Miracle-dz/UniFAR-FTUs huggingface dataset:
Blood Cytokines as Biomarkers of In Vivo Toxicity in Preclinical Safety Assessment: Considerations for Their Use

In the drive to develop drugs with well-characterized and clinically monitorable safety profiles, there is incentive to expand the repertoire of safety biomarkers for toxicities without routine markers or premonitory detection. Biomarkers in blood are pursued because of specimen accessibility, opportunity for serial monitoring, quantitative measurement, and the availability of assay platforms. Cytokines, chemokines, and growth factors (here referred to collectively as cytokines) show robust modulation in proximal events of inflammation, immune response, and repair. These are key general processes in many toxicities; therefore, cytokines are commonly identified during biomarker discovery studies. In addition, multiplexed cytokine immunoassays are easily applied to biomarker discovery and routine toxicity studies to measure blood cytokines. However, cytokines pose several challenges as safety biomarkers because of a short serum half-life; low to undetectable baseline levels; lack of tissue-specific or toxicity-specific expression; complexities related to cytokine expression with multiorgan involvement; and species, strain, and interindividual differences. Additional challenges to their application are caused by analytical, methodological, and study design–related variables. A final consideration is the strength of the relationship between changes in cytokine levels and the development of phenotypic or functional manifestations of toxicity. These factors should inform the integrated judgment-based qualification of novel biomarkers in preclinical, and potentially clinical, risk assessment. The dearth of robust, predictive cytokine biomarkers for specific toxicities is an indication of the significant complexity of these challenges. This review will consider the current state of the science and recommendations for appropriate application of cytokines in preclinical safety assessment.
Inferring the Importance of Product Appearance: A Step Towards the Screenless Revolution

Nowadays, almost all the online orders were placed through screened devices such as mobile phones, tablets, and computers. With the rapid development of the Internet of Things (IoT) and smart appliances, more and more screenless smart devices, e.g., smart speaker and smart refrigerator, appear in our daily lives. They open up new means of interaction and may provide an excellent opportunity to reach new customers and increase sales. However, not all the items are suitable for screenless shopping, since some items' appearance play an important role in consumer decision making. Typical examples include clothes, dolls, bags, and shoes. In this paper, we aim to infer the significance of every item's appearance in consumer decision making and identify the group of items that are suitable for screenless shopping. Specifically, we formulate the problem as a classification task that predicts if an item's appearance has a significant impact on people's purchase behavior. To solve this problem, we extract features from three different views, namely items' intrinsic properties, items' images, and users' comments, and collect a set of necessary labels via crowdsourcing. We then propose an iterative semi-supervised learning framework with three carefully designed loss functions. We conduct extensive experiments on a real-world transaction dataset collected from the online retail giant JD.com. Experimental results verify the effectiveness of the proposed method.
Machine Vision in the Context of Robotics: A Systematic Literature Review

Machine vision is critical to robotics due to a wide range of applications which rely on input from visual sensors such as autonomous mobile robots and smart production systems. To create the smart homes and systems of tomorrow, an overview about current challenges in the research field would be of use to identify further possible directions, created in a systematic and reproducible manner. In this work a systematic literature review was conducted covering research from the last 10 years. We screened 172 papers from four databases and selected 52 relevant papers. While robustness and computation time were improved greatly, occlusion and lighting variance are still the biggest problems faced. From the number of recent publications, we conclude that the observed field is of relevance and interest to the research community. Further challenges arise in many areas of the field.
Analysis of MLP-Based Hierarchical Phoneme Posterior Probability Estimator

We analyze a simple hierarchical architecture consisting of two multilayer perceptron (MLP) classifiers in tandem to estimate the phonetic class conditional probabilities. In this hierarchical setup, the first MLP classifier is trained using standard acoustic features. The second MLP is trained using the posterior probabilities of phonemes estimated by the first, but with a long temporal context of around 150-230 ms. Through extensive phoneme recognition experiments, and the analysis of the trained second MLP using Volterra series, we show that 1) the hierarchical system yields higher phoneme recognition accuracies-an absolute improvement of 3.5% and 9.3% on TIMIT and CTS respectively-over the conventional single MLP-based system, 2) there exists useful information in the temporal trajectories of the posterior feature space, spanning around 230 ms of context, 3) the second MLP learns the phonetic temporal patterns in the posterior features, which include the phonetic confusions at the output of the first MLP as well as the phonotactics of the language as observed in the training data, and 4) the second MLP classifier requires fewer number of parameters and can be trained using lesser amount of training data.
Pretrain Soft Q-Learning with Imperfect Demonstrations

