Miracle-dz/UniFAR-FTUs huggingface dataset:
negative symptoms Therapeutic effects of cerebrolysin added to risperidone in patients with schizophrenia dominated

Objective: Cerebrolysin is a nootropic drug with unique neurotrophic activities directly affecting cerebral neurons. This study evaluated the efficacy and safety of cerebrolysin added to risperidone in patients with schizophrenia dominated by negative symptoms. Methods: The trial was a double-blind, placebo-controlled, parallel-group design. A total of 109 patients who met the DSM-IV diagnostic criteria for schizophrenia were randomly assigned to cerebrolysin (cerebrolysin plus risperidone, n=55) or placebo (risperidone only, n=54) groups. Intravenous infusions of 30 ml cerebrolysin or placebo were given once daily from Monday to Friday for 4 weeks. Efficacy was assessed with measurements including the Positive and Negative Symptoms Scale (PANSS), Wechsler Memory Scale (WMS), modified Chinese Wechsler Adult Intelligence Scale (mWAIS). and Clinical Global Impression (CGI). Results: Patients in both groups demonstrated improvements in psychiatric symptoms and cognitive and memory performance as assessed by PANSS, mWAIS, and WMS over the trial. There was no difference in rates of change in the PANSS total score or negative score between the two treatment groups. Patients treated with cerebrolysin showed significantly greater improvements in cognitive and memory function from week 2. No severe treatment adverse events were observed in either group. The frequency of adverse events was comparable between the two groups at the end of the treatment. Conclusion: Cerebrolysin added to risperidone did not augment the efficacy of risperidone in treating the psychotic symptoms of schizophrenia patients over an 8-week trial. Cerebrolysin at 30 ml per day as an adjunctive treatment was safe and may improve cognitive and memory functions of patients with schizophrenia dominated by negative symptoms.
Clinical Study Oxytocin and Psychological Factors Affecting Type 2 Diabetes Mellitus

1 1st Department of Psychiatry, Eginition Hospital, Athens University Medical School, 11528 Athens, Greece 2 University Mental Health Research Institute, 11527 Athens, Greece 3 2nd Department of Internal Medicine, Research Institute and Diabetes Center, Athens University Medical School, Attikon University Hospital, 12462 Athens, Greece 4 Hellenic National Center for Research, Prevention and Treatment of Diabetes Mellitus and Its Complications (HNDC), Athens, Greece
Traces of Newtonian-Sobolev, Hajlasz-Sobolev, and BV functions on metric spaces

We study the boundary traces of Newton-Sobolev, Hajlasz-Sobolev, and BV (bounded variation) functions. Assuming less regularity of the domain than is usually done in the literature, we show that all of these function classes achieve the same "boundary values", which in particular implies that the trace spaces coincide provided that they exist. Many of our results seem to be new even in Euclidean spaces but we work in a more general complete metric space equipped with a doubling measure and supporting a Poincare inequality.
PtLnc-BXE: Prediction of plant lncRNAs using a Bagging-XGBoost-ensemble method with multiple features

Motivation: Long non-coding RNAs (lncRNAs) are a diverse class of RNA molecules with a length above 200 nucleotides that do not encode proteins. Since lncRNAs have involved in a wide range of functions in cellular and developmental processes, an increasing number of methods or tools for distin-guishing lncRNAs from coding RNAs have been proposed. However, most of the existing methods are designed for lncRNAs in animal systems, and only a few methods focus on the plant lncRNA identifica-tion. Different from lncRNAs in animal systems, plant lncRNAs have distinct characteristics. It is desira-ble to develop a computational method for accurate and rapid identification of plant lncRNAs. Results: Herein, we present a plant lncRNA prediction approach PtLnc-BXE, which combines multiple sequence features in two steps to develop an ensemble mode. First, a diverse number of plants lncRNA features are collected and filtered by feature selection and subsequently used to represent RNA se-quences. Then, the training dataset is sampled into several subsets using the bootstrapping technique, and base learners are constructed on data subsets by using XGBoost, and multiple base learners are further combined into a single meta-learner by using logistic regression. PtLnc-BXE outperformed other state-of-the-art plant lncRNA prediction methods, achieving higher AUC (> 95.9%) on the benchmark datasets. The studies across different plant species reveal that the different species have a high overlap between their selected features for modeling. Therefore, it is possible to build the cross-species predic-tion models for plant lncRNAs. Availability: The scripts and data can be downloaded at this https URL Contact: example@example.org Supplementary information: Supplementary data are available at Bioinformatics online.
Ground-State Properties of the Hydrogen Chain: Dimerization, Insulator-to-Metal Transition, and Magnetic Phases

