Miracle-dz/UniFAR-FTUs huggingface dataset:
Implementation of Highly Stable Memristive Characteristics in an Organic–Inorganic Hybrid Resistive Switching Layer of Chitosan-Titanium Oxide with Microwave-Assisted Oxidation

This study proposes a high-performance organic–inorganic hybrid memristor for the development of neuromorphic devices in the memristor-based artificial synapse. The memristor consists of a solid polymer electrolyte (SPE) chitosan layer and a titanium oxide (TiOx) layer grown with a low-thermal-budget, microwave-assisted oxidation. The fabricated Ti/SPE–chitosan/TiOx/Pt-structured memristor exhibited steady bipolar resistive switching (BRS) characteristics and demonstrated excellent endurance in 100-cycle repetition tests. Compared to SPE–chitosan memristors without a TiOx layer, the proposed organic–inorganic hybrid memristor demonstrated a higher dynamic range and a higher response to pre-synaptic stimuli such as short-term plasticity via paired-pulse facilitation. The effect of adding the TiOx layer on the BRS properties was examined, and the results showed that the TiOx layer improved the chemical and electrical superiority of the proposed memristor synaptic device. The proposed SPE–chitosan organic–inorganic hybrid memristor also exhibited a stable spike-timing-dependent plasticity, which closely mimics long-term plasticity. The potentiation and depression behaviors that modulate synaptic weights operated stably via repeated spike cycle tests. Therefore, the proposed SPE–chitosan organic–inorganic hybrid memristor is a promising candidate for the development of neuromorphic devices in memristor-based artificial synapses owing to its excellent stability, high dynamic range, and superior response to pre-synaptic stimuli.
Membrane fouling behavior and microbial community succession in a submerged membrane bioreactor treating harbor oily wastewater

A membrane bioreactor (MBR) was established for treating harbor oily wastewater. It showed good removal performance for chemical oxygen demand (COD), oil content, suspended solids (SS), and other pollutants. However, serious membrane fouling occurred. It was recognized that the extracellular polymeric substances (EPS) accumulated on the membrane surface, especially the proteins, were of great importance for the transmembrane pressure (TMP) increment and membrane fouling. The MBR was optimized via improving aeration rate and reducing the ratio of Ar/Ad (Ar and Ad are the cross-sectional areas of the riser and the downcomer of the MBR). The increasing rate of TMP was slowed, indicating that the optimization strategy could effectively mitigate membrane fouling. Microbial community evolution was monitored and analyzed by polymerase chain reaction-denaturing gradient gel electrophoresis (PCR-DGGE), cloning, and sequencing of 16S ribosomal ribonucleic acid (rRNA) fragments. Results revealed that low community shift occurred during the whole operational period. Geobacter sp. and Rhodocyclales sp. , which have also been identified by other studies in a petroleum refinery wastewater MBR or an infiltration basin receiving highway runoff, dominated in the MBR system throughout. Comamonas sp. was thought to accommodate the lower aeration rate in this study, while Rhodocyclales sp. preferred the higher aeration rate. In addition, during the operational time under high organic loading rate, a few species were present in abundance, and may have been responsible for the good removal performance at this time. 中文概要目 的采用膜生物反应器处理港口含油废水。 考察运行过程中膜污染行为特征, 分析引起膜污染的关键影响因素, 研究减弱膜污染的途径与方法; 考察反应器内群落结构演变, 分离鉴定优势菌种, 揭示运行条件和进水水质等宏观环境与微生物微环境之间的对应关系。 创新点1. 分析出膜表面累积的胞外蛋白是膜污染严重和过膜压力增大的关键诱因; 2. 基于Yusuf Chisti和Murray Moo Young 提出的气提液体上升流速模型, 结合胞外蛋白变化趋势, 提出降低过膜压力上升速度和缓解膜污染进程的有效措施; 3. 针对处理港口含油废水的生物系统进行种群结构研究及优势菌属的分离与鉴定。 方 法1. 借助环境扫描电子显微镜(SEM)、能量色散X射线光谱仪(EDX)和傅里叶红外光谱仪(FTIR)等分析手段表征膜污染物的主要成分; 2. 基于Yusuf Chisti 和Murray Moo Young 提出的气提液体上升流速模型, 提出缓解膜污染进程的措施, 并验证所提措施的有效性(图3 和9); 3. 通过巢式PCR-DGGE 技术对处理港口含油废水的膜生物反应器不同运行阶段的微生物群落结构进行研究, 并采用克隆技术对优势微生物进行菌种鉴定(图10 和表3)。 结 论1. 采用一体化厌氧/好氧-膜生物反应器来处理港口含油废水的优点是对有机污染物和油类等污染物的去除效率高, 但在其运行过程中膜污染严重。 2. 分析发现胞外聚合物是引起膜污染物的主要因素(图3∼6); 组分分析进一步表明膜表面累积的胞外蛋白是膜污染严重和过膜压力增大的关键诱因(图7∼9)。 3. 通过增大反应器曝气量, 调整反应器结构参数可有效降低过膜压力上升速度, 缓解膜污染进程。 4. 反应器内细菌群落结构平稳。 5. 克隆测序结果表明: 膜生物反应器内丛毛单胞菌属在低曝气环境下繁殖能力强, 且可能是引起膜污染的特征微生物; 红环菌科细菌更适应高曝气条件, 是降解含有油类污染物的特征细菌。
Study of TTS for a 20-inch dynode PMT

