Miracle-dz/UniFAR-FTUs huggingface dataset:
From Speech Illusions to Onset of Psychotic Disorder: Applying Network Analysis to an Experimental Measure of Aberrant Experiences

Aberrant perceptional experiences are a potential early marker of psychosis development. Earlier studies have found experimentally assessed speech illusions to be associated with positive symptoms in patients with psychotic disorders, but findings for attenuated symptoms in individuals without psychotic disorders have been inconsistent. Also, the role of affect is unclear. The aim of this study was to use the network approach to investigate how speech illusions relate to individual symptoms and onset of a psychotic disorder. We estimated a network model based on data from 289 Clinical High-Risk (CHR) subjects, participating in the EU-GEI project. The network structure depicts statistical associations between (affective and all) speech illusions, cross-sectional individual attenuated positive and affective symptoms, and transition to psychotic disorder after conditioning on all other variables in the network. Speech illusions were assessed with the White Noise Task, symptoms with the BPRS and transition during 24-month follow-up with the CAARMS. Affective, not all, speech illusions were found to be directly, albeit weakly, associated with hallucinatory experiences. Hallucinatory experiences, in turn, were associated with delusional ideation. Bizarre behavior was the only symptom in the network steadily predictive of transition. Affective symptoms were highly interrelated, with depression showing the highest overall strength of connections to and predictability by other symptoms. Both speech illusions and transition showed low overall predictability by symptoms. Our findings suggest that experimentally assessed speech illusions are not a mere consequence of psychotic symptoms or disorder, but that their single assessment is likely not useful for assessing transition risk.
UV-Ozone Treatment on Cs2CO3 Interfacial Layer for the Improvement of Inverted Polymer Solar Cells

Inverted configuration polymer solar cells (IPSCs) were prepared by using Cs2CO3 modified indium tin oxide (ITO) substrates as cathode and MoO3/Al as anode, ITO/Cs2CO3/P3HT:PCBM/MoO3/Al. The interfacial Cs2CO3 layers were conducted with annealing treatment and different time UV-Ozone treatment. The power conversion efficiency (PCE) of IPSCs was improved to 1% when the UV-Ozone treatment time is 15 minutes, with the open-circuit voltage of 0.48 V, short-circuit current density of 5.4 mA/cm2, and fill factor of 39%. The improvement of IPSCs should be attributed to the increased electron transporting and collection ability of Cs2CO3 layer induced by UV-Ozone treatment. The underlying mechanism of PCE improvement was discussed in terms of series and shunt resistance of cells induced by UV-Ozone treatment on Cs2CO3 layer, and the mole ratio of Cs to O of Cs2CO3 layer with different UV-Ozone treatment was investigated by scanning electron microscopy operating in the mode for in situ energy dispersive X-ray (EDX) spectra.
Forecasting Cattle Prices in the Presence of Structural Change

Abstract Recent empirical research and developments in the cattle industry suggest several reasons to suspect structural change in economic relationships determining cattle prices. Standard forecasting models may ignore structural change and may produce biased and misleading forecasts. Vector autoregressive (VAR) models that allow parameters to vary with time are used to forecast quarterly cattle prices. The VAR procedures are flexible in that they allow the identification of structural change that begins at an a priori unknown point and occurs gradually. The results indicate that the lowest RMSE for out-of-sample forecasts of cattle prices is obtained using a gradually switching VAR model. However, differences between the gradually switching VAR model and a univariate ARIMA model are not strongly significant. Impulse response functions indicate that adjustments of cattle prices to new information have become faster in recent years.
PhD position in medical imaging Unified framework for change detection in multimodal brain MRI Application to longitudinal analysis of multiple sclerosis

