Miracle-dz/UniFAR-FTUs huggingface dataset:
UNETR: Transformers for 3D Medical Image Segmentation

Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have shown prominence for the majority of medical image segmentation applications since the past decade. In FCNNs, the encoder plays an integral role by learning both global and local features and contextual representations which can be utilized for semantic output prediction by the decoder. Despite their success, the locality of convolutional layers in FCNNs, limits the capability of learning long-range spatial dependencies. Inspired by the recent success of transformers for Natural Language Processing (NLP) in long-range sequence learning, we reformulate the task of volumetric (3D) medical image segmentation as a sequence-to-sequence prediction problem. We introduce a novel architecture, dubbed as UNEt TRansformers (UNETR), that utilizes a transformer as the encoder to learn sequence representations of the input volume and effectively capture the global multi-scale information, while also following the successful "U-shaped" network design for the encoder and decoder. The transformer encoder is directly connected to a decoder via skip connections at different resolutions to compute the final semantic segmentation output. We have validated the performance of our method on the Multi Atlas Labeling Beyond The Cranial Vault (BTCV) dataset for multi-organ segmentation and the Medical Segmentation Decathlon (MSD) dataset for brain tumor and spleen segmentation tasks. Our benchmarks demonstrate new state-of-the-art performance on the BTCV leaderboard.
linear subspace clustering: a comparative study of nonlinear manifold clustering algorithms

Summary: Subspace clustering is an important unsupervised clustering approach. It is based on the assumption that the high-dimensional data points are approximately distributed around several low-dimensional linear subspaces. The majority of the prominent subspace clustering algorithms rely on the representation of the data points as linear combinations of other data points, which is known as a self-expressive representation. To overcome the restrictive linearity assumption, numerous nonlinear approaches were proposed to extend successful subspace clustering approaches to data on a union of non-linear manifolds. In this comparative study
Extending Sparse Tensor Accelerators to Support Multiple Compression Formats

Sparsity, which occurs in both scientific applications and Deep Learning (DL) models, has been a key target of optimization within recent ASIC accelerators due to the potential memory and compute savings. These applications use data stored in a variety of compression formats. We demonstrate that both the compactness of different compression formats and the compute efficiency of the algorithms enabled by them vary across tensor dimensions and amount of sparsity. Since DL and scientific workloads span across all sparsity regions, there can be numerous format combinations for optimizing memory and compute efficiency. Unfortunately, many proposed accelerators operate on one or two fixed format combinations. This work proposes hardware extensions to accelerators for supporting numerous format combinations seamlessly and demonstrates $\sim 4 \times$ speedup over performing format conversions in software.
HW-NAS-Bench: Hardware-Aware Neural Architecture Search Benchmark

HardWare-aware Neural Architecture Search (HW-NAS) has recently gained tremendous attention by automating the design of DNNs deployed in more resource-constrained daily life devices. Despite its promising performance, developing optimal HW-NAS solutions can be prohibitively challenging as it requires cross-disciplinary knowledge in the algorithm, micro-architecture, and device-specific compilation. First, to determine the hardware-cost to be incorporated into the NAS process, existing works mostly adopt either pre-collected hardware-cost look-up tables or device-specific hardware-cost models. Both of them limit the development of HW-NAS innovations and impose a barrier-to-entry to non-hardware experts. Second, similar to generic NAS, it can be notoriously difficult to benchmark HW-NAS algorithms due to their significant required computational resources and the differences in adopted search spaces, hyperparameters, and hardware devices. To this end, we develop HW-NAS-Bench, the first public dataset for HW-NAS research which aims to democratize HW-NAS research to non-hardware experts and make HW-NAS research more reproducible and accessible. To design HW-NAS-Bench, we carefully collected the measured/estimated hardware performance of all the networks in the search spaces of both NAS-Bench-201 and FBNet, on six hardware devices that fall into three categories (i.e., commercial edge devices, FPGA, and ASIC). Furthermore, we provide a comprehensive analysis of the collected measurements in HW-NAS-Bench to provide insights for HW-NAS research. Finally, we demonstrate exemplary user cases to (1) show that HW-NAS-Bench allows non-hardware experts to perform HW-NAS by simply querying it and (2) verify that dedicated device-specific HW-NAS can indeed lead to optimal accuracy-cost trade-offs. The codes and all collected data are available at https://github.com/RICE-EIC/HW-NAS-Bench.
Method validation for determination of heavy metals in wine and slightly alcoholic beverages by ICP-MS

