Miracle-dz/UniFAR-FTUs huggingface dataset:
Research and Application of Sinec L2 and Modbus Plus Networks on Industrial Automation

In order to raise industrial production efficiency and automation level, there is need for enterprises to integrate two or more heterogeneous industrial controlling networks to a single network. The system introduced in the paper integrated two different industrial networks of SINEC L2 and MODBUS PLUS to one network, which formed the network architecture of three levels. That is, the first level could obtain information and controlling instructions from operations; the second level could coordinate information via Programmable Logical Controller (PLC); the third level could supervise the controlling units. Considering this problem that heterogeneous PLC networks employ different ladder charts programming, the system realized the network control by means of a kind of PLC-oriented programming software CADEPA, which was legible, understandable and modified conveniently.
Egocentric Pose Recognition in Four Lines of Code

We tackle the problem of estimating the 3D pose of an individual's upper limbs (arms+hands) from a chest mounted depth-camera. Importantly, we consider pose estimation during everyday interactions with objects. Past work shows that strong pose+viewpoint priors and depth-based features are crucial for robust performance. In egocentric views, hands and arms are observable within a well defined volume in front of the camera. We call this volume an egocentric workspace. A notable property is that hand appearance correlates with workspace location. To exploit this correlation, we classify arm+hand configurations in a global egocentric coordinate frame, rather than a local scanning window. This greatly simplify the architecture and improves performance. We propose an efficient pipeline which 1) generates synthetic workspace exemplars for training using a virtual chest-mounted camera whose intrinsic parameters match our physical camera, 2) computes perspective-aware depth features on this entire volume and 3) recognizes discrete arm+hand pose classes through a sparse multi-class SVM. Our method provides state-of-the-art hand pose recognition performance from egocentric RGB-D images in real-time.
Swing State: Consistent Updates for Stateful and Programmable Data Planes

With the rise of stateful programmable data planes, a lot of the network functions that used to be implemented in the controller or at the end-hosts are now moving to the data plane to benefit from line-rate processing. Unfortunately, stateful data planes also mean more complex network updates as not only flows, but also the associated states, must now be migrated consistently to guarantee correct network behaviors. The main challenge is that data-plane states are maintained at line rate, according to possibly runtime criteria, rendering controller-driven migration impossible. We present Swing State, a general state-management framework and runtime system supporting consistent state migration in stateful data planes. The key insight behind Swing State is to perform state migration entirely within the data plane by piggybacking state updates on live traffic. To minimize the overhead, Swing State only migrates the states that cannot be safely reconstructed at the destination switch. We implemented a prototype of Swing State for P4. Given a P4 program, Swing State performs static analysis to compute which states require consistent migration and automatically augments the program to enable the transfer of these states at runtime. Our preliminary results indicate that Swing State is practical in migrating data-plane states at line rate with small overhead.
Task allocation for decoding multiple hard real-time video streams on homogeneous NoCs

Hard-real time video systems require deterministic admission control decisions to maintain high levels of predictability. These decisions can be based on the state-of-the-art schedulability analysis of tasks and flows. However, due to the pessimistic behaviour of the schedulability analysis and the uncertainties in the application, the multi-core system resources are usually under-utilised. In this paper we propose two task allocation techniques that exploit application and platform characteristics in order to increase the number of simultaneous, fully schedulable, video streams handled by the system. The first, more generic technique, uses the worst-case remaining slack of the mapped tasks as a heuristic to determine the task to processing core allocation. The paper also investigates a second technique that maps the heavily communicating, critical path tasks of the applications onto same core to reduce the communication overhead. We compare against other heuristic based dynamic mapping techniques in the literature, and show that an overall improvement of up to 10%-15% can be obtained, in admission rates and system utilisation.
Constrained portfolio liquidation in a limit order book model

We consider the problem of optimally placing market orders so as to minimize the expected liquidity costs from buying a given amount of shares. The liquidity price impact of market orders is described by an extension of a model for a limit order book with resilience that was proposed by Obizhaeva and Wang (2006). We extend their model by allowing for a time-dependent resilience rate, arbitrary trading times, and general equilibrium dynamics for the una! ected bid and ask prices. Our main results solve the problem of minimizing the expected liquidity costs within a given convex set of predictable trading strategies by reducing it to a deterministic optimization problem. This deterministic problem is explicitly solved for the case in which the convex set of strategies is defined via finitely many linear constraints. A detailed study of optimal portfolio liquidation in markets with opening and closing call auctions is provided as
An Evidence-based Prehospital Guideline for External Hemorrhage Control: American College of Surgeons Committee on Trauma