Accurate and predictive computations of the quantum-mechanical behavior of many interacting electrons in realistic atomic environments are critical for the theoretical design of materials with desired properties, and they require solving the grand-challenge problem of the many-electron Schrodinger equation. An infinite chain of equispaced hydrogen atoms is perhaps the simplest realistic model for a bulk material, embodying several central themes of modern condensed-matter physics and chemistry while retaining a connection to the paradigmatic Hubbard model. Here, we report a combined application of cutting-edge computational methods to determine the properties of the hydrogen chain in its quantum-mechanical ground state. Varying the separation between the nuclei leads to a rich phase diagram, including a Mott phase with quasi-long-range antiferromagnetic order, electron density dimerization with power-law correlations, an insulator-to-metal transition, and an intricate set of intertwined magnetic orders.
Quinoa: a Q-function You Infer Normalized Over Actions

We present an algorithm for learning an approximate action-value soft Q-function in the relative entropy regularised reinforcement learning setting, for which an optimal improved policy can be recovered in closed form. We use recent advances in normalising flows for parametrising the policy together with a learned value-function; and show how this combination can be used to implicitly represent Q-values of an arbitrary policy in continuous action space. Using simple temporal difference learning on the Q-values then leads to a unified objective for policy and value learning. We show how this approach considerably simplifies standard Actor-Critic off-policy algorithms, removing the need for a policy optimisation step. We perform experiments on a range of established reinforcement learning benchmarks, demonstrating that our approach allows for complex, multimodal policy distributions in continuous action spaces, while keeping the process of sampling from the policy both fast and exact.
False-Belief Representation and Attribution in Preschoolers: Testing a Graded-Representation hypothesis

On attribue generalement les difficultes des jeunes enfants a la tâche de la fausse croyance a un defaut de representation. Cependant, la version standard de l’epreuve ne permet pas de distinguer les capacites de representation et d’attribution d’une fausse croyance. Afin de mieux apprecier chacune de ces capacites, on a propose a 114 enfants de 3 et 4 ans deux nouvelles versions ou la version standard de l’epreuve. Dans une premiere variante, on a introduit un second personnage dont le point de vue correspondait a celui des participants afin de conferer des statuts similaires aux deux points de vue en jeux dans la tâche, et ainsi d’aider a la comprehension que differentes croyances a propos d’une meme realite peuvent co-exister. Dans une seconde variante, l’introduction du second personnage etait accompagnee d’etiquettes afin de favoriser l’attribution correcte des croyances. Ces nouvelles versions ont effectivement ete mieux reussies que la standard. En outre, certains enfants ont attribue une vraie et une fausse croyances mais ont inverse les personnages, laissant apparaitre un eventuel niveau intermediaire de connaissance. Les resultats sont discutes en reference au modele des representations graduees de Munakata (2001).
Shortcut-to-adiabaticity quantum Otto refrigerator

We investigate the performance of a quantum Otto refrigerator operating in finite time and exploiting local counterdiabatic techniques. We evaluate its coefficient of performance and cooling power when the working medium consists a quantum harmonic oscillator with a time-dependent frequency. We find that the quantum refrigerator outperforms its conventional counterpart, except for very short cycle times, even when the driving cost of the local counterdiabatic driving is included. We moreover derive upper bounds on the performance of the thermal machine based on quantum speed limits and show that they are tighter than the second law of thermodynamics.
Comparison of Intrusion Detection Systems/Intrusion Prevention Systems – A Selection Criterion

