Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Maximum Likelihood: Learning Undirected Graphical Models using Persistent Sequential Monte Carlo

Along with the emergence of algorithms such as persistent contrastive divergence (PCD), tempered transition and parallel tempering, the past decade has witnessed a revival of learning undirected graphical models (UGMs) with sampling-based approximations. In this paper, based upon the analogy between Robbins-Monro’s stochastic approximation procedure and sequential Monte Carlo (SMC), we analyze the strengths and limitations of state-of-the-art learning algorithms from an SMC point of view. Moreover, we apply the rationale further in sampling at each iteration, and propose to learn UGMs using persistent sequential Monte Carlo (PSMC). The whole learning procedure is based on the samples from a long, persistent sequence of distributions which are actively constructed. Compared to the above-mentioned algorithms, one critical strength of PSMCbased learning is that it can explore the sampling space more effectively. In particular, it is robust when learning rates are large or model distributions are high-dimensional and thus multi-modal, which often causes other algorithms to deteriorate. We tested PSMC learning, also with other related methods, on carefully-designed experiments with both synthetic and real-world data, and our empirical results demonstrate that PSMC compares favorably with the state of the art.
An anti-windup self-tuning fuzzy PID controller for speed control of brushless DC motor

ABSTRACT This paper presents an anti-windup self-tuning euzzy proportional-integral-derivative (PID) controller for the speed control of brushless direct-current (BLDC) motor. The proposed controller consists of a self-tuning fuzzy PID controller and an snti-windup controller, which combines the merits of these two controllers. The anti-windup controller is designed as a fuzzy proportion controller and is switched by a logic function based on the output of proposed controller and the speed error. A detailed simulation study in MATLAB/Simulink is performed to investigate the performance of proposed controller, and the simulation experiments are conducted on different conditions. The control system parameters of the proposed controller such as overshoot, settling time and rising time are compared with that of the conventional PID controller. The simulation results show that the proposed controller can accelerate the response speed of BLDC motor, strengthen the ability of anti-windup and make motor runs smoothly, it is proven that anti-windup self-tuning fuzzy PID controller has better performance than conventional PID controller.
Adapting Regularized Low-Rank Models for Parallel Architectures

We introduce a reformulation of regularized low-rank recovery models to take advantage of GPU, multiple CPU, and hybridized architectures. Low-rank recovery often involves nuclear-norm minimization through iterative thresholding of singular values. These models are slow to fit and difficult to parallelize because of their dependence on computing a singular value decomposition at each iteration. Regularized low-rank recovery models also incorporate non-smooth terms to separate structured components (e.g. sparse outliers) from the low-rank component, making these problems more difficult. Using Burer-Monteiro splitting and marginalization, we develop a smooth, non-convex formulation of regularized low-rank recovery models that can be fit with first-order solvers. We develop a computable certificate of convergence for this non-convex program, and use it to establish bounds on the suboptimality of any point. Using robust principal component analysis (RPCA) as an example, we include numerical experiments showing that this approach is an order-of-magnitude faster than existing RPCA solvers on the GPU. We also show that this acceleration allows new applications for RPCA, including real-time background subtraction and MR image analysis.
Preventing UDP Flooding Amplification Attacks with Weak Authentication

An attacker wishing to flood a network with excess amounts of network traffic may send UDP packets with a spoofed IP source address corresponding to the target network. In many cases servers then amplify the attack by replying to the target network with more data than was sent by the attacker. This kind of attack has been successful in the past using both DNS and NTP servers.The AllNet protocol has been designed to deliver data over UDP as well as other media. Once an AllNet peer receives a suitable UDP packet, it records the sender’s IP address and begins to forward AllNet data to that address. This is a legitimate form of traffic amplification, with one packet being used to request that a limited number of other packets (currently 100) be sent to this IP address.To keep attackers from using AllNet peers for flooding amplification attacks, AllNet peers require potential contacts to return a bitstring that was sent to that specific IP address. In this way, legitimate contacts can receive and return the bitstring and start receiving their data. In contrast, attackers who spoof their IP address and do not receive the bitstring, are unable to direct amplified traffic to other networks.This very weak form of authentication, conceptually related to TCP SYN cookies, only verifies that the packet comes from a system that is able to receive packets sent to that specific IP address. Such weak authentication is sufficient to prevent flooding amplification attacks.
The Academic Warning Model Based on TLBO-BP Algorithm

Tracking students' learning situations, analyzing test scores and predicting possible states of learning in the future are helpful for students who need academic intervention. And it is an important task with practical meaning and research value for teachers to provide targeted guidance and a decision-making basis. In this paper, a group intelligent optimization algorithm and the neural network theory are applied to the students' academic intervention problem. The TLBO-BP algorithm is proposed to optimize the initial weights in the BP neural network. It can speed up the convergence of the algorithm and avoid the instability of the training result caused by the random initialization of the BP neural network. The model shows satisfactory results in two examples, which further supports its validity.
Design Considerations for RINA Congestion Control over WiFi Links