Abstract This report describes the development of an evidence-based guideline for external hemorrhage control in the prehospital setting. This project included a systematic review of the literature regarding the use of tourniquets and hemostatic agents for management of life-threatening extremity and junctional hemorrhage. Using the GRADE methodology to define the key clinical questions, an expert panel then reviewed the results of the literature review, established the quality of the evidence and made recommendations for EMS care. A clinical care guideline is proposed for adoption by EMS systems. Key words: tourniquet; hemostatic agents; external hemorrhage
Loss of PTB or Negative Regulation of Notch mRNA Reveals Distinct Zones of Notch and Actin Protein Accumulation in Drosophila Embryo

Polypyrimidine Tract Binding (PTB) protein is a regulator of mRNA processing and translation. Genetic screens and studies of wing and bristle development during the post-embryonic stages of Drosophila suggest that it is a negative regulator of the Notch pathway. How PTB regulates the Notch pathway is unknown. Our studies of Drosophila embryogenesis indicate that (1) the Notch mRNA is a potential target of PTB, (2) PTB and Notch functions in the dorso-lateral regions of the Drosophila embryo are linked to actin regulation but not their functions in the ventral region, and (3) the actin-related Notch activity in the dorso-lateral regions might require a Notch activity at or near the cell surface that is different from the nuclear Notch activity involved in cell fate specification in the ventral region. These data raise the possibility that the Drosophila embryo is divided into zones of different PTB and Notch activities based on whether or not they are linked to actin regulation. They also provide clues to the almost forgotten role of Notch in cell adhesion and reveal a role for the Notch pathway in cell fusions.
A point-pass-based action prediction method

The paper describes a basic idea on how to realize an intelligent learning room system. Such a system needs to have a dynamic adaptive capability for each user. We have proposed a method to predict user action using inductive learning with N-gram. The system based on our proposed method is able to acquire rules automatically from data pairs through inductive learning. As unified with N-gram, the system demonstrates a high predictive accuracy. However, the acquired rules express the user's habits and preferences. Consequently, it is possible that the system adapts dynamically to each user. The user needs to proof-read the errors in the prediction results. Therefore, the prediction ability improves. As a result, the number of errors decreases. This paper unifies N-gram and inductive learning to develop the point-pass-based prediction system. The system was found to have good accuracy of which the highest prediction accuracy was about 89.3%. The system was proved to have high dynamic adaptive ability.
Pattern Recognition and Analysis of the Blocked Traffic Queuing in Traffic Coordinated Control

Traffic queuing and delay can’t be detected accurately by traditional loop detection due to technical limit. In recent years, video technology has been applied to the traffic control detection, which has a greater deal of information and larger detection area, and therefore, it will bring revolutionary change to the traffic control algorithm. This paper provides discussions on the characteristics of vehicle arrival in urban arterial based on video traffic detection, and puts forward the pattern recognition approach of the traffic queuing arrival and calculation method of the related parameters, which can offer the reliable basis for the generation of traffic control scheme. Furthermore, the feasibility of this method is illustrated by example analysis.
Iterative enhancement of event related potentials through sparsity constraints

In this paper we propose an iterative technique that enhances the average event related potential (ERP) by correcting the delay associated with the ERP in each trial. This correction is done in three steps: in the first step a sparse template function is estimated. In the second step, this template is utilized in estimating the inter-trial ERP delays. The ERPs fromeach trial are time-aligned using the estimated delays. In the third step, a new estimate of the ERP waveform is obtained by averaging these time-aligned signals over the trials. The algorithm iterates through these three steps until convergence. The sparse template is estimated in each iteration through the minimization of a convex objective function which compromises between the fit of the estimated ERP waveform to the template, and the sparsity of the estimated ERP waveform.
Analysis of Biological Networks: Random Models 1.1 Random (er) Graphs

In this lecture we discuss fundamental concepts in network analysis and present random network models. 1 Concepts We begin with introducing important concepts in network analysis. The Erdös-Rényi (ER) random graphs model, also called simply random graphs, was presented by Erdös and Rényi [4] in the 1950s and 1960s. Erdös and Rényi characterized random graphs and showed that many of the properties of such networks can be calculated analytically. Construction of an ER random graph with parameter 0 ≤ p ≤ 1 and N nodes is by connecting every pair of nodes with probability p. Note that the resulting graph is a simple graph. Figure 1: Source [4]. Degree distribution of a random graph, and an example of such a graph. Many complex networks found in the real world feature an important property-most nodes have a few links to other nodes, but a small number of nodes are highly connected and have a huge number of links to other nodes. This leads to the observation that these networks do not have nodes with a typical number of neighbors, and in this sense these networks are scale-free. The modern investigation of scale-free networks began with Barabási and Albert[2]. In order to analyze these networks, we require several definitions. The degree sequence for a graph is defined as the vector (d(1), d(2),. , d(n)) holding the degree information d(v) of each node v in the graph.
Adaptive synergy control of a dexterous artificial hand to rotate objects in multiple orientations via EMG facial recognition

