Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantifying cancer cell receptors with paired-agent fluorescent imaging: a novel method to account for tissue optical property effects

Dynamic fluorescence imaging approaches can be used to estimate the concentration of cell surface receptors in vivo. Kinetic models are used to generate the final estimation by taking the targeted imaging agent concentration as a function of time. However, tissue absorption and scattering properties cause the final readout signal to be on a different scale than the real fluorescent agent concentration. In paired-agent imaging approaches, simultaneous injection of a suitable control imaging agent with a targeted one can account for non-specific uptake and retention of the targeted agent. Additionally, the signal from the control agent can be a normalizing factor to correct for tissue optical property differences. In this study, the kinetic model used for paired-agent imaging analysis (i.e., simplified reference tissue model) is modified and tested in simulation and experimental data in a way that accounts for the scaling correction within the kinetic model fit to the data to ultimately extract an estimate of the targeted biomarker concentration.
Unsupervised Abstractive Sentence Summarization using Length Controlled Variational Autoencoder

In this work we present an unsupervised approach to summarize sentences in abstractive way using Variational Autoencoder (VAE). VAE are known to learn a semantically rich latent variable, representing high dimensional input. VAEs are trained by learning to reconstruct the input from the probabilistic latent variable. Explicitly providing the information about output length during training influences the VAE to not encode this information and thus can be manipulated during inference. Instructing the decoder to produce a shorter output sequence leads to expressing the input sentence with fewer words. We show on different summarization data sets, that these shorter sentences can not beat a simple baseline but yield higher ROUGE scores than trying to reconstruct the whole sentence.
Dynamic Optimization of Employees Work Strategies in a WeChat-Based Evaluation System

In recent years, social media platforms and, especially, instant messaging applications have been witnessed to play an increasingly important role as a communication channel among employees to build effective organizations, which results in a considerably large part of individual activities moving onto the cyber-based workspace. In this paper, we collect the unique real-world data set from a WeChat-based work performance evaluation system built and used by a medium-sized organization in China, in order to study the optimization of the employees’ work strategies, especially the work time determination, with the purpose of maximizing the total utility comprised of the work and nonwork utilities. In the system, the performance evaluation is completely based on the employees’ work reports and includes three major measures including score, level, and rank. First, we formulate a dynamic optimization model, incorporating the work utility into the reputational and the substantive utility, so as to examine the employees’ work strategy. Then, we conduct both empirical studies and computational experiments to make an in-depth analysis to probe the employees’ optimal work strategies. The main results include: 1) the optimal work time is greatly affected by the weight of the reputational utility; 2) the optimal work time is always the threshold time to achieve a certain level or a certain rank; 3) the total utility is with the trend of decreasing after increasing with the growing work time; and 4) the optimal work time does not always make the employee achieve a good work performance.
Drug discovery strategies and the preclinical development of D-amino-acid oxidase inhibitors as antipsychotic therapies

ABSTRACT Introduction: D-amino-acid oxidase (DAAO) degrades D-serine, a co-agonist of the NMDA receptor whose dysfunction is involved in the positive, negative, and cognitive symptoms of schizophrenia. The inhibition of DAAO appears to be a viable strategy to increase D-serine level and to have therapeutic potential in schizophrenia. Areas covered: This review describes the efforts to develop DAAO inhibitors and to optimize their in vitro and in vivo effects in preclinical settings. The structural evolution of DAAO inhibitors is presented from simple carboxylic acid derivatives via small, planar compounds with carboxylic acid mimetics to extended compounds whose binding is possible owing to DAAO flexibility. Inhibitory potency and pharmacokinetic properties are discussed in the context of compounds’ ability to increase D-serine level and to show efficacy in animal models of schizophrenia. Expert opinion: The accumulated knowledge on the structural requirements of DAAO inhibitors and on their in vitro and in vivo effects provides appropriate basis to develop inhibitors with optimized potency, selectivity and pharmacokinetic profile including blood-brain penetration. In addition, the validation of DAAO inhibition therapy in alleviating the symptoms of schizophrenia requires further studies on the efficacy of DAAO inhibitors in behavioral assays of animals and on the species differences in D-serine metabolism.
A novel system of SSVEP-based human–robot coordination

