Miracle-dz/UniFAR-FTUs huggingface dataset:
Atmospheric Chemistry and Physics Water Activity and Activation Diameters from Hygroscopicity Data – Part I: Theory and Application to Inorganic Salts

A method is described that uses particle hygro-scopicity measurements, made with a humidified tandem differential mobility analyzer (HTDMA), to determine solution water activity as a function of composition. The use of derived water activity data in computations determining the ability of aerosols to serve as cloud condensation nu-clei (CCN) is explored. Results for sodium chloride and ammonium sulfate are shown in Part I. The methodology yields solution water activities and critical dry diameters for ammonium sulfate and sodium chloride in good agreement with previously published data. The approach avoids the assumptions required for application of simplified and modified Köhler equations to predict CCN activity, most importantly , knowledge of the molecular weight and the degree of dissociation of the soluble species. Predictions of the dependence of water activity on the mass fraction of aerosol species are sensitive to the assumed dry density, but predicted critical dry diameters are not.
The Dopamine Prediction Error: Contributions to Associative Models of Reward Learning

Phasic activity of midbrain dopamine neurons is currently thought to encapsulate the prediction-error signal described in Sutton and Barto’s (1981) model-free reinforcement learning algorithm. This phasic signal is thought to contain information about the quantitative value of reward, which transfers to the reward-predictive cue after learning. This is argued to endow the reward-predictive cue with the value inherent in the reward, motivating behavior toward cues signaling the presence of reward. Yet theoretical and empirical research has implicated prediction-error signaling in learning that extends far beyond a transfer of quantitative value to a reward-predictive cue. Here, we review the research which demonstrates the complexity of how dopaminergic prediction errors facilitate learning. After briefly discussing the literature demonstrating that phasic dopaminergic signals can act in the manner described by Sutton and Barto (1981), we consider how these signals may also influence attentional processing across multiple attentional systems in distinct brain circuits. Then, we discuss how prediction errors encode and promote the development of context-specific associations between cues and rewards. Finally, we consider recent evidence that shows dopaminergic activity contains information about causal relationships between cues and rewards that reflect information garnered from rich associative models of the world that can be adapted in the absence of direct experience. In discussing this research we hope to support the expansion of how dopaminergic prediction errors are thought to contribute to the learning process beyond the traditional concept of transferring quantitative value.
Cajal‐Retzius cells in early postnatal mouse cortex selectively express functional metabotropic glutamate receptors

Glutamate receptors have been linked to the regulation of several developmental events in the CNS. By using cortical slices of early postnatal mice, we show that in layer I cells, glutamate produces intracellular calcium ([Ca2+]i) elevations mediated by ionotropic and metabotropic glutamate receptors (mGluRs). The contribution of mGluRs to these responses was demonstrated by application of tACPD, an agonist to groups I and II mGluRs, which evoked [Ca2+]i increases that could be reversibly blocked by MCPG, an antagonist to groups I and II mGluRs. In the absence of extracellular Ca2+, repetitive applications of tACPD or quisqualate, an agonist to group I mGluRs, elicited decreasing [Ca2+]i responses that were restored by refilling a thapsigargin‐sensitive Ca2+ store. The use of specific group I mGluR agonists CHPG and DHPG indicated that the functional mGluR in layer I was of the mGluR1 subtype. Subtype specific antibodies confirmed the presence of mGlur1α, but not mGluR5, in Cajal‐Retzius (Reelin‐immunoreactive) neurons.
Fast semi-device-independent quantum random number generator based on unambiguous state discrimination

The generation of random numbers is of paramount importance in modern science and technology, in particular cryptography. Here, we develop a new approach to quantum random number generation based on unambiguous quantum state discrimination. We consider a prepare-and-measure protocol, where two non-orthogonal quantum states can be prepared, and a measurement device aims at unambiguously discriminating between them. Because the states are non-orthogonal, this necessarily leads to a minimal rate of inconclusive events whose occurrence must be genuinely random and which provide the randomness source that we exploit. Our protocol is semi-device-independent in the sense that the output entropy can be lower bounded based on experimental data and few general assumptions about the setup alone. It is also practically relevant, which we demonstrate by realising a simple optical implementation achieving rates of 16.5 Mbits/s. Combining ease of implementation, high rate, and real-time entropy estimation, our protocol represents a promising approach intermediate between fully device-independent protocols and commercial QRNGs.
'Self-Fulfilling Prophecy': A Literature Review