The neutrino detector for the Jiangmen Underground Neutrino Observatory (JUNO) requires a large number of photomultiplier tubes (PMTs), including 15000 MCP PMTs and 5000 dynode PMTs. The TTS (transit time spread) of the PMTs is very important for vertex and track reconstruction of the neutrinos in the detector. In this paper, we study the TTS of a 20-inch dynode PMT (R12860) from Hamamatsu for different high voltage, light intensity, light spot size and different photocathode regions. The impact from Earth's magnetic field is also studied. The results achieved in this paper will be very useful for the JUNO experiment.
Factors associated with lifetime history of eating disorder in non-psychotic patients with suicidal ideation

BACKGROUND: Data on the sociodemographic, biographical, and clinical factors associated with a lifetime diagnosis of eating disorders (ED) in patients with non-psychotic mental disorders (NPMD) and suicidal ideation (SI) are scarce. METHODS: A cohort study was conducted at the Moscow Research and Clinical Center for Neuropsychiatry. The sample consisted of consecutive patients with non-psychotic mental disorders and SI, aged 1845 years. Participants with a lifetime diagnosis of anorexia and/or bulimia (then in remission or recovery) were compared with those without ED in terms of their sociodemographic profile, clinical characteristics, lifetime traumatic events, and some behavioral patterns. All participants underwent the Russian version of the Self-Injurious Thoughts and Behaviors Interview and completed the Brief Reasons for Living Inventory, the State and Trait Anxiety Inventory, and the Beck Depression Inventory. RESULTS: A total of 892 patients with non-psychotic mental disorders and SI were included in the study. The mean age was 25.7 years, and 84% were assigned female at birth. Same-sex experience was more common in the ED group. Patients with an ED were more likely to have a history of physical and sexual abuse and to have witnessed domestic violence. The proportion of participants with piercings, tattoos, or severe body modifications was significantly higher in the ED group. Patients with a lifetime ED were more likely to engage in nonsuicidal self-injurious behaviors and to have a history of suicide attempts. CONCLUSION: Lifetime ED in NPMD patients with SI is associated with younger age, being assigned female at birth, having an alternative gender identity, having same-sex experience, having more than one psychiatric diagnosis, having been diagnosed with bipolar disorder, experiencing severe depression and anxiety, being exposed to multiple traumatic experiences, having various body modifications, practicing NSSI, and having a lifetime story of suicide attempts.
ESTIMATION OF OBJECT SURFACE REFLECTION PROPERTIES BASED ON MEASURING OF SPECTRAL REFLECTION DATA

This paper describes a method for estimating surface properties of an object, including an index of refraction, surface spectral reflectance, and reflection model parameters. These reflection properties of the object surface are modeled using optical parameters of the surface material. These parameters are estimated from camera RGB sensor measurements for reflection intensity of an object surface at different conditions of illumination and viewing. In order to estimate these parameters of the reflection model, we develop an apparatus for measuring reflection intensity on the object surface. The spectral reflectance is estimated by applying the linear finite model and the camera RGB sensor data. Finally, all estimates are combined for rendering of the object under different illumination and viewing conditions.
SoTL Best Practices: 21st Century College Students’ Perceptions of Learning Styles and Instructional Design Materials’ Influence on the Successful Completion of Assignments