Objective. Human–robot coordination (HRC) aims to enable human and robot to form a tightly coupled system to accomplish a task. One of its important application prospects is to improve the physical function of the disabled. However, the low level of the coordination between human and robot and the limited potential users still hamper the efficiency of such systems. Approach. To deal with such challenges, a novel steady-state visual evoked potential (SSVEP) based human–robot coordinated brain–computer interface (BCI) system was proposed to finish a target capturing task. In this system, the robot, by combining the information obtained during the human’s natural interaction with itself to capture a target, could optimize the same object capturing task and yield a better performance automatically. The combination of human dealing with the uncertainty problem and the robot dealing with the complexity problem was the key to the system. Meanwhile, an asynchronous BCI based on SSVEP was used as the system interface, and a novel asynchronous recognition algorithm was used to discriminate the electroencephalogram (EEG) signal. Main results. The results show that the proposed system can lower the fatigue level of the subject and simplify the operation of the system. Meanwhile, the signal recognition accuracy and the efficiency of the system were also improved. Significance. Under the help of the close and natural coordination relationship design between human and robot, and the asynchronous SSVEP based BCI design which requires no limb movement to control a robot, the users would be provided with an accurate and efficient control experience. Moreover, people with severe motor diseases might potentially benefit from such a system. Also, the proposed methods can be easily integrated into other BCI diagrams, which would ameliorate the predicament of the HRC.
Image Categorization using Thepade's Sorted Block Truncation Coding N-ary with Data Mining Classifiers

Due to increase in use of social Networking sites uploading of multimedia data on internet has exponentially increased. The search and retrieval through such data can be made faster by categorizing the data according to predefined classes. Here the image classification technique is proposed using Thepade's Sorted Block Truncation Coding (TSTBTC) to classify the images into predefined classes. Image color content based signatures ares used for image classification. Assorted sizes of feature vectors are formed by taking Thepade's Sorted N-ary Block Truncation Coding. The various classification algorithms from assorted classifier families are deployed in proposed image classification techniques such as Function (Simple Logistic, RBF Network) Rule(Part, Decision), Tree (J48, BFTree, Random Forest, Random tree) and Bayes(BayesNet, Navie Bayes) the performance of various proposed techniques are analyzed using classification accuracy. Simple Logistic Classifier has given the highest classification accuracy of 87.5% with TSTBTC ‘Hexa ary’ version.
Combined Direct and Indirect Adaptive Control of Nonlinear Four-bar Linkage Robot Manipulator

In this paper, a combined direct and indirect adaptive control for a nonlinear a robot manipulator is introduced. The combined direct and indirect adaptive control improves accuracy and speed of adaptation, which would lead to increase tracking performance. The adaptive mechanism can be viewed as a modified version of the direct adaptive controller. The parameters are updated based on the information provided by the tracking error and identification error. The closed-loop system stability is proven by the Lyapunov theory of stability. The proposed scheme is applied to a nonlinear four-bar linkage robot manipulator using a FPGA and the simulation results are compared with the direct adaptive method. Although this method is applied to four-bar linkage robot it can be generalized to all types of nonlinear robots.
Application of Higher-Order Sliding-Modes to a Ball and Plate System

This paper presents a control strategy that combines feedback-linearization with higher-order sliding-mode control for the tracking control of a cam-plate driven ball and plate system. The concept of the practical relative degree legitimates to reconsider the system representation as a simpler feedback-linearizable system. For the remaining linear dynamics, a higher-order sliding-mode algorithm serves to estimate the states required for a super-twisting controller. The effectiveness of the proposed approach is demonstrated on a laboratory test-bench imposing a circular trajectory for tracking.
CDCA3 mediates p21-dependent proliferation by regulating E2F1 expression in colorectal cancer

Dysregulated cell cycle progression serves a crucial role in tumor development. Cell division cycle- associated 3 (CDCA3) is considered a trigger of mitotic entry; it is an important part of the S phase kinase-associated protein 1/Cullin/F-box ubiquitin ligase complex and mediates the destruction of mitosis-inhibitory kinase wee1. However, little is known about the role of CDCA3 in cancer, particularly colorectal cancer (CRC). The present study aimed to explore the biological and clinical significance of CDCA3 in CRC growth and progression. CDCA3 expression was significantly associated with tumor progression and poor survival. Overexpression of CDCA3 increased proliferation in LoVo CRC cells, whereas CDCA3 knockdown in SW480 CRC cells led to decreased proliferation, in vitro and in vivo. Further mechanistic investigations demonstrated that reduced CDCA3 expression resulted in G1/S phase transition arrest, which was attributed to a significant accumulation of p21 in SW480 cells; conversely, increased CDCA3 expression promoted G1/S phase transition through decreased p21 accumulation in LoVo cells. It was also demonstrated that CDCA3 was able to regulate the expression of transcription factor E2F1, thereby repressing p21 expression. Taken together, these results suggested that overexpression of CDCA3 may serve a crucial role in tumor malignant potential and that CDCA3 may be used as a prognostic factor and a potential therapeutic target in CRC.
An Object Manipulation System Architecture for Humanoid Robots Based on Primate Cognition

