Miracle-dz/UniFAR-FTUs huggingface dataset:
Detecting Multiple Disfluencies from Speech using Pre-linguistic Automatic Syllabification with Acoustic and Prosody Features

In this paper, a new method to detect disfluencies directly from speech is explored. The method makes use of pre-linguistic automatic syllabification - the process of segmenting input speech signals into perceptually distinct syllable-like regions, to develop syllable-level disfluency detection systems. Statistical prosody features related to fundamental frequency, energy and duration are extracted from each syllable-like region and used to train a DNN classifier for automatic detection of speech disfluencies. Further, complementary information useful for the task of disfluency detection is added to the pipeline with the help of acoustic features. A BiLSTM feature extractor is used to get complex acoustic representation from the baseline MFCC features for each syllable-like region. This acoustic representation is concatenated with the prosody features and used in the proposed system for detecting multiple speech disfluencies. Experiments are conducted for four types of disfluencies in the UCLASS and the IIITH-IED datasets to test the proposed disfluency detection system. Overall, it is found that the proposed system gives a detection accuracy of 88.75% for the disfluencies in the UCLASS dataset, whereas for the IIITH-IED dataset, the accuracy obtained is 91.24%, showing the effectiveness of considering perceptually distinct syllable-like regions as representational units for detecting disfluencies.
Simulation of precipitation in 40 alpha world cities for the 20th century by means of random walk

Totally unexpected and unpredictable droughts and floods frequently occur around the world. The unexpectedness and unpredictability strongly suggest that random factors should play an important role in those unexpected and unpredictable droughts and floods. The historical precipitations provide an important way to analyze the precipitation trend and also provide a possibility to use various models to simulate the historical precipitations, which is important too because it is almost impossible to conduct a controlled experiments on precipitations to illustrate their patterns and results. In this study, we use the random walk to simulate the annual precipitation in 40 alpha world cities for the 20th century. The results show that the random walk can simulate the year-to-year precipitations and can catch their general trend. Thus this study provides a way to describe the unexpectedness and unpredictability of precipitation patterns in these cities during this period of time.
WiTricity:Wireless Power Transfer By Non-radiative Method

A non-radiative energy transfer, commonly referred as WiTricity and based on ‘strong coupling’ between two coils which are separated physically by medium-range distances, is proposed to realize efficient wireless energy transfer. WiTricity idea is spear-headed by MIT researcher Marin Soljacic, which describes the ability to provide electricity to remote objects without wires. The advent of WiTricity technology is though old of 1899, explored by Nikola Tesla, but has founded its grip in recent years with numerous gadgets and there snaking cables around us. The technology is in turn expels E-waste and will free us from the power cords. WiTricity depends upon strong coupled resonance between transmitter and receiver coils. The transmitter emits a non-radiative magnetic field resonating at MHz frequencies and the receiving unit load resonates in that field. Thin film WiTricitycells, consisting of a tape coil on one layer, which is separated by an insulation layer to conductive strips on another layer, are fabricated. These cells are light, compact, and flexible provide convenience and flexibility in the design of wireless devices. Experiments are conducted to showcase the performance of this energy transfer system. Keywords— WiTricity, Inductive magnetic coupling, Resonant wireless energy transfer, strong coupling.
Case studies of low socioeconomic Mexican American graduates in the Austin Independent School District: why they didn't drop out

Case Studies of Low Socioeconomic Mexican American Graduates in the Austin Independent School District: Why They Didn’t Drop Out. (December 2006) Lucila Diaz Duncan, B.S., Texas Woman’s University; M.Ed., Sul Ross State University Chair of Advisory Committee: Dr. Linda Skrla Research data revealed that Mexican Americans have the highest dropout rate of any other Hispanic group. Since Hispanics are becoming the highest minority group in the U.S., the primary purpose of this qualitative study was to investigate what helped third generation low socioeconomic Mexican American A.I.S.D. students successfully graduate from high school. Three former students were selected from three different high schools. The researcher collected data through individual interviews with each participant and with key informants such as parents and school personnel. Issues examined included participant perceptions of factors that kept them in school, the support systems they believed were important, and the challenges they encountered. The overall findings revealed that the three participants had a strong sense of control, positive relationships with peers and school staff, parents who valued education, and strong school support through programs or personnel. Recommendations for educators based on the findings include 1) staff
Neural Articulated Radiance Field Supplemental Material