Pretraining reinforcement learning methods with demonstrations has been an important concept in the study of reinforcement learning since a large amount of computing power is spent on online simulations with existing reinforcement learning algorithms. Pretraining reinforcement learning remains a significant challenge in exploiting expert demonstrations whilst keeping exploration potentials, especially for value based methods. In this paper, we propose a pretraining method for soft Q-learning. Our work is inspired by pretraining methods for actor-critic algorithms since soft Q-learning is a value based algorithm that is equivalent to policy gradient. The proposed method is based on $\gamma$-discounted biased policy evaluation with entropy regularization, which is also the updating target of soft Q-learning. Our method is evaluated on various tasks from Atari 2600. Experiments show that our method effectively learns from imperfect demonstrations, and outperforms other state-of-the-art methods that learn from expert demonstrations.
A Quasi-Steady Lifting Line Theory for Insect-Like Hovering Flight

A novel lifting line formulation is presented for the quasi-steady aerodynamic evaluation of insect-like wings in hovering flight. The approach allows accurate estimation of aerodynamic forces from geometry and kinematic information alone and provides for the first time quantitative information on the relative contribution of induced and profile drag associated with lift production for insect-like wings in hover. The main adaptation to the existing lifting line theory is the use of an equivalent angle of attack, which enables capture of the steady non-linear aerodynamics at high angles of attack. A simple methodology to include non-ideal induced effects due to wake periodicity and effective actuator disc area within the lifting line theory is included in the model. Low Reynolds number effects as well as the edge velocity correction required to account for different wing planform shapes are incorporated through appropriate modification of the wing section lift curve slope. The model has been successfully validated against measurements from revolving wing experiments and high order computational fluid dynamics simulations. Model predicted mean lift to weight ratio results have an average error of 4% compared to values from computational fluid dynamics for eight different insect cases. Application of an unmodified linear lifting line approach leads on average to a 60% overestimation in the mean lift force required for weight support, with most of the discrepancy due to use of linear aerodynamics. It is shown that on average for the eight insects considered, the induced drag contributes 22% of the total drag based on the mean cycle values and 29% of the total drag based on the mid half-stroke values.
A method for demand-driven information requirements analysis in data warehousing projects

Information requirements analysis for data warehouse systems differs significantly from requirements analysis for conventional information systems. Existing data warehouse specific approaches are reviewed. A comprehensive methodology that supports the entire process of determining information requirements of data warehouse users, matching information requirements with actual information supply, evaluating and homogenizing resulting information requirements, establishing priorities for unsatisfied information requirements, and formally specifying the results as a basis for subsequent phases of the data warehouse development (sub)project is proposed. Its components as well as its overall design are based partially on literature review, but mainly on findings from a four year collaboration project with several large companies, mostly from the service sector. While an application of the entire methodology is still outstanding, some components have been successfully applied in actual data warehouse development projects of the participating companies.
Non-linear electrodynamics and the variation of the fine structure constant

It has been claimed that during the late time history of our universe, the fine structure constant of electromagnetism, �, has been increasing (Webb et al. 2001; Murphy et al. 2003). The conclusion is achieved after looking at the separation between lines of ions like CIV, MgII, SiII, FeII, among others in the absorption spectra of very distant quasars, and comparing them with their counterparts obtained in the laboratory. However, in the meantime, other teams has claimed either a null result or a de- creasingwith respect to the cosmic time (Chand et al. 2004; Levshakov et al. 2004). Also, the current precision of laboratory tests does not allow one to either comfort or reject any of these astronomical observations. Here we suggest that as photons are the sidereal messengers, a nonlinear electrodynamics (NLED) description of the interac- tion of photons with the weak local background magnetic fields of a gas cloud absorber around the emitting quasar can reconcile the Chand et al. (2004) and Levshakov et al. (2004) findings with the negative variation found by Murphy et al. (2001a, 2001b, 2001c, 2001d) and Webb et al. (2001), and also to find a bridge with the positive variation argued more recently by Levshakov et al. (2006a, 2007). We also show that nonlinear electrodynamics photon propagation in a vacuum permeated by a back- ground magnetic field presents a full agreement with constraints from Oklo natural reactor data. Finally, we show that NLED may render a null result only in a narrow range of the local background magnetic field which should be the case of both the claims by Chand et al.(2004) and by Srianand et al. (2004).
An improved phase-error based dual-microphone noise reduction method

In this paper, an improved phase-error based filter (IPBF) for dual-microphone noise reduction is proposed. First, instead of the Wiener filter, a statistical-model-based amplitude estimator is employed in IPBF algorithm to make further reduction of the directional noise. Then, in order to take advantage of the single channel speech enhancement method and the PBF method, we present a decision rule to merge the spectral gains of these two methods, which is very effective in the reduction of musical noise caused by the PBF algorithm and the performance of the directional noise reduction is not corrupted at the same time. Experimental results show that the proposed method outperforms two reference methods.
A novel speckle reduction and contrast enhancement method based on fuzzy anisotropic diffusion

