Miracle-dz/UniFAR-FTUs huggingface dataset:
A hand segmentation scheme using clustering technique in homogeneous background

Segmentation serves as the first step in any image analysis and it plays a very vital role as the success of the image analysis in the later stage depends very much on a suitable and robust segmentation scheme. Hand segmentation on the other hand is the first step for hand image analysis such as hand gesture recognition. An image subtraction method is implemented on a gray level image, RGB color image and image in normalized RGB color space under homogeneous background to investigate their appropriateness for segmentation. A skin color model based on the clustering property of skin color is then built to improve the segmentation result obtained from the image subtraction on the normalized RGB image. It is found that the proposed skin color modeling technique is able to improve the segmentation and provide a faster and reliable method for hand segmentation.
Memory-efficient array redistribution through portable collective communication

Modern large-scale deep learning workloads highlight the need for parallel execution across many devices in order to fit model data into hardware accelerator memories. In these settings, array redistribution may be required during a computation, but can also become a bottleneck if not done efficiently. In this paper we address the problem of redistributing multi-dimensional array data in SPMD computations, the most prevalent form of parallelism in deep learning. We present a type-directed approach to synthesizing array redistributions as sequences of MPI-style collective operations. We prove formally that our synthesized redistributions are memory-efficient and perform no excessive data transfers. Array redistribution for SPMD computations using collective operations has also been implemented in the context of the XLA SPMD partitioner, a production-grade tool for partitioning programs across accelerator systems. We evaluate our approach against the XLA implementation and find that our approach delivers a geometric mean speedup of $1.22\times$, with maximum speedups as a high as $5.7\times$, while offering provable memory guarantees, making our system particularly appealing for large-scale models.
The Physics of Machine Learning: An Intuitive Introduction for the Physical Scientist

This article is intended for physical scientists who wish to gain deeper insights into machine learning algorithms which we present via the domain they know best, physics. We begin with a review of two energy-based machine learning algorithms, Hopfield networks and Boltzmann machines, and their connection to the Ising model. This serves as a foundation to understand the phenomenon of learning more generally. Equipped with this intuition we then delve into additional, more"practical,"machine learning architectures including feedforward neural networks, convolutional neural networks, and autoencoders. We also provide code that explicitly demonstrates training a neural network with gradient descent.
Parallel Electromagnetically Induced Transparency near Ground-State Cooling of a Trapped-Ion Crystal

We theoretically propose and experimentally demonstrate a parallel-electromagnetically-induced transparency (parallel-EIT) cooling technique for ion crystals in the Paul trap. It has less stringent requirements on the cooling resonance condition than the standard electromagnetically-induced transparency (EIT) cooling, thus allowing, in principle, to simultaneously cool the motional mode spectrum with an arbitrary range. A proof-of-principle validation for this cooling scheme is experimentally demonstrated with up to 4 trapped 40Ca+ ions. We observe simultaneous near-ground-state cooling for all motional modes with best average phonon number about 0.2. By tuning the trap frequency in a large range to imitate a broadband motional mode spectrum, we can still reach almost the same cooling limit for all the modes while standard EIT cooling shows limited cooling range. Our method has a simple experimental configuration, requiring only appropriate modulation of the probe beam of standard EIT cooling, and can be applied to various types of ions (e.g., 171Yb+, 40Ca+). This cooling scheme provides a powerful tool for the initialization of the trapped-ion quantum computers and simulators.
DROPLET SPECTRA AND WIND TUNNEL EVALUATION OF VENTURI AND PRE-ORIFICE NOZZLES

