Miracle-dz/UniFAR-FTUs huggingface dataset:
Full-State Tracking Control for Flexible Joint Robots With Singular Perturbation Techniques

This paper proposes a practical method to realize multivariable full-state tracking control for industrial robots with elastic joints. Unlike existing methods, the proposed method does not require high-order derivatives of the link states such as acceleration and jerk. Therefore, the proposed method does not suffer from chatter related to inaccurate estimation of high-order derivatives. The method is derived by adopting a singular perturbation technique. A decoupled error dynamics is achieved by two decoupling control loops: a fast loop that controls the deflection error and a slow loop for tracking control on the link side. Our stability analysis based on a linear system shows that the proposed control system is stable as long as the fast system is at least twice as fast as the slow system. A practical method to select the gain is also presented such that the closed-loop poles are placed at the desired locations. In simulation, we compare the proposed method with feedback linearization. The results indicate that in an ideal scenario the proposed method can obtain a similar performance as feedback linearization. However, the proposed method obtains a superior performance in a realistic scenario. A real-world experiment with a six degree-of-freedom commercial industrial robot is carried out to further validate our approach.
Machine Learning for Expert Systems in Data Analysis

Imagine a system that automates the job of data science in such a way that if given sales data of a store nationwide can analyze the rate of sales of a product relative to its supply and demand and other necessary variables and is able to decide on the best places for the company to open new stores and help them to decide on the best possible ways to distribute their products. Data Science is an interdisciplinary field that is very much like data mining and Knowledge Discovery in Databases (KDD), involving the analysis of data to make useful inferences and deduction. However, taking into consideration the information overload of this age, it has become a pressing need to automate the process of analyzing this data. At the point of this need is where data science meets machine learning. Machine learning being a powerful tool for automation can be merged with data science and analysis to make for a more effective faster way to analyze data. In this paper, an application of expert systems for data analysis would be discussed as a case study.
2018 International Conference on Control, Automation and Information Sciences (ICCAIS)

Following the success of ICCAIS 2017 (Chiang Mai, Thailand), ICCAIS 2016 (Ansan, Korea), ICCAIS 2015 (Changshu, China), ICCAIS 2014 (Gwangju, Korea), ICCAIS 2013 (Nha Trang, Vietnam), ICCAIS 2012 (Ho Chi Minh, Vietnam), Hangzhou Dianzi University is proud to host the 2018 International Conference on Control, Automation and Information Sciences (ICCAIS) at the beautiful city of Hangzhou from October 24-27, 2018. ICCAIS 2018 is technically co-sponsored by IEEE, the international journal Sensors, Intelligent Service Committee of CAAI, Granular Computing and Multi-Scale Analysis Committee of CAA. With the generous help and contributions from many of our notable colleagues in the field, we have been reasonably successful with our aims. This year seems no exception as we continue to see the strong support of our conference by the academic and professional communities, particularly in terms of the international contributions.
Synthesis of a Non-Integer Controller for Fractional Order Systems

This paper deals with the design of non-integer-controller for fractional order systems with temporal specifications such that rise time and overshoot. These performances are defined by a target model using the Characteristic Ratio Assignment (CRA) method. The proposed controller is recommended to a high order transfer function. In fact, to find the controller parameters, it is needed to minimize an objective function using a Least Square Estimation (LSE) method. For certain cases, constraints can be added to the LSE problem.
Evaluation of GaN Based Multilevel Converters

With the significant reduction in board space occupied by the smaller GaN transistors, topologies that require a greater number of active devices as a tradeoff for reduced passive size, the main barrier to higher density, have become attractive. Switched capacitor multilevel converters are good examples of topologies that can effectively reduce or eliminate passive components. Two GaN based proof-of-concept designs (three-level converters), one for a low voltage (LV) 48 V server application and the other for a higher voltage (HV) 400 V power factor correction (PFC) circuit are discussed in this paper. As a result of using lower figure-of-merit (FOM) devices, significant efficiency gains are observed for the LV and HV converters developed in this paper compared to a two-level topology. Smaller passive size (mainly inductors) also provides a significant increase in power density for the LV converter. A startup scheme is discussed which eliminates the need for extraneous control loops or high voltage rated devices for the top switch. Finally, an electrical performance comparison is also shown for a two-stage intermediate bus architecture (IBA) developed with the GaN based three-level converter for the front end and a high frequency GaN-based second buck stage for 48 V to 1 V point-of-load (PoL) conversion. Two choices of intermediate bus voltage are investigated: 12 V and 6 V. Each bus voltage demonstrates its advantage in a certain range of load current.
Crystal structure of the complex between venom toxin and serum inhibitor from Viperidae snake

