Miracle-dz/UniFAR-FTUs huggingface dataset:
Structure of the Cannabis sativa olivetol‐producing enzyme reveals cyclization plasticity in type III polyketide synthases

In the native pathway to therapeutic cannabinoid biosynthesis in Cannabis sativa, the three‐step production of a key intermediate, olivetolic acid, is catalysed by the enzymes tetraketide synthase (TKS; linear tetraketide intermediate production in two stages) and olivetolic acid cyclase (OAC; final C2 → C7 aldol condensation). In the absence of OAC, a nonenzymatic C2 → C7 decarboxylative aldol condensation of the tetraketide intermediate occurs forming olivetol. TKS is a type III polyketide synthase, and the question arises why it is unable to form olivetolic acid directly, but instead forms this unwanted side product. We determined the TKS, CoA complex structure, and performed structurally guided mutagenesis studies to identify potential residues responsible for cyclization pathway discrimination in type III polyketide synthases. Prior studies suggested an ‘aldol switch’ is necessary to allow linear tetraketide intermediate release prior to cyclization, thereby enabling subsequent olivetolic acid production by OAC. However, our studies do not support the presence of a universal or predictable ‘aldol switch’ consensus sequence. Instead, we propose the mode of ordered active site water activation between type III polyketide synthases catalysing different cyclization mechanisms is subtle and homologue‐specific. Our work indicates that subtle structural variations between homologous enzymes can have a major mechanistic impact on the catalytic outcome. This highlights the importance of embedding high‐resolution structural analysis of multiple enzyme homologues with classical site‐directed mutagenesis studies when investigating highly similar enzymes with different mechanistic pathway outcomes.
Level-1 Track Finding with an all-FPGA system at CMS for the HL-LHC

With the High Luminosity LHC upgrades, incorporating tracking information into the CMS Level-1 trigger becomes necessary in order to maintain a manageable trigger rate and good trigger performance e.g. to retain thresholds for electroweak physics. The main challenges Level-1 track finding faces are the large data throughput from the detector at a collision rate of 40 MHz and a 4 microsecond latency budget to reconstruct charged particle tracks with sufficiently low transverse momentum to be used in the Level-1 trigger decision. Dedicated all-FPGA hardware systems with time-multiplexed architecture have been developed for track finding to address these challenges. The algorithm and performance of the pattern recognition and particle trajectory determination are discussed. The implementation on customized electronics with commercially available FPGAs is presented as well.
Le système monétaire international face aux critères du développement durable

Les Accords de Bretton Woods en 1944 ont mis en place un systeme monetaire international qui n’en etait pas un puisqu’il etait un systeme monetaire sans monnaie. C’est le dollar qui peu a peu a rempli cette fonction. Soixante-dix ans apres, quelques reformes ont ete adoptees mais rien de fondamental n’a ete change a cet egard. Quelques autres monnaies sont utilisees entre les pays, mais le dollar reste la principale monnaie internationale. Avec le bilan des OMD, l’actualite accorde une large place aux objectifs du developpement durable. Le systeme monetaire international n’a guere d’impact a cet egard, mais il peut etre interessant d’analyser les mecanismes qui sous-tendent ce systeme du dollar a la lumiere des criteres du developpement durable. Apres un rapide retour sur le systeme de Bretton Woods, nous verrons que la creation du dollar comme monnaie internationale se fait chaque jour en meconnaissance de l’interet des parties prenantes, puis que l’accumulation des avoirs et des engagements en dollars dans le monde se fait au fil des annees en meconnaissance de l’interet des generations futures.
Nonparametric Activity Recognition System in Smart Homes Based on Heterogeneous Sensor Data

Throughout the course of life, there is time when we live independently in our house without anyone to look after each other. In order to support these people and ensure their safety with limited medical resources and human labors, it is important to constantly monitor one’s activity of daily living (ADL). Therefore, we propose an activity recognition (AR) system for people living independently in smart homes to achieve the concept of “aging in place. ” The AR model adopted by the proposed system is powerful to recognize meaningful ADL by integrating heterogeneous data from both ambient and on-body sensors. Moreover, this proposed system adopts a nonparametric approach, which requires much fewer efforts from humans. The average AR precision and recall rates of this proposed system are up to 98.7% and 99.0%, which indicates its feasibility of deployment in a real-life home environment for monitoring users’ ADL with promising performance and thus helps realize “aging in place. ” Note to Practitioners—With the advance of Internet of Things technologies, it is convenient to collect data about ADL in a smart home environment via ambient sensors and on-body sensors. This proposed system integrates data from these two heterogeneous sensors and discovers potential activities automatically without user labeling or parameter setting. By reducing the above-mentioned user efforts, it is more suitable for users and thus greatly helps realize the concept of “aging in place. ”
Analysis and Simulation Result of Uplink SC-FDMA System’s Energy Efficient Resource Allocation