In Table 1 of the main paper, we quantitatively evaluate our model in the case of a single articulated 3D object, while in Section 5 in the main paper, we only show qualitative results in the case of Autoencoder. Here, we supplement those results with the quantitative ablation study for Autoencoder in Table A. The same test data settings (namely “same pose/same view”, “novel pose/same view”, “same pose/novel view”, and “novel pose/novel view”), metrics (namely PSNR, SSIM, and Mask), and baseline methods (namely NARFP , NARFH and NARFD, CNN-based, PNeRF and D-NARF) are used for comparison. The same dataset as in Section 5 of the main paper is used for experimentation. At testing phase, when extracting the latent shape and appearance vectors (zs and za) using the encoder, we use images under the same viewpoint distribution as in the training images as input. Then, images from novel views and poses are rendered by combining zs and za with unseen views and poses in the training data.
Report of Echinococcus granulosus sensu stricto ( G 1 ) in a Domestic Cat in Turkey

Echinococcus granulosus sensu lato is a zoonotic helminth with a life cycle that include of definitive hosts (dogs and wild carnivores) and intermediate hosts (usually the even-toed ungulates, Artiodactyla). Intermediate hosts become infected by ingesting the parasite eggs in contaminated food and water.Accidental intermediate hosts acquire infection in a similar way as other intermediate hosts. A two-year-old female cat was presented to the Van (Turkey) Animal Care and Rehabilitation Center with abdominal tension. Multiple intraperitoneal vesicles, which were found to be E. granulosus (s.1.) metacestodes, were observed during the ultrasound imaging. Then, the animal was laparotomized. Phylogenetic analysis based the partial cytochrome c oxidase 1 (pcox1) mitochondrial gene region was performed on metacestode samples (hydatid cysts). The isolate was identified as sensu stricto genotype G1, which is most commonly found in Turkey.
Analyzing the Common Features of Public Bicycle Share Data for Decision Support

—While public bicycle share (PBS) systems are becoming more popular around the world, the amount of public transportation data continuously increases. Analyzing this data can provide us with a better understanding of human mobility patterns and urban dynamics, as well as help us improve existing bicycle sharing solutions and the infrastructure for all bike users. This mapping study aims to give an overview of different features of the PBS data that have been used to visualize and analyze the usage of bicycle sharing solutions in order to provide decision support.
Performance Analysis of Energy Optimized LTE-V2X Networks for Delay Sensitive Real-Time Services

Energy-efficient relaying technology in multi-hop data transmission can help the challenges faced in cellular Vehicle-to-Everything (cellular-V2X) communication. However, due to high demand of emergency service requirements of the systems such as Public Protection and Disaster Relief (PPDR), National Security and Public Safety (NSPS), Intelligent Transport System (ITS) etc., least energy consumed user equipment (UEs)/Vehicular-UEs are required which can either run real-time applications or relay the application data. To support these scenarios, we present a high way based system model in rural area and enhance its scope for applying single-hop direct, relay assisted multi-hop cellular-V2X and Store-CarryForward (SCF) modes of uplink data transmission. We compare the performance of three modes of transmissions in terms of overall energy consumption and overall transmission delay with specific delay constraints of VoIP and video applications. With the varying cell radius and irrespective type of applications, our numerical results, validated with ns-3 show that, least energy is always consumed in SCF mode due to its inherent property but applications suffer a lot due to high delay incurred whereas singlehop direct mode shows the reverse. When compared with cellular-V2X mode, overall transmission delay for single-hop direct mode is acceptable within cell radius 600m but beyond that, relay assisted multi-hop cellular-V2X mode always outperforms (with low latency and moderate energy consumption).
A Survey on the Performance Optimization of CMOS - Operational Amplifier using Evolutionary Techniques

: The growing complexity in the formulation of integrated circuit (IC) design requires a suitable optimization technique to ensure “nil faults”. The formulation techniques designed for determining the best solutions with statistical theory requires long duration for executing the design. Design techniques should be capable of handling the stability, correctness, toughness and reliability. Characteristic analog ICs like offset voltage, DC gain, gain – bandwidth product etc., are mainly depends on the design optimization. This paper presents a comprehensive review on performance optimization of CMOS-operational amplifier using evolutionary techniques with respect to speed, power and area.
Improving a Bayesian Network Based Recognition of Spontaneous Facial Expressions of a Person who Watches Web News - Utilizing Image Features for Blinks -

Recently reading news through web news media is becoming popular. Most of web news is delivered together with a comment system, which asks the user to rate items such as “ the news let you think ” , and “ boring ” . However, many users do not like manual operations for rating; therefore, not many users actually input the rates. One solution for this issue is to utilize results of recognizing facial expressions from the video sequence acquired by the camera that observes the user. So far, there are very many works on recognizing six fundamental expressions such as sad, surprise and happy, for example, by HMM (Hidden Markov Models) [1]. However, not many works dealt with recognizing expressions that could appear when the user watches web news; main difficulty in recognizing these expressions could be caused by the fact that these expressions are spontaneously generated. It is difficult to recognize the spontaneous expressions, which come with tiny movements hard to be detected. This paper proposes a method that aims at recognizing spontaneous expressions.
GPT-2 for Emily Dickinson poetry generation