Automatic change detection in series of MRI acquisitions is essential for analyzing the evolution of pathologies over time. It is particularly useful in clinical routine for diagnosis and longitudinal monitoring of patients subject to treatments. It is also an important tool for large scale studies using imaging biomarkers to evaluate the efficiency of therapeutic strategies. Intensity changes between MRI acquisitions of a patient at different times can have multiple origins. They can reveal the appearance or the evolution of a lesion, the deformation of brain structures (e.g. atrophy), or they can be caused by artefacts due to the acquisition device. The major issue is to distinguish these different sources to quantify their individual impact. Existing methods have a sequential approach that considers each phenomenon successively, without taking into account their interdependency. Thus, the detection of a given source of change is potentially biased by the presence of others. For example, the appearance of a new lesion can be interpreted as a deformation of the brain, and can be discarded by the registration process [1]. Another crucial point is the multimodal nature of the data. The features of different MRI modalities (T1, T2, FLAIR.) are complementary and should be combined to provide a complete description of a pathology. However, the changes are often estimated separately in each modality.
Asymptotic behavior of Hamilton-Jacobi equations defined on two domains separated by an oscillatory interface

We consider a family of optimal control problems in the plane with dynamics and running costs possibly discontinuous across an oscillatory interface Γ e. The oscillations of the interface have small period and amplitude, both of the order of e, and the interfaces Γ e tend to a straight line Γ. We study the asymptotic behavior as e → 0. We prove that the value function tends to the solution of Hamilton-Jacobi equations in the two half-planes limited by Γ, with an effective transmission condition on Γ keeping track of the oscillations of Γ e .
PrivKV: Key-Value Data Collection with Local Differential Privacy

Local differential privacy (LDP), where each user perturbs her data locally before sending to an untrusted data collector, is a new and promising technique for privacy-preserving distributed data collection. The advantage of LDP is to enable the collector to obtain accurate statistical estimation on sensitive user data (e.g., location and app usage) without accessing them. However, existing work on LDP is limited to simple data types, such as categorical, numerical, and set-valued data. To the best of our knowledge, there is no existing LDP work on key-value data, which is an extremely popular NoSQL data model and the generalized form of set-valued and numerical data. In this paper, we study this problem of frequency and mean estimation on key-value data by first designing a baseline approach PrivKV within the same "perturbation-calibration" paradigm as existing LDP techniques. To address the poor estimation accuracy due to the clueless perturbation of users, we then propose two iterative solutions PrivKVM and PrivKVM+ that can gradually improve the estimation results through a series of iterations. An optimization strategy is also presented to reduce network latency and increase estimation accuracy by introducing virtual iterations in the collector side without user involvement. We verify the correctness and effectiveness of these solutions through theoretical analysis and extensive experimental results.
Differential Network for Video Object Detection

Object detection in streaming videos has three requirement: consistency, online and real time. For consistency, we adopt incremental Seq-NMS [9] to link the current bounding boxes with previous ones to see whether the object is new or not. And we adopt Deep Feature Flow (DFF) [34] to achieve on-line and real time. However, DFF is sensitive to the selection of a good key frame, so we proposed the Differential Network (DiffNet) to achieve an automatic key frame scheduling. DiffNet combines both information from raw image and optical flow to tell whether two frames need to transfer feature, which is applied ahead of the detection pipeline. We firstly evaluate the classification ability of DiffNet and then examine such method on ImageNet VID dataset and compare it with the fixed-step key frame scheduling.
Effective Prediction of Bearing Fault Degradation under Different Crack Sizes Using a Deep Neural Network

Exact evaluation of the degradation levels in bearing defects is one of the most essential works in bearing condition monitoring. This paper proposed an efficient evaluation method using a deep neural network (DNN) for correct prediction of degradation levels of bearings under different crack size conditions. An envelope technique was first used to capture the characteristic fault frequencies from acoustic emission (AE) signals of bearing defects. Accordingly, a health-related indicator (HI) calculation was performed on the collected envelope power spectrum (EPS) signals using a Gaussian window method to estimate the fault severities of bearings that served as an appropriate dataset for DNN training. The proposed DNN was then trained for effective prediction of bearing degradation using the Adam optimization-based backpropagation algorithm, in which the synaptic weights were optimally initialized by the Xavier initialization method. The effectiveness of the proposed degradation prediction approach was evaluated through different crack size experiments (3, 6, and 12 mm) of bearing faults.
Socio-Economic Differences in Mortality