Abstract There is a long history of interest in individual differences in learning styles. Beginning in the 1960s, academic research endeavors began examining the concept of personalizing teaching as the best scholarship of teaching and learning best practice (SoTL). This current series of interconnected empirical studies take a fresh look at SoTL by examining students’ self-perception of their learning styles and whether their perceptions relate to how they learn. Today’s college students are growing up in the information age of the 21st Century. Many educators believe that a best practice is to focus on delivering personalized instructional material through technology. Thus, the current mixed methods study adds value to SoTL research by examining these concepts through a representative sample of the subject university in the United States. To assure the reliability and validity of the complex series of three integrated studies, research assistants were trained by a researcher experienced in experimental and survey designs. Data were analyzed using SPSS27®. The study's findings indicated that focusing instructional materials on individual learning styles does not equate to learning success; in fact, the data showed no relationship. The data showed that a combined instructional delivery methodology (kinetic and audio) had a positive influence on learning success. The findings revealed that explicit instructions with or without audio were the most effective in leading to students’ ability to follow instructions successfully. Managing students’ self-perceptions of learning styles is important to assure successful learning experiences. Findings, conclusions, implications, recommendations, and limitations are presented herein.
GENOMIC CHARACTERIZATION OF CDKN2A ALTERATIONS IN MENINGIOMAS (TR)

10. Nebras M. Warsi (SSTP), Simeon M. Wong, Olivia N. Arski, Hrishikesh Suresh, Jürgen Germann, Alexandre Boutet, Lauren Erdman, Flavia Venetucci Gouveia, Elizabeth Kerr, Mary Lou Smith, Ayako Ochi, Hiroshi Otsubo, Rohit Sharma, Puneet Jain, Shelly Weiss, Elizabeth J. Donner, Andres M. Lozano, O. Carter Snead, Margot J. Taylor, George M. Ibrahim: CLOSEDLOOP DECODING AND CONTROL OF ATTENTION IN CHILDREN (TR)
What Role Does Education Play in Improving Gender Equality?

This paper illustrates how important education’s role is in improving gender equality. This work analyzes the phenomenon of gender inequality in the workplace and investigates the gender income gap, female participation ratio, and educational years in urban and rural areas data after 1986. Besides, this research. Hypothesizes that an increase in educational years raises female wages and the female employment rate will shorten the income gap. The controlled variables are the birth rate of males and females. As a result, the employment rate and women’s wage growth followed by increasing education years, and the income gap between males and females will decrease at the same time.
Incremental Nonlinear Dynamic Inversion based Optical Flow Control for Flying Robots: An Efficient Data-driven Approach

This paper presents a novel approach for optical flow control of Micro Air Vehicles (MAVs). The task is challenging due to the nonlinearity of optical flow observables. Our proposed Incremental Nonlinear Dynamic Inversion (INDI) control scheme incorporates an efficient data-driven method to address the nonlinearity. It directly estimates the inverse of the time-varying control effectiveness in real-time, eliminating the need for the constant assumption and avoiding high computation in traditional INDI. This approach effectively handles fast-changing system dynamics commonly encountered in optical flow control, particularly height-dependent changes. We demonstrate the robustness and efficiency of the proposed control scheme in numerical simulations and also real-world flight tests: multiple landings of an MAV on a static and flat surface with various tracking setpoints, hovering and landings on moving and undulating surfaces. Despite being challenged with the presence of noisy optical flow estimates and the lateral and vertical movement of the landing surfaces, the MAV is able to successfully track or land on the surface with an exponential decay of both height and vertical velocity at almost the same time, as desired.
Survey: Automatic Speech Recognition For Indian Languages

This survey paper provides a comprehensive overview of Automatic Speech Recognition (ASR), an area of research that has garnered significant attention due to its numerous real-world applications. The paper begins with a thorough exploration of the basic concepts and underlying theory of ASR, demystifying the fundamental principles of the field. It then delves into various types of end-to-end speech recognition systems, discussing their respective strengths and weaknesses, with a particular focus on Connectionist Temporal Classification (CTC), RNN-Transducers, and transformer-based models. The paper further highlights the unique challenges associated with ASR for Indian languages, a particularly diverse and less-resourced linguistic area, which poses interesting obstacles and opportunities for researchers. Key challenges discussed include the immense linguistic diversity and scarcity of labeled training data. Our aim with this survey is to provide readers with a holistic understanding of ASR, its progress, and potential future directions, particularly within the context of Indian languages.
Uncertainty Estimation for Connectionist Temporal Classification Based Automatic Speech Recognition