The Organisation International de la Vigne et du Vin (OIV) fixed an uppermost level for some heavy metals in wine. Consequently, the need to determine very low concentration of elements that may be present in wine in trace and ultra trace levels occurred. Inductively coupled plasma mass spectrometry ICP-MS is considered an excellent tool for detailed characterization of the elementary composition of many samples, including samples of drinks. In this study a method of quantitative analysis for the determination of toxic metals (Cr, As, Cd, Ni, Hg, Pb) in wines and slightly alcoholic beverages by ICP-MS was validated. Several parameters have been taken into account and evaluated for the validation of method, namely: linearity, the minimum detection limit, the limit of quantification, accuracy and uncertainty.
Efficient Deep Reinforcement Learning With Imitative Expert Priors for Autonomous Driving

Deep reinforcement learning (DRL) is a promising way to achieve human-like autonomous driving. However, the low sample efficiency and difficulty of designing reward functions for DRL would hinder its applications in practice. In light of this, this article proposes a novel framework to incorporate human prior knowledge in DRL, in order to improve the sample efficiency and save the effort of designing sophisticated reward functions. Our framework consists of three ingredients, namely, expert demonstration, policy derivation, and RL. In the expert demonstration step, a human expert demonstrates their execution of the task, and their behaviors are stored as state-action pairs. In the policy derivation step, the imitative expert policy is derived using behavioral cloning and uncertainty estimation relying on the demonstration data. In the RL step, the imitative expert policy is utilized to guide the learning of the DRL agent by regularizing the KL divergence between the DRL agent’s policy and the imitative expert policy. To validate the proposed method in autonomous driving applications, two simulated urban driving scenarios (unprotected left turn and roundabout) are designed. The strengths of our proposed method are manifested by the training results as our method can not only achieve the best performance but also significantly improve the sample efficiency in comparison with the baseline algorithms (particularly 60% improvement compared with soft actor-critic). In testing conditions, the agent trained by our method obtains the highest success rate and shows diverse and human-like driving behaviors as demonstrated by the human expert. We also find that using the imitative expert policy trained with the ensemble method that estimates both policy and model uncertainties, as well as increasing the training sample size, can result in better training and testing performance, especially for more difficult tasks. As a result, the proposed method has shown its potential to facilitate the applications of DRL-enabled human-like autonomous driving systems in practice. The code and supplementary videos are also provided. [https://mczhi.github. io/Expert-Prior-RL/]
Effect of Feed Moisture and Barrel Temperature on Physical Properties of Extruded Cowpea Meal

Whole cowpeas were decorticated, adjusted to moisture contents of 20, 30 and 40%, chopped into coarse meals and extruded on a Wayne pilot-scale extrudel (19 mm × 475 mm barrel, 5:1 compression screw, 4.76 mm i.d. die) at barrel temperatures of 150°, 175° and 200°C. Measured product temperatures ranged from 130-164°C. Product moistures ranged from 10–34% and were affected mainly by initial moisture. Expansion at the die was highest for 20% moisture extrudates (1.7–1.9), and lowest for 40% samples (0.9- 1.3). The 20%-150° extrudate was dense (0.45 g/cm3) and twisted. The 20%-175° product was highly expanded (0.23 g/cm3). Other products were intermediate in density. Tristimulus color values were determined.
Masked Conditional Random Fields for Sequence Labeling

Conditional Random Field (CRF) based neural models are among the most performant methods for solving sequence labeling problems. Despite its great success, CRF has the shortcoming of occasionally generating illegal sequences of tags, e.g. sequences containing an “I-” tag immediately after an “O” tag, which is forbidden by the underlying BIO tagging scheme. In this work, we propose Masked Conditional Random Field (MCRF), an easy to implement variant of CRF that impose restrictions on candidate paths during both training and decoding phases. We show that the proposed method thoroughly resolves this issue and brings significant improvement over existing CRF-based models with near zero additional cost.
The trapping of electrons in polystyrene

A thin polystyrene film is charged with a short pulse of low-energy electrons (2 keV). Most of these electrons are deeply trapped in the polymer; the remaining electrons are excited from shallow traps into conduction levels and are swept out of the polymer. At short times, when most of these shallow-trapped electrons are excited into the conduction levels, the current approaches the space-charge limit: hence a value for electron mobility is obtained. At long times (from about five seconds to thousands of seconds) the conduction process is dominated by the rate of excitation from deeper-lying trapping levels; the excitation of electrons from these traps can be analyzed in terms of a time-dependent demarcation energy that is related to elapsed time. The time dependence of current is thus related to the distribution of electron traps in the polymer. <<ETX>>
Phytochemical and Pharmacological Evaluation of Methanolic Extract of Knoxia sumatrensis Leaves