Most of the devices and systems nowadays are complex connected devices that perform critical functions. Security in these devices is a critical task and of the highest importance. The protection of the data is mandatory for any organization, so there is a demand for the security mechanism to protect the data. Security is a challenging issue that should be taken into consideration when designing and building business-based web applications, as well as during its maintenance stage. Security can be provided to a system in various ways at different layers. This can be done either by an Intrusion Prevent System (IPS) or Intrusion Detection Systems (IDS). Usually deployed in a network to monitor the traffic, these systems use their own methodology to prevent, mitigate, and arrive at conclusions. The main objective of the paper is to discuss various kinds of IPS/IDS in detail, and their uniqueness which makes them stand out for various reasons. An additional discussion point will indicate which IDS/IPS can be used according to the security requirement, their functionality, and performances with their effectiveness to stop the malicious activity over a computer network. Reasons to choose a specific IDS/IPS will be listed. One of the high-level objectives of the paper is to create awareness about the availability of IDS/IPS and information on which one to choose for their requirements.
Chemical observations of a polar vortex intrusion

[1] An intrusion of vortex edge air into the interior of the Arctic polar vortex was observed on the 31 January 2005 flight of the NASA DC-8 aircraft. This intrusion was identified as anomalously high values of ozone by Airborne Raman Ozone, Temperature, and Aerosol Lidar (AROTAL) and Differential Absorption Lidar (DIAL). Our analysis shows that this intrusion formed when a blocking feature near Iceland collapsed, allowing edge air to sweep into the vortex interior. Analysis of Aura Microwave Limb Sounder (MLS) observations made along the DC-8 flight track also shows the intrusion in both ozone and HNO3. Polar stratospheric clouds (PSCs) were observed by the DIAL lidar on the DC-8. The spatial variability of the PSCs can be explained using MLS HNO3 and H2O observations and meteorological analysis temperatures. We also estimate vortex denitrification using the relationship between N2O and HNO3. Reverse domain fill back trajectory calculations are used to focus on the features in the MLS data. The trajectory results improve the agreement between lidar measured ozone and MLS ozone and also improve the agreement between the HNO3 measurements and PSC locations. The back trajectory calculations allow us to compute the local denitrification rate and reduction of HCl within the filament. We estimate a denitrification rate of about 10% per day after exposure to below–PSC formation temperature.
Phase lags of solar hemispheric cycles

The north–south asymmetry of solar activity is variable in time and strength. We analyse the long-term variation of the phase lags of hemispheric cycles and check a conjectured relationship between these phase lags and the hemispheric cycle strengths. Sunspot data are used from cycles 12 to 23, in which the separation of Northern and Southern hemispheres is possible. The centres of mass of the hemispheric cycle profiles were used to study the phase relations and relative strengths of the hemispheric cycles. This approach considers a cycle as a whole and disregards the short-term fluctuations of the cycle’s time profile. The phase of the hemispheric cycles shows an alternating variation: the northern cycle leads in four cycles and follows in four cycles. No significant relationship is found between the phase and strength differences of the hemispheric cycles. The period of 4+4 cycles appears to be close to the Gleissberg cycle and may provide a key to its physical background. It may raise a new aspect in the solar dynamo mechanism because it needs a very long memory.
Curie temperature of emerging two-dimensional magnetic structures

Recent realizations of intrinsic, long-range magnetic orders in two-dimensional (2D) van der Waals materials have ignited tremendous research interests. In this work, we employ the XXZ Heisenberg model and Monte Carlo simulations to study a fundamental property of these emerging 2D magnetic materials, the Curie temperature (Tc). By including both onsite and neighbor couplings extracted from first-principles simulations, we have calculated Tc of monolayer chromium trihalides and Cr2Ge2Te6, which are of broad interests currently, and the simulation results agree with available measurements. We also clarify the roles played by anisotropic and isotropic interactions in deciding Tc of magnetic orders. Particularly, we find a universal, linear dependence between Tc and magnetic interactions within the parameter space of realistic materials. With this linear dependence, we can predict Tc of general 2D lattice structures, omitting the Monte Carlo simulations. Compared with the widely used Ising model, mean-field theory, and spin-wave theory, this work provides a convenient and quantitative estimation of Tc, giving hope to speeding up the search for novel 2D materials with higher Curie temperatures.
Automatic Calibration of Dual-LiDARs Using Two Poles Stickered with Retro-Reflective Tape