Currently one of the most important challenges is to bring robots out of factory floors to work alongside humans. Because these environments are characterized by a very large variety of objects, a key factor is to provide them with better adaptive object manipulation skills. This means that robots are required to connect, in a meaningful way, a high level task to the robot body movements. Understanding the objects at a physical level can give a robot a connecting mechanism to the higher level system. A previous experiment showed that a robot can skillfully manipulate an object if it is provided with the right mathematical models and controllers [1]. We want to expand this experiment by creating a system that can generalize this type of object manipulation capabilities to many more objects and tasks. In this paper we propose an architecture that helps bridge this gap by using insights from primate cognition. This system enables robots to handle more objects, deal better with tools, and facilitate the process of reasoning about actions and their expected outcomes. We exercised our implementation with some simple testing object models, and were able to corroborate its proper behavior under the proposed circumstances.
AlSub: Fully Parallel Subdivision for Modeling and Rendering

Mesh subdivision is a key geometric modeling task which forges smooth, supple, eye-pleasing surfaces out of coarse polygonal outlines. Since its rise to mainstream around the turn of the century, subdivision has become an unavoidable production tool. With industrial demands in sight, there has been a steady effort for developing faster and more efficient subdivision implementations. Despite the tremendous parallelism potential of subdivision algorithms, state-of-the-art implementations are only partially parallel as they are riddled by intermediate serial steps and therefore fail to unleash the compute power of massively parallel devices such as graphics processing units (GPUs). To fully parallelize the subdivision process, we discard traditional linked list data structures in favor of a sparse matrix linear algebra formalism. Subdivision algorithms are written in the language of linear algebra with customized operators which readily demonstrate a performance edge over existing approaches. To further increase performance, we automatically identify critical matrix operations and replace them by specialized, heavily tuned GPU kernels. To substantiate the versatility of our approach we apply it to $\sqrt{3}$, Loop and Catmull-Clark subdivision schemes and show support for adaptive subdivision, semi-sharp creases, and a split evaluation scheme that separates topology and topological changes from positional updates. Our results indicate substantial performance gains over the state-of-the-art and current industry standard.
How to Interconnect for Massive Mimo Self-Calibration?

In time-division duplexing (TDD) systems, massive multiple-input multiple-output (MIMO) relies on the channel reciprocity to obtain the downlink (DL) channel state information (CSI) with the acquired uplink (UL) CSI at the base station (BS). However, the mismatches in the radio frequency (RF) analog circuits at different antennas at the BS break the end-to-end UL and DL channel reciprocity. To restore the channel reciprocity, it is necessary to calibrate all the antennas at the BS. This paper addresses the interconnection strategy for the internal self-calibration at the BS where different antennas are interconnected via hardware transmission lines. Specifically, the paper reveals the optimality of the star interconnection and the daisy chain interconnection respectively. From the results, we see the star interconnection is the optimal interconnection strategy when the B S are given the same number of measurements. On the other hand, the daisy chain interconnection outperforms the star interconnection when the same amount of time resources are consumed. Numerical results corroborate our theoretical analyses.
Cloud Computing as a Source of Independent, Large Scale Computing Capabilities for Small Science Teams, Based on Pierre Auger Collaboration Example

Small science teams often have to conduct researches that needs heavy calculations without access to adequate processing powers of computation clusters. However, cloud computing can be used as a reliable source of calculation potential. Development of software for Pierre Auger Surface Detectors needs large amounts of data, that can be obtained only by simulations conducted by CORSIKA and OffLine packages. With help of scripts, it is easy to create a nearly fully automated, cost and time efficient system, that can be used to many different simulation cases. Detailed instruction is presented, along with solutions of problems encountered during the simulations. Architecture of the system was designed in such way, that minimal configuration changes are needed to develop new simulation cases and they are provided as outside module. As an example, comparison of calculations from Lodz team will be presented as a proof of efficiency of the system, that will be later presented for the whole Pierre Auger Collaboration, to help other research teams.
Autonomous Fixed-Wing Aerobatics: From Theory to Flight

Unmanned aerial vehicles (UAVs) are increasingly being proposed for a wide range of applications. A promising new class of these vehicles, known as agile fixed-wing UAV s, is intended to bridge the gap between conventional fixed-wing aircraft, which can cover long distances efficiently, and rotorcraft, which are typically very maneuverable. This paper addresses the implementation of a controller for agile UAVs, beginning with a hardware-in-the-loop (HIL) simulator, followed by testing on a real platform, both implemented on the Pixhawk microcontroller. We replace the Xplane physics engine used in the standard Pixhawk HIL with our own in-house Matlab/Simulink high-fidelity simulation of an agile UA V. The HIL simulator is found to provide substantial advantages in the transition from pure simulation to experimental testing. Once the controller is integrated into the flight platform, flight tests are conducted, and the results of those tests are compared to those from the HIL simulation and those obtained from the pure simulation environment, for maneuvers including hover, aggressive turnaround, knife-edge, and rolling Harrier. The desired position and orientation time histories were successfully tracked with the proposed implementation, demonstrating the impressive autonomous maneuverability that can be achieved by this type of aircraft.
Efficient Structured Surrogate Loss and Regularization in Structured Prediction

