Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Facial Dataset Capture and Processing for Visual Speech Recognition using an RGB-D Sensor

— This work presents a new RGB-D acquisition system to capture a comprehensive dynamic facial dataset that can be used for visual speech recognition. The RGB-D facial dataset acquisition system uses a Kinect to record detailed facial features of a person. The dynamic facial dataset is comprised of the facial data of 20 individuals saying 20 common English words or phrases. The acquisition system employs Kinect facial tracking, which records a large number of dynamic facial features. These features include: facial points, facial outline, RGB data, depth data, mapping between RGB and depth data, facial animation units, facial shape units, and finally 2D and 3D face representations of the face along with the 3D head orientation. The effectiveness of acquired RGB-D dynamic facial dataset is demonstrated by presenting a new visual speech recognition method that employs three-dimensional spatiotemporal data of different facial feature points. A number of visual speech recognition methods from the literature are also tested on the new dataset and they obtain a comparable or favorable visual speech recognition results. The results demonstrate the effectiveness of the proposed RGB-D dynamic facial dataset and show that it can be effectively employed in a visual speech recognition system.
Productive disasters ? – Evidence from European firm level data

This paper examines the impact of floods on firms’ capital accumulation, employment growth and productivity by using a difference-indifference approach and considering firms’ asset structure. We find evidence that after a flooding companies in flooded regions show on average higher growth of total assets and employment than firms in areas not affected by flooding. The positive effect prevails for companies with larger shares of intangible assets. Regarding firms’ productivity a negative flood effect is observable which declines with increasing share of intangible assets. JEL Codes: D24, Q54, R10, C21,
Adaptation to Climate Change through Sustainable Management and Development of Agroforestry Systems

This paper describes the potential role of agroforestry systems in the adaptation to expected changes in climate by smallholder farmers in the tropical regions in general and in sub-Saharan Africa in particular. There is enough scientific evidence to conclude that climate change is happening and to link climate change with the observed changes in the earth's physical systems. Agriculture is one of the high priority sectors where the impacts of climate change exceed tolerance limits with implications for the livelihoods of millions of smallholder farmers dependent on this sector. Agroforestry interventions, because of their ability to provide economic and environmental benefits, are considered to be the best “no regrets” measures in making communities adapt and become resilient to the impacts of climate change. The important elements of agroforestry systems that can play a significant role in the adaptation to climate change include changes in the microclimate, protection through provision of permanent cover, opportunities for diversification of the agricultural systems, improving efficiency of use of soil, water and climatic resources, contribution to soil fertility improvement, reducing carbon emissions and increasing sequestration, and promoting gender equity. These are discussed and limitations are highlighted. While agroforestry systems clearly offer economic and ecological advantages, the development of robust systems compliant with stakeholder needs and requirements is constrained by our limited understanding of the tradeoffs between subsistence requirements, acceptable risks, and the costs involved.
JOINT PILOT AND DATA LOADING TECHNIQUE FOR MIMO SYSTEMS OPERATING WITH COVARIANCE FEEDBACK

We consider a multiple input multiple output (MIMO) system wherein channel correlation as well as noisy channel estimation are both taken into account. For a given block fading duration, pilot-assisted channel estimation is carried out with the aid of a minimum mean square error estimator. The pilot sequences and data are jointly optimized to maximize the ergodic capacity while fulfilling a total transmit energy constraint. Using the optimization results, we study the effect of various system parameters and different types of channel knowledge. We show that systems with channel covariance knowledge and imperfect channel estimates can even outperform those with perfect channel estimation at the receiver, but without any channel knowledge at the transmitter. Further, we show that the signal to noise ratio and the block fading duration determine the qualitative behavior of ergodic capacity as a function of antenna spacing or angular spread. Finally, we find that the optimum pilot-to-data power ratio is always greater than one. It increases as the total transmit power decreases or as the block fading length increases.
Meteorological Measurements and Instrumentation

This book describes the fundamental scientific principles underlying high quality instrumentation used for environmental measurements. It discusses a wide range of in situ sensors employed in practical environmental monitoring and, in particular, those used in surface based measurement systems. It also considers the use of weather balloons to provide a wealth of upper atmosphere data. To illustrate the technologies in use it includes many examples of real atmospheric measurements in typical and unusual circumstances, with a discussion of the electronic signal conditioning, data acquisition considerations and data processing principles necessary for reliable measurements. This also allows the long history of atmospheric measurements to be placed in the context of the requirements of modern climate science, by building the physical science appreciation of the instrumental record and looking forward to new and emerging sensor and recording technologies.
Statistica Sinica Preprint