ABSTRACT Knoxia sumatrensis leaf extracts in methanol were analyzed for the phytochemical compositions, antioxidant properties (DPPH, ABTS, and hydroxyl radical assay), anti-proliferative activity (lung cancer A549 and breast cancer MCF-7 cell lines), and mosquito larvicidal properties. The GC-MS analysis showed the presence of eight bioactive compounds of which the hexadecanoic Acid, 2-Propyl-, methyl Ester, and 2,3-anhydro-D-Mannosan were the two major compounds present. The bioactive compounds present included phenolics and flavonoids (55.0 ± 1.3 mg GAE g−1 d.w. and 42.5 ± 0.5 mg RE g−1 d.w.). FT-IR analysis detected the presence of aldehydes and anhydrites functional groups. Antioxidant properties were positively associated with the secondary metabolites. The anti-proliferative activity of methanolic leaf extracts showed the IC50 values 135.82 and 181.73 μg mL−1 for A549 and MCF-7, respectively. The methanolic extract also showed larvicidal activity on three mosquito species with highest toxicity on Culex quinquefasciatus larvae with LC50 4.84 mg mL−1 and LC90 19.33 mg mL−1.
In Vivo Antioxidant, anti-Inflammatory and anti-Hyperglycemic Effects of Salvia Splendens Aerial Parts in Male Albino Rat Models

ABSTRACT The in vivo antioxidant, anti-inflammatory, and anti-hyperglycemic activities of Salvia splendens extracts (100 mg kg−1d−1 P.O.) in male albino rats were studied. The total 70% methanolic, total aqueous and successive ethyl acetate extracts demonstrated antioxidant and anti-inflammatory activities. The results suggested that S. splendens may have supportive role in controlling diabetic conditions due to the presence of poly-phenolic compounds.
Sum things are not what they seem: Problems with point-wise interpretations and quantitative analyses of proxies based on aggregated radiocarbon dates

Radiocarbon-date assemblages are commonly used as proxies for past human and environmental phenomena. Prominent examples of target phenomena include past population levels and sea level fluctuations. These processes are thought to have affected the amount of organic carbon deposited into the archaeological and/or palaeoenvironmental record. Time-series representing through-time fluctuations in the frequency of radiocarbon samples are, therefore, often used as proxies for such processes. However, there are critical problems with using radiocarbon “dates-as-data” in point-wise comparisons and these problems have gone largely underappreciated. The key problem is that the established proxies are easily misinterpreted. They conflate process variation and chronological uncertainty, which makes them unsuitable for point-wise comparisons aimed at identifying rates of change, comparing variables directly, or estimating parameters in regression models. Here we explore the interpretive and analytical problems in detail in an effort to raise awareness and promote skepticism about the use of the established proxies in point-wise comparisons. We also provide suggestions for future research and point to potential methodological alternatives that may improve the viability of dates-as-data approaches.
Adsorption of Ni (II) Ions from Aqueous Solution by Pyrolytic Chars Derived from the Cottonseed Cake

Abstract The adsorption of Ni (II) ions from aqueous solution by pyrolytic char obtained from the cottonseed cake was studied in thisarticle. The sample was pyrolysed with a heating rate of 7°C min−1 up to select final pyrolysis temperatures of 400°C, 550°C, and 700°C under a steam atmosphere with sweep gas velocities of 2.7 cm sec−1. Three pyrolytic char were used as adsorbent obtained at final pyrolysis temperatures. The influence of experimental parameters such as pyrolysis temperature, adsorbent dosage, condition time, and nickel (II) concentration were investigated. Optimal condition was determined as: 0.35 g/50 ml adsorbent dosage, 10 mg/l Ni (II) concentration, 60 min contact time for temperature 700°C of pyrolytic char. Adsorption parameters were determined by using Langmuir isotherm. The adsorption capacity of the pyrolytic char is 25 mg/g and the maximum surface area (47.98 m2/g) was obtained at 700°C. According to the experimental results, the pyrolytic char can be used as carbon adsorbent for adsorption processes.
Classification of Wines Using Principal Component Analysis

Abstract Classification of wines with a large number of correlated covariates may lead to classification results that are difficult to interpret. In this study, we use a publicly available dataset on wines from three known cultivars, where there are 13 highly correlated variables measuring chemical compounds of wines. The goal is to produce an efficient classifier with straightforward interpretation to shed light on the important features of wines in the classification. To achieve the goal, we incorporate principal component analysis (PCA) in the k-nearest neighbor (kNN) classification to deal with the serious multicollinearity among the explanatory variables. PCA can identify the underlying dominant features and provide a more succinct and straightforward summary over the correlated covariates. The study shows that kNN combined with PCA yields a much simpler and interpretable classifier that has comparable performance with kNN based on all the 13 variables. The appropriate number of principal components is chosen to strike a balance between predictive accuracy and simplicity of interpretation. Our final classifier is based on only two principal components, which can be interpreted as the strength of taste and level of alcohol and fermentation in wines, respectively. (JEL Classifications: C10, Cl4, D83)
Accessible interactive 3D models for blind and low-vision people

