Miracle-dz/UniFAR-FTUs huggingface dataset:
Cutting-edge 3D Medical Image Segmentation Methods in 2020: Are Happy Families All Alike?

Segmentation is one of the most important and popular tasks in medical image analysis, which plays a critical role in disease diagnosis, surgical planning, and prognosis evaluation. During the past five years, on the one hand, thousands of medical image segmentation methods have been proposed for various organs and lesions in different medical images, which become more and more challenging to fairly compare different methods. On the other hand, international segmentation challenges can provide a transparent platform to fairly evaluate and compare different methods. In this paper, we present a comprehensive review of the top methods in ten 3D medical image segmentation challenges during 2020, covering a variety of tasks and datasets. We also identify the"happy-families"practices in the cutting-edge segmentation methods, which are useful for developing powerful segmentation approaches. Finally, we discuss open research problems that should be addressed in the future. We also maintain a list of cutting-edge segmentation methods at \url{https://github.com/JunMa11/SOTA-MedSeg}.
SmartDeal: Remodeling Deep Network Weights for Efficient Inference and Training

The record-breaking performance of deep neural networks (DNNs) comes with heavy parameter budgets, which leads to external dynamic random access memory (DRAM) for storage. The prohibitive energy of DRAM accesses makes it nontrivial for DNN deployment on resource-constrained devices, calling for minimizing the movements of weights and data in order to improve the energy efficiency. Driven by this critical bottleneck, we present SmartDeal, a hardware-friendly algorithm framework to trade higher-cost memory storage/access for lower-cost computation, in order to aggressively boost the storage and energy efficiency, for both DNN inference and training. The core technique of SmartDeal is a novel DNN weight matrix decomposition framework with respective structural constraints on each matrix factor, carefully crafted to unleash the hardware-aware efficiency potential. Specifically, we decompose each weight tensor as the product of a small basis matrix and a large structurally sparse coefficient matrix whose nonzero elements are readily quantized to the power-of-2. The resulting sparse and readily quantized DNNs enjoy greatly reduced energy consumption in data movement as well as weight storage, while incurring minimal overhead to recover the original weights thanks to the required sparse bit-operations and cost-favorable computations. Beyond inference, we take another leap to embrace energy-efficient training, by introducing several customized techniques to address the unique roadblocks arising in training while preserving the SmartDeal structures. We also design a dedicated hardware accelerator to fully utilize the new weight structure to improve the real energy efficiency and latency performance. We conduct experiments on both vision and language tasks, with nine models, four datasets, and three settings (inference-only, adaptation, and fine-tuning). Our extensive results show that 1) being applied to inference, SmartDeal achieves up to $2.44\times $ improvement in energy efficiency as evaluated using real hardware implementations and 2) being applied to training, SmartDeal can lead to $10.56\times $ and $4.48\times $ reduction in the storage and the training energy cost, respectively, with usually negligible accuracy loss, compared to state-of-the-art training baselines. Our source codes are available at: https://github.com/VITA-Group/SmartDeal.
A Case Study of a Successful Lake Rehabilitation Project in South-Central Nebraska

Cottonmill Lake, a 17.4 ha impoundment located in Buffalo County, Nebraska, was a fishery dominated by common carp (Cyprinus carpio). As a result of the poor sportfish populations, angler participation in May and June of 1993 was low (503 ± 210 angler hours) and angler catch rates for all fish species (0.5 ± 0.4 fish/angler hour) was less than desired. In 1995, before rehabilitation, bluegill (Lepomis macrochirus) trap net catch per unit effort (CPUE) was 1.5 ± 0.9, largemouth bass (Micropterus salmoides) catch per hour of electrofishing was 8.0 ± 0.5, and channel catfish (Ictalurus punctatus) CPUE in gill nets was 7.5 ± 2.5. In 1999, the Nebraska Game and Parks Commission (NGPC) and city of Kearney completed a lake rehabilitation project at Cottonmill Lake by removing 84,995 m3 (300,000 ft3) of sediment and adding two islands and four breakwater jetties. Standardized NGPC fishery survey conducted in 2003, four years after rehabilitation, found a significant increase in number of bluegill (CPUE = 28.3 ± 7.4; p = 0.012; F = 12.86; df= 1) and largemouth bass (CPUE = 496.0 ± 5.8; p < 0.001; F = 34.33; df= 1). In addition, angler participation in May and June 2006 was higher after rehabilitation (11,122 ± 1,333 angler hours), and angler catch rates for all species (1.5 ± 0.4 fish/angler hour) increased. The estimated angler expenditure while fishing at Cottonmill Lake during May and June 2006 increased to $367,026 in 2006 from an estimated $26,004 during May and June 1993. lCorresponding author. E-mail address: brad.newcomb@nebraska.gov 96 The Prairie Naturalist 40(3/4): September/December 2008
Axial vibration in a poppet valve based on fluid–structure interaction