Venomous snakes have endogenous proteins that neutralize the toxicity of their venom components. We previously identified five small serum proteins (SSP-1–SSP-5) from a highly venomous snake belonging to the family Viperidae as inhibitors of various toxins from snake venom. The endogenous inhibitors belong to the prostate secretory protein of 94 amino acids (PSP94) family. SSP-2 interacts with triflin, which is a member of the cysteine-rich secretory protein (CRISP) family that blocks smooth muscle contraction. However, the structural basis for the interaction and the biological roles of these inhibitors are largely unknown. Here, we determined the crystal structure of the SSP-2–triflin complex at 2.3 Å resolution. A concave region centrally located in the N-terminal domain of triflin is fully occupied by the terminal β-strands of SSP-2. SSP-2 does not bind tightly to the C-terminal cysteine-rich domain of triflin; this domain is thought to be responsible for its channel-blocker function. Instead, the cysteine-rich domain is tilted 7.7° upon binding to SSP-2, and the inhibitor appears to sterically hinder triflin binding to calcium channels. These results help explain how an endogenous inhibitor prevents the venomous protein from maintaining homeostasis in the host. Furthermore, this interaction also sheds light on the binding interface between the human homologues PSP94 and CRISP-3, which are up-regulated in prostate and ovarian cancers.
Examining the nature of feedback within the Mini Clinical Evaluation Exercise (Mini-CEX): an analysis of 1427 Mini-CEX assessment forms

Background: Despite the growing use, studies have demonstrated some limitations related to the feedback provided in the context of the increasing use of the Mini Clinical Evaluation Exercise (Mini-CEX) in undergraduate medical education. This study examined the written feedback provided on the Mini-CEX form to determine its usefulness as a learning tool for students. Methods: 1427 Mini-CEX assessment forms of final year medical students were collected. Written feedback, both on students’ strength and weakness, was categorized and correlated with the variables of clinical case complexity, assessors’ clinical position and students’ clinical performance rating. Results: The number of general feedback comments for students’ strengths and development were 953 (65.3%) and 604 (38.64%) respectively. Less than 30% of the feedback for each domain was categorized as specific feedback. Significant associations were found between feedback on strength and clinical case complexity (Χ2=17.48, p<.01); and also with assessor clinical position (Χ2=37.10, p<.01). There was also an association between feedback for students’ development and assessor clinical position (Χ2=27.22, p<.01). Conclusion: Based on the Mini-CEX forms of student cohort this study examined, it can be concluded that the written feedback provided in the Mini-CEX assessment form was general and lacked specificity. This finding leads to the need to train clinical teachers in the provision of feedback.
Cholesterol Increases Lipid Binding Rate and Changes Binding Behavior of Bacillus thuringiensis Cytolytic Protein

Cytolytic protein (Cyt) is a member of insecticidal proteins produced by Bacillus thuringiensis. Cyt protein has activity against insect cells and mammalian cells, which differ in lipid and cholesterol composition. This study presents the lipid binding behavior of Cyt2Aa2 protein on model membranes containing different levels of cholesterol content by combining Quartz Crystal Microbalance with Dissipation (QCM-D) and Atomic Force Microscopy (AFM). QCM-D results revealed that cholesterol enhances the binding rate of Cyt2Aa2 protein onto lipid bilayers. In addition, the thicker lipid bilayer was observed for the highest cholesterol content. These results were confirmed by AFM. The analysis of protein surface coverage as a function of time showed a slower process for 5:0 and 5:0.2 (POPC:Chol) ratios than for 5:1 and 5:2 (POPC:Chol) ratios. Significantly, the Cyt2Aa2-lipid binding behavior and the protein–lipid layer were different for the 5:3 (POPC:Chol) ratio. Furthermore, AFM images revealed a transformation of Cyt2Aa2/lipid layer structure from strip pattern to ring shape structures (which showed a strong repulsion with AFM tip). In summary, cholesterol increases the binding rate and alters the lipid binding behavior of Cyt2Aa2 protein, although it is not required for Cyt2Aa2 protein binding onto lipid bilayers.
NLPRL@INLI-2018: Hybrid gated LSTM-CNN model for Indian Native Language Identification

Native language identiﬁcation (NLI) focuses on determining the native language of the author based on the writing style in English. Indian native language identiﬁcation is a challenging task based on users comments and posts on social media. To solve this problem, we present a hybrid gated LSTM-CNN model to solve this problem. The ﬁnal vector of a sentence is generated at hybrid gate by joining the two distinct vector of a sentence. Gate seeks the optimum mixture of the LSTM and CNN level outputs. The input word for LSTM and CNN are projected into high-dimensional space by embedding technique. We obtained 88.50% accuracy during training on the provided social media dataset, and 17.10% is reported in the ﬁnal testing done by Indian native language identiﬁcation (INLI) workshop organizers.
A clinic compatible, open source electrophysiology system

