Miracle-dz/UniFAR-FTUs huggingface dataset:
Six domesticated PiggyBac transposases together carry out programmed DNA elimination in Paramecium

The domestication of transposable elements has repeatedly occurred during evolution and domesticated transposases have often been implicated in programmed genome rearrangements, as remarkably illustrated in ciliates. In Paramecium, PiggyMac (Pgm), a domesticated PiggyBac transposase, carries out developmentally programmed DNA elimination, including the precise excision of tens of thousands of gene-interrupting germline Internal Eliminated Sequences (IESs). Here, we report the discovery of five groups of distant Pgm-like proteins (PgmLs), all able to interact with Pgm and essential for its nuclear localization and IES excision genome-wide. Unlike Pgm, PgmLs lack a conserved catalytic site, suggesting that they rather have an architectural function within a multi-component excision complex embedding Pgm. PgmL depletion can increase erroneous targeting of residual Pgm-mediated DNA cleavage, indicating that PgmLs contribute to accurately position the complex on IES ends. DNA rearrangements in Paramecium constitute a rare example of a biological process jointly managed by six distinct domesticated transposases.
TV-SVM: Support Vector Machine with Total Variational Regularization

It is required that input features are represented as vectors or scalars in machine learning for classification, e.g. support vector machine (SVM). However, real world data such as 2D images is naturally represented as matrices or tensors with higher dimensions. Thus, structural information of the data whose dimensions are more than two is not successfully considered. One typical structural information which is useful for the classification task is the spatial relationship of the nearby data points. In this paper, to leverage this kind of structural information, we propose a novel classification method which combines total variational (TV) regularization with SVM, called TV-SVM. Since TV achieves a local smoothing property by penalizing the local discontinuity of data, TV-SVM preserves better local structure than the original SVM due to TV regularization. We solve the objective function of TV-SVM via the alternating direction method of multipliers (ADMM) algorithm. Experimental results on image classification show that TV-SVM is competitive to the state-of-the-art learning method in both classification accuracy and computational complexity.
Quantitative Analysis of Abnormal and Normal Gait based on Inertial Sensors

Quantitative gait research is an effective auxiliary means to provide effective clinic diagnosis and surgery guidance for patients mobility. The traditional gait analysis carried out in the laboratory cannot be easily applied into the clinical medicine due to its complexity and high operating costs. To overcome this limitation, inertial sensors fixed on the ankle could perform out-of-lab measurements on subjects walking patterns. In this paper, a low-cost and intelligent wearable gait analysis system based on the inertial sensors is proposed, which can measure spatiotemporal gait parameters and help clinicians with diagnosis. The paper solves sensor drift problem by gradient descent method and zero velocity update algorithm. Using the gait analysis system, we conducted the contrast test of the gait parameters between the patients with abnormal gait and normal person. By contrasting two group of volunteers, it could be found that there is a significant difference about most spatiotemporal gait parameters. These results also indicate that our gait analysis system has potential to be used for out-of-lab gait analysis and can effectively detect the gait disorders and provide the reference for clinicians.
Theoretical and experimental dynamic evaluations of revolute-prismatic joints manipulator contains N-rigid links and hubs

in this paper, a rigid manipulator that is composed of revolute-prismatic joints has been designed and modeled by recursive Gibbs-Appell formulation. The manipulator contains N-rigid links and sleeves and in comparison to previous researches, the mass of joint's hub has been added in dynamic equations. Therefore, the obtained equations are complicated, because of the combined reciprocating and rotating link's motion, as well as, the joint's hub act as one extra part considered in a collection with the link as one arm. The results are evaluated by the experimental setup that is designed and manufactured in Robotic Laboratory of Iran University of Science and Technology and appropriate system simulated in MATLAB SimMechanics simulator. The concluded system with the dynamic equation and simulated system can be used for precise positioning, repetitive task and object manipulations.
Multimodal Student Engagement Recognition in Prosocial Games

In this paper, we address the problem of recognizing student engagement in prosocial games by exploiting engagement cues from different input modalities. Since engagement is a multifaceted phenomenon with different dimensions, i.e., behavioral, cognitive, and affective, we propose the modeling of student engagement using real-time data from both the students and the game. More specifically, we apply body motion and facial expression analysis to identify the affective state of students, while we extract features related to their cognitive and behavioral engagement based on the analysis of their interaction with the game. For the automatic recognition of engagement, we adopt a machine learning approach based on artificial neural networks, while for the annotation of the engagement data, we introduce a novel approach based on the use of games with different degrees of challenge in conjunction with a retrospective self-reporting method. To evaluate the proposed methodology, we conducted real-life experiments in 4 classes, in 3 primary schools, with 72 students and 144 gameplay recordings in total. Experimental results show the great potential of the proposed methodology, which improves the classification accuracy of the three distinct dimensions with a detection rate of 85%. A detailed analysis of the role of each component of the Game Engagement Questionnaire, i.e., immersion, presence, flow, and absorption, in the classification process is also presented in this paper.
Contributions of nucleus accumbens dopamine to cognitive flexibility