: In a completely randomized experiment, the variances of the treatment effect estimators in a finite population are usually not identifiable, and hence not estimable. Although some estimable bounds of such variances have been established in the literature, few are derived in the presence of covariates. We consider the difference-in-means estimator and the Wald estimator in completely randomized experiments with perfect compliance and noncompliance, respectively. We also establish sharp bounds for the variances of these two estimators when covariates are available. Furthermore, we obtain consistent estimators for such bounds that can be used to shorten the confidence intervals and improve the power of tests. The confidence intervals are constructed based on the consistent estimators of the upper bounds, and have coverage rates that are uniformly asymptotically guaranteed. We use analyses based on simulations and real data to evaluate and demonstrate the proposed methods.
Thompson 𝛆 -Greedy algorithm: An improvement to the regret of Thompson Sampling and 𝛆 -Greedy on multi-armed bandit problems

The multi-armed bandit problem is one of the most classic reinforcement learning problems, aiming to find balanced decisions of exploration and exploitation and to increase the total reward of the actions from each round. To solve multi-armed bandit problems, algorithms were designed, including some of the most typical and widely used ones, like the Explore-Then-Commit algorithm, Upper Confidence Bound algorithm, Epsilon-Greedy algorithm, and Thompson Sampling algorithm. Some of them are improvements upon others, while all of them seek to increase total reward but contain specific weaknesses. Epsilon-Greedy algorithm, as a simple method to balance exploration and exploitation of multi-armed bandit problems, has the disadvantage of still picking non-optimal actions even if it appears to be non-optimal for a very long time. Thompson Sampling algorithm, though performing well in many scenarios, costs a significantly long time to update its prior distribution each round and tends to explore too much in initial tries when the real distribution of reward is scattered. To further fix their weaknesses and improve their performance, this paper proposed a newly designed algorithm, Thompson ε - Greedy (TEG), which seeks to utilize the advantages of both algorithms to complement each other’s disadvantages. The TEG algorithm is not only proved to perform better than ε -Greedy in most cases, but also turned out to be more adaptive in environments with true reward distributions that weaken Thompson Sampling Algorithm. Beyond the comparison of regrets, the paper further analyzed the time cost of applying TEG with those two existing methods and their best arm selection rates to illustrate the significance of the TEG algorithm.
Molecular Docking-based Screening of Natural Heterocyclic Compounds as a Potential Drug for COVID-19

The coronavirus pandemic poses significant challenges for the pharmaceutical industry. Coronavirus enters host cells via the angiotensin-converting enzyme 2 receptors (ACE2). The SARS-CoV-2 spike glycoprotein is a potential target for medicinal chemists in the development of specific drugs. The current study investigates molecular modeling studies to identify potential drug candidates. Molecular docking simulations were run on 11 natural heterocyclic compounds/flavonoids. When tested against the viral spike protein receptor, isoquercetin had a docking binding energy of -6.74kcal/mol (PDBID:6LU7). A docking study revealed the interaction of the receptor-binding domain with various flavonoid compounds.
Association Proceedings of the Third USENIX Conference on File and Storage Technologies San Francisco , CA , USA March 31 – April 2 , 2004

This paper explores the concept of early discard for interactive search of unindexed data. Processing data inside storage devices using downloaded searchlet code enables Diamond to perform efficient, applicationspecific filtering of large data collections. Early discard helps users who are looking for “needles in a haystack” by eliminating the bulk of the irrelevant items as early as possible. A searchlet consists of a set of application-generated filters that Diamond uses to determine whether an object may be of interest to the user. The system optimizes the evaluation order of the filters based on run-time measurements of each filter’s selectivity and computational cost. Diamond can also dynamically partition computation between the storage devices and the host computer to adjust for changes in hardware and network conditions. Performance numbers show that Diamond dynamically adapts to a query and to run-time system state. An informal user study of an image retrieval application supports our belief that early discard significantly improves the quality of interactive searches.
ADP-Based Robust Tracking Control for a Class of Nonlinear Systems With Unmatched Uncertainties

In this paper, an approximately optimal control strategy is developed for the tracking control of a class of continuous-time nonlinear systems with unmatched uncertainties. By transforming the unmatched uncertain term, the auxiliary system associated with the uncertain nonlinear system is established. The auxiliary system is divided into steady and transient parts, and the related controllers are separately solved, meanwhile the transient tracking error system is also obtained by introducing the reference system. A neural network-based adaptive dynamic programming method is used to get the approximately optimal tracking control law of uncertain nonlinear systems with a predefined cost function. Furthermore, the ultimately uniform boundedness of neural network weights and the stability of tracking error systems are both proved through Lyapunov theory. Two cases of nonlinear systems with unmatched uncertainties are investigated to illustrate the effectiveness of the proposed robust tracking control strategy.
Internet-delivered Cognitive Behaviour Therapy for Depressive Symptoms: An Exploratory Examination of Therapist Behaviours and their Relationship to Outcome and Therapeutic Alliance.