Small- to medium-size droplets are desirable when applying insecticides and fungicides because they provide better penetration into the canopy and better coverage than larger sizes. However, small droplets can drift long distances. Several agricultural nozzle manufacturers have recently introduced so-called “low-drift” nozzles. Although manufacturers of low-drift nozzles claim these nozzles are considerably more effective in reducing spray drift than standard flat-fan nozzles, no independent data are available to support their claim. The objective of this study was to determine the effectiveness of two low-drift nozzles (TurboDrop® and Turbo TeeJet®) in reducing drift. The TurboDrop nozzle design incorporates a venturi air intake port and a pre-orifice chamber while the Turbo TeeJet nozzle design includes only a pre-orifice chamber. Nozzle evaluations were accomplished by measuring droplet sizes with a laser particle sizer and deposition distances of droplets in a wind tunnel (5 m/s). Data from measurements obtained with the low-drift nozzles were compared to data from a standard flat-fan nozzle. The low-drift nozzles produced fewer drift prone droplets and significantly lower downwind airborne deposits than the standard flat-fan nozzle (XR). In general, droplet size measurements taken along the long axis of the spray patterns showed less variation in volume median diameters for the XR and Turbo TeeJet (TT) nozzles than for the TurboDrop (TD) nozzles. The TD nozzles produced lower downwind deposits than TT nozzles operated at similar pressures (276 kPa); however, a larger orifice TT nozzle operated at a lower pressure (176 kPa) produced significantly lower downwind airborne deposits than the TD nozzle operated at 276 kPa. Covering or restricting the venturi air intake port on the TD nozzle increased nozzle output but had little impact on the overall droplet spectrum and downwind drift.
On Two XAI Cultures: A Case Study of Non-technical Explanations in Deployed AI System

Explainable AI (XAI) research has been booming, but the question"$\textbf{To whom}$ are we making AI explainable? "is yet to gain sufficient attention. Not much of XAI is comprehensible to non-AI experts, who nonetheless, are the primary audience and major stakeholders of deployed AI systems in practice. The gap is glaring: what is considered"explained"to AI-experts versus non-experts are very different in practical scenarios. Hence, this gap produced two distinct cultures of expectations, goals, and forms of XAI in real-life AI deployments. We advocate that it is critical to develop XAI methods for non-technical audiences. We then present a real-life case study, where AI experts provided non-technical explanations of AI decisions to non-technical stakeholders, and completed a successful deployment in a highly regulated industry. We then synthesize lessons learned from the case, and share a list of suggestions for AI experts to consider when explaining AI decisions to non-technical stakeholders.
Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems

In recent years, owing to the outstanding performance in graph representation learning, graph neural network (GNN) techniques have gained considerable interests in many real-world scenarios, such as recommender systems and social networks. In recommender systems, the main challenge is to learn the effective user/item representations from their interactions. However, many recent publications using GNNs for recommender systems cannot be directly compared, due to their difference on datasets and evaluation metrics. Furthermore, many of them only provide a demo to conduct experiments on small datasets, which is far away to be applied in real-world recommender systems. To address this problem, we introduce Graph4Rec, a universal toolkit that unifies the paradigm to train GNN models into the following parts: graphs input, random walk generation, ego graphs generation, pairs generation and GNNs selection. From this training pipeline, one can easily establish his own GNN model with a few configurations. Besides, we develop a large-scale graph engine and a parameter server to support distributed GNN training. We conduct a systematic and comprehensive experiment to compare the performance of different GNN models on several scenarios in different scale. Extensive experiments are demonstrated to identify the key components of GNNs. We also try to figure out how the sparse and dense parameters affect the performance of GNNs. Finally, we investigate methods including negative sampling, ego graph construction order, and warm start strategy to find a more effective and efficient GNNs practice on recommender systems. Our toolkit is based on PGL https://github.com/PaddlePaddle/PGL and the code is opened source in https://github.com/PaddlePaddle/PGL/tree/main/apps/Graph4Rec.
How to reach optimal estimates of confidence intervals in microscopic counting of phytoplankton?