The power of positivity is one of the hottest emerging topics of the time. People today are becoming more and more awar e about the power of human mind and thoughts. Psychologists and thinkers have increasingly focused on the importance of positive thinking in human life. Though it is not a new concept according to the Indian mythology. Indian sages use d to say that a person‘s destiny is in his/her own hands. Their intension was to make people aware about the tremendous power possessed by the human mind and the need to channelize that energy for the upliftment of the mankind. Even the modern psychologists and scientists have found a significant positive relation between a person‘s mental beliefs and situations he/she encounters in life. It is said that whatever we think and spend our mental energy on, manifests itself in ph ysical form in our lives. This phenomenon is the essence of the concept known as ―self-fulfilling prophecy‖. The aim of this paper is to study the meaning of this term and the implementation pro cess. This paper also highlights the interplay of self-f ulfilling prophecy with the concepts like- stereotyping and expectati ons. This paper intends to study the step by step implementa tion of this concept in real life as suggested by various research ers and talk about the factors impacting it.
Large Eddy Simulation of a High Pressure Turbine Stage: Effects of Sub-Grid Scale Modeling and Mesh Resolution

The use of Computational Fluid Dynamics (CFD) tools for integrated simulations of gas turbine components has emerged as a promising way to predict undesired component interactions thereby giving access to potentially better engine designs and higher efficiency. In this context, the ever-increasing computational power available worldwide makes it possible to envision integrated massively parallel combustion chamber-turbomachinery simulations based on Large-Eddy Simulations (LES). While LES have proven their superiority for combustor simulations, few studies have employed this approach in complete turbomachinery stages. The main reason for this is the known weaknesses of near wall flow modeling in CFD. Two approaches exist: the wall-modeled LES, where wall flow physics is modeled by a law-of-the-wall, and the wall-resolved LES where all the relevant near wall physics is to be captured by the grid leading to massive computational cost increases. This work investigates the sensitivity of wall-modeled LES of a high-pressure turbine stage. The code employed, called TurboAVBP, is an in-house LES code capable of handling turbomachinery configurations. This is possible through an LES-compatible approach with the rotor/stator interface treated based on an overset moving grids method. It is designed to avoid any interference with the numerical scheme, allow the proper representation of turbulent structures crossing it and run on massively parallel platforms. The simulations focus on the engine-representative MT1 transonic high-pressure turbine, tested by QinetiQ. To control the computational cost, the configuration employed is composed of 1 scaled stator section and 2 rotors. The main issues investigated are the effect of mesh resolution and the effect of sub-grid scale models in conjunction with wall modeling. The pressure profiles across the stator and rotor blades are in good agreement with the experimental data for all cases. Radial profiles at the rotor exit (in the near and far field) show improvement over RANS predictions. Unsteady flow features, inherently present in LES, are, however, found to be affected by the modeling parameters as evidenced by the obtained shock strengths and structures or turbulence content of the different simulations.Copyright © 2014 by ASME
Research on Deep Learning Techniques in Breaking Text-Based Captchas and Designing Image-Based Captcha

The ability of hackers to infiltrate computer systems using computer attack programs and bots led to the development of Captchas or Completely Automated Public Turing Tests to Tell Computers and Humans Apart. The text Captcha is the most popular Captcha scheme given its ease of construction and user friendliness. However, the next generation of hackers and programmers has decreased the expected security of these mechanisms, leaving websites open to attack. Text Captchas are still widely used, because it is believed that the attack speeds are slow, typically two to five seconds per image, and this is not seen as a critical threat. In this paper, we introduce a simple, generic, and fast attack on text Captchas that effectively challenges that supposition. With deep learning techniques, our attack demonstrates a high success rate in breaking the Roman-character-based text Captchas deployed by the top 50 most popular international websites and three Chinese Captchas that use a larger character set. These targeted schemes cover almost all existing resistance mechanisms, demonstrating that our attack techniques are also applicable to other existing Captchas. Does this work then spell the beginning of the end for text-based Captcha? We believe so. A novel image-based Captcha named Style Area Captcha (SACaptcha) is proposed in this paper, which is based on semantic information understanding, pixel-level segmentation, and deep learning techniques. Having demonstrated that text Captchas are no longer secure, we hope that our proposal shows promise in the development of image-based Captchas using deep learning techniques.
A method for extracting high-quality total RNA from plant rich in polysaccharides and polyphenols using Dendrobium huoshanense

Acquiring high quality RNA is the basis of plant molecular biology research, plant genetics and other physiological investigations. At present, a large number of nucleotide isolation methods have been exploited or modified, such as commercial kits, CTAB, SDS methods and so on. Due to the nature of different plants, extraction methods vary. Moreover, efficiency of certain approach cannot be guaranteed due to composition of different plants and extracting high quality RNA from plants rich in polysaccharides and polyphenols are often difficult. The physical and chemical properties of polysaccharides which are similar to nucleic acids and other secondary metabolites will be coprecipitated with RNA irreversibly. Therefore, how to remove polysaccharides and other secondary metabolites during RNA extraction is the primary challenge. Dendrobium huoshanense is an Orchidaceae perennial herb that is rich in polysaccharides and other secondary metabolites. By using D. huoshanense as the subject, we improved the method originated from CHAN and made it suitable for plants containing high amount of polysaccharides and polyphenols. The extracted total RNA was clear and non-dispersive, with good integrity and no obvious contamination with DNA and other impurities. And it was also evaluated by gel electrophoresis, nucleic acid quantitative detector and PCR assessment. Thus, as a simple approach, it is suitable and efficient in RNA isolation for plants rich in polysaccharides and polyphenols.
GFDM Interference Mitigation Without Noise Enhancement