The analyses included in the report show that there are big socio-economic differences in mortality, especially for men, and they appear to have become bigger over time. The report discusses implications of mortality differentials for five major areas of pension policy: the progressivity of the pension system, the pension eligibility age, the retirement incentives, future pension expenditures and private pensions. The empirical work shows that the mortality differentials reduce progressivity in pension systems. Moreover, there is empirical evidence that raising retirement age is not more unfair to socio-economic groups with lower life expectancy.
Tools and Techniques for Teaching Computer Programming: A Review

Courses on computer programming are included in the curricula of almost all engineering disciplines. We surveyed the research literature and identified the techniques that are commonly used by instructors for teaching these courses. We observed that visual programming and game-based learning can enhance computational thinking and problem-solving skills in students and may be used to introduce them to programming. Robot programming may be used to attract students to programming, but the success of this technique is subjected to the availability of robots. Pair and collaborative programming allows students to learn from one another and write efficient programs. Assessment systems help instructors in evaluating programs written by students and provide them with timely feedback. Furthermore, an analysis of citations showed that Scratch is the most researched tool for teaching programming. We discuss how these techniques may be used to teach introductory courses, advanced courses, and massive open online courses on programming.
The Toxicity and Metabolism Properties of Herba Epimedii Flavonoids on Laval and Adult Zebrafish

Zebrafish is being increasingly used for metabolism and toxicity assessment. The drugs consumed in zebrafish metabolism studies are far less than those used in rat studies. In our study, zebrafish embryos were exposed to icariin, Baohuoside I (BI), Epimedin A (EA), Epimedin B (EB), Epimedin C (EC), Sagittatoside A (SA), Sagittatoside B (SB), and 2′′-O-rhamnosylicariside II (SC), respectively, to examine the toxicity and metabolic profiles of these flavonoids. The order of toxicity was SC, SB > EC, SA > BI, icariin, EA, EB. After 24 h exposure to SB and SC, the mortality of zebrafish larvae reached 100% and yolk sac swollen was obvious. Both SC and SB caused severe hepatocellular vacuolization and liver cells degeneration in adult zebrafish after 15 consecutive days' treatment. The metabolic profiles of these flavonoids with trace amount were also monitored in larvae. BI was the common metabolite shared by icariin, EA, EB, SA, and SB, via deglycosylation. Both BI and SC remained as the prototype in the medium, suggesting that it is hard for BI and SC to cleave the rhamnose residue. EC was metabolized into SC and BI in zebrafish, inferring that SC might be responsible for the toxicity observed in EC group. The metabolites of icariin, EA, EB, EC, and BI in zebrafish larvae coincided with results from rats and intestinal flora. These data support the use of this system as a surrogate in predicting metabolites and hepatotoxicity risk, especially for TCM compound with trace amount.
Synchrophasor based Power System Monitoring and Control using Real Time Digital Simulation Facility

This paper briefly presents the details of Real Time Digital Simulation (RTDS) setup at IIT Kanpur to carry out research works related to wide-area monitoring and control of power system. The RTDS have been integrated with synchrophasor equipments, such as Phasor Measurement Units (PMU) and Phasor Data Concentrator (PDC) to setup Wide-Area Monitoring and Control System (WAMCS) lab. The application of this physical real time simulation facility has been demonstrated for wide area voltage stability monitoring and stabilizing control of the power system. Keywords-Real time digital simulation; synchrophasor; phasor measurmeent unit; wide-area monitoring and control;
Title : ! ! Stochastic ! Quantitative ! Reasoning ! for ! Autonomous ! Mission ! Planning !