Blind and low-vision (BLV) people experience difficulty accessing graphical information, particularly regarding travel and education. Tactile diagrams and 3D printed models can improve access to graphical information for BLV people; however, these formats only allow limited detailed and contextual information. Interactive 3D printed models (I3Ms) exist, but many rely on passive audio labels that don't particularly empower BLV people in independent knowledge building and interpretation. This project investigates the creation of I3Ms that offer more engaging experiences with a focus on facilitating independent exploration and knowledge discovery. Specifically, this project explores how BLV people want to interact with I3Ms, interactive functionalities and behaviours that I3Ms should support, such as conversational interfaces and model agency, and to understand the relationship between I3Ms and conventional accessible graphics.
CircusTent: A Benchmark Suite for Atomic Memory Operations

A paradigm shift is currently taking place in the field of computer architecture. Consistent silicon-level processor improvements, relied upon in the past to drive the augmentation of system scalability, have stalled. As such, it is widely believed that future systems, wherein the design of hardware and software are more closely coupled, will need to leverage an increased degree of heterogeneity in order to realize further improvements. Parallel processing and corresponding programming models, already ubiquitous to high performance computing, will play a crucial role in these systems. Consequently, it is critically important to understand the interaction between these components. However, the behavior of atomic operations and associated synchronization primitives, which already represent a bottleneck in current systems, is difficult to quantify. Therefore, in this work, we introduce CircusTent, an open source, modular, and natively extensible benchmark suite for shared and distributed memory systems that is designed to measure the performance of a target architecture’s memory subsystem with respect to atomic operations. Herein, we first detail the design of CircusTent, which includes eight different kernels designed to replicate common atomic memory access patterns using two atomic primitives. We then demonstrate the capabilities of CircusTent through an evaluation of fourteen different platforms using our OpenMP benchmark implementation. In short, we believe CircusTent will prove to be an invaluable tool for the design and prototyping of emerging architectures.
The application and influence of big data in medicine

At present, with the gradual expansion of the application scope of big data technology, its application frequency in medicine is becoming more and more frequent. The purpose of using medical data for different projects is mainly to reduce the pressure of patients and improve the accuracy. However, the use of medical data has also caused some adverse effects, so this paper will study it from three aspects: source, application and impact.
DELTA: Validate GPU Memory Profiling with Microbenchmarks

With the advent of GPU computing, profiling tools are now widely used to assist developers in identifying and solving performance bottlenecks. Those tools are commonly relying on hardware performance counters to grant users the access to low-level activities. Considering the increasing complexity of modern GPUs and also the efforts needed to associate program behaviors with hardware events, it is not trivial to construct the profiling tools with assured correctness. As a result, profiling tools should be strictly validated to make sure that the program behaviors and resource usages can be correctly captured and analyzed. To aid the validation, we create a testing prototype DELTA, on top of the open-source Radeon Open Compute platform (ROCm), to investigate the values of the derived profiling metrics and their underlying basic counters. The tests of DELTA are generally based on the classical microbenchmarks, which are capable to control program behaviors to generate predictable statistics. Differing from prior dissecting works, our tests are to examine the profiled results and to compare against desired patterns, reporting whether the profiling tools are correctly working with appropriate data collection and processing. This paper presents the validation methodology and experimental results of cache and main memory on the recent GPUs and ROCm platform, and the case studies demonstrate that the tests are helpful to scrutinize the profiling tools.
A model for the individual empowerment using intrinsic personalization of crowdsourcing tasks

Crowdsourcing is a paradigm of outsourcing work that is done using human capabilities through the Internet. Given the various possibilities of overcoming cultural and social barriers, crowdsourcing provides an opportunity for people with disabilities to have a financial compensation and help them feel realised. In crowdsourcing, people with disabilities face problems related with the lack of task description or usability. This article it is presented the main threads for my PhD thesis which main goal is to prove, that it is possible to map crowdsourcing tasks effectively to each individual, focusing particularly on the cognitive abilities.
Analysis method of Tissue-specific gene set weight

Gene set testing is one of the indispensable analysis methods for analyzing high-dimensional genome data. Although the expression and function of many genes are tissue-specific, gene set testing is performed in a way that does not distinguish between tissue types. In order to cope with this challenge, this article uses the tissue-specific gene activity information in the human protein atlas to calculate the tissue-specific gene weights, and uses the filtered gene set in the molecular signature database to generate the tissue-specific gene set weights. In order to prove the validity of these weights, this article use the weights to perform gene set tests on the gene expression data of the three diseases, and use the p-values generated by the tests to weight. Through the weighted FDR analysis, this research can conclude the proposed tissue-specific gene set weights significantly improve the statistical power of gene set testing, and more accurately identified the biological gene set association information of high-dimensional genome data.