Miracle-dz/UniFAR-FTUs huggingface dataset:
An FPGA-based Emulation Platform for Edge Computing Node Design Exploration

Recent advances in machine learning have made it possible to consider the implementation of smart applications in constrained systems at the edge of the network. These memory and Central Processing Unit (CPU) intensive applications may require specific exploration methodologies to design efficient node computing devices. To better guide and validate these explorations, we need to perform energy and performance evaluations of the system. Software-based evaluation tools are application-oriented and do not consider real-time and hardware constraints. Alternatively, hardware prototyping allows an accurate and real-time evaluation but offers limited flexibility and does not allow agile design exploration of the microcontroller unit (MCU). In this work, we propose a Field Programmable Gate Arrays (FPGA) based edge computing node emulation platform. Our solution combines the flexibility and the real-time capability of programmable logic with hardware prototype evaluation. We present an open-source microcontroller architecture for design exploration which integrates an activity monitor to collect traces at run-time. These activity traces are then used to profile the energy consumption of different components in the edge computing node. Importantly, our FPGA is connected to real sensors and communication modules to enable interactions with the environment during the node evaluation and exploration.
International Journal of Public Health Science (IJPHS)

Received Apr 12, 2021 Revised Dec 30, 2021 Accepted Feb 16, 2022 Good perceptions on accreditation have an important role to play in achieving five-star accreditation and continuous implementation of accreditation. In the pre-survey, we found that the problem, a low perceived accreditation benefits, participation, and organizational commitment, as well as employees’ performance of the accreditation process that did not meet the target. The purpose of this study was to analyze the impact of the perceived benefits of accreditation on the performance of employees in the accreditation process. The research method used was a causal analysis study with a cross-sectional quantitative approach and a path analysis hypothesis test. The sample was taken by means of an objective sampling of X mother & children hospital (Rumah Sakit Ibu dan Anak/RSIA) health personnel. The results found that the perception of the benefits of accreditation had a positive and significant effect through mediation of participation and commitment to performance. The impact of the perceived benefits of accreditation, participation and organizational commitment on employee performance is 46.9%. This research implies for hospital management to optimize the performance of the accreditation process by increasing the perception of the benefits of accreditation through training, sharing, assessment, award and monitoring and evaluation involving all employees.
Developing a Robotic Kit for Mechatronic Engineering Education

This paper discusses the development of a robotic kit for the courses in the Mechatronic Engineering program. Mechatronics is a discipline that combines elements of Mechanical Engineering, Electrical Engineering, Computer Engineering and Control Engineering. Students in the program are required to possess not only broad multi-disciplinary knowledge but also strong hands-on technical skills. To achieve the program's educational objective, a robotic kit - the VEX® robotic arm 1 with a granular jamming gripper has been developed. Using the robotic arm and gripper system, students can visually understand the design process of a robotic manipulator based on the theorem they learned from the classes, such as forward and inverse kinematics, robotic dynamics and trajectory planning. Particularly, the granular jamming gripper is a creative and universal solution for robotic gripper designs. The flexible VEX® robotic arm in combination with the gripper can be used as an ideal educational platform. The easily implemented robotic system with the creative gripper design can inspire students to explore more novel and feasible solutions in their future careers in engineering.
Green Industrial Revolution in Indo-Pacific Region: Economic and Political Implications

: The dynamics of International Economy and Politics is strongly shaped by the industrial revolution an era has. A few decades ago, the dominant industry was mining industry. In comparison to the mining industry, the contemporary green industry is more sustainable and environmental friendly. The green industry is used often in the world of developmental policy, hence the shift from the unsustainable policies towards the more sustainable ones is a very significant change in the modern history. The shift can be seen in the ratification of Paris D’accord and the governments’ official statements of their commitment to be a part of Sustainable Development Goals. The trend of green industry is caused by the public awareness of its necessity, and therefore the trend creates a huge market for entrepreneurs and job opportunities. However, the implication of the green industry is not always positive due to amount of reasons. For instance, the U.S market was getting better when Trump administration announced their withdrawal from the Paris D’accord, and therefore in some cases the absence of green industry results in positive economic outcome which implies that the presence of green industry can result in negative economic outcome. Besides the economic implication, the rise of green industry also impacts other sectors including the political sector. The green industry and sustainable development that once was a second-tier political issue has now entered the main political stage. The implication of green industry as one of the main political issues results in the possibility of politicization of a necessity that green industry is. In this paper, the author(s) will try to understand the gap of what theoretically ought to happen and what really is happening in regards of green industry rising trend and its implication via qualitative methodology.
D ON ’ T F REEZE Y OUR E MBEDDING : L ESSONS FROM P OLICY F INETUNING IN E NVIRONMENT T RANSFER