Spatio-temporal data streams generated from sensors can be erroneous and could lead to serious problems. For example, pitot tubes icing which occurred to Air France flight 447 (AF447) in June 2009 led to faulty airspeed readings and eventually caused a fatal accident killing all 228 people on board. As an effort to develop self-healing spatio-temporal data stream systems, we have developed a highly declarative programming language called PILOTS that enables error detection and data correction based on error signatures. Error signatures are mathematical function patterns with constraints and are used to stochastically identify and categorize errors in redundant spatiotemporal data streams. In this paper, we refine the error detection and correction methods previously reported by the authors and apply these methods to real flight data of a private Cessna flight and the AF447 flight. The results show that the error detection and correction methods successfully work for both sets of flight data. For the private Cessna flight, three error scenarios are simulated: pitot tube failure, GPS failure, and simultaneous pitot tube and GPS failures. The error detection accuracy is approximately 93% and the response time to correct data is at most 5 seconds. For the AF447 flight, 162 seconds of available flight data including the pitot tubes failure is collected from the accident report and examined accordingly. The pitot tube failure of the AF447 flight is successfully detected and corrected after 5 seconds from the beginning of the failure. Overall error mode detection accuracy reaches 96.31%. Furthermore, our simulations show that the system never corrects data incorrectly, i.e., all inaccurate mode detections produce either unknown or unrecoverable errors. These results suggest that the presented error signature-based detection and correction methods can fix erroneous data readings caused by sensor failures within a few seconds and thereby keep flight systems working properly. Such self-healing flight systems could have prevented the tragic AF447 accident from happening and saved the lives of all crew members and passengers.
Neuromarketing and Psychology of Cors in the process of Making Purchase

— The present work is a qualitative research, with the intention of improving the measurement or the reach of the results used as a data collection procedure or focus group, or that had a team of participants participating in a non-research sample. probabilistic in performance with the research objectives, striving for the heterogeneity of the group. Did a research try to answer a starting question like neuromarketing and a psychology of the nuclei that are influencing the purchase decision making? A general research aimed to identify the influence of Neuromarketing and core psychology on consumer behavior, and how to define criteria that show the concept of neuromarketing, expose the perception of the influence of core psychology and diagnosis the effect of neuromarketing on the behavior of consumer using a use of colors. Through oras a result of the focus group, it is not allowed how the nuclei can influence the decisive buying process of a particular individual, since the nuclei remain unaware of consumers and can be triggered as a mind trigger, using the tools and methods of neuromarketing .
Dimensions Influencing Business Intelligence Usage in Thailand SMEs

Today, many companies are interested to being Business Intelligence (BI) to use. There are many factors that influent BI usage. This research studied BI environments and how they affect companies’ BI usage. Information Evolution Model (IEM) is used to explain BI environments and also to state the level of companies’ BI maturity. Information Evolution Model considers BI environment in 4 dimensions, which are Human capital, Knowledge process, Infrastructure and Culture. Technology Acceptance Model (TAM) is used to explain BI usage. This study focused on Thailand SMEs. Keywords-Business Intelligence, Decision Support Systems, Culture, Human Capital, Knowledge Process, Infrastructure
Wormhole Attacks als Sicherheitsrisiko in Wireless Sensor Networks und Gegenmaßnahmen von RPL

Sicherheit spielt in nahezu jedem Netzwerk eine große Rolle. Angreifer finden in der heutigen Zeit immer fortgeschrittenere und komplexere Methoden, eine ernstzunehmende Bedrohung darzustellen. Insbesondere in Wireless Sensor Networks (WSN) ist es deshalb wichtig, dem entgegenzuwirken. Dies stellt sich aber häufig als große Herausforderung dar, weil meist ein Kompromiss zwischen Sicherheit, Aufwand und somit Performance gemacht werden muss. Im Folgenden wird eine der häufigsten Angriffsmethoden auf WSNs, der Wormhole-Attack, und Lösungsansätze des Routing Protocol for Low Power and Lossy Networks (RPL)
Data Mining Approach for Analysis of Variable Speed Refrigeration System