An adaptive synergy controller is presented which allows a dexterous artificial hand to unscrew and screw an object using facial expressions derived from electromyogram (EMG) signals. In preliminary experiments, the finger joint motions of nine human test subjects were recorded as they unscrewed a bottle cap in multiple orientations of their hands with respect to the object. These data were used to develop a set of adaptive sinusoidal joint synergies to approximate the orientation-dependent human motions, which were then implemented on a dexterous robotic manipulator via the proposed adaptive synergy controller. The controller is driven through a noninvasive interface which allows a single input to drive the bioinspired human motions using facial expressions. The adaptive synergy controller was evaluated by four able-bodied subjects who were able to unscrew and screw an instrumented object using the artificial hand in two orientations with a 100% success rate.
A data annotation architecture for semantic applications in virtualized wireless sensor networks

Wireless Sensor Networks (WSNs) have become very popular and are being used in many application domains (e.g. smart cities, security, gaming and agriculture). Virtualized WSNs allow the same WSN to be shared by multiple applications. Semantic applications are situation-aware and can potentially play a critical role in virtualized WSNs. However, provisioning them in such settings remains a challenge. The key reason is that semantic applications' provisioning mandates data annotation. Unfortunately it is no easy task to annotate data collected in virtualized WSNs. This paper proposes a data annotation architecture for semantic applications in virtualized heterogeneous WSNs. The architecture uses overlays as the cornerstone, and we have built a prototype in the cloud environment using Google App Engine. The early performance measurements are also presented.
A Portfolio Approach to Procurement Contracts

The purpose of this paper is to develop a general framework for supply contracts in which portfolios of contracts can be analyzed and optimized. We focus on a multi‐period environment with convex contract, spot market, and inventory holding costs. We specialize the model to the case of a portfolio consisting of option contracts. We characterize the optimal replenishment policy and show that it has a simple structure. Namely, the use of every different option contract and the spot market is dictated by a modified base‐stock policy. In addition, we derive conditions to determine when an option is relatively attractive compared to other options or the spot market. Finally, we present our computational study, where we report the sensitivity of the results to the parameters of the model. Our experiments indicate that portfolio contracts not only increase the manufacturer's expected profit, but can also reduce its financial risk.
Writing on insertion paper

The capacity of insertion channels, even in the presence of feedback, is an open problem in information theory. In this paper, we prove that the capacity of insertion channel with non-causal insertion information at the transmitter is 1 even when the receiver do not know the insertion pattern. This paper considers two insertion models, namely random insertion model and the sticky insertion model. For both these models, interference-free capacity of 1 is obtained even when the receiver does not know the interference (insertion pattern).
STRUCTURE AND STABILITY OF MATTER

The main motivation of this article is to try to address such questions as why our universe is as it is from the smallest elementary particles to stars, galaxies and the largest cosmos. We attempt to offer our view on this fundamental problem by introducing energy levels for leptons and quarks as well as for hadrons, and by exploring both essential characteristics of four forces derived using the unified field theory derived recently by the authors.
Detecting outliers on arbitrary data streams using anytime approaches

Data streams are gaining importance in many sensoring and monitoring environments. Frequent mining tasks on data streams include classification, modeling and outlier detection. Since often the data arrival rates vary, anytime algorithms have been proposed for stream clustering and classification, which can deliver a fast first result and improve their result if more time is available. In this work, we propose the novel concept of anytime outlier detection and introduce an algorithm for anytime outlier detection based on a hierarchical cluster representation. We show promising results in preliminary experiments and discuss future research for anytime outlier detection.
“I Hope to Remain the Same”: Continuity and Change in College Students' Sexual Possible Selves across the First Semester