Suboptimal TCP congestion control performance over wireless 802.11 (WiFi) networks is a well known issue. We will argue that some of these problems are difficult to overcome when employing end-to-end congestion control such as in TCP. Using measurements, we show that control could better be applied locally, using knowledge about the local WiFi connection and adapting to its characteristics. Under the Recursive InterNetworking Architecture (RINA), using such schemes is a natural architectural consequence, making deployment simpler and more efficient than in the “normal” Internet.
An Optimal Power Scheduling for Smart Home Appliances with Smart Battery using Grey Wolf Optimizer

In this paper, Grey Wolf optimizer (GWO) is adapted for Power Scheduling Problem (PSP) of Smart Home with Smart Battery. GWO is the recent metaheuristic swarm-based optimization method stemmed by grey pack behaviour in hunting process. It has been successfully tailored to a wide variety of real-world optimization problems. PSP is tackled by scheduling the home appliances over a certain time horizon to minimize both the electricity bill and the peak-to-average ratio (PAR) as well as to improve the users comfort level. A new formulation for smart battery (SB) and its impact to achieve the objectives are also utilized and considered as the main part of this paper. The simulation results prove the efficiency of SB in minimizing the electricity bill and PAR and improving the user comfort. Furthermore, GWO proves its efficiency in obtaining the best schedule results in comparison with genetic algorithm (GA) results. In conclusion, SB has a high impact in power scheduling of appliances for smart home.
Gabor feature-based composite kernel method for hyperspectral image classification

Different from the traditional kernel classifiers that map the original data into high-dimensional kernel space, a novel classifier that projects Gabor features of the hyperspectral image into the kernel induced space through composite kernel technique is presented. The proposed method can not only improve the flexibility of the exploitation of spatial information but also successfully apply the kernel technique from a very different perspective to strengthen the discriminative ability. Experiments on the Indian Pines dataset demonstrate the superiority of the proposed method.
Development of an Airfoil-Based Aeroelastic Energy Harvester Driven by Alternating Airflow

Wireless sensor networks (WSNs)have been widely used for intelligent monitoring applications. However, due to the short lifetime of chemical batteries, the maintenance of WSNs system has been high-cost and time-consuming. This paper presents an airfoil-based energy harvester aiming to scavenging aeroelastic energy from alternating airflows. Two types of airfoil model (NACA6412 and NACA23012)are used in the experiments to verify the performance of prototypes in a wind tunnel. 15V peak-to-peak voltage can be reached under airflow speed of 8.9m/s and −5 degree attack angle, and the maximum power is about 7μW with 20kΩ load resistance.
High voltage gain dc–dc converters based on coupled inductors

In this study, two new high voltage gain dc-dc converters are proposed. In the proposed converters, two coupled inductors are used to increase and decrease the output voltage more than the traditional dc-dc converters in boost and buck operations, respectively. In these converters, by increasing the turns ratio of the coupled inductors, the voltage conversion ratio is increased for the whole range of duty cycles. The proposed topologies are analysed in all operating modes and the values of current stresses of all switches, voltage stresses on all switches, input current ripples and voltage gains are calculated for boost and buck operations. Finally, the accurate performance of the proposed converters is verified through experimental and Power System Computer Aided Design (PSCAD)/Electro Magnetic Transient Design and Control (EMTDC) simulation results.
Vision-Based Leader–Follower Formation Control of Multiagents With Visibility Constraints

In this brief, a vision-based tracking scheme is studied for the leader–follower formation control of multiagent system under visibility constraints without communication among agents. A kinect is the sole sensor installed on each agent to estimate the pose, including relative orientations and relative positions of the local leader. A dipolar vector field is established to generate and maintain a desired formation given the desired displacement on positions among agents. The visibility constraints of kinect are converted into input constrains given the pose of the local leader, and an auxiliary system is designed to analyze the effect of visibility constraints. Combining the state of the auxiliary system with the vector filed, the proposed control protocol is able to achieve the formation tracking without global or local communication among agents. Simulation studies on the robot operating system using the Gazebo simulator and experimental results on Turtlebot2 are presented to illustrate the performance of the proposed approaches.
Judging clinical competence using structured observation tools: A cautionary tale