Two major problems of ultrasound imaging are low-contrast and speckle noise. Traditionally, before speckle reduction, an enhancement algorithm is employed to improve the quality of the image. However, the noise is enhanced as well. To overcome this drawback, we introduce a novel fuzzy anisotropic diffusion approach for speckle reduction and contrast enhancement. Maximum fuzzy entropy principle is used to map the image from space domain to fuzzy domain. Then, fractional-order partial differential equation is used to remove noise and to preserve edges. Finally, the subpixel operator is utilized as a tuning parameter to achieve the optimal result. We test the proposed method on synthetic and real breast ultrasound (BUS) images. The experimental results demonstrate that the proposed method can preserve the edges and enhance the structural details of the BUS images well while removing speckle noise.
High-Resolution Near-Infrared Images and Models of the Circumstellar Disk in HH 30

We present Hubble Space Telescope near-infrared camera and multiobject spectrometer observations of the reflection nebulosity associated with the T Tauri star HH 30. The images show the scattered-light pattern characteristic of a highly inclined, optically thick disk with a prominent dust lane whose width decreases with increasing wavelength. The reflected nebulosity exhibits a lateral asymmetry in the upper lobe on the opposite side to that reported in previously published Wide Field Planetary Camera 2 images. The radiation transfer model that most closely reproduces the data has a flared accretion disk with dust grains larger than standard interstellar medium grains by a factor of approximately 2.1. A single hot spot on the stellar surface provides the necessary asymmetry to fit the images and is consistent with previous modeling of the light curve and images. Photometric analysis results in an estimated extinction of AV ≳ 80; however, since the photometry measures only scattered light rather than direct stellar flux, this a lower limit. The radiative transfer models require an extinction of AV = 7900.
Accelerated Muscle Fatigability of Latent Myofascial Trigger Points in Humanspme

Objective. Muscle fatigue is prevalent in acute and chronic musculoskeletal pain conditions in which myofascial trigger points (MTPs) are involved. The aim of this study was to investigate the association of latent MTPs with muscle fatigue. Design. Intramuscular electromyographic (EMG) recordings were obtained from latent MTPs and nonMTPs together with surface EMG recordings from the upper trapezius muscles during sustained isometric muscle contractions in 12 healthy subjects. Outcome Measures. Normalized root mean square (RMS) EMG amplitude and mean power frequency (MNF) were analyzed. The rate of perceived exertion and pain intensity from MTP side and non-MTP side were recorded. Results. Pain intensity on the MTP side was significantly higher than the non-MTP side (P < 0.05). Intramuscular EMG from latent MTPs showed an early onset of decrease in MNF and a significant decrease at the end of fatiguing contraction as compared with non-MTPs (P < 0.05). Surface EMG from muscle fibers close to latent MTPs presented with an early onset of the increase in RMS amplitude and the increase was significantly higher than that from non-MTPs at the end of sustained isometric contraction (P < 0.05). Conclusions. A latent MTP is associated with an accelerated development of muscle fatigue and simultaneously overloading active motor units close to an MTP. Elimination of latent MTPs and inactivation of active MTPs may effectively reduce accelerated muscle fatigue and prevent overload spreading within a muscle.
Exploration of Instructional Strategies and Individual Difference within the Context of Web-Based Learning.

Individual differences have been identified as important factors that might have significant impact on students’ learning. This study investigated the effect of student’s cognitive styles, achievement motivation, prior knowledge, and attitudes on student’s achievement in web-based learning. A web-based course was designed for second year university students in an educational psychology class. Cognitive Style Analysis (CSA), Achievement Motivation Scale (AMS), and Attitude Scale (AS) were administered to a sample of 71 second-year university students enrolled in the educational psychology class. Findings of the study revealed that no significant differences (0.05) in achievement between field-dependent and field-independent students. Also, students with different characteristics learned equally well in webbased learning. The students enjoyed the convenience of web based learning. Competition and high expectation were the motivating forces in web based learning. Prior knowledge and motivation seemed to be the only significant factors that explained more than 25 per cent of student achievement measured by class grade.
Immune Characterization of Plasmodium falciparum Parasites with a Shared Genetic Signature in a Region of Decreasing Transmission