Open source electrophysiology (ephys) recording systems have several advantages over commercial systems such as customization and affordability enabling more researchers to conduct ephys experiments. Notable open source ephys systems include Open-Ephys, NeuroRighter and more recently Willow, all of which have high channel count (64+), scalability, and advanced software to develop on top of. However, little work has been done to build an open source ephys system that is clinic compatible, particularly in the operating room where acute human electrocorticography (ECoG) research is performed. We developed an affordable (<; $10,000) and open system for research purposes that features power isolation for patient safety, compact and water resistant enclosures and 256 recording channels sampled up to 20ksam/sec, 16-bit. The system was validated by recording ECoG with a high density, thin film device for an acute, awake craniotomy study at UC San Diego, Thornton Hospital Operating Room.
SIMD-X: Programming and Processing of Graph Algorithms on GPUs

With high computation power and memory bandwidth, graphics processing units (GPUs) lend themselves to accelerate data-intensive analytics, especially when such applications fit the single instruction multiple data (SIMD) model. However, graph algorithms such as breadth-first search and k-core, often fail to take full advantage of GPUs, due to irregularity in memory access and control flow. To address this challenge, we have developed SIMD-X, for programming and processing of single instruction multiple, complex, data on GPUs. Specifically, the new Active-Compute-Combine (ACC) model not only provides ease of programming to programmers, but more importantly creates opportunities for system-level optimizations. To this end, SIMD-X utilizes just-in-time task management which filters out inactive vertices at runtime and intelligently maps various tasks to different amount of GPU cores in pursuit of workload balancing. In addition, SIMD-X leverages push-pull based kernel fusion that, with the help of a new deadlock-free global barrier, reduces a large number of computation kernels to very few. Using SIMD-X, a user can program a graph algorithm in tens of lines of code, while achieving 3? , 6? , 24? , 3? speedup over Gunrock, Galois, CuSha, and Ligra, respectively.
HyperReconNet: Joint Coded Aperture Optimization and Image Reconstruction for Compressive Hyperspectral Imaging

Coded aperture snapshot spectral imaging (CASSI) system encodes the 3D hyperspectral image (HSI) within a single 2D compressive image and then reconstructs the underlying HSI by employing an inverse optimization algorithm, which equips with the distinct advantage of snapshot but usually results in low reconstruction accuracy. To improve the accuracy, existing methods attempt to design either alternative coded apertures or advanced reconstruction methods, but cannot connect these two aspects via a unified framework, which limits the accuracy improvement. In this paper, we propose a convolution neural network-based end-to-end method to boost the accuracy by jointly optimizing the coded aperture and the reconstruction method. On the one hand, based on the nature of CASSI forward model, we design a repeated pattern for the coded aperture, whose entities are learned by acting as the network weights. On the other hand, we conduct the reconstruction through simultaneously exploiting intrinsic properties within HSI—the extensive correlations across the spatial and spectral dimensions. By leveraging the power of deep learning, the coded aperture design and the image reconstruction are connected and optimized via a unified framework. Experimental results show that our method outperforms the state-of-the-art methods under both comprehensive quantitative metrics and perceptive quality.
Multidrug-resistant tuberculosis.

ABSTRACT: BACKGROUND: With almost 9 million new cases each year, tuberculosis remains one of the most feared diseases on the planet. Led by the STOP-TB Partnership and WHO, recent efforts to combat the disease have made considerable progress in a number of countries. However, the emergence of mutated strains of Mycobacterium tuberculosis that are resistant to the major anti-tuberculosis drugs poses a deadly threat to control efforts. Multidrug-resistant tuberculosis (MDR-TB) has been reported in all regions of the world. More recently, extensively drug resistant-tuberculosis (XDR-TB) that is also resistant to second line drugs has emerged in a number of countries. To ensure that adequate resources are allocated to prevent the emergence and spread of drug resistance it is important to understand the scale of the problem. In this article we propose that current methods of describing the epidemiology of drug resistant tuberculosis are not adequate for this purpose and argue for the inclusion of population based statistics in global surveillance data. DISCUSSION: Whereas the prevalence of tuberculosis is presented as the proportion of individuals within a defined population having disease, the prevalence of drug resistant tuberculosis is usually presented as the proportion of tuberculosis cases exhibiting resistance to anti-tuberculosis drugs. Global surveillance activities have identified countries in Eastern Europe, the former Soviet Union and regions of China as having a high proportion of MDR-TB cases and international commentary has focused primarily on the urgent need to improve control in these settings. Other regions, such as sub-Saharan Africa have been observed as having a low proportion of drug resistant cases. However, if one considers the incidence of new tuberculosis cases with drug resistant disease in terms of the population then countries of sub-Saharan Africa have amongst the highest rates of transmitted MDR-TB in the world. We propose that inclusion of population based statistics in global surveillance data is necessary to better inform debate on the control of drug resistant tuberculosis. SUMMARY: Re-appraisal of global MDR-TB data to include population based statistics suggests that the problem of drug resistant tuberculosis in sub-Saharan Africa is more critical than previously perceived.
Disgust, politics, and responses to threat