Abstract Background: One method for appraising the competence with which psychological therapy is delivered is to use a structured assessment tool that rates audio or video recordings of therapist performance against a standard set of criteria. Aims: The present study examines the inter-rater reliability of a well-established instrument (the Cognitive Therapy Scale – Revised) and a newly developed scale for assessing competence in CBT. Method: Six experienced raters working independently and blind to each other’s ratings rated 25 video recordings of therapy being undertaken by CBT therapists in training. Results: Inter-rater reliability was found to be low on both instruments. Conclusions: It is argued that the results represent a realistic appraisal of the accuracy of rating scales, and that the figures often cited for inter-rater reliability are unlikely to be generalizable outside the specific context in which they were achieved. The findings raise concerns about the use of these scales for making summative judgements of clinical competence in both educational and research contexts.
Bipartite Consensus in Networked Euler–Lagrange Systems With Uncertain Parameters Under a Cooperation-Competition Network Topology

This letter studies the bipartite consensus control problem of networked Euler–Lagrange systems under directed network topology with the coexistence of positive and negative interaction weights. An adaptive bipartite consensus control law is proposed for solving bipartite consensus in networked Euler–Lagrange systems with uncertain parameters. By exploiting the specific structure of the Laplacian matrix associated with cooperation-competition networks, we introduce a novel coordinate transformation based on which the corresponding convergence analysis of closed-loop controlled systems is provided. Finally, simulations conducted on a network composed by seven two-link revolute joint manipulators are provided to illustrate the theoretical results.
Six-Phase Space Vector PWM under Stator One-Phase Open-Circuit Fault Condition

: The torque pulsation and speed pulsation of a six-phase induction machine with double Y-connected windings displaced by 30 ◦ increase after one phase of the stator winding is open-circuited. To reduce the pulsations, a method of six-phase space vector pulse width modulation (PWM) with fault-tolerance is proposed. The star point of the open-circuit winding group is connected to the centre point of the converter DC voltage. The 32 column matrices, which represent 32 stator winding voltages, are mapped to the d - q and z 1 - z 2 planes using a new ﬁve-dimensional orthogonal matrix; then, 32 basic vectors are obtained. By using an optimal model, six auxiliary vectors and an equivalent null vector are constructed, the magnitudes of which are all zero in the z 1 - z 2 plane. By using the auxiliary vectors and equivalent null vector, the reference vector is synthesized, the zero magnitude of which in the z 1 - z 2 plane can suppress the harmonic voltages, and the circular trajectory of which in the d - q plane can make the torque smooth. The simulation and experimental results show that the torque pulsation range and speed pulsation range of the proposed method with fault-tolerance are 30% and 25% of those of the classical method without fault-tolerance, respectively. Thus, the phase-deﬁcient operation performance of the system is improved.
Robust assignment of customer orders with uncertain configurations in a production network for aircraft manufacturing

Production of multi-variant products in a network requires the assignment of customer orders to locations and periods. This is a highly complex planning task, as requirements of procurement, production, distribution, and sales have to be considered. Providing customers with the flexibility of configuring their ordered products after order assignment further increases the complexity of the planning task by taking uncertainty into account. Therefore, a robust optimisation model, using scenarios representing potential customer-specific order configurations, is introduced. By providing enough flexibility to handle maximum work overload caused by the potential order configurations at locations, a robust assignment of orders can be guaranteed in order to avoid undesirable situations causing delays and additional costs. Therefore, the mid-term adjustments of the flexibility limits are enabled by the changeability of workforce supply by making use of external workers. An industrial application of the model in manufacturing of the Airbus A320 Family of aircrafts is presented. The costs for offering configuration flexibility to customers are quantified by the expected value of perfect information. The explicit consideration of configuration uncertainty through the use of scenarios is discussed based on the value of the stochastic solution in comparison to the results attained by simplistically using the expected value.
Unifying Wi-Fi® and VLANs with the RINA Model

This paper reports the results of applying the fundamental networking principles embodied in the Recursive InterNet Architecture (RINA) to unifying VLANs and WiFi. There are two purposes to this: 1) to explore what these principles tell us about VLANs and WiFi that we might not have been previously aware and to explore whether they indicate any improvements or simplifications, and 2) to test the universality of the principles underlying RINA. This work was prompted by the striking similarity of WiFi and VLAN-both are multiple layers of the same rank on a common media. Applying the IPC Model and its instantiation in RINA, we first construct minimal layers tailored to each media and then tailor a universal layer for the resulting environment unifying the two. This has two purposes: 1) to simplify and improve WiFi and VLAN technology, without many of the issues and complexity currently plaguing both 802.11 and 802.1; and 2) to test the limits of the IPC Model and RINA.
Mapping the GPS multipath environment using the signal‐to‐noise ratio (SNR)