BACKGROUND A previous study of therapist-assisted Internet-delivered cognitive behaviour therapy (ICBT) for generalized anxiety (Paxling et al., 2013) identified eight distinct therapist behaviours in ICBT (task reinforcement, self-efficacy shaping, task prompting, alliance bolstering, psychoeducation, empathetic utterances, deadline flexibility, and self-disclosure). It is unknown how generalizable these behaviours are across ICBT programs. AIMS We systematically examined the frequency of these eight therapist behaviours and additional newly identified behaviours in e-mails sent to patients during the course of ICBT for depressive symptoms. We also conducted exploratory analyses to examine relationships between therapist behaviours, symptom improvement, and therapeutic alliance. METHOD Data was obtained from a previously published open trial (Hadjistavropoulos et al., 2014). A total of 1013 e-mails sent from therapists (n = 24) to patients (n = 41) during ICBT for depressive symptoms were analyzed. Therapist behaviours were correlated with symptom change scores and ratings of therapeutic alliance at mid- and post-treatment. RESULTS Therapist behaviours described by Paxling et al. were reliably identified in the e-mails using qualitative content analysis; the frequencies of these behaviours differed, however, from the Paxling et al. study and three additional therapist behaviours were identified (administrative statements, questionnaire feedback, asking clarifying questions). Several therapist behaviours (e.g. administrative statements, task prompting) were associated with lower symptom improvement at post-treatment. Questionnaire feedback and task reinforcement were associated with higher patient ratings of therapeutic alliance. CONCLUSIONS The study provides partial support for the generalizability of therapist-assistance across ICBT programs. Experimental research is needed to examine the impact of varying therapist-assistance on patient outcomes.
On the estimation of yield from exploited marine stocks withreference to south-east Asia

Accurate estimates of yield and eflfort are necessary for a proper apraisal of exploited stocks. In the South-east Asian region where fishing is of a diffused nature and landings are by indigenous boats all along the coastline using various types of gear, complete record of catch and effort is impossible and a suitable sampling scheme for their estimation is the only logical alternative. The various factors influencing the choice of a sample design Imve been discussed in relation to the existing conditions in the region. The following points in particular have been dealt with in the paper: (a) the nature of stratification, (6) the choice of units, (c) intensity of sampling and (rf) precision of estimate. The sampling design employed in India for the purpose of estimation of catch and effort has also been discussed in detail.
Vehicle Identification and Surveillance Using Efficient Machine Learning Techniques

Identification of vehicles is an important area of research. It has several applications namely toll gate fee management systems, automatic parking applications, theft prevention, traffic monitoring and ticket(fine) imposing etc. Presently, there are many vehicles manufacturers marketing various types of vehicles, and this has an increasing growth rate. It has become a difficult task to analyze various attributes of vehicles and to identify each one of them. Vehicle identification is the main objective of this project. Using license plate, different vehicles can be identified but it is troublesome when the license plate recognition fails in scenarios where the license plate is forged, missing, or covered. The attributes such as the model and make of the vehicle cannot be easily changed and this helps in recognition of vehicles easily. In this project, vehicle recognition mainly focuses on identifying the name of the vehicle manufacturing company. Further recognition is done by the extraction of the license plate number by employing Haar Cascade classifier.
Utilizing Gold Nanoparticles as Prospective Radiosensitizers in 3D Radioresistant Pancreatic Co-Culture Model

Pancreatic cancer stands among the deadliest forms of cancer, and the existing treatments fall short of providing adequate efficacy. Novel and more effective treatment approaches are urgently required to address this critical medical challenge. In this study, we aimed to evaluate the anti-cancer efficacy of gold nanoparticles (GNPs) in combination with radiotherapy (RT). A 3D pancreatic cancer co-culture spheroid model of MIA PaCa-2 cancer cells and patient-derived cancer-associated fibroblasts (CAF-98) was used. The spheroids were treated with GNPs (7.5 μg/mL) and 2 Gy of RT. The spheroids’ cell viability was assessed through the CellTiter-Glo 3D assay, and an immunofluorescence assay was used to assess the DNA DSBs via the expression of the DNA damage marker 53BP1. Co-culture samples showed a 10.8% (p < 0.05) increase in proliferation and a 13.0% (p < 0.05) decrease in DNA DSB when compared to monoculture samples, However, they displayed a 175% (p < 0.001) increase in GNPs uptake when compared to monoculture spheroids. Using GNPs/RT, we were able to show a significant reduction of 6.2% (p < 0.05) in spheroid size and an increase of 14.3% (p < 0.05) in DNA DSB damage in co-culture samples. The combination of GNPs with RT demonstrated remarkable radiosensitization effects, representing a promising approach to enhance cancer treatment efficacy. These effects were particularly noteworthy in the more treatment-resistant co-culture spheroid model.
Identification of Distinct Endosymbionts of Whiteflies-infesting Cassava (Manihot esculenta Crantz), using Diagnostic PCR and Sanger Sequencing