In 1985, Steve Jobs gave a talk at the University of Lunds outlining his vision for the future. The following excerpt of what he said about artificial intelligence caught our attention: “My hope is someday, when the next Aristotle is alive, we can capture some underlying worldview of Aristotle in a computer. And someday some student will be able to not only read the words Aristotle wrote but ask Aristotle a question and get an answer” [1]. Fast forward 36 years to now, and Jobs’ vision of machine intelligence has come to life, in a sense. In this project we test Jobs’ idea of “capturing some underlying worldview of Aristotle” with a famous 19th century poet, Emily Dickinson. Through a technical and literary analysis, we explore a GPT-2 model trained on a dataset of Dickinson’s poetry and fine tuned to output new Dickinson poems. We explore the implications of uploading the “consciousnesses” of famous thinkers using deep learning models, and conclude with a brief analysis of our model’s generated poetry.
Art and the City: Contemporary Art Galleries Districts in Paris from the End of the 19th Century until Today

The space invested by contemporary art galleries is of utmost importance. Not only is it essential to have suitable premises, but they must also be in the right place: The physical address carries a lot of weight. The benefits to galleries of being concentrated in the same areas are twofold: They are close to their competitors, which means they are close to the art market, and thus, by their collective presence, can boost the market by encouraging collectors to go to the same places built up as art districts. Moreover, the district’s qualifying function comes about through the collective construction of this grouping of galleries from which it benefits. Today in Paris, it is the Marais neighborhood—a sector that started developing in the 1970s but even more in the 1980s and 1990s—that epitomizes the place to be for contemporary art galleries. The implantation of contemporary galleries in Paris clearly results from a historical process that led them from the 8th arrondissement to the Marais, stopping briefly at Saint-Germain-des-Prés (or the 6th arrondissement) mostly for small avant-garde structures. Studying the implantation—here in Paris—of contemporary art galleries over time illustrates the dynamics that gird the installation choices and also shows how alive the urban fabric is. Galleries enter the transformations of the urban fabric, and when they are numerous enough, they also participate directly in its development.
Willingness to Receive the COVID-19 Vaccination and the Psychological State of Japanese University Students: A Cross-Sectional Study

Vaccinations may be one of the solutions to end the COVID-19 pandemic. One’s psychological state may be strongly related to one’s willingness to be vaccinated. This study investigated the relationship between the psychological state of Japanese university students and their willingness to be vaccinated. A self-report questionnaire on COVID-19, its vaccines (vaccination status, and perceived efficacy and safety), and psychological state (anxiety and depressive mood) was administered online, and 560 valid responses were obtained. The unvaccinated group reported significantly lower perceived vaccine effectiveness and importance than the vaccinated group. However, there were no differences in anxiety and depressive mood symptoms between the two groups. Multivariate logistic regression analysis was performed on the unvaccinated participants to identify the factors associated with their unwillingness to be vaccinated; there was a significant association between anxiety and unwillingness to receive the vaccine (p < 0.05). However, there was no significant association between depressive mood and unwillingness to receive the vaccine. The results suggest that timely psychological support for Japanese university students experiencing high levels of anxiety is important in accelerating vaccination programs.
Modelling the optical and electronic transport properties of AlGaAs and AlGaN intersubband devices and optimisation of quantum cascade laser active regions