In this research work, investigate quality-of-service (QoS)-driven energy-efficient style for the uplink of long term evolution (LTE) networks in M2M/H2H co-existence situations. the precise constraints of single carrier frequency division multiple access (uplink air interface in LTE networks) bearing on power and resource block allocation not only complicate the resource allocation drawback, however additionally render the quality Lagrangian duality techniques unsuitable. There are several energy-efficient style is compared with the spectral efficient style alongside round robin (RR) and best channel quality indicator (BCQI) algorithms. In Previous work uplink resource allocation algorithmic rule for LTE systems, that focuses on QoS provision in time period applications and energy efficiency, however therein work answer of multicell situation shouldn't be taken part and therein interference thought rejection ought to be taken.
Privatisation et croissance dans les pays de l'est

Nous estimons une fonction de croissance des vingt-cinq pays de l'Est sur la periode 1990-1999. Les resultats montrent que la stabilisation macroeconomique (la lutte contre l'inflation) et les modalites de mise en oeuvre de la privatisation (le renoncement a la methode de privatisation de masse, le recours a la methode favorisant les insiders) exercent une action significative et positive sur la croissance a l'Est. Ces resultats vont a rebours des affirmations initiales du courant standard selon lesquelles les deux methodes de privatisation favorables a la croissance sont la privatisation de masse et la vente directe des entreprises a des outsiders. Ils resultats suggerent, enfin, que le developpement des micro-entreprises associe a la petite privatisation exerce un impact non negligeable sur la croissance.
Active disturbance rejection control technology-based method for controlling synchronization of photovoltaic power generation system

The invention provides an active disturbance rejection control-based method for controlling synchronization of a photovoltaic power generation system. The photovoltaic power generation system is a non-linear system, influenced by power grid and environment and strongly disturbed by the outside and has nonlinear uncertain factors. According to the work characteristics of the system, the active disturbance rejection control technology is adopted to realize effective control of the system. The system dynamically observes the uncertain factors in the system model and external disturbance by utilizing an extended state observer of an active disturbance rejection controller (ADRC) to ensure that the system has good adaptability to disturbance, can adapt to changes of an object model and the surrounding environment in a large range without influencing the control quality of the controller, and represents well robustness.
Exploring the Web and Semantic Knowledge-Driven Automatic Question Answering System

The growth of information retrieval from the web sources are increased day by day, proving an effective and efficient way to the user for retrieving relevant documents from the web is an art. Asking the right question and retrieving a right answer to the posted query is a service which provide by the Natural Language Processing. Question Answering System is one of the best ways to identify the candidate answer with high accuracy. The web and Semantic Knowledge Driven Question Answering System (QAS) used to determine the candidate answer for the posted query in the NLP tools. This method includes Query expansion techniques and entity linking method to identify the information source snippets with ontology structure, also ranking the sentences by applying conditional probability between query and Answer to identify the optimal answer from the web corpus. The result provides an exact answer with high accuracy than the baseline method.
Psychological Stress Detection from Social Media Data using a Novel Hybrid Model

Psychological stress is considered as the biggest threat to individual’s health. Hence, it is vital to detect and manage stress before it turns into severe problem. However, conventional stress detection strategies rely on psychological scales and physiological devices, which require active individual participation making it labor-consuming, complex and expensive. With the rapid growth of social networks, people are willing to share moods via social media platforms making it practicable to leverage online social interaction data for stress detection. The developed novel hybrid model Psychological Stress Detection (PSD), automatically detect the individual’s psychological stress from social media. It comprises of three modules Probabilistic Naive Bayes Classifier, Visual (Hue, Saturation, Value) and Social, to leverage text, image post and social interaction information we have defined the set of stress-related textual ‘F = {f1, f2, f3, f4}’, visual ‘vF = {vf1, vf2}’, social ‘sf’ to detect and predict stress from social media content. Experimental results show that the proposed PSD model improves the detection performance when compared to TensiStrength and Teenchat framework, PSD achieves 95% of Precision rate. PSD model would be useful in developing stress detection tools for mental health agencies and individuals.
Research of Distinct Algorithm of Short Text Based on Simhash