Multi-LiDAR systems have been prevalently applied in modern autonomous vehicles to render a broad view of the environments. The rapid development of 5G wireless technologies has brought a breakthrough for current cellular vehicle-to-everything (C-V2X) applications. Therefore, a novel localization and perception system in which multiple LiDARs are mounted around cities for autonomous vehicles has been proposed. However, the existing calibration methods require specific hard-to-move markers, ego-motion, or good initial values given by users. In this paper, we present a novel approach that enables automatic multi-LiDAR calibration using two poles stickered with retro-reflective tape. This method does not depend on prior environmental information, initial values of the extrinsic parameters, or movable platforms like a car. We analyze the LiDAR-pole model, verify the feasibility of the algorithm through simulation data, and present a simple method to measure the calibration errors w.r. t the ground truth. Experimental results demonstrate that our approach gains better flexibility and higher accuracy when compared with the state-of-the-art approach.
P- and S-wave velocity model estimation from surface wave data

Surface wave analysis performed on data acquired on purpose or extracted from seismic gathers acquired for body wave surveying is a powerful tool for S-wave velocity model estimation. S-wave velocity is relevant for many engineering applications, but the recent application for hydrocarbon exploration data processing (static corrections) makes the estimation of P-wave velocity a desired additional target of the method. Several approaches ranging from joint inversion to approximated direct estimation have been implemented in recent here and are here reviewed. These approaches, applied here on synthetic 1D examples have been also extended to 2D real world applications.
Modelling of dynamic and metadynamic recrystallisation during bar rolling of a medium carbon spring steel

A constitutive model for hot deformation of a medium-carbon spring steel has been developed and validated using isothermal compression experiments at monotonic and abruptly changed strain rate conditions, providing data for the flow stress and softening kinetics. The integrated deformation-softening constitutive model is based on the two hypotheses: (a) instantaneous response of the microstructure to varying temperature–strain rate conditions and (b) invariance of the kinetics of different strain states having equal effective plastic strain. It has been i considered i mills. A c RX cannot p ©
Credit imperfections, labor market frictions and unemployment: a DSGE approach

We construct and estimate a new-Keynesian DSGE model, integrating sticky prices in goods market and frictions in labor and credit markets. A search and matching labor market and a costly-state verication framework in the credit market are introduced. Capital spending, vacancy posting costs and wage bill require to be paid in advance of production and thus require external nancing in a frictional credit market. Search and matching in labor market is uncertain and costly. We nd that the procyclicality of the risk premium (the cost of external over internal funds) impacts the vacancy posting decision and thus the level of unemployment in the economy. Credit markets frictions may be the source of lower posting vacancies, lower bargained wages and higher unemployment level. Then, the transmission channel of shocks among both markets is investigated. We also analyze to what extent frictions alter main variables response to monetary and credit shocks. An empirical evidence is presented by regressing principle variables in both markets using a Bayesian VAR method. The shocks identication is based on sign restrictions and penalty function strategies. Moreover, the theoretical model is log-linearized around the steady-state and estimated using a Bayesian approach. The calibration is based on post-war US data and observed variables cover the period 1960-2007. We nd that asymmetric information in the credit market pushes up the marginal cost of labor by adding a risk premium to the negotiated wage, to compensate for uncertainty related to the recruitment process eciency. Although the wage is a result of a right-to-manage bargaining process, credit market imperfections aect labor supply through the nal good mark-up.
On the dynamic consistency of hierarchical risk-averse decision problems