There is a compelling evidence that midbrain dopamine (DA) neurons and their projections to the ventral striatum provide a mechanism for motivating reward‐seeking behavior, and for utilizing information about unexpected reward prediction errors (RPEs) to guide behavior based on current, rather than historical, outcomes. When this mechanism is compromised in addictions, it may produce patterns of maladaptive behavior that remain obdurate in the face of contrary information and even adverse consequences. Nonetheless, DAergic contributions to performance on behavioral tasks that rely on the ability to flexibly update stimulus‐reward relationships remains incompletly understood. In the current study, we used a discrimination and reversal paradigm to monitor subsecond DA release in mouse NAc core (NAc) using in vivo fast‐scan cyclic voltammetry (FSCV). We observed post‐choice elevations in phasic NAc DA release; however, increased DA transients were only evident during early reversal when mice made responses at the newly rewarded stimulus. Based on this finding, we used in vivo optogenetic (eNpHR) photosilencing and (Channelrhodopsin2 [ChR2]) photostimulation to assess the effects of manipulating VTA‐DAergic fibers in the NAc on reversal performance. Photosilencing the VTA → NAc DAergic pathway during early reversal increased errors, while photostimulation did not demonstrably affect behavior. Taken together, these data provide additional evidence of the importance of NAc DA release as a neural substrate supporting adjustments in learned behavior after a switch in expected stimulus‐reward contingencies. These findings have possible implications for furthering understanding the role of DA in persistent, maladaptive decision‐making characterizing addictions.
A Unified Estimator for Source Positioning and DOA Estimation Using AOA

Angles of Arrival (AOAs) are popular measurements to locate a signal source. They can yield a position if the source is near the sensors or a DOA if it is far away. Point positioning and DOA localization require different estimators and the knowledge if the source is near or far is needed. Nevertheless, such knowledge about the source range is often not available in practice. This paper first analyzes the consequences of point positioning of a distant source and DOA estimation of a near one. It next proposes a unified estimator that provides a position estimate if it is near or a DOA estimate if it is far, without requiring any prior knowledge about the range region of where it lies. The estimator is derived using the Maximum Likelihood criterion with the Gauss Newton implementation, using the modified polar coordinates to represent the source location. A preliminary solution using the semi-definite relaxation is developed to initialize the estimator. Simulations validate the performance of the proposed estimator in reaching the CRLB performance.
A Novel Doppler-based Direction-of-Arrival Estimation Scheme

Direction-of-arrival (DoA) estimation is an essential means for localization issues in various applications. DoA estimation can be extended, for example, to source triangulation. Many approaches in this field, such as beamforming, exploit the knowledge of all signal amplitudes and phases of an antenna array. That is, as many receivers and analog-to-digital converters as antenna elements are required, followed by array signal processing. In contrast to that, Doppler-based DoA estimation typically involves switching between circularly arranged antenna elements and further processing of the resulting signal stream. In this paper, a novel Doppler-based DoA scheme is presented which makes use of the same hardware set-up as classical beamforming approaches on the one hand. On the other hand, very simple signal processing in analogy to Doppler direction finding is applied. This results in the advantage of having a common hardware that now can be used for a wider range of DoA algorithms. Also, the proposed scheme as well as the well-known MUSIC algorithm have been implemented using software-defined-radio (SDR) modules. Measurement results for validation and comparison are presented in this paper.
Fatigue Detection System for the Drivers Using Video Analysis of Facial Expressions

Tiredness, and sleeping of the drivers while driving is one of the main causes of traffic accidents. Observations of the drivers to detect tiredness and fatigue is a complex task which includes behavioral and physical elements. Different approaches were made, including computer vision with the ability to track and monitor the drivers behind the wheel without the intervention in the driving. The proposed system can observe the alertness or tiredness of drivers in order to prevent them from sleeping while driving. In this work, a framework is developed to detect the fatigue-ness from tracking the cornea of eyes by focusing on the face of the driver. It utilizes applying image morphological changes like dilution, erosion, image segmentation like background elimination and Circular Hough Transform (CHT) to detect cornea of an eye on the captured images on drivers. Whenever the fatigue is recognized it will buzz an alarm at the system and necessary actions can be taken. It additionally decreases incorrect outcomes of by disposing of low-quality faces that happened to a video sequence because of issues in sensible lighting, head movement, and posture variety.
A Linear Least Square Initialization Method for 3D Pose Graph Optimization Problem

