity (L*), tonality (TON), saturation, loss of water by cooking (LWC), moisture, crude protein (CP), sensory attributes, and consumers’ intention to buy were analysed. Beef and pork produced differences in L*, saturation, TON, and CP, appearance, and colour of the sun-dried product. The SDM cured for 30 hours was higher in moisture, LWC and L* ( P =0.023), and had greater CP content than that cured for 40 hours. The appearance and salinity of the product were affected by the level of salt that was used in making it. In conclusion, various NaCl levels did not influence the physical-chemical and qualitative characteristics of the SDM, but these characteristics were influenced by the curing and meat type. In addition, the meat type and the salt level affected some aspects of the sensory evaluation. ______________________________________________________________________________________ micro-Kjeldahl meat a of g used in tasters, SDM. by tasters. mean and denoting the interaction of the treatment effects. The ε ijlk was the random residual error. For the sensory analysis, the scores of the individual tasters were averaged to obtain one score for each sample. The indications of sample preference were summarized as frequencies. Significance was declared with P ≤ 0.05. The effects with 0.05 < P ≤ 0.10 were deemed a tendency. Treatment means were compared using Tukey’s test. Normality of the residuals was tested with Shapiro-Wilk’s test. Values that were three or more standard deviations from the mean were considered outliers. Homogeneity of variance was tested by the Bartlett’s test. All analyses were performed by the statistical program R Core Team (2019).
Double Encoder-Decoder Model for Improving the Accuracy of the Electricity Consumption Prediction in Manufacturing

This paper investigated methods to improve the forecasting accuracy of the electricity consumption prediction model. Currently, the demand for electricity has continuously been rising more than ever. Since the industrial sector uses more electricity than any other sectors, the importance of a more precise forecasting model for manufacturing sites has been highlighted to lower the excess energy production. We propose a double encoder-decoder model, which uses two separate encoders and one decoder, in order to adapt both long-term and short-term data for better forecasts. We evaluated our proposed model on our electricity power consumption dataset, which was collected in a manufacturing site of Sehong from January 1st, 2019 to June 30th, 2019 with 1 minute time interval. From the experiment, the double encoder-decoder model marked about 10% reduction in mean absolute error percentage compared to a conventional encoder-decoder model. This result indicates that the proposed model forecasts electricity consumption more accurately on manufacturing sites compared to an encoder-decoder model
B RAIN INSIGHTS IMPROVE RNN S ’ ACCURACY AND ROBUSTNESS FOR HIERARCHICAL CONTROL OF CON TINUALLY LEARNED AUTONOMOUS MOTOR MOTIFS

We study the problem of learning dynamics that can produce hierarchically or-ganized continuous outputs consisting of the ﬂexible chaining of re-usable motor motifs from which complex behavior is generated. Can a motif library be efﬁciently and extendably learned without interference between motifs, and can these motifs be chained in arbitrary orders without ﬁrst learning the corresponding motif transitions during training? This requires (i) parameter updates while learning a new motif that do not interfere with the parameters used for the previously acquired ones; and (ii) successful motif generation when starting from the network states reached at the end of any of the other motifs, even if these states were not present during training (a case of out-of-distribution generalization). We meet the ﬁrst requirement by designing recurrent neural networks (RNNs) with speciﬁc architectures that segregate motif-dependent parameters (as customary in continual learning works), and try a standard method to address the second by training with random initial states. We ﬁnd that these standard RNNs are very unreliable during zero-shot transfer to motif chaining. We then use insights from the motor thalamocortical circuit, featuring a speciﬁc module that shapes motif transitions. We de-velop a method to constrain the RNNs to function similarly to the thalamocortical circuit during motif transitions, while preserving the large expressivity afforded by gradient-based training of non-analytically tractable RNNs. We then show that this thalamocortical inductive bias not only acts in synergy with gradient-descent RNN training to improve accuracy during in-training-distribution motif production, but also leads to zero-shot transfer to new motif chains with no performance cost. Besides proposing an efﬁcient, robust and ﬂexible RNN architecture, our results shed new light on the function of motor preparation in the brain.
Varactor‐Tunable Frequency Selective Surface With an Appropriate Embedded Bias Network

In this work, a varactor‐tunable frequency selective surface (FSS) is proposed; it is based on a common FSS composed of annular ring slot cells, in which four varactors are loaded on each annular ring slot to dynamically tune its resonant frequency in real time. In addition, an appropriate embedded bias network is added to introduce a same direct current bias voltage for each varactor. Numerical simulations demonstrate that the resonant‐frequency of the proposed FSS can be tuned over a 77.2% wide frequency range from 2.73 to 6.16 GHz by altering the capacitance of the varactors from 1.0 to 0.1 pF; moreover, the embedded bias network has little effect on the band‐pass response, and the minimum of the reflection coefficients is less than −24 dB at all times. Finally, one effective experimental validation is carried out. The proposed tunable FSS possesses the merits of wide tuning range and high transmittance; thus, it has great potential applications in the design of tunable radomes or adaptive screening of unwanted wireless transmissions.
Advisory Board (Other European Members) Advisory Board (Members from the rest of the world)