A common occurrence in reinforcement learning (RL) research is making use of a pretrained vision stack that converts image observations to latent vectors. Using a visual embedding in this way leaves open questions, though: should the vision stack be updated with the policy? In this work, we evaluate the effectiveness of such decisions in RL transfer settings. We introduce policy update formulations for use after pretraining in a different environment and analyze the performance of such formulations. Through this evaluation, we also detail emergent metrics of benchmark suites and present results on Atari and AndroidEnv.
Rehabilitation of navigation lock walls : case histories

Abstract : Approximately half of the Corps 269 navigation lock chambers were built prior to 1940. Consequently, the concrete in these structures does not contain intentionally entrained air and is therefore susceptible to deterioration by freezing and thawing, Since more than three-fourths of these older structures are located in the Corps North Central and Ohio River Divisions, areas of relatively severe climatic exposure, it is not surprising that the concrete in many of these structures exhibits significant freeze-thaw deterioration. Depending upon exposure conditions, depths of concrete deterioration can range from surface scaling to several feet. The general approach in lock wall rehabilitation has been to remove the deteriorated concrete and replace it with concrete or shotcrete. Explosive blasting has been used successfully at several Corps projects and appears to be the most cost-effective and expedient means for removing large quantities of concrete. Once the deteriorated concrete has been removed, conventional cast-in-place concrete has been used as the replacement material in past lock wall rehabilitation projects. Other replacement systems that have been used or proposed include shotcrete, preplaced-aggregate concrete, and precast concrete stay-in-place forms. In addition, several materials including latex-modified concrete, polymer mortars and grouts, conventional shotcrete, and latex-modified, fiber-reinforced shotcrete have been used as thin overlays on existing lock walls. Applications of the various rehabilitation systems and their performance to date are described in selected case histories.
Improved identifiability of cardiac AP model parameters by physiological constraints Gradient-based parameter optimization to determine membrane ionic current composition of human 4 induced pluripotent stem cell-derived cardiomyocytes

37 Premature cardiac myocytes derived from human-induced pluripotent stem cells (hiPSC-CMs) show 38 heterogeneous action potentials (APs), most probably because of different expression patterns of membrane 39 ionic currents. We aim to develop a method of determining expression patterns of functional channels in terms 40 of the whole-cell ionic conductances ( G x ) using individual spontaneous AP configurations. However, it has 41 been suggested that apparently identical AP configurations were obtained by different sets of ionic currents in 42 a mathematical model of cardiac membrane excitation. If so, the inverse problem of G x estimation might not be 43 solved. We computationally tested the feasibility of the gradient-based optimization method. For realistic 44 examination, conventional 'cell-specific models' were prepared by superimposing the model output of AP on 45 each experimental AP record by the conventional manual adjustment of G x s of the baseline model. Then, G x s 46 of 4 ~ 6 major ionic currents of the 'cell-specific models' were randomized within a range of ± 5 ~ 15% and 47 were used as initial parameter sets for the gradient-based automatic G x s recovery by decreasing the mean 48 square error (MSE) between the target and model output. When plotted all data points of MSE - G x relation 49 during the optimization, we found that the randomized population of G x s progressively converged to the 50 original value of the cell-specific model with decreasing MSE. To confirm the absence of any other local 51 minimum in the global search space, we mapped the MSE by randomizing G x s over a range of 0.1 ~ 10 times 52 the control. No additional local minimum of MSE was obvious in the whole parameter space besides the global 53 minimum of MSE at the default model parameter.
Overrun-Resilient Multiprocessor Real-Time Locking

Existing real-time locking protocols require accurate worst-case execution time (WCET) estimates for both tasks and critical sections (CSs) in order to function correctly. On multicore platforms, however, the only seemingly viable strategy for obtaining such estimates is via measurements, which cannot produce a true WCET with certainty. The absence of correct WCETs can be partially ameliorated by enforcing execution budgets at both the task and CS levels and by using a locking protocol that is resilient to budget overruns , i.e. , that ensures that the schedulability of non-overrunning tasks is not compromised by tasks that do overrun their budgets. Unfortunately, no fully overrun-resilient locking protocol has been proposed to date for multiprocessor systems. To remedy this situation, this paper presents two such protocols, the OR-FMLP and the OR-OMLP , which introduce overrun-resiliency mechanisms to two existing multiprocessor protocols, the spin-based FMLP and suspension-based global OMLP , respectively. In devising such mechanisms, undo code can be problematic. For the important locking use case of protecting shared data structures, it is shown that such code can be avoided entirely by using abortable critical sections , a concept proposed herein that leverages obstruction-free synchronization techniques. Experiments are presented that demonstrate both the effectiveness of the mechanisms introduced in this paper and their cost.
Hybrid Model for Sequential and Simultaneous Choice with Search Cost