Predictive uncertainty estimation of deep neural networks is important when their outputs are used for high stakes decision making. We investigate token-level uncertainty of connectionist temporal classification (CTC) based automatic speech recognition models. We propose an approach, which considers that not all changes at frame-level lead to a change at token-level after CTC decoding. The approach shows promising performance for prediction of recognition errors on TIMIT, Mozilla Common Voice (MCV) and kidsTALC, a corpus of children’s speech, using two different model architectures, while introducing only negligible computational overhead. Our approach identifies over 80 % of a wav2vec2.0 model’s errors on MCV by selecting 10 % of the tokens. We further show, that the predictive uncertainty estimate relates to the uncertainty of a human annotator, by re-annotating 500 utterances of kidsTALC.
ADAPTIVE: Agent-Based Learning for Bounding Time in Mixed-Criticality Systems

In Mixed-Criticality (MC) systems, the high Worst-Case Execution Time (WCET) of a task is a pessimistic bound, the maximum execution time of the task under all circumstances, while the low WCET should be close to the actual execution time of most instances of the task to improve utilization and Quality-of-Service (QoS). Most MC systems consider a static low WCET for each task which cannot adapt to dynamism at run-time. In this regard, we consider the run-time behavior of tasks and propose a learning-based approach that dynamically monitors the tasks’ execution times and adapts the low WCETs to determine the ideal trade-off between mode-switches, utilization, and QoS. Based on our observations on running embedded real-time benchmarks on a real platform, the proposed scheme improves the QoS by 16.4% on average while reducing the utilization waste by 17.7%, on average, compared to state-of-the-art works.
Demonstrating Mobile Manipulation in the Wild: A Metrics-Driven Approach

We present our general-purpose mobile manipulation system consisting of a custom robot platform and key algorithms spanning perception and planning. To extensively test the system in the wild and benchmark its performance, we choose a grocery shopping scenario in an actual, unmodified grocery store. We derive key performance metrics from detailed robot log data collected during six week-long field tests, spread across 18 months. These objective metrics, gained from complex yet repeatable tests, drive the direction of our research efforts and let us continuously improve our system's performance. We find that thorough end-to-end system-level testing of a complex mobile manipulation system can serve as a reality-check for state-of-the-art methods in robotics. This effectively grounds robotics research efforts in real world needs and challenges, which we deem highly useful for the advancement of the field. To this end, we share our key insights and takeaways to inspire and accelerate similar system-level research projects.
Unified Syntactic Annotation of English in the CGEL Framework

We investigate whether the Cambridge Grammar of the English Language (2002) and its extensive descriptions work well as a corpus annotation scheme. We develop annotation guidelines and in the process outline some interesting linguistic uncertainties that we had to resolve. To test the applicability of CGEL to real-world corpora, we conduct an interannotator study on sentences from the English Web Treebank, showing that consistent annotation of even complex syntactic phenomena like gapping using the CGEL formalism is feasible. Why introduce yet another formalism for English syntax? We argue that CGEL is attractive due to its exhaustive analysis of English syntactic phenomena, its labeling of both constituents and functions, and its accessibility. We look towards expanding CGELBank and augmenting it with automatic conversions from existing treebanks in the future.
Absolute phase unwrapping for dual-camera system without embedding statistical features

This paper proposes an absolute phase unwrapping method for 3D measurement that uses two cameras and one projector. On the left camera image, each pixel has one wrapped phase value which corresponds to multiple projector candidates with different absolute phase values. We use geometric relationship of the system to map projector candidates into right camera candidates. By applying a series of candidate rejection criteria, a unique correspondence pair between two camera images can be determined. Then the absolute phase is obtained by tracing the correspondence point back to projector space. Experimental results demonstrate that the proposed absolute phase unwrapping algorithm can successfully work on both complex geometry and multiple isolated objects measurement.
Electronic and magnetic properties of an iron perovskite slab