Generalized frequency division multiplexing (GFDM) is a promising candidate for the next generation of wireless communications. Using non-orthogonal pulse shapes in GFDM, the transmitted symbols overlap on each other according to a self-interference matrix. Because of those small singular values of this matrix, the interference compensation leads to noise enhancements at the receiver of GFDM. In this letter, we develop a novel scheme called coded GFDM (CGFDM) that transmits a coded version of symbols. It is shown that the self-interference matrix of CGFDM is unitary with the singular values all equal to one; as a result, the interference removal does not enhance the power of noise.
Mbps experimental acoustic through-tissue communications: MEAT-COMMS

Methods for digital, phase-coherent acoustic communication date to at least the work of Stojanjovic, et al [20], and the added robustness afforded by improved phase tracking and compensation of Johnson, et al [21]. This work explores the use of such methods for communications through tissue for potential biomedical applications, using the tremendous bandwidth available in commercial medical ultrasound transducers. While long-range ocean acoustic experiments have been at rates of under 100kbps, typically on the order of 1-10kbps, data rates in excess of 120Mb/s have been achieved over cm-scale distances in ultrasonic testbeds [19]. This paper describes experimental transmission of digital communication signals through samples of real pork tissue and beef liver, achieving data rates of 20-30Mbps, demonstrating the possibility of real-time video-rate data transmission through tissue for in-body ultrasonic communications with implanted medical devices.
Elevation of Serum Alpha‐fetoprotein and Proliferation of Oval Cells in the Livers of LEC Rats

Alpha‐fetoprotein (AFP) in the sera of 35 LEC (Long‐Evans with a cinnamon‐like coat color) rats between 7 and 25 weeks of age was evaluated by enzyme‐linked immunosorbent assay (ELISA). Elevation of serum AFP and proliferation of oval cells in the liver were observed in most LEC rats, which suffered from acute hepatitis. On the other hand, the serum AFP level was within the normal range before the onset of hepatitis. Immunohistochemical staining for AFP revealed that some of the proliferating oval cells produced AFP, Morphotnetric analysis of AFP‐positive cells and ELISA for serum AFP demonstrated that there was a statistically significant correlation between the number of AFP‐positive cells in the liver and the concentration of AFP in the serum. Histological examination revealed the transition and differentiation of the oval cells to small hepatocytes. These results suggested that the phenomena which occurred in LEC rats suffering from acute hepatitis were similar to those that occurred during the early stage of azo dye hepatocarcinogenesis, although the extent of the oval cell proliferation and the elevation of serum AFP in LEC rats were not as great as those in rats treated with azo dye. This is the first report on a rat strain with proliferation of AFP‐producing oval cells during its natural history.
Perceived interpersonal sources of life satisfaction in Chinese and American students: Cultural or gender differences?

Abstract This study evaluated cultural and gender differences in perceived interpersonal sources of life satisfaction between 341 Chinese and 222 American college students. Comparative analyses indicated overall lower life satisfaction in Chinese, than in Americans. However, Chinese, compared to Americans, reported greater life satisfaction derived from perceived sources involving the self, parents, teachers and peers. Results of regression analyses indicated that, for both Chinese and Americans, the self and parents were unique sources associated with life satisfaction. However, for Americans, close friends also emerged as an additional unique source. When gender differences were examined, additional nuances were found within each cultural group. Thus, some evidence for both cultural and gender differences in perceived interpersonal sources of life satisfaction across Chinese and Americans was found. Some implications of the present findings are discussed.
Towards Visual Software Analytics

Since its inception, a large amount of software has been written in Java and surprisingly little is known about the structure of Java programs in the wild. There are very few software visualization tools for analytical reasoning of Java software. We are creating a visual software analytics tool that will help to characterize a corpus of Java software. Our tool will help to provide insight into a collection of Java programs, confirm the expected, and expose the unexpected.
Impact of axonal delay on structure development in a Linsker type network.