In this paper we propose a hybrid model to study sequential and simultaneous choice behavior in the presence of search cost. After examining all products in an assortment, a rational consumer decides whether to continue searching. Switching from one assortment to another often incurs a search cost that is often related to transportation expense or time. We combine the classic sequential search model with the famous simultaneous discrete choice model, and develop a uniﬁed framework to investigate the impact of search cost on the sequential and simultaneous choice behavior. The proposed framework involves a deterministic utility in the discrete choice model under the random utility maximization theory. We consider the associated operations problems including price optimization and competition, assortment planning, search cost management and model estimation. In particular, the prices or markups are product-invariant at optimality for each assortment, and the optimal prices for the products in the ﬁrst assortment are increasing in search cost while they are decreasing for the products in the second assortment. The assortment optimization problems are generally NP-hard, so we develop eﬃcient heuristics and approximation schemes. Finally, we characterize the likelihood functions, and develop eﬃcient estimation algorithms for complete and incomplete data scenarios. An empirical study shows that the hybrid search and choice model can improve the goodness-of-ﬁt and prediction accuracy for consumer choice, sales and revenue in the shopping scenarios with multiple stores in diﬀerent locations. Failure to account for the hybrid features of consumer choice and the impact of search cost may lead to substantial losses in operations management. The developed solution approaches are readily applicable in practice.
Manufacturers’ Assortment Planning and Pricing in a Competitive Two-Tier Supply Chain

We consider a two-tier supply chain in which competing manufacturers choose assortments of their products to be sold to consumers through a single wholesaler. Consumers’ purchasing decisions are described by the celebrated Multinomial Logit (MNL) model, and they pay the wholesaler market prices set by the latter to maximize its proﬁt. We formulate non-cooperative games to model assortment competitions among manufacturers, for both cases when each manufacturer’s wholesale prices are given inputs to its assortment decision, and when they are optimized jointly to maximize the manufacturer’s proﬁt. We also consider situations in which the maximum number of products in each manufacture’s assortment is restricted by a cardinality constraint. For each case, we develop eﬃcient solution procedures to determine a manufacturer’s optimal assortment in response to its competitors’ decisions and prove the existence of a pure-strategy Nash equilibrium. We also conduct numerical studies to compare equilibrium assortments between this two-tier supply chain and the case when manufacturers sell their products directly to consumers, and discuss related proﬁt and consumer surplus implications.
P300 single-trial classiﬁcation using deep belief networks for a BCI system

—A brain-computer interface (BCI) aims to provide their users the capability to interact with machines only through their though processes. BCIs targeted at subjects with mild and severe motor impairments are of special interest since this kind of technology would improve their lifestyles. This paper focuses on the classiﬁcation of the P300 waveform from single trials in EEG to be used in a BCI using deep belief networks. This deep learning algorithm has the capability to identify relevant features automatically from the subject’s EEG data, making its training requiring less preprocessing stages. The network is tested on healthy subjects and post-stroke victims. The highest accuracy achieved was of 91.6% for a healthy subject and 88.1% for a post-stroke victim.
DA-BASED MULTIPLE GAUSSIANS PARTICLE FILTER FOR ORBIT DETERMINATION

The nonlinear ﬁltering problem plays a fundamental role in multiple space related appli- cations. This paper offers a new ﬁltering technique that combines the classical ensemble Kalman ﬁlter with a Gaussian mixture model. Differential Algebra (DA) techniques are used as a tool to reduce the computational effort required by particle ﬁlters. Moreover, the use of Expectation Maximization (EM), as a clustering algorithm, leads to a better approximation of the propagated probability density function and to a multiple weighted measurement update. The performances of the new method is assessed in the nonlinear Orbit Determination problem, for the challenging case of low observations frequency.
Do Skirts on Board Impact Figures in Books?