Abstract Present practices in the microscopic counting of phytoplankton to estimate the reliability of results rely on the assumption of a random distribution of taxa in sample preparations. In contrast to that and in agreement with the literature, we show that aggregated distribution is common and can lead to over-optimistic confidence intervals, if estimated according to the shortcut procedure of Lund et al. based on the number of counted cells. We found a good linear correlation between the distribution independent confidence intervals for medians and those for parametric statistics so that 95% confidence intervals can be approximated by using a correction factor of 1.4. Instead, the recommendation to estimate confidence intervals from the total number of counted cells according to Lund et al. should be categorically rejected. We further propose the adoption of real-time confidence intervals during microscopic counting as the criterion to define how long counting should be continued. Then each sample can be counted in its individual way to reach the necessary reliability independent of highly different samples. Such a dynamic counting strategy would be the most significant development in the quality control of phytoplankton counting since the early pioneers established the present counting practices in the late 1950s.
Phylodynamic analysis and spike protein mutations in porcine deltacoronavirus with a new variant introduction in Taiwan

Abstract Porcine deltacoronavirus (PDCoV) is a highly transmissible intestinal pathogen that causes mild to severe clinical symptoms, such as anorexia, vomiting, and watery diarrhea in pigs. By comparing the genetic sequences of the spike glycoprotein between historical and current Taiwanese PDCoV strains, we identified a novel PDCoV variant that displaced the PDCoV responsible for the 2015 epidemic. This PDCoV variant belongs to a young population within the US lineage, and infected pigs carry high concentrations of the virus. It also has several critical point mutations and an amino acid insertion at position 52 that may enhance the affinity between the B-cell epitopes located in the N-terminal domain with its complementarity regions, consequently facilitating binding or penetration between the fusion peptide and cellular membrane. Furthermore, viral protein structure prediction demonstrated that these amino acid changes may change the ability of the virus to bind to the receptor, which may consequently alter virus infectivity. Our results hence suggest the emergence of new PDCoV strains in Taiwan with the potential for greater transmission and pathogenesis.
Sequential spatially balanced sampling

Sequential sampling occurs when an entire population is unknown in advance and data are received one by one or in groups of units. This article proposes a new algorithm to sequentially select a balanced sample. The algorithm respects equal and unequal inclusion probabilities. The method can also be used to select a spatially balanced sample if the population of interest contains spatial coordinates. A simulation study is proposed, and the results show that the proposed method outperforms other methods.
Genome-wide association analysis of chickpea germplasms differing for salinity tolerance based on DArTseq markers

Soil salinity is significant abiotic stress that severely limits global crop production. Chickpea (Cicer arietinum L.) is an important grain legume that plays a substantial role in nutritional food security, especially in the developing world. This study used a chickpea population collected from the International Center for Agricultural Research in the Dry Area (ICARDA) genebank using the focused identification of germplasm strategy. The germplasm included 186 genotypes with broad Asian and African origins and genotyped with 1856 DArTseq markers. We conducted phenotyping for salinity in the field (Arish, Sinai, Egypt) and greenhouse hydroponic experiments at 100 mM NaCl concentration. Based on the performance in both hydroponic and field experiments, we identified seven genotypes from Azerbaijan and Pakistan (IGs: 70782, 70430, 70764, 117703, 6057, 8447, and 70249) as potential sources for high salinity tolerance. Multi-trait genome-wide association analysis (mtGWAS) detected one locus on chromosome Ca4 at 10618070 bp associated with salinity tolerance under hydroponic and field conditions. In addition, we located another locus specific to the hydroponic system on chromosome Ca2 at 30537619 bp. Gene annotation analysis revealed the location of rs5825813 within the Embryogenesis-associated protein (EMB8-like), while the location of rs5825939 is within the Ribosomal Protein Large P0 (RPLP0). Utilizing such markers in practical breeding programs can effectively improve the adaptability of current chickpea cultivars in saline soil. Moreover, researchers can use our markers to facilitate the incorporation of new genes into commercial cultivars.
Visual facility layout design system