Pose Graph Optimization (PGO) is an important optimization problem arising in robotics and machine vision applications like 3D reconstruction and 3D SLAM. Each node of pose graph corresponds to an orientation and a location. The PGO problem finds orientations and locations of the nodes from relative noisy observation between nodes. Recent investigations show that well-known iterative PGO solvers need good initialization to converge to good solutions. However, we observed that state-of-the-art initialization methods obtain good initialization only in low noise problems, and they fail in challenging problems having more measurement noise. Consequently, iterative methods may converge to bad solutions in high noise problems. In this paper, a new method for obtaining orientations in the PGO optimization problem is presented. Like other well-known methods the initial locations are obtained from the result of a least-squares problem. The proposed method iteratively approximates the problem around current estimation and converts it to a least-squares problem. Therefore, the method can be seen as an iterative least-squares method which is computationally efficient. Simulation results show that the proposed initialization method helps the most well-known iterative solver to obtain better optima and significantly outperform other solvers in some cases.
Autonomous Bio-Inspired Small-Object Detection and Avoidance

Small-object detection and avoidance in unknown environments is a significant challenge to overcome for small autonomous vehicles that are generally highly agile and restricted in payload and computational processing power. Typical machine-vision and range measurement based solutions suffer either from restricted fields-of-view or significant computational complexity and are not easily portable to small platforms. In this paper, a novel bio-inspired navigation technique is introduced that is modeled using analogues of the small-field motion-sensitive interneurons of the insect visuomotor system. The proposed technique achieves small-field object detection based on Fourier residual analysis of instantaneous optic flow. The small field signal is used to extract relative range and bearing of the nearest obstacle, which is then combined with an artificial potential function-based low-order steering control law. The proposed sensing and control scheme is experimentally validated with a quadrotor vehicle that is able to effectively navigate an unknown environment laden with small-field clutter. This bio-inspired approach is computationally efficient and serves as a robust, reflexive solution to the problem of small-object detection and avoidance for autonomous robots.
Error Probability Based Resource Allocation for Device-to-Device Communications Underlaying Cellular Networks

In this paper, we propose a new Error Probability based D2D Resource Allocation (EPDRA) scheme to increase the total throughput of cellular networks. In EPDRA, error probabilities of packets, positions of all users, and scheduling information of cellular users (CUEs) are sent to D2D users (DUEs) and are used in DUE pairs to locally decide which resource blocks DUE pairs can use. Finally, simulations are completed using the NS-3 (network simulator). It is shown that the proposed method shows higher total throughput than reference D2D resource allocation schemes.
Switchable inteins for conditional protein splicing

Abstract Synthetic biologists aim at engineering controllable biological parts such as DNA, RNA and proteins in order to steer biological activities using external inputs. Proteins can be controlled in several ways, for instance by regulating the expression of their encoding genes with small molecules or light. However, post-translationally modifying pre-existing proteins to regulate their function or localization leads to faster responses. Conditional splicing of internal protein domains, termed inteins, is an attractive methodology for this purpose. Here we discuss methods to control intein activity with a focus on those compatible with applications in living cells.
Adoption Challenges of the Internet of Things: ASurvey

The internet had been considered as pre-dominantly human-to-human interaction. However, this is changing rapidly with the onset of “smart” devices. The potential of a connected world is very much dependent on the adoption of the technologies to realize Internet of Things or IoT. IoT devices produces a unique set of challenges in which the current deployment standards, models & technologies fall short. In this survey paper, the various architectures are explored including the newly introduced fog architecture. The challenges in the cloud based IoT architectures and the IoT paradigm are discussed and categorized. Furthermore, some of the identified solutions and future directions toward overcoming the challenges are described.
Temporal Decision Trees In Diagnostics Systems

In this paper the problem of diagnostics in dynamic systems using artificial intelligence methods is considered. An analysis of the dynamic situation at a technical object involves two stages: building of a model (in particular case, a decision tree) and using it to classify new situations occurring at this object. Data, on the basis of which it is necessary to carry out diagnostics, can be represented by sets of time series. As a classification model in such situations, temporal decision trees can be used. The definition of the temporal decision tree is given, an algorithm for constructing such decision trees is described. A new algorithm for constructing Temporal ID3 temporary decision trees is proposed. It is characterized by the new criterion for selecting decision tree nodes. The process of faults and anomaly situations diagnostics for various methods of building decision trees is modeled and compared. The analysis of the dynamic situations diagnostics results by temporal trees constructed by different algorithms is given and followed by recommendations on the use cases of these algorithms
Safety and Guaranteed Stability Through Embedded Energy-Aware Actuators