This article proposes a model to measure Community-Based Tourism (CBT). Its starting point is the need to better quantify and explain CBT in emerging countries. The authors build it by drawing a connection between local competitiveness and CBT frameworks, therefore calling the proposed model C-CBT. By grounding this measurement model, the pushers for this type of tourism, as well as its deterrents, are established and grouped. This model is then used as a comparative tool and applied to two territories in Colombia that have suffered from civil unrest in the last decades and have subsequently developed CBT. As for the field work in the two areas, it is based on interviews and participative observation carried out in the context of tourism projects. The outcome is a clearer measurement system for CBT initiatives by connecting these to competitiveness frameworks, with the added interest that the cases presented come from a severely conflict-burdened country.
Selecting Teachers in Indonesia: Predicting Teacher Performance U sing Pre-e mployment Information

A wave of teacher retirement in Indonesia provides an opportunity to replace them with better-performing teachers. We study whether teacher candidates' screening tests into Pendidikan Profesi Guru (PPG) or Teacher Professional Education, a postgraduate education programme in Indonesia, can predict their performance at the end of the programme and in an actual classroom situation at the beginning of their teaching career. Using administrative data of 1,291 primary school teacher candidates, we find that admission criteria, including undergraduate grade point average (GPA), online admission tests, and interview scores, can predict a candidate's performance on their knowledge and teaching practice exams at the end of their education programme. A one standard deviation higher online admission test score is associated with a 0.30 standard deviation higher score in the knowledge examination. Teacher candidates with a one standard deviation higher interview score perform 0.07 standard deviation better on the teaching practice examination. For teacher candidates with one standard deviation higher undergraduate GPA, their knowledge examination performance is 0.15–0.17 standard deviation higher on average, and their teaching practice exam score is 0.06-0.07 standard deviation higher on average. We then estimate the predictive ability of the admission criteria on student learning outcomes in numeracy and literacy, which uses 1,530 randomly sampled students taught by 114 teacher candidates. We find no evidence that the selection criteria predicted student learning in a meaningful way. Our results contribute to a nascent body of research on the selection of teachers using ex-ante criteria to identify effective teachers in developing countries.
Sub-Frame Analysis-based Object Detection for Real-Time Video Surveillance

We introduce a vision-based object detection method for real-time video surveillance system in low-end edge computing environments. Recently, the accuracy of object detection has been improved due to the performance of approaches based on deep learning algorithm such as Region Convolutional Neural Network(R-CNN) which has two stage for inferencing. On the other hand, one stage detection algorithms such as single-shot detection (SSD) and you only look once (YOLO) have been developed at the expense of some accuracy and can be used for real-time systems. However, high-performance hardware such as General-Purpose computing on Graphics Processing Unit(GPGPU) is required to still achieve excellent object detection performance and speed. To address hardware requirement that is burdensome to low-end edge computing environments, We propose sub-frame analysis method for the object detection. In specific, We divide a whole image frame into smaller ones then inference them on Convolutional Neural Network (CNN) based image detection network, which is much faster than conventional network designed for full frame image. We reduced its computational requirement significantly without losing throughput and object detection accuracy with the proposed method.
MicroenvironmentMacrophage Survival in an Estrogen Phosphorylation Is Associated with Human Up-Regulation of Bcl-2 through ERK

Estrogen is a known immunomodulator with pleiotropic effects on macrophage function that partly accounts for the gender bias observed in numerous autoimmune, cardiovascular, and neurodegenerative disorders. The effect of estrogen on the survival of human macrophages is largely unknown, and in this study we demonstrate that 17 (cid:1) -estradiol (E2) provokes a death response in human THP-1 macrophages by initiating Bax translocation from cytosol to the mitochondria; however, a concomitant up-regu-lation of Bcl-2 creates a Bax to Bcl-2 ratio favorable for Bcl-2, thus ensuring cell survival. Both Bcl-2 up-regulation and Bax translocation are estrogen receptor-dependent events; however, Bcl-2 augmentation but not Bax translocation is dependent on Ca 2 (cid:2) increase, activation of protein kinase C, and ERK phosphorylation. This estrogen-induced Bcl-2 increase is crucial for the survival of THP-1 macrophages as well as that of human peripheral blood monocyte-derived macrophages, which is evident from E2-induced cell death under small interfering RNA-mediated Bcl-2 knockdown conditions. Hence, this study demonstrates that E2-induced Bcl-2 up-regulation is a homeostatic survival mechanism necessary for the manifestation of immunomodulatory effect of estrogen on human macrophages. The Journal of Immunology, 2007, 179: 2330–2338.
To Inhibit T Cell Proliferation and Function Alters Redox Balance 3 Au-ACRAMTU-PEt