This paper presents an integrated visual facility layout design system to solve layout problems with geometric constraints on a continual planar site. The system employs a tabu search algorithm as the search engine with heuristic neighbourhood control and a manual interference mechanism to obtain good solutions. The Windows®-based system provides layout designers with a friendly environment that can automatically search a good solution aided by the visual inspection function. The visual inspection function is a mechanism embedded in the search process that dynamically displays the improvement status and allows manual adjustment of facility location during the search. The system also serves as a powerful tool for performing 'what if' analyses for layout designers.
Slot‐loaded gap‐coupled microstrip array antenna for wide impedance bandwidth

A novel design of four‐element gap‐coupled slot rectangular microstrip array antenna fed by aperture coupling is presented for wideband operation. From the experimental results it is seen that a −10 dB return loss impedance bandwidth of 34.17% is achieved at X‐band frequencies. This impedance bandwidth is 2.43 times more than that of conventional corporate fed four‐element gap‐coupled rectangular microstrip array antenna, which operates at triple band of frequencies. The effect of slot in the radiating and parasitic elements and use of aperture coupling is studied for converting its triple bands into a single band. © 2007 Wiley Periodicals, Inc. Microwave Opt Technol Lett 49: 3014–3017, 2007; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.22894
Persistent eczema leads to both impaired growth and food allergy: JECS birth cohort

Skin inflammation leads to altered cytokine/chemokine production and causes systemic inflammation. The systemic mechanism of atopic dermatitis (AD) is recognized to affect systemic metabolism. This study aimed to examine the relationship between early-onset persistent eczema and body weight, height, and body mass index (BMI), in addition to food allergy in a birth cohort among infants. This study design was a nationwide, multicenter, prospective birth cohort study—the Japan Environment and Children’s Study (JECS). Generalized linear models were fitted for z scores of weight, height, BMI, and food allergy to evaluate the relationship between eczema and these outcomes for infants at age1, 2, and 3 years. Persistent eczema was negatively associated with height at the age of 2 years (estimated coefficient, −0.127; 95% confidence interval [CI], −0.16 to −0.095) and 3 years (−0.177; 95% CI, −0.214 to −0.139). The same tendency was also observed with weight and BMI. Early disease onset at younger than 1 year and persistent eczema had the strongest association with development of food allergy at age 3 years (OR, 11.794; 95% CI, 10.721–12.975). One phenotype of eczema with early-onset and persistent disease creates a risk of both physical growth impairment and development of food allergy. Infants who present with the early-onset and persistent type of eczema should be carefully evaluated daily for impaired physical growth and development of food allergy.
A low-power and low-complexity continuous-time Gm-C based Delta-Sigma modulator for WCDMA/UMTS

This article describes the implementation of a continuous-time Delta-Sigma modulator for WCDMA/UMTS in wireless communication. The Delta-Sigma modulator employs a Gm-C based integrator to form a fourth-order noise-shaping loop. The modulator samples at 160 MHz and has an over-sampling ratio of 40 in 2 MHz bandwidth. To reduce power consumption and design complexity, single-bit quantisation is employed. The modulator is implemented in 0.25 µm 1-ploy 5-metal CMOS technology and has an area of 0.13 mm2. The modulator can achieve 70.7 dB of signal-to-noise-plus-distortion-ratio and 74 dB of dynamic range. Finally, the modulator consumes 3.5 mW of power with a reduced power supply of 1.8 V.
Simplifying heterogeneous migration between x86 and ARM machines

Heterogeneous computing is the strategy of deploying multiple types of processing elements within a single workflow, and allowing each to perform the tasks to which is best suited. To fully harness the power of heterogeneity, we want to be able to dynamically schedule portions of the code and migrate processes at runtime between the architectures. Also, migration includes transforming the execution state of the process, which induces a significant overhead that offsets the benefits of migrating in the first place. The goal of my PhD is to work on techniques that allow applications to run on heterogeneous cores under a shared programming model, and to tackle the generic problem of creating a uniform address space between processes running on these highly diverse systems. This would greatly simplify the migration process. We will start by examining a common stack layout between x86 and ARM binaries, focusing on these two widely spread architectures, and later we will try to generalize to other more diverse execution environments. On top of that, the performance and energy efficiency of the above effort compared to current approaches will be benchmarked.
Alpha and theta oscillations are inversely related to progressive levels of meditation depth