ABSTRACT As the intensity of malaria transmission has declined, Plasmodium falciparum parasite populations have displayed decreased clonal diversity resulting from the emergence of many parasites with common genetic signatures (CGS). We have monitored such CGS parasite clusters from 2006 to 2013 in Thiès, Senegal, using the molecular barcode. The first, and one of the largest observed clusters of CGS parasites, was present in 24% of clinical isolates in 2008, declined to 3.4% of clinical isolates in 2009, and then disappeared. To begin to explore the relationship between the immune responses of the population and the emergence and decline of specific parasite genotypes, we have determined whether antibodies to CGS parasites correlate with their prevalence. We measured (i) antibodies capable of inhibiting parasite growth in culture and (ii) antibodies recognizing the surfaces of infected erythrocytes (RBCs). IgG obtained from volunteers in 2009 showed increased reactivity to the surfaces of CGS-parasitized erythrocytes over IgG from 2008. Since P. falciparum EMP-1 (PfEMP-1) is a major variant surface antigen, we used var Ups quantitative reverse transcription-PCR (qRT-PCR) and sequencing with degenerate DBL1α domain primers to characterize the var genes expressed by CGS parasites after short-term in vitro culture. CGS parasites show upregulation of UpsA var genes and 2-cysteine-containing PfEMP-1 molecules and express the same dominant var transcript. Our work indicates that the CGS parasites in this cluster express similar var genes, more than would be expected by chance in the population, and that there is year-to-year variation in immune recognition of surface antigens on CGS parasite-infected erythrocytes. This study lays the groundwork for detailed investigations of the mechanisms driving the expansion or contraction of specific parasite clones in the population.
Performance Investigations of Adaptive ANN-SVPWM Controlled Shunt Active Power Filter

Efficient and Adaptive Artificial Neural Network (ANN) based Shunt Active Power Filter (SAPF) for mitigating harmonics is presented in the paper. ANN based reference current generation is implemented using Neural Synchronous method while Levenberg Marquardt method is used for training algorithm. ANN based Space Vector Pulse Width Modulation (ANN-SVPWM) controller is realized for the effective control of SAPF. Performance of the proposed ANN-SVPWM controller is compared with conventional Hysteresis Current Controller (HCC). Hence, both the reference compensating currents as well as controller of the proposed SAPF are realized using Artificial Neural Network. Effective compensation of the load harmonics and good dynamic response of the ANN based SAPF is proved by the presented simulation results using MATLAB. The proposed SAPF effectively compensates for the load harmonics in steady-state as well as during dynamic conditions.
Learning to Rank Using Semantic Features in Document Retrieval

This paper describes an approach to retrieve documents adopting machine learning method. The application of machine learning to document retrieval, which can be so called “learning to rank”, has been a hot research topic in the information retrieval and machine learning communities recently. One of the characters that discriminates this work from other studies is the use of semantic features while classifying the documents. Firstly, we extract the semantic structures from collection and index documents on these structures. Later, we construct a SVM classifier and generate it using pairwise training data. Lastly, we use this SVM to judge the relevance of documents according to given queries. In the extracting of semantic structures phrase, an analysis system that we developed previously is used. The analysis system is based on modifying relations (MR) and on the help of a knowledge base called MRKB. We give a general introduction about the system in this paper. The experiment is done on the benchmark dataset OHSUMED, and the experimental results shows that the proposed method outperforms other approaches based barely on string frequency.
Decoupled Control of Dual-Split-Phase IMs for Full Power Range Using Capacitive Filters

variety of applications require co-ordinated and independent control of multiple motors. At present, each machine in a multi-motor drive system is controlled independently using its own voltage source inverter (VSI) connected to a common DC-link. Mostly, three phase machines and inverters are used in such applications. The problem with three phase drive systems is increase in inverter count, lower reliability, and difficulty in co-ordinating two motors using only one VSI. For complete decoupled control of two machines (of same or different ratings) at different speeds and loading conditions, while being fed from a single VSI, use of split-phase machines is a viable solution. The stator windings of split-phase machines can be configured to enable independent control of each machine, although the whole system is supplied from a single VSI. This paper describes the decoupled control and operation of two parallel connected split phase induction machines (IM) operated from a six-phase VSI which acts as a power source. To limit the flow of non-torque producing current, two switched capacitor filters are added. As opposed to series connected machines, the proposed scheme allows operation of both machines at full rated power. This is possible because the individual machines in the proposed configuration do not carry the load current of other machine (as in the case of series connected machines). Proposed system is tested with different operating conditions and provided with the experimental results.
VOT4CS: A Virtualization Obfuscation Tool for C#

Software protection is a difficult task especially for managed code, which executes only on a runtime environment such as C# or Java. Applications developed in such languages can be accurately decompiled, as opposed to x86 machine code. This facilitates reverse engineering attacks, with the goal of extracting proprietary algorithms. Due to the ease of distributing software copies across different jurisdictions, software developers cannot only rely on legal means for protection against reverse engineering attacks. Therefore, they have to employ technical means for software protection such as obfuscation. This paper presents an open source tool for virtualization obfuscation of programs written in the C# language, called VOT4CS. Our tool offers several possibilities for randomization that aim to confuse attacks based on pattern recognition. An evaluation of VOT4CS is performed based on several case-studies, which show the performance-security trade-off offered by the tool.