The aim of this study is to carry out performance modeling of an experimental refrigeration system driven by variable speed compressor using Data Mining techniques with small data sets. In order to vary the capacity of the refrigeration systems, one of the best methods is controlling the rotational speed of the compressor motor with a frequency inverter. For this aim, an experimental refrigeration system is setup with a frequency inverter for controlling the speed of compressor electric motor. The experiments are made for 35 Hz to 50 Hz electric motor frequencies. Data mining technique is applied to determine the system performance parameters using actual data obtained from the measurements. From the results, it is observed that data mining procedure is suitable for forecasting the system characteristics for different compressor frequencies and cooling loads instead of making several experiments. Degisken Hizli Sogutma Sisteminin Analizi Icin Veri Madenciligi Yaklasimi
Energy Efficient Scheduling of Independent Tasks on Multicore Processors with Software Controlled Dynamic Voltage Scaling

Energy-efficient task scheduling for multicore processors is a vital design issue in parallel and distributed computing. Nowadays multicore processors are designed by using software controlled Dynamic Voltage Scaling (DVS) to reduce the energy consumption of cores. We propose a modified EETSA algorithm to schedule the independent tasks on multicore processors that have to be completed within a given common deadline. The proposed algorithm works in two phases. In the first phase, tasks are scheduled on different cores of multicore processors and in the second phase, energy consumption is optimized. The complexity of proposed algorithm is same as the EETSA. We perform the experiments on a system with a single multi-core processor with software controlled DVS having a limited set of core speeds. The results of proposed algorithm are compared with EETSA and OPT algorithms. The results show that proposed algorithm outperforms the compared algorithms regarding scheduling time and energy consumption. Keywords—Dynamic Voltage Scaling, Energy efficient scheduling, Multi-core processor, Task allocation.
Supplementary Material of Subspace Learning Based Low-Rank Representation

Since we only conduct one experiment on the Extended Yale B to test the performance of the compared methods on multiple subspaces, we do a subspace segmentation experiment with 10 subspaces and 20 subspaces in this section. CMU PIE [1] is also a face image database. It contains more than 40,000 facial images of 68 subjects. We select 40 images of the first 10 and 20 subjects to test the performance of all the compared methods. Table 1 shows the segmentation error of all the compared methods on the database of CMU PIE. With respect to 10 subjects, SLLRR performs a little better than other methods. However, when the number of subjects is 20, only LS3C and SLLRR achieve the clustering errors lower than 20%. Because SLLRR can learn a subspace favoring the selfrepresentation to extract the similarity, it achieves the lowest segmentation error 16.50% with α = 50, β = 1.
A Study on Online Purchase Motivation, Perceived Risk and Behavioral Intention of Sports Goods Consumption

The purpose of this study is to analyze the characteristics of consumer behavior, and the relationship among purchase motivation, perceived risk, and behavioral intention. In this study, 594 valid questionnaires were collected from consumers who bought sports goods online. After statistical analysis, this study found that: 1. Sports shoes are the main products consumers buy online. 2. There is no difference in purchase motivation, perceived risk, and behavioral intention between genders.3. Consumers of different ages have different purchase motives and perceived risks. 4. Consumer purchase motivation and perceived risk have predictive effect on behavioral intention.
The Political Economy of Financial Systems

This survey reviews the literature on the political economy of financial structure, broadly defined to include the size of capital markets and banking systems as well as the distribution of access to external finance across firms.The theoretical literature on the institutional basis for financial development and the recent evidence suggests that unconstrained political power undermines financial accumulation. Even under limited government, unaccountable institutions lead to regulatory capture, favor connected interests, and undermine finance access and entry. Thus the degree of access to political rights by citizens thus strongly affects their access to finance. Finally, we review the recent literature on the time variation of financial development across democracies during the XX century.