After the financial crisis of 2007, a wave of discussion started that who sits on board is a critical factor in deciding the success and failure of a company. Research has found links between diverse boards and its positive effects on board inputs (Daniel Ferreira). Countries have rigorously passed laws and legislations without considering the fact there a complex relationship between women on board and performance and several other factors are needed to consider before making a strong assumption. The perceived importance of gender equality in board rooms (Nielson & Huse, 2010) do not points towards a clear opinion on whether gender equality in boardrooms will definitely yield better economic results (Hamdani & Buckley, 2010; Jonas Vob, 2015). A data for FTSE 100 companies was analyzed from 2006-2012 using Random Effects regression model. The results of this research does not support the business case of higher gender diversity for higher profits. This does not imply gender diversity should not be promoted. It is good to have gender diverse boards because of the positive behavioral impact they create on overall environment of an organization. Higher representation of women on board might not relate to the higher profits but diversified boards are critical in better decision making because of the benefit of diverse perspectives’. Equality should be promoted in work environment for ethical reasons and everyone should get a chance to make it to the top level if they are good enough.
VivoCauses Attraction of DC Both In Vitro and In Human CXCL14 Expression in Tumor Cells Dendritic Cells (DC), while Restoration of Tissue Is Associated with Low Infiltration by Loss of New Chemokine CXCL14 in Tumor

Breast and kidney-expressed chemokine (BRAK) CXCL14 is a new CXC chemokine with unknown function and receptor selectivity. The majority of head and neck squamous cell carcinoma (HNSCC) and some cervical squamous cell carcinoma do not express CXCL14 mRNA, as opposed to constitutive expression by normal oral squamous epithelium. In this study, we demonstrate that the loss of CXCL14 in HNSCC cells and at HNSCC primary tumor sites was correlated with low or no attraction of dendritic cell (DC) in vitro, and decreased inﬁltration of HNSCC mass by DC at the tumor site in vivo. Next, we found that recombinant human CXCL14 and CXCL14-positive HNSCC cell lines induced DC attraction in vitro, whereas CXCL14-negative HNSCC cells did not chemoattract DC. Transduction of CXCL14-negative HNSCC cell lines with the human CXCL14 gene resulted in stimulation of DC attraction in vitro and increased tumor inﬁltration by DC in vivo in chimeric animal models. Furthermore, evaluating the biologic effect of CXCL14 on DC, we demonstrated that the addition of recombinant human CXCL14 to DC cultures resulted in up-regulation of the expression of DC maturation markers, as well as enhanced proliferation of allogeneic T cells in MLR. Activation of DC with recombinant human CXCL14 was accompanied by up-regulation of NF- (cid:1) B activity. These data suggest that CXCL14 is a potent chemoattractant and activator of DC and might
Deep Classiﬁers trained with the Square Loss

Here we consider a model of the dynamics of gradient ﬂow under the square loss in overparametrized ReLU networks. We show that convergence to a solution with the absolute minimum ρ , which is the product of the Frobenius norms of each layer weight matrix, is expected when normalization by a Lagrange multiplier (LN) is used together with Weight Decay (WD). We prove that SGD converges to solutions that have a bias towards 1) large margin (i.e. small ρ ) and 2) low rank of the weight matrices. In addition, we predict the occurence of Neural Collapse without ad hoc assumptions such as the unconstrained features hypothesis. Abstract Recent results of [1] suggest that square loss performs on par with cross-entropy loss in classiﬁcation tasks with deep networks. While the theoretical understanding of training deep networks with the cross-entropy loss has been growing ([2] and [3]) in terms of margin maximization, the study of square loss for classiﬁcation has been lagging behind. Here we consider a speciﬁc model of the dynamics of, ﬁrst, gradient ﬂow (GF), and then SGD, in overparametrized ReLU networks under the square loss. Under the assumption of convergence to zero loss minima, we show that solutions have a bias toward small ρ , deﬁned as the product of the Frobenius norms of each layer unnormalized weight matrix. We assume that during training there is normalization using a Lagrange multiplier (LM) of each layer weight matrix but the last one, together with Weight Decay (WD). For λ → 0 the solution would be the interpolating solution with minimum ρ . In the absence of LM+WD, good solutions for classiﬁcation may still be achieved because of the implicit bias towards small norm solutions in the GD dynamics introduced by carefully chosen close-to-zero initial conditions on the norms of the weights, similar to the case of overparametrized linear networks (see Appendix E). However, for λ = 0 we often observe solutions with large ρ that are suboptimal and probably in the NTK regime. We show that convergence to an ideal equilibrium of SGD ( ˙ V k = 0 for all minibatches) with λ > 0 would imply rank one weight matrices. This is impossible generically, implying that SGD never converges to the same set of V k across all minibatches. We claim that this is the origin of
Syllabus for the course “Topology of geometric structures” Local master course on Diﬀerential Topology

You may also observe that these structures are not arbitrary sections: metrics are required to be positive definite, symplectic forms have to be non-degenerate and closed, Poisson bivectors have to be involutive with respect to the Schouten bracket, and immersions must have non-degenerate differential. It follows that we want to think of geometric structures as sections of a bundle that additionally satisfy some constraint that can be expressed in terms of derivatives. For instance, the condition of being closed for a symplectic structure ω amounts to its “first derivative” dω being zero.