From the [001] grown Sr2FeMoO6 double perovskite (SFMO) it was excised a slab with (001) free surfaces and half c wide. A supercell was constructed with that slab, leaving a 10 Å vacuum region along the [001] direction, in such a way that it is possible to deal with the slab as an insulated system. After the corresponding geometry optimization, the electronic and magnetic properties of the oriented slab were calculated. All calculations were made within the Density Functional Theory (DFT) scheme, with the Generalized Gradient Approximation (GGA), using the Perdew-Burke-Ernzerhof functional (PBE). The calculated values for each atomic magnetic moment were such that Fe atoms have the higher spin value (2.947ħ) for the SFMO system and are ferromagnetically aligned. Also the Density of States (DOS) and band structure were calculated, because the main SFMO bulk feature is the half-metallic behavior.
Approximation methods for latent variable models

Modern statistical models are often intractable, and approximation methods can be required to perform inference on them. Many different methods can be employed in most contexts, but not all are fully understood. The current thesis is an investigation into the use of various approximation methods for performing inference on latent variable models. Composite likelihoods are used as surrogates for the likelihood function of state space models (SSM). In chapter 3, variational approximations to their evaluation are investigated, and the interaction of biases as composite structure changes is observed. The bias effect of increasing the block size in composite likelihoods is found to balance the statistical benefit of including more data in each component. Predictions and smoothing estimates are made using approximate Expectation- Maximisation (EM) techniques. Variational EM estimators are found to produce predictions and smoothing estimates of a lesser quality than stochastic EM estimators, but at a massively reduced computational cost. Surrogate latent marginals are introduced in chapter 4 into a non-stationary SSM with i.i. d. replicates. They are cheap to compute, and break functional dependencies on parameters for previous time points, giving estimation algorithms linear computational complexity. Gaussian variational approximations are integrated with the surrogate marginals to produce an approximate EM algorithm. Using these Gaussians as proposal distributions in importance sampling is found to offer a positive trade-off in terms of the accuracy of predictions and smoothing estimates made using estimators. A cheap to compute model based hierarchical clustering algorithm is proposed in chapter 5. A cluster dissimilarity measure based on method of moments estimators is used to avoid likelihood function evaluation. Computation time for hierarchical clustering sequences is further reduced with the introduction of short-lists that are linear in the number of clusters at each iteration. The resulting clustering sequences are found to have plausible characteristics in both real and synthetic datasets.
Design and Experiment of a Prescribed-Time Trajectory Tracking Controller for a 7-DOF Robot Manipulator

We present an analytical design and experimental veriﬁcation of trajectory tracking con- trol of a 7-DOF robot manipulator, which achieves convergence of all tracking errors to the origin within a ﬁnite terminal time, also referred to as the “settling time. ” A key feature of this control strategy is that the settling time is explicitly assigned by the control designer to a value desired, or “prescribed” by the user and that the settling time is independent of the initial conditions and of the reference signal. In order to achieve this bene- ﬁcial property with the controller, a scaling of the state by a function of time that grows unbounded toward the terminal time is employed. Through Lyapunov analysis, we ﬁrst demonstrate that the proposed controller achieves regulation of all tracking errors within the prescribed time as well as the uniform boundedness of the joint torques, even in the presence of a matched, nonvanishing disturbance. Then, through both simulation and experiment, we demonstrate that the proposed controller is capable of converging to the desired trajectory within the prescribed time, despite large distance between the initial conditions and the reference trajectory, i.e., in spite of large initial tracking errors, and in spite of a sinusoidal disturbance being applied in each joint. [DOI: 10.1115/1.4055023]
DRC-NET for The 2nd Clarity Enhancement Challenge

This technique report summarises an end-to-end system based on DRC-NET for the 2nd clarity enhancement challenge. In this hearing aid system, first, the DRC-NET recovers target speech from a noisy mixture sampled by the Behind-The-Ear (BTE) hearing aid microphone array. Then, a personalized amplification filter calculated by NAL-R is applied to the estimated speech signal to adopt audiograms of hearing-impaired listeners. The DRC-NET combines convolutional neural networks (CNN) and RNNs as a basic neural operator in dense U-Net architecture, which helps the system could better modeling the long-term variations and short-term local structure of the speech spectrum. Experimental result shows that the system significantly outperforms the baseline system in the development test set.