In this paper, we consider a risk-averse decision problem for controlled-diffusion processes, with dynamic risk measures, in which there are two risk-averse decision makers (i.e., {\it leader} and {\it follower}) with different risk-averse related responsibilities and information. Moreover, we assume that there are two objectives that these decision makers are expected to achieve. That is, the first objective being of {\it stochastic controllability} type that describes an acceptable risk-exposure set vis-\'a-vis some uncertain future payoff, and while the {\it second one} is making sure the solution of a certain risk-related system equation has to stay always above a given continuous stochastic process, namely {\it obstacle}. In particular, we introduce multi-structure, time-consistent, dynamic risk measures induced from conditional $g$-expectations, where the latter are associated with the generator functionals of two backward-SDEs that implicitly take into account the above two objectives along with the given continuous obstacle process. Moreover, under certain conditions, we establish the existence of optimal hierarchical risk-averse solutions, in the sense of viscosity solutions, to the associated risk-averse dynamic programming equations that formalize the way in which both the {\it leader} and {\it follower} consistently choose their respective risk-averse decisions. Finally, we remark on the implication of our result in assessing the influence of the {\it leader'}s decisions on the risk-averseness of the {\it follower} in relation to the direction of {\it leader-follower} information flow.
A POWERFUL AGN OUTBURST IN RBS 797

Utilizing ∼50 ks of Chandra X-Ray Observatory imaging, we present an analysis of the intracluster medium (ICM) and cavity system in the galaxy cluster RBS 797. In addition to the two previously known cavities in the cluster core, the new and deeper X-ray image has revealed additional structure associated with the active galactic nucleus (AGN). The surface brightness decrements of the two cavities are unusually large and are consistent with elongated cavities lying close to our line of sight. We estimate a total AGN outburst energy and mean jet power of ≈(3–6) × 1060 erg and ≈(3–6) × 1045 erg s−1, respectively, depending on the assumed geometrical configuration of the cavities. Thus, RBS 797 is apparently among the most powerful AGN outbursts known in a cluster. The average mass accretion rate needed to power the AGN by accretion alone is ∼1 M☉ yr−1. We show that accretion of cold gas onto the AGN at this level is plausible, but that Bondi accretion of the hot atmosphere is probably not. The brightest cluster galaxy (BCG) harbors an unresolved, non-thermal nuclear X-ray source with a bolometric luminosity of ≈2 × 1044 erg s−1. The nuclear emission is probably associated with a rapidly accreting, radiatively inefficient accretion flow. We present tentative evidence that star formation in the BCG is being triggered by the radio jets and suggest that the cavities may be driving weak shocks (M ∼ 1.5) into the ICM, similar to the process in the galaxy cluster MS 0735.6+7421.
Developments in Business Simulation and Experiential Learning , Volume 30 , 2003 STRATEGIC MANAGEMENT : AN EVALUATION OF THE USE OF THREE LEARNING METHODS IN HONG KONG

The paper examines the use of three learning methods in the teaching of a strategic management course for a Masters programme in Postgraduate Scheme in Business and Management at the Hong Kong Polytechnic University. The three methods are an in-company consultancy project; a new business venture simulation; and the case method. The results of a survey of postgraduate students' perceptions show that overall the new business venture simulation provides a more successful learning experience than in-company consultancy project and the case method.
Introduction to Univalent Foundations of Mathematics with Agda

We introduce Voevodsky's univalent foundations and univalent mathematics, and explain how to develop them with the computer system Agda, which is based on Martin-Lof type theory. Agda allows us to write mathematical definitions, constructions, theorems and proofs, for example in number theory, analysis, group theory, topology, category theory or programming language theory, checking them for logical and mathematical correctness. Agda is a constructive mathematical system by default, which amounts to saying that it can also be considered as a programming language for manipulating mathematical objects. But we can assume the axiom of choice or the principle of excluded middle for pieces of mathematics that require them, at the cost of losing the implicit programming-language character of the system. For a fully constructive development of univalent mathematics in Agda, we would need to use its new cubical flavour, and we hope these notes provide a base for researchers interested in learning cubical type theory and cubical Agda as the next step. Compared to most expositions of the subject, we work with explicit universe levels.