The study was conducted during March–December 2021 in Thiruvananthapuram, Kerala for the identification of distinct endosymbionts of cassava whiteflies (Bemisia tabaci (Gennadius) and Aleurodicus dispersus (Russell). Endosymbiotic bacteria found in whiteflies have been reported to interact and provide specific advantages to the hosts and they can be one of the reasons for whiteflies’ insecticidal resistance. In the present study, endosymbiotic bacteria were isolated from the insects, allowed to grow in media after insecticidal treatments and after observing colony morphology, distinct bacterial colonies were sub-cultured and selected for further molecular identification procedures. PCR analysis was carried out for bacteria using 16S rRNA primer, with an annealing temperature of 49°C and yielded fragments at 1500 bp. Based on the sequencing report the observations were that the culturable endosymbionts Providencia present in B. tabaci is resistant to chlorpyrifos and Paenibacillus alvei present in A. dispersus is resistant to imidacloprid and chlorpyrifos. Clostridium senegalens and Acinetobacter are found in control populations of Bemisia and Aleurodicus respectively.The sequences were submitted to NCBI and the accession numbers obtained were OP303253, OP303256, OP303254 and OP295124. Also, through diagnostic PCR using specific bacterial primers, presence of Portiera aleyrodidarum at fragment length of 1000 bp and Wolbachia at 650 bp in B. tabaci and Rickettsia at fragment length of 800 bp in A. dispersus are confirmed. The study could identify bacteria which are decisive in their presence and activity in whiteflies.
Exposure to insulating materials and risk of coronary artery diseases: a cross-sectional study

Background Although previous reports link exposure to insulating materials with an increased risk of mesothelioma and chronic respiratory diseases, studies evaluating their associations with the risk of coronary artery diseases (CAD) are lacking. Aims We aimed at evaluating the associations between exposure to insulating materials and the 10-year risk of CAD among insulators. Methods In this cross-sectional study, we recruited 643 adults (≥18 years), full-time insulators from the Local 110 Heat and Frost Insulators and Allied Workers Union in Edmonton, Alberta. We obtained demographic information, personal and family history, and job-exposure history, including experience (years) and types of exposure to insulating materials. Clinical profiling including Framingham risk scores (FRS) was assessed. Results Of all insulators, 89% were men (mean ± SD age: 47 ± 12 years), 27% had a parental history of cardiac diseases, and 22% had a comorbid chronic respiratory disease. In total, 53% reported exposure to asbestos, while 61, 82, and 94% reported exposure to ceramic fibers, fiberglass, and mineral fibers, respectively. In single-exposure multivariable regression models adjusted for experience, marital status, and body mass index (BMI), asbestos was found to be associated with higher FRS (β: 1.004; 95%CI: 0.003–2.00). The association remained consistent in multi-exposure models and a higher association was found between asbestos exposure and FRS among insulators with comorbid chronic respiratory disease. Conclusion Our study demonstrates that apart from cancer and chronic respiratory diseases, asbestos exposure may also have a cardiac effect, thus warranting the need for systematic surveillance to protect workers from the adverse effects of these materials.
A Low-Voltage Submicrowatt, High-Speed CMOS Dynamic Comparator

We present an energy-efficient dynamic comparator that is suitable for low-voltage and high-speed applications. The preamplifier circuit prevents fully discharging the output nodes without utilizing an extra capacitor, which improves power consumption and operating speed compared to other solutions. The comparator is designed in a 65-nm CMOS technology at a power supply of1.2 V. The designed comparator operates at a frequency of 1.2 MHz, consumes 931.1 nW, and has a delay of 0.64 ns at $\Delta \mathrm{V}_{\mathrm{i}\mathrm{n}}$=1mV. To estimate the offset of the proposed circuit, Monte Carlo simulations were performed and resulted in a standard deviation of 1.54 mV and a mean of 1.06 mV.
Mindfulness-based stress reduction (MBSR).

MBSR by Dr. Jon Kabat-Zinn is adapted from an ancient meditation practice called Vipassana (Insight) meditation. Both meditation practices involve consciously and intentionally being aware of unfolding sensory experiences as they occur in the present moment. In Buddhist psychology as well as MBSR mental phenomenon such as thinking, daydreams and self-talk are considered sensory as they can be observed and experienced.