With the development of social network, microblog is the typical application in big data era. However, there are two aspects: the one is Chinese language is more various and flexible than English, the other is that microblog with Simhash is not good. This paper analyzes the data of microblog, which is the big number of data and short text, then the innovational algorithm is introduced. We used Simhash as the baseline, then we improved it and proposed B-Simhash algorithm. Meanwhile, we focused on the data quality of microblog by special character processing. Through experiments we can gain the results of B-Simhash, which is better in processing the short text. Adding the special character processing, the result is better than the original. According to the result of experiments, the higher precision and recall rate are gained. The efficiency is also improved for microblog de-duplication.
Real-time pedestrian detection for driver assistance systems based on deep learning

We present a model for real-time pedestrian detection based on a deep learning framework. With respect to the base network for feature extraction, we have improved the network based on Mobilenet which is a simple and fast convolutional neural network. We only use the front part of its network and then build several new multi-scale convolutional layers to calculate multi-scale feature maps. With respect to the detection network behind the feature extraction, we use a simplified SSD(single shot multibox detector) model to detect pedestrians with fewer feature maps. In addition, we design detection boxes with specific sizes according to pedestrian’s shape characteristics. To avoid overfitting, we apply data augmentation and dropout techniques to training. Experimental results on PASCAL VOC and KITTI confirm that the speed of our detection model has been increased by 22.2% while precision remains almost unchanged. Our approach makes a trade-off between speed and precision, and has an obvious speed advantage over other detection approaches.
Data/pilot combination strategies for Compass B1C signal acquisition

The new Compass B1C signal, with a power ratio of the data channel to the pilot channel of 1:3, adopts BOC(1,1) in its data channel; a new modulation, namely, a quadrature multiplexed binary offset carrier (QMBOC) in its pilot channel modulates the narrowband BOC(1,1) and the wideband BOC(6,1) components in phase quadrature with each other. To fully exploit the B1C signal and to improve the acquisition sensitivity in a degraded signal or an indoor environment, this paper proposes to combine the data and the pilot channels for B1C signal acquisition. The noncoherent, coherent, and differential coherent strategies are considered for application in the B1C signal. Considering the power inequality between the data and the pilot channels, the maximum signal‐to‐noise ratio combination (MRC) is utilized in these strategies to obtain an optimal combination. This paper also proposes to simplify the differential coherent strategy to obtain a better anti‐noise performance under stable conditions. The computer simulation results are given to verify the theoretical expressions, and some discussions with respect to the proposed strategies are provided.
Encouraging Learners to Explore Websites: Hyperlinks as Invitations

We applied a user interface (UI) design approach based on a constructivist perspective for web-based learning that conceptualizes interactions between learners and websites as conversations. Hyperlinks on the UI of a distributed interactive learning environment were treated as invitations for interaction to encourage learners’ active exploration. An experiment for examining the effects of two UI features, namely, additional cues attached to hyperlinks and indication of the website owner, was conducted on the basis of the literature on invitation designs for web surveys. Results reveal that, on a website owned by someone with a high-power status, learners explore further when additional cues are present on the UI. Such effects indirectly enhance learning outcomes.
Use of a Hysteresis Loop Activation Function to Enable an Analog Perceptron to Gain Memory

With the advent of memristors, analog artificial neural networks are closer than ever. Neural computing is growing as a topic of research. In the context of analog artificial neural networks, the purpose of this research is to verify that a perceptron could gain a discrete memory from implementing a hysteresis loop in the activation function. The discrete memory is represented by the difference path of the hysteresis activation function that took from logic 1 to logic 0. To write to the memory, the input to the hysteresis loop would have to exceed threshold. To read the stored value, the input would have to be between the thresholds of the hysteresis function. In order to verify the perceptron’s memory, a network with manually chosen weights is selected which acts as a shift register. The components of this network are assembled in a circuit simulation program. Functionally, the network receives two inputs: a data signal and an enable signal. The output of the network is a time-shifted version of previous input signals. A system whose output is a time-shifted version of the previous inputs is considered to have memory.
Intracranial Electrophysiology in Language Research