GPS multipath, where a signal arrives by more than one path, is a source of positioning error which cannot be easily neutralized. Better understanding of the multipath environment, i.e., the direction of and distance to reflecting objects, is important for multipath mitigation during the site construction phase as well as discerning the impact of multipath on positioning estimates for existing sites. This paper presents a tool called power spectral mapping that visually represents the multipath environment of a GPS site. This technique uses the spectral content (frequency and magnitude) of signal‐to‐noise ratio (SNR) time series to determine which satellites, and therefore which portions of the antenna environment, contribute significant multipath error and at what frequencies. Wavelet analysis is used to extract the time‐varying frequency and magnitude content of various multipath constituents, and these data are projected onto a map representing the GPS antenna surroundings. Power spectral map examples from stations with very different multipath environments are presented. The maps are interpreted in terms of potential sources of multipath reflections, and how these multipath signals contribute to positioning error at each station is also assessed.
Full-Order Terminal Sliding-Mode Control for LCL Type Grid-Side Converter

This paper investigates the robust control strategies for the LCL type grid-side converter. The voltage outer loop adopts the traditional voltage vector orientation control, and the main function is to stabilize the DC side voltage. The current inner loop is designed in the synchronous rotating coordinate system, and adopts full-order sliding mode control strategy. Two full-order sliding-mode controllers are proposed to control the grid current to realize the unity power factor and on-grid. It is the most widely used control strategy that facilitates the design of grid-side filtering parameters. The time-varying gains in the controllers are constructed to reduce the waste of the control energy and avoid the overestimation of the bound of the system uncertainties. Finally, the simulation results validate the proposed method.
Social Perception of Facial Color Appearance for Human Trichromatic Versus Dichromatic Color Vision

Typical human color vision is trichromatic, on the basis that we have three distinct classes of photoreceptors. A recent evolutionary account posits that trichromacy facilitates detecting subtle skin color changes to better distinguish important social states related to proceptivity, health, and emotion in others. Across two experiments, we manipulated the facial color appearance of images consistent with a skin blood perfusion response and asked participants to evaluate the perceived attractiveness, health, and anger of the face (trichromatic condition). We additionally simulated what these faces would look like for three dichromatic conditions (protanopia, deuteranopia, tritanopia). The results demonstrated that flushed (relative to baseline) faces were perceived as more attractive, healthy, and angry in the trichromatic and tritanopia conditions, but not in the protanopia and deuteranopia conditions. The results provide empirical support for the social perception account of trichromatic color vision evolution and lead to systematic predictions of social perception based on ecological social perception theory.
A User-Centered Mobile Cloud Computing Platform for Improving Knowledge Management in Small-to-Medium Enterprises in the Chilean Construction Industry

Knowledge management (KM) is a key element for the development of small-to-medium enterprises (SMEs) in the construction industry. This is particularly relevant in Chile, where this industry is composed almost entirely of SMEs. Although various KM system proposals can be found in the literature, they are not suitable for SMEs, due to usability problems, budget constraints, and time and connectivity issues. Mobile Cloud Computing (MCC) systems offer several advantages to construction SMEs, but they have not yet been exploited to address KM needs. Therefore, this research is aimed at the development of a MCC-based KM platform to manage lessons learned in different construction projects of SMEs, through an iterative and user-centered methodology. Usability and quality evaluations of the proposed platform show that MCC is a feasible and attractive option to address the KM issues in SMEs of the Chilean construction industry, since it is possible to consider both technical and usability requirements.
High-resolution Anorectal Manometry in the Diagnosis of Functional Defecation Disorder in Patients With Functional Constipation: A Retrospective Cohort Study

Background/Aims High-resolution anorectal manometry (HRAM) has been considered a first-line diagnostic tool for functional defecation disorder. However, clinical studies on HRAM used in constipation patients are very limited and few studies have reported the characteristics of anorectal pressure in Chinese patients. The aim of this study is to investigate the characteristics of motility data in a cohort of Chinese patients with functional constipation. Methods A total of 82 consecutive patients with functional constipation who underwent a standardized HRAM were retrospectively enrolled in this study. The functional defecation disorder was classified into Rao’s types. Results The mean age of 82 patients was 51 years (range, 16–83 years). Indications for anorectal manometry were functional constipation for all patients. The mean resting pressure was 69.2 ± 21.2 mmHg (range, 24.5–126.9 mmHg). The mean maximum squeezing pressure was 198.4 ± 75.6 mmHg (range, 54.2–476.9 mmHg). The mean length of the anal high pressure zone was 3.4 ± 1.0 cm (range, 0.6–4.9 cm). Sixty (73.2%) patients were diagnosed as functional defecation disorder. In attempted defecation, type I was most common (n = 24), followed by type II (n = 12), type III (n = 11), and type IV (n = 13) that were present on HRAM according to Rao’s classification. In all 60 patients with functional defecation disorder, 37 were women and 23 were men. Men were significantly more likely than women to have functional defecation disorder (92.0% vs 64.9%, P = 0.014). Conclusion HRAM could be used as a test for the diagnosis of functional defecation disorder and functional defecation disorder is common in Chinese patients with functional constipation.