Safety is essential for robots in unknown environments, especially when there is physical Human-Robot Interaction (pHRI). Control over energy, or passivity, is an effective safety mechanism. However, when the control algorithm is implemented in a discrete-time computer, computation and communication delays readily lead to loss of passivity and to instability. In this paper, a way to make the actuators aware of the energy that they inject into the system is presented. Passivity and stability are then always guaranteed, even in situations of total communication loss. These Embedded Energy-Aware Actuators are a model-free passivity and safety layer that make complex robotic systems dependable, well-behaved and safe. The proposed method is validated in simulation and experiments.
DPATCH: An Adversarial Patch Attack on Object Detectors

Object detectors have emerged as an indispensable module in modern computer vision systems. In this work, we propose DPatch -- a black-box adversarial-patch-based attack towards mainstream object detectors (i.e. Faster R-CNN and YOLO). Unlike the original adversarial patch that only manipulates image-level classifier, our DPatch simultaneously attacks the bounding box regression and object classification so as to disable their predictions. Compared to prior works, DPatch has several appealing properties: (1) DPatch can perform both untargeted and targeted effective attacks, degrading the mAP of Faster R-CNN and YOLO from 75.10% and 65.7% down to below 1%, respectively. (2) DPatch is small in size and its attacking effect is location-independent, making it very practical to implement real-world attacks. (3) DPatch demonstrates great transferability among different detectors as well as training datasets. For example, DPatch that is trained on Faster R-CNN can effectively attack YOLO, and vice versa. Extensive evaluations imply that DPatch can perform effective attacks under black-box setup, i.e., even without the knowledge of the attacked network's architectures and parameters. Successful realization of DPatch also illustrates the intrinsic vulnerability of the modern detector architectures to such patch-based adversarial attacks.
Systems of Bounded Rational Agents with Information-Theoretic Constraints

Specialization and hierarchical organization are important features of efficient collaboration in economical, artificial, and biological systems. Here, we investigate the hypothesis that both features can be explained by the fact that each entity of such a system is limited in a certain way. We propose an information-theoretic approach based on a free energy principle in order to computationally analyze systems of bounded rational agents that deal with such limitations optimally. We find that specialization allows a focus on fewer tasks, thus leading to a more efficient execution, but in turn, it requires coordination in hierarchical structures of specialized experts and coordinating units. Our results suggest that hierarchical architectures of specialized units at lower levels that are coordinated by units at higher levels are optimal, given that each unit's information-processing capability is limited and conforms to constraints on complexity costs.
GamingVideoSET: A Dataset for Gaming Video Streaming Applications

This paper presents GamingVideoSET1, a dataset consisting of twenty-four uncompressed raw gaming videos of 30 seconds duration, 1080p resolution, and 30 fps for the research community working on gaming video quality assessment. Furthermore, the data set includes subjective quality assessment results for 90 video sequences obtained by encoding six different gaming videos using the H.264/MPEG-AVC codec standard in 15 different resolution-bitrate pairs (three resolution, five bitrates each). In addition to the reference videos, the dataset offers a total of 576 distorted videos in MP4 format, obtained by encoding the videos in 24 different resolution-bitrate pairs, and their objective quality assessment results (average and per-frame) using three video quality assessment metrics. 1The database is available at https://kingston.box. com/v/GamingVideoSET
Semi-Supervised Training Using Adversarial Multi-Task Learning for Spoken Language Understanding

Spoken language understanding (SLU) usually requires human semantic annotation on collected data, but the process is expensive. In order to make better use of unlabeled data for robust SLU, we propose an adversarial multi-task learning method by merging a bidirectional language model (BLM) and a slot tagging model (STM). As a secondary objective, the BLM is used to learn generalized and unsupervised knowledge with abundant unlabeled data and improve the performance of STM on unseen data samples. We construct a shared space for both tasks and independent private spaces for each task respectively. Additional adversarial task discriminator is also used to obtain more task - independent sharing information. Experiments show that the proposed approaches achieve the state-of-the-art performance on the small scale ATIS benchmark and significantly improve the semi -supervised performance on a large-scale dataset.
Hybridnet for Depth Estimation and Semantic Segmentation

Semantic segmentation and depth estimation are two important tasks in the area of image processing. Traditionally, these two tasks are addressed in an independent manner. However, for those applications where geometric and semantic information is required, such as robotics or autonomous navigation, depth or semantic segmentation alone are not sufficient. In this paper, depth estimation and semantic segmentation are addressed together from a single input image through a hybrid convolutional network. Different from the state of the art methods where features are extracted by a sole feature extraction network for both tasks, the proposed HybridNet improves the features extraction by separating the relevant features for one task from those which are relevant for both. Experimental results demonstrate that HybridNet results are comparable with the state of the art methods, as well as the single task methods that HybridNet is based on.