Intracranial electrophysiological recording in humans has been a long-standing technique in neurosurgical treatment for epilepsy. Due to the clinical constraints as well as the necessity to map critical language sites prior to resection of tissue, electrode coverage often involves brain areas relevant for language function. This cortical sampling as well as the excellent temporal and spatial resolution provided by intracranial recordings have made the technique particularly important for our understanding of how the human brain processes speech and language. This chapter provides an introduction to the technique and describes how it historically contributed to language mapping. Furthermore, it discusses the advantages and disadvantages of intracranial recordings as a research tool and illustrates the use of the technique with examples of novel findings and approaches in the area of language processing.
Violations of the Law of One Price in Credit and Swap Markets During Market Distress

This thesis is devoted to investigating the dynamic properties of Law of One Price (LOP) violations around periods of market distress. It studies the extent to which these violations are state-dependent and how they are related to different market conditions. The role of monetary policies and hedge funds in contributing to LOP equilibrium are also analysed. First part focuses on the construction of an LOP deviation proxy called Basisbond, derived from the foreign-currency sovereign bond markets (in USD and Euro) of three main emerging economies (i.e. Brazil, Mexico and Turkey). It is observed that Basisbond moves close to zero (supporting LOP condition) during good states of economy (Pre-Crisis), whereas it becomes markedly large, persistent and volatile during bad states of economy (Credit Crisis). Second part delves deeper into why Basisbond deviated so markedly from zero during 2008-2009 Credit Crisis. We find strong evidence that rising funding costs, falling bond supply, intensified risk aversion and deteriorated default risk coupled with macroeconomic shocks are responsible in deterring arbitrageurs from exploiting the trading strategy, and inducing relative price divergence. Depending on the geography of bond issuance, there exist cross-sectional idiosyncrasies regarding the sign of deviation. While Turkey tends to pay smaller credit risk premium in Euro compared to USD, the opposite is true for Brazil and Mexico. When investors face discordant default risks across two equivalent securities, the underlying trading strategy is left unexploited. Moreover, there is strong evidence that different sets of monetary policies have different implications in resolving LOP disequilibrium. Third part investigates the impact of arbitrageurs (particularly hedge funds) in supplying liquidity to markets, and thus contributing to correct relative valuation of mispriced securities. It is confirmed that hedge fund withdrawal, due to severe capital reduction, contributes adversely to the growth of LOP deviations in credit and swap markets.
Regrasp Planning Using Stable Object Poses Supported by Complex Structures

Using regrasp planning, a robot could pick up an object, place it down to an intermediate stable state, and reorient the object into certain poses by grasping and picking it up again. Regrasp is an important skill when a robot cannot reorient the object directly with one grasp due to kinematic constraints and collisions. It uses intermediate object states to release, regrasp, and reorient objects. In our previous work, we developed regrasp algorithms considering intermediate stable states on simple fixtures like a flat table surface. This paper further develops our previous studies by enabling regrasp planning using stable object poses on complex structures. The complex structures have high variety of contact elements. They not only provide flat surface supports but also point supports, line supports, and a combination of them. In detail, the developed regrasp planner includes two parallel processes. One is a dynamic simulator that computes immediate stable poses on given supporting structures. The other builds a regrasp graph using the stable states and finds a sequence of reorient motion by searching the graph. We performed thousands of simulation and real-world verification to analyze the performance of the developed planner. We conclude that our planner has higher performance on regrasping than the previous ones. The number of candidate regrasp sequences increased and the lengths became shorter. We also compared simulation results with real-world executions, and gave suggestions on the selection of supporting structures considering real-world implementations.
Value creation in European M&As

This paper looks at the value generated to shareholders by the announcement of mergers and acquisitions involving firms in the European Union over the period 1998-2000. Target firm shareholders receive on average a statistically significant excess return of 9% in a onemonth window centered on the announcement date. Acquirers’ excess returns are null on average. When distinguishing in terms of the geographical and sectoral dimensions of the merger deals, our main finding is that mergers in industries that had been previously under government control or that are still heavily regulated generate lower value than M&A announcements in unregulated industries. This low value creation in regulated industries becomes significantly negative when the merger involved two firms from different countries and was primarily due to the lower positive return that shareholders of the target firm enjoyed upon the announcement of the merger. This evidence is consistent with the existence of obstacles (such as cultural, legal, or transaction barriers) to the successful conclusion of this type of transaction, which decrease the probability that the merger will actually be completed as announced and, therefore, reduce its expected value.