Abstract We address two questions regarding the relationship between political ideology and responses to threatening or aversive stimuli. The first concerns the reason for the connection between disgust and specific political and moral attitudes; the second concerns the observation that some responses to threat (i.e., neuroticism/anxiety) are associated with a more left-wing political orientation.
The cabABC Operon Essential for Biofilm and Rugose Colony Development in Vibrio vulnificus

A transcriptome analysis identified Vibrio vulnificus cabABC genes which were preferentially expressed in biofilms. The cabABC genes were transcribed as a single operon. The cabA gene was induced by elevated 3′,5′-cyclic diguanylic acid (c-di-GMP) and encoded a calcium-binding protein CabA. Comparison of the biofilms produced by the cabA mutant and its parent strain JN111 in microtiter plates using crystal-violet staining demonstrated that CabA contributed to biofilm formation in a calcium-dependent manner under elevated c-di-GMP conditions. Genetic and biochemical analyses revealed that CabA was secreted to the cell exterior through functional CabB and CabC, distributed throughout the biofilm matrix, and produced as the biofilm matured. These results, together with the observation that CabA also contributes to the development of rugose colony morphology, indicated that CabA is a matrix-associated protein required for maturation, rather than adhesion involved in the initial attachment, of biofilms. Microscopic comparison of the structure of biofilms produced by JN111 and the cabA mutant demonstrated that CabA is an extracellular matrix component essential for the development of the mature biofilm structures in flow cells and on oyster shells. Exogenously providing purified CabA restored the biofilm- and rugose colony-forming abilities of the cabA mutant when calcium was available. Circular dichroism and size exclusion analyses revealed that calcium binding induces CabA conformational changes which may lead to multimerization. Extracellular complementation experiments revealed that CabA can assemble a functional matrix only when exopolysaccharides coexist. Consequently, the combined results suggested that CabA is a structural protein of the extracellular matrix and multimerizes to a conformation functional in building robust biofilms, which may render V. vulnificus to survive in hostile environments and reach a concentrated infective dose.
Radioresistance of mesenchymal glioblastoma initiating cells correlates with patient outcome and is associated with activation of inflammatory program

Glioblastoma (GBM) still remains an incurable disease being radiotherapy (RT) the mainstay treatment. Glioblastoma intra-tumoral heterogeneity and Glioblastoma-Initiating Cells (GICs) challenge the design of effective therapies. We investigated GICs and non-GICs response to RT in a paired in-vitro model and addressed molecular programs activated in GICs after RT. Established GICs heterogeneously expressed several GICs markers and displayed a mesenchymal signature. Upon fractionated RT, GICs reported higher radioresistance compared to non-GICs and showed lower α- and β-values, according to the Linear Quadratic Model interpretation of the survival curves. Moreover, a significant correlation was observed between GICs radiosensitivity and patient disease-free survival. Transcriptome analysis of GICs after acquisition of a radioresistant phenotype reported significant activation of Proneural-to-Mesenchymal transition (PMT) and pro-inflammatory pathways, being STAT3 and IL6 the major players. Our findings support a leading role of mesenchymal GICs in defining patient response to RT and provide the grounds for targeted therapies based on the blockade of inflammatory pathways to overcome GBM radioresistance.
Identification of Compounds with pH-Dependent Bactericidal Activity against Mycobacterium tuberculosis

To find new inhibitors of Mycobacterium tuberculosis that have novel mechanisms of action, we miniaturized a high throughput screen to identify compounds that disrupt pH homeostasis. We adapted and validated a 384-well format assay to determine intrabacterial pH using a ratiometric green fluorescent protein. We screened 89000 small molecules under nonreplicating conditions and confirmed 556 hits that reduced intrabacterial pH (below pH 6.5). We selected five compounds that disrupt intrabacterial pH homeostasis and also showed some activity against nonreplicating bacteria in a 4-stress model, but with no (or greatly reduced) activity against replicating bacteria. The compounds selected were two benzamide sulfonamides, a benzothiadiazole, a bissulfone, and a thiadiazole, none of which are known antibacterial agents. All of these five compounds demonstrated bactericidal activity against nonreplicating bacteria in buffer. Four of the five compounds demonstrated increased activity under low pH conditions. None of the five compounds acted as ionophores or as general disrupters of membrane potential. These compounds are useful starting points for work to elucidate their mechanism of action and their utility for drug discovery.