In his seminal, three paper series, Linsker provided a mechanism for how random activity in the visual pathway could give rise to many of the features observed experimentally in the early stages of visual processing. Owing to the complexity of multilayer models, an implicit assumption in Linsker's and subsequent papers has been that propagation delay is homogeneous and, consequently, plays little functional role in neural behaviour. In this paper, we relax this assumption to examine the impact of axonal distance dependent propagation delay on neural learning. We show that propagation delay induces lowpass filtering by dispersing the arrival times of spikes from presynaptic neurons, providing a natural correlation cancellation mechanism for distal connections. The cutoff frequency decreases as the radial propagation delay within a layer, relative to propagation delay between the layers, increases, introducing an upper limit on temporal resolution. Given that the postsynaptic potential also acts as a lowpass filter, we show that the effective time constant of each should enable the processing of similar scales of temporal information. This result has implications for the visual system, in which receptive field size and, thus, radial propagation delay, increases with eccentricity. Furthermore, the network response is frequency dependent since higher frequencies require increased input amplitude to compensate for attenuation. This concords with frequency dependent contrast sensitivity in the visual system, which changes with eccentricity and receptive field size. Finally, we determine the eigenfunctions of both Linsker's network, and the network with propagation delay. We show that the addition of propagation delay stabilizes the leading eigenfunction to changes in homeostatic parameters, and hence stabilizes the resulting receptive field structure.
Managing the Stroke Rehabilitation Triage Process

The challenge of constructing an exemplary stroke rehabilitation system is balancing the attempt to maximize patient outcomes while controlling costs. This review, using findings from Chapters 3, 5, 7, 21, and 22 and from evidence-based consensus opinions, presents research on the stroke rehabilitation triage process. Evidence on patient screening, establishing assessment criteria, stroke severity, and unit location is reported. As well, a potential stroke rehabilitation triage system is proposed.
Performance of the ARX-derived Auditory Evoked Potential Index as an Indicator of Anesthetic Depth: A Comparison with Bispectral Index and Hemodynamic Measures during Propofol Administration

Background Autoregressive modeling with exogenous input of middle-latency auditory evoked potential (A-Line autoregressive index [AAI]) has been proposed for monitoring anesthetic depth. The aim of the current study was to compare the accuracy of this new index with the Bispectral Index (BIS), predicted effect-site concentration of propofol, and hemodynamic measures. Methods Twenty female patients scheduled for ambulatory gynecologic surgery received effect compartment controlled infusion of propofol. Target effect-site concentration was started at 1.5 &mgr;g/ml and increased every 4 min by 0.5 &mgr;g/ml. At every step, sedation level was compared with monitoring values using different clinical scoring systems and reaction to noxious stimulus. Results Bispectral Index, AAI, and predicted propofol effect-site concentration were accurate indicators for the level of sedation and loss of consciousness. Hemodynamic variables were poor indicators of the hypnotic-anesthetic status of the patient. BIS correlated best with propofol effect-site concentration, followed by AAI. Hemodynamic measurements did not correlate well. No indicators predicted reaction to noxious stimulus. Poststimulus, BIS and AAI showed an increase as a result of arousal. This reaction occurred more rapidly with the AAI than with BIS. Conclusion Bispectral Index, AAI, and predicted propofol effect-site concentration revealed information on the level of sedation and loss of consciousness but did not predict response to noxious stimulus.
Ranking Influencers in Social Collaboration Networks

The emergence of social collaboration platforms has created new ways for people or groups to interact and work collaboratively to achieve mutual goals. The research community has highlighted the importance of measuring influence in such social collaboration networks to recognize key contributors and better facilitate collaboration. However, classic graph-based approaches often fail to elaborate on relations between people and the items they collaborate on, which is the essence of such social collaboration platforms. Moreover, with traditional methods it is usually difficult to incorporate auxiliary information about users and items into the modeling process. Therefore, in this paper we draw an analogy between recommender systems and social collaboration networks, and propose a method to harness information about users and items to model influence diffusion in collaboration networks. With this analogy, we quantify social influence by measuring the latent influence of each user-item pair under a unified framework based on factorization machines. Experiments conducted on a real-world dataset collected from GitHub attest the effectiveness of the proposed framework in providing better rankings for top influencers than several baseline methods. In addition, interesting analyses and discussions on the results are also provided in the paper.
Order-Planning Neural Text Generation From Structured Data

Generating texts from structured data (e.g., a table) is important for various natural language processing tasks such as question answering and dialog systems. In recent studies, researchers use neural language models and encoder-decoder frameworks for table-to-text generation. However, these neural network-based approaches typically do not model the order of content during text generation. When a human writes a summary based on a given table, he or she would probably consider the content order before wording. In this paper, we propose an order-planning text generation model, where order information is explicitly captured by link-based attention. Then a self-adaptive gate combines the link-based attention with traditional content-based attention. We conducted experiments on the WikiBio dataset and achieve higher performance than previous methods in terms of BLEU, ROUGE, and NIST scores; we also performed ablation tests to analyze each component of our model.