Abstract Meditation training is proposed to enhance mental well-being by modulating neural activity, particularly alpha and theta brain oscillations, and autonomic activity. Although such enhancement also depends on the quality of meditation, little is known about how these neural and physiological changes relate to meditation quality. One model characterizes meditation quality as five increasing levels of ‘depth’: hindrances, relaxation, concentration, transpersonal qualities and nonduality. We investigated the neural oscillatory (theta, alpha, beta and gamma) and physiological (respiration rate, heart rate and heart rate variability) correlates of the self-reported meditation depth in long-term meditators (LTMs) and meditation-naïve controls (CTLs). To determine the neural and physiological correlates of meditation depth, we modelled the change in the slope of the relationship between self-reported experiential degree at each of the five depth levels and the multiple neural and physiological measures. CTLs reported experiencing more ‘hindrances’ than LTMs, while LTMs reported more ‘transpersonal qualities’ and ‘nonduality’ compared to CTLs, confirming the experiential manipulation of meditation depth. We found that in both groups, theta (4–6 Hz) and alpha (7–13 Hz) oscillations were related to meditation depth in a precisely opposite manner. The theta amplitude positively correlated with ‘hindrances’ and increasingly negatively correlated with increasing meditation depth levels. Alpha amplitude negatively correlated with ‘hindrances’ and increasingly positively with increasing depth levels. The increase in the inverse association between theta and meditation depth occurred over different scalp locations in the two groups—frontal midline in LTMs and frontal lateral in CTLs—possibly reflecting the downregulation of two different aspects of executive processing—monitoring and attention regulation, respectively—during deep meditation. These results suggest a functional dissociation of the two classical neural signatures of meditation training, namely, alpha and theta oscillations. Moreover, while essential for overcoming ‘hindrances’, executive neural processing appears to be downregulated during deeper meditation experiences.
The voice of few, the opinions of many: evidence of social biases in Twitter COVID-19 fake news sharing

Online platforms play a relevant role in the creation and diffusion of false or misleading news. Concerningly, the COVID-19 pandemic is shaping a communication network which reflects the emergence of collective attention towards a topic that rapidly gained universal interest. Here, we characterize the dynamics of this network on Twitter, analysing how unreliable content distributes among its users. We find that a minority of accounts is responsible for the majority of the misinformation circulating online, and identify two categories of users: a few active ones, playing the role of ‘creators’, and a majority playing the role of ‘consumers’. The relative proportion of these groups (approx. 14% creators—86% consumers) appears stable over time: consumers are mostly exposed to the opinions of a vocal minority of creators (which are the origin of 82% of fake content in our data), that could be mistakenly understood as representative of the majority of users. The corresponding pressure from a perceived majority is identified as a potential driver of the ongoing COVID-19 infodemic.
Recent Advances in Optics (RAO): Contribution of D. Narayana Rao in Optics and Photonics research in India

The fist documented speculations on light were found in oriental and Greek schools of philosophy. The investigations on the light in India were rooted way back to the ancient era. The Indian Samkhya, Nyaya, and Vaisheshika identified light or fie (Tejas) as one of the key elements among the fie elementary things of the universe. The pioneering results of optics and spectroscopy were witnessed in the era of stellar people like Sir Jagadish Chandra Bose and Sir C V Raman. The research has then taken a new directive into the development of nonlinear optics and photonics after the invention of the laser. The present article reviews the past few decades pioneering works of Prof. D Narayana Rao, an experimental physicist, in the context of optics and photonics in India. The most notable contributions of Prof Rao, introduced in India for the fist time, are white-light interferometry, degenerate four-wave mixing (DFWM), electric field induced second harmonic generation (EFISHG), incoherent laser spectroscopy (using dye laser), and femtosecond lasers for creating nano/microstructures.