Abstract This study replicates and extends research on college students’ sexual possible selves by longitudinally examining their sexual expectations, fears, and behavioral strategies at the beginning and end of the first semester in college. Open-ended responses were collected at two time points (T1, N = 78; T2, N = 40) and were analyzed using directed content analysis. First semester students’ salient expectations and fears focused on abstinence, interpersonal relationships, quantity, conditional sex, decreasing/avoidant behaviors, and fears related to sexual health/well-being, sexual assault/coercion, self-focus, and increased sexual risk at both T1 and T2. However, two new themes emerged at T2: maintain (expected) and partner-focus (feared). At both time points, behavioral strategies included abstinence, making opportunities, restricting boundaries, sexual health/well-being, thoughtful/informed decisions, and self-assertion/protection. Quantitative frequency comparisons were also conducted to examine changes in the prevalence of the reported categories and indicated shifts in salient expectations, fears, and strategies across the first semester. Implications for sexual education are discussed.
From HMM's to segment models: a unified view of stochastic modeling for speech recognition

Many alternative models have been proposed to address some of the shortcomings of the hidden Markov model (HMM), which is currently the most popular approach to speech recognition. In particular, a variety of models that could be broadly classified as segment models have been described for representing a variable-length sequence of observation vectors in speech recognition applications. Since there are many aspects in common between these approaches, including the general recognition and training problems, it is useful to consider them in a unified framework. The paper describes a general stochastic model that encompasses most of the models proposed in the literature, pointing out similarities of the models in terms of correlation and parameter tying assumptions, and drawing analogies between segment models and HMMs. In addition, we summarize experimental results assessing different modeling assumptions and point out remaining open questions.
Relay selection based on the log likelihood ratio for cooperative communication networks

This paper proposes a relay selection scheme for wireless cooperative communication systems (CCS) utilizing a decode and forward protocol. The proposed scheme is suitable for systems employing M-ary PSK modulation schemes. A single relay node is chosen to cooperate with the direct link in delivering the message signal. This choice is based on the absolute value of the log-likelihood ratio (LLR) of the received signal at each relay node. The proposed scheme is derived for BPSK and QPSK as illustrative examples. Numerical simulations are carried out and presented to evaluate the system performance. The bit-error-rate (BER) curves of the proposed scheme are evaluated for known and estimated channels and are compared to other relay selection methods. Channel estimation is performed by the least square algorithm. Convergence curves of known and estimated channels are employed to evaluate the performance of the estimation algorithm.
Experiments and Models Bearing on the Role of Chromite as a Collector of Platinum Group Minerals by Local Reduction

Chromite is widely recognized to act as a collector for platinum group elements (PGE), which tend to be observed as discrete grains of platinum group minerals included within magmatic chromite grains. In the course of experiments involving the re-equilibration or growth of chromite and Cr-spinel in molten silicate, we observe that platinum group minerals (PGM; including metal alloys and laurite) form at the mineral^melt interface. The formation of PGM to the extent observed requires a mechanism involving sustained transport of PGE from a source within the experiment to the site of deposition. We propose that the driving force for this process is a redox gradient developed in response to mineral growth or re-equilibration with the surrounding melt. The mechanism is local reduction within the mineral^melt interfacial region as a consequence of the selective uptake of trivalent Cr and Fe from the melt by spinel relative to the divalent species.We have modeled the transient perturbation of fO2 in a compositional boundary layer melt around spinel for both crystal growth and diffusive re-equilibration of mineral and melt.We find that metal solubilities decrease by several per cent in the silicate melt at the melt^crystal interface during crystal growth, providing the driving force for PGM formation. In magmas that are saturated with PGM, as a result of falling temperature and oxygen fugacity during spinel crystallization, nucleation of PGM will be impeded by interfacial tension everywhere except in the reduced boundary layer around spinel crystals. The resulting concentration and trapping of alloy particles in the growing chromite crystals can produce apparent bulk chromite/melt partition coefficients exceeding 20 even if there is no solid solution of PGE in the chromite.The introduction of spinel grains, initially equilibrated with a mafic magma, into a more primitive magma, with higher Cr/Al, would lead to disequilibrium between chromite and melt. The perturbation of fO2 in the compositional boundary layer surrounding a chromite xenocryst would exceed 0 1 log unit, leading to local reduction of alloy solubility of the order of 13^18%. A small number of spinel xenocrysts could serve as collection sites for all of the excess PGE in the magma, leading to the eventual observation that a few chromite crystals contain many PGM inclusions, whereas the rest of the chromite population may be relatively free of PGM.
Consensus inference with multilayer graphs for multi-modal data

Emergence of numerous modalities for data generation necessitates the development of machine learning techniques that can perform efficient inference with multi-modal data. In this paper, we present an approach to learn discriminant low-dimensional projections from supervised multi-modal data. We construct intra- and inter-class similarity graphs for each modality and optimize for consensus projections in the kernel space. Features obtained with these projections can then be used to train a classifier for consensus inference. We also provide methods for out-of-sample extensions with novel test data. Classification results with standard multi-modal data sets demonstrate the efficacy of our method.