Terahertz quantum cascade lasers (THz QCLs) have many potential applications such as medical and security screening. While their output power has recently exceeded 1 W, their highest operating temperature is currently limited to approximately 200K due to mechanisms such as thermal backfilling and non-radiative phonon emission between lasing states. To achieve higher operating temperatures, theoretical models are key to suppressing these degradation mechanisms either through further design optimisation or new material systems. This work investigates the opto-electronic properties of state-of-the-art intersubband devices in AlGaAs/GaAs and AlGaN/GaN material systems as well as the applications of QCLs. A density matrix model is investigated and used to predict the electron distribution, gain and current density in an arbitrary QCL active region. This model is validated with a comparison to rate equation, non-equilibrium Green’s function, and experimental data for AlGaAs/GaAs QCLs. Novel designs using tall AlAs barriers to suppress leakage current are modelled, and the effect of long and short range interface roughness is investigated. An increased sensitivity to roughness is shown for tall barrier structures which have a larger conduction band offset discontinuity and thinner epitaxial layers. The model is then used to optimise both AlGaAs and AlGaN QCL structures to propose new designs for a desired emission wavelength. The use of the density matrix approach to model possible applications is demonstrated by modelling the origin of the self-mixing (optical feedback) interferometry terminal voltage variations. It is shown that the self-mixing voltage amplitude is highly dependent on the differential resistance of the QCL, and the increased sensitivity of a particular QCL is explained. The feasibility of nitride QCLs is shown by comparing the calculated and experimental absorption linewidth of near-infrared and THz AlGaN/GaN quantum wells grown by molecular beam epitaxy. Finally, a novel adaptation of the density matrix approach is used to investigate the transport properties of nitride resonant tunnelling diodes alongside sequential tunnelling devices. This allows the extent of transport due to bound defect states and interface roughness values to be estimated.
Data Assimilation of Nowcasted Observations

We investigate the assimilation of nowcasted information into a classical data assimilation cycle. As a reference setup we employ the assimilation of standard observations such as direct observations of particular variables into a forecasting system. The pure advective movement extrapolation of observations as a simple nowcasting (NWC) is usually much better for the first minutes to hours, until outperformed by numerical weather prediction (NWP) based on data assimilation. Can nowcasted information be used in the data assimilation cycle? We study both an oscillator model and the Lorenz 63 model with assimilation based on the Localized Ensemble Transform Kalman Filter (LETKF). We investigate and provide a mathematical framework for the assimilation of nowcasted information, approximated as a local tendency, into the LETKF in each assimilation step. In particular, we derive and discuss adequate observation error and background uncertainty covariance matrices and interprete the assimilation of nowcasted information as assimilation with an H1-type metric in observation space. Further, we show numerical results which prove that nowcasted information in data assimilation has the potential to significantly improve model based forecasting.
“I Definitely Wouldn’t Have Been Able to Make it Through College Without My Dad’s Money”: American College Students’ Reflections Regarding Caregiver Financial Support

American college students (N = 49) participated in semi-structured qualitative interviews focused on the manner in which their caregivers did (or did not) provide financial support related to their college-enrollment. Transcript data were coded and analyzed using a cross-case, variable-oriented approach. Students described variability in their perceptions of (a) the extent of caregiver financial resources and (b) caregiver representations of financial contributions to college education as a source of support or a source of control. Placement with respect to these two dimensions yielded groups of students that varied in meaningful ways in terms of feelings of obligation to share school-related information with caregivers and attitudes toward caregivers’ support. Findings have implications for informing the manner in which researchers, parents, and campus professionals understand the nature of financial support and the importance of considering the emotional climate within which it is provided.
Selection and Resource Allocation Model for Upgrading Fire Safety of Historic Buildings

AbstractFire safety constitutes an important component of cultural heritage protection since fire constitutes a major hazard for historic buildings. In the past many examples have been reported of where fire caused significant damage to priceless monuments of the world’s cultural heritage (e.g., Chiado area in Lisbon, Portugal, in 1988; Fenice Theatre in Venice, Italy, in 1996). A holistic approach to the preservation of historic buildings should include, among other things, a concern for suitable fire protection design. Such actions, however, are frequently subjected to stringent budget restrictions that confront designers with challenging trade-offs in their decision-making process. In this context, the current study provides an integrated scheme that embodies innovative tools for the solution of the budget allocation problem with respect to protecting historic buildings from fire. In particular, a generic selection and resource allocation model for updating the fire safety of historic buildings is prop.
Taxonomy grooming algorithm ‐ An autodidactic domain specific dimensionality reduction approach for fast clustering of social media text data

Social media being the most eminent source toward the growth of big data is important for information retrieval‐based applications to improve the efficiency in proportional to the volume it must deal with. One way to achieve better performance is to upgrade the processing capacity and the alternative option is to improve the processing methodology. The latter can be achieved using smarter processing techniques and/or better algorithms. Reducing the data volume that needs to be processed is a good strategy and it can be achieved by extracting only the relevant information via user segmentation by adopting an appropriate clustering technique. However, while dealing with text content, clustering algorithms do suffer due to the very high dimensions to be dealt with. Since the domain‐specific aspects are getting lost while applying traditional dimensionality reduction approaches, it is important to device an alternate strategy. This work proposes a taxonomy grooming algorithm (TGA), an autodidactic domain‐specific dimensionality reduction approach, for fast clustering of social media text data. Our experiment results are very promising and the dimensionality reduction using TGA resulted in better results in comparison with the traditional dimensionality reduction approaches.