In this dissertation, we focus on several important problems in structured prediction. In structured prediction, the label has a rich intrinsic substructure, and the loss varies with respect to the predicted label and the true label pair. Structured SVM is an extension of binary SVM to adapt to such structured tasks. In the first part of the dissertation, we study the surrogate losses and its efficient methods. To minimize the empirical risk, a surrogate loss which upper bounds the loss, is used as a proxy to minimize the actual loss. Since the objective function is written in terms of the surrogate loss, the choice of the surrogate loss is important, and the performance depends on it. Another issue regarding the surrogate loss is the efficiency of the argmax label inference for the surrogate loss. Efficient inference is necessary for the optimization since it is often the most time-consuming step. We present a new class of surrogate losses named bi-criteria surrogate loss, which is a generalization of the popular surrogate losses. We first investigate an efficient method for a slack rescaling formulation as a starting point utilizing decomposability of the model. Then, we extend the algorithm to the bi-criteria surrogate loss, which is very efficient and also shows performance improvements. In the second part of the dissertation, another important issue of regularization is studied. Specifically, we investigate a problem of regularization in hierarchical classification when a structural imbalance exists in the label structure. We present a method to normalize the structure, as well as a new norm, namely shared Frobenius norm. It is suitable for hierarchical classification that adapts to the data in addition to the label structure.
Improved Maximum-Likelihood Decoding Using Sparse Parity-Check Matrices

Maximum-likelihood (ML) decoding is an important and powerful method in communications to obtain the optimal performance of a channel code. However, ML decoding is a challenging problem whose complexity grows exponentially with the blocklength of the code, making it unfeasible to decode optimally for large codes. In this paper, we present a new approach to accelerate ML decoding by minimizing the number of ones in the code's underlying matrix representation. We formulate this minimization problem as an integer program and use a heuristic algorithm to solve it. Using these minimized matrices, we significantly reduce the runtime of different ML decoders for various codes, resulting in speedups of up to 81% compared to the runtime on the original matrices.
Reactive Power Compensation of Solid State Transformer for WECS

Series of power electronic based high frequency transformer i.e. Solid state transformer (SST) is one of the most emerging technologies for renewable energy integration. In wind energy conversion system (WECS) the fundamental frequency steps up transformer is replaced by advanced power electronic based solid-state transformer with advantage of smaller size and improved power quality. In this paper a configuration presents that combines the DFIG based wind turbine and SST operation. SST has three stage that is AC-DC, DC -DC (dual active bridge with high frequency transformer) and DC-AC. In this paper SST improve power qualities that is compensate reactive power, harmonic and unity power factor from dc-ac converter, by using hysteresis current control without using PLL thus the proposed system can eliminate the use STATCOM in DFIG based WECS and the system meets the recent grid code requirement of wind turbine.
LED Selection and MAP Detection for Generalized LED Index Modulation

In this letter, we propose light-emitting diode (LED) selection that can be applied not only to the conventional multiple-input multiple-output (MIMO) case but also to a larger MIMO configuration of a generalized LED index modulation (GLIM) system with optical orthogonal frequency division multiplexing in visible light communication. Moreover, we derive an implementation of the maximum <italic>a posteriori</italic> (MAP) detector when the number of LEDs is an even number larger than four. Simulation results show that the performance of MAP and LED selection is better than other detection algorithms for larger even numbers of LEDs and conventional GLIM for <inline-formula> <tex-math notation="LaTeX">$4\times 4$ </tex-math></inline-formula> transmission, respectively.
Design and Kinematics Modeling of a Novel Haptic Device

In recent years use of haptic devices in different fields and their application increased. Novel mechanical design for a 3DOF haptic device presented according to the problems. Design the novel 3DOF haptic device done according several purposes such as decrease the inertia, support different workspaces and transmission the motion by new approach. Common and commercial impedance haptic devices analyses from the viewpoint of workspace and price. Parallel and serial mechanism compared. The kinematics formulas of designed 3DOF haptic device calculated. In order to simulation and validation the formulas, two scenarios defined. Initial and final values of revolute joints define in scenarios. To perform the scenarios MATLAB and RoboAnalyze softwares used. The results confirm that formulas are accurate.