Although T cells play a critical role in protection from viruses, bacteria, and tumors, they also cause autoimmune diseases such as systemic lupus erythematosus, rheumatoid arthritis, and multiple sclerosis. Unwanted T cell responses during organ transplant, graft-versus-host disease, and allergies are also major clinical problems. Although drugs are available to suppress unwanted immune responses, they have limited efﬁcacy with serious side effects. Thus, new therapeutics limiting T cell activation, proliferation, and function can make an immediate clinical impact. To identify new suppressors of lymphocyte activation, proliferation, and function, we examined the immunosuppressive activity of gold(I) analogs of platinum-acridine antitumor agents. We found that the gold complex Au-ACRAMTU-PEt 3 is a potent suppressor of murine and human T cell activation. Preincubation with Au-ACRAMTU-PEt 3 suppresses the proliferation of CD4 + and CD8 + T cells at a similar concentration as pharmaceutical grade cyclosporine A. Au-ACRAMTU-PEt 3 pretreatment decreases the production of IFN- g , TNF- a , IL-2, and IL-17 by human and murine CD4 + and CD8 + T cells. When mice were treated with Au-ACRAMTU-PEt 3 during viral infection, the expansion of virus-speciﬁc CD8 + T cells was decreased 10-fold and viral load was elevated. Taken together, these results demonstrate that Au-ACRAMTU-PEt 3 has potent immunosuppressive activity that could be used to suppress immune responses during transplantation and autoimmunity. The Journal of Immunology , 2015, 195: 000–000. cells are critical for protection from viruses, bacteria, and tumors (1). Prior to infection, naive T cells exist in a quiescent, nondividing state (2),
Antidepressant-like effect of ginsenoside Rb1 on potentiating synaptic plasticity via the miR-134 e mediated BDNF signaling pathway in a mouse model of chronic stress-induced depression

Background: Brain-derived neurotrophic factor (BDNF) e tropomyosin-related kinase B (TrkB) plays a critical role in the pathogenesis of depression by modulating synaptic structural remodeling and functional transmission. Previously, we have demonstrated that the ginsenoside Rb1 (Rb1) presents a novel antidepressant-like effect via BDNF e TrkB signaling in the hippocampus of chronic unpredictable mild stress (CUMS)-exposed mice. However, the underlying mechanism through which Rb1 counteracts stress-induced aberrant hippocampal synaptic plasticity via BDNF e TrkB signaling remains elusive. Methods: We focused on hippocampal microRNAs (miRNAs) that could directly bind to BDNF and are regulated by Rb1 to explore the possible synaptic plasticity-dependent mechanism of Rb1, which affords protection against CUMS-induced depression-like effects. Results: Herein, we observed that brain-speci ﬁ c miRNA-134 (miR-134) could directly bind to BDNF 3 0 UTR and was markedly downregulated by Rb1 in the hippocampus of CUMS-exposed mice. Furthermore, the hippocampus e targeted miR-134 overexpression substantially blocked the antidepressant-like effects of Rb1 during behavioral tests, attenuating the effects on neuronal nuclei-immunoreactive neurons, the density of dendritic spines, synaptic ultrastructure, long-term potentiation, and expression of synapse-associated proteins and BDNF e TrkB signaling proteins in the hippocampus of CUMS-exposed mice. Conclusion: These data provide strong evidence that Rb1 rescued CUMS-induced depression-like effects by modulating hippocampal synaptic plasticity via the miR-134-mediated BDNF signaling pathway. onthe BDNF signaling cascade pathway during chronic stress, promoting gene transcription and expression and increasing hippocampal synaptic plasticity. These molecular changes be the basis for the antidepressant-like effects of
A Review on Taste Masking Oral Dispersible Tablets of Antihypertensive Drug

: The fast-dissolving drug delivery system is rapidly gaining interest in the pharmaceutical industry among the various novel drug delivery systems. The most convenient route for drug administration for pharmaceutical dosage forms is the oral route. More than 50% of pharmaceutical products are orally administered for several Reasons, and undesirable taste is one of the essential formulation problems encountered with such oral products. According to governing compliance, the taste of a pharmaceutical product is an important parameter. Hence taste masking of oral Pharmaceuticals has become an essential tool to improve patient compliance and the Quality of treatment. In pharmaceutical oral dosage forms have unpleasant or obnoxious tastes, including bitter, sour, salt, sweet and umami tastes founded by the majority of active pharmaceutical ingredients (APIs). Taste is now one of the most important factors influencing the Quality of the product, according to therapeutic value, compliance, and acceptance of the patient. This reason is an initiative for the development of various taste-masking technologies by which the characteristics of the dosage form are improved, and good patient compliance is achieved. The present article reviews the earlier applications and methodologies of taste masking and discusses the most recent developments and approaches to bitterness reduction to give an idea about the traditional and recent taste masking evaluation techniques whereby they increase palatability for oral pharmaceuticals.