In this paper, fluid–structure interaction modeling of poppet fluid was presented, response of poppet under the action of step and periodic excitation signal was simulated, and dynamic characteristics of viscous force and hydraulic force on poppet surface were analyzed. The investigation indicates that poppet surface before valve port can be divided into jet impingement area and static pressure area under the action of submerged jet, and the dynamic characteristics of hydraulic force on these areas and axial vibration of poppet are closely related with length of jet impingement and poppet structure. Furthermore, for hydraulic force on jet impingement area, hydraulic force on static pressure area, and viscous force, if amplitude and phase have a reasonable configuration, axial vibration amplitude of poppet can decrease greatly.
Phosphorus in Urban Sewage Sludges as Assessed by Isotopic Exchange

Sewage sludges have been used for years as fertilizers but little information exists on the availability of P in these complex materials. Isotopically exchangeable P was measured in 12 urban sewage sludges using the isotopic exchange kinetics method. The results obtained with this method were then interpreted according to the multicompartmental model recently proposed. The two activated sludges and the composted sludge had the highest quantities of P isotopically exchangeable within 1 min (Elmin higher than 0.77 g P kg -1 sludge). The flocculated and heat-treated sludges contained low quantities of rapidly exchangeable P but high quantities of slowly exchangeable P (E >12wk higher than 9 g P kg -1 sludge). The quantity of P exchangeable within 1 d was positively correlated to the sum of water- and bicarbonate-extractable P while slowly exchangeable P was positively correlated to the quantity of P present in precipitated forms deduced from a sequential extraction (i.e., HCl plus NaOH-extractable P for FeSO 4 -flocculated sludges and HCl-extractable P for the other sludges). Results of this study demonstrate that the isotopic exchange kinetics method can give valuable information on inorganic P forms and exchangeability in urban sewage sludges.
Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing

The early and robust detection of anomalies occurring in discrete manufacturing processes allows operators to prevent harm, e.g. defects in production machinery or products. While current approaches for data-driven anomaly detection provide good results on the exact processes they were trained on, they often lack the ability to flexibly adapt to changes, e.g. in products. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks. Therefore, this article discusses different continual learning approaches from the group of regularization strategies, which are implemented, evaluated and compared based on a real industrial metal forming dataset.
Sage: Using Unsupervised Learning for Scalable Performance Debugging in Microservices

Cloud applications are increasingly shifting from large monolithic services to complex graphs of loosely-coupled microservices. Despite the advantages of modularity and elasticity microservices offer, they also complicate cluster management and performance debugging, as dependencies between tiers introduce backpressure and cascading QoS violations. We present Sage, a machine learning-driven root cause analysis system for interactive cloud microservices. Sage leverages unsupervised ML models to circumvent the overhead of trace labeling, captures the impact of dependencies between microservices to determine the root cause of unpredictable performance online, and applies corrective actions to recover a cloud service's QoS. In experiments on both dedicated local clusters and large clusters on Google Compute Engine we show that Sage consistently achieves over 93% accuracy in correctly identifying the root cause of QoS violations, and improves performance predictability.
Deep-Learned Approximate Message Passing for Asynchronous Massive Connectivity

This paper considers the massive connectivity problem in an asynchronous grant-free random access system, where a huge number of devices sporadically transmit data to a base station (BS) with imperfect synchronization. The goal is to design algorithms for joint user activity detection, delay detection, and channel estimation. By exploiting the sparsity on both user activity and delays, we formulate a hierarchical sparse signal recovery problem in both the single-antenna and the multiple-antenna scenarios. While traditional compressed sensing algorithms can be applied to these problems, they suffer high computational complexity and often require the perfect statistical information of channel and devices. This paper solves these problems by designing the Learned Approximate Message Passing (LAMP) network, which belongs to model-driven deep learning approaches and ensures efficient performance without tremendous training data. Particularly, in the multiple-antenna scenario, we design three different LAMP structures, namely, distributed, centralized and hybrid ones, to balance the performance and complexity. Simulation results demonstrate that the proposed LAMP networks can significantly outperform the conventional AMP method thanks to their ability of parameter learning. It is also shown that LAMP has robust performance to the maximal delay spread of the asynchronous users.
Outline to Story: Fine-grained Controllable Story Generation from Cascaded Events

Large-scale pretrained language models have shown thrilling generation capabilities, especially when they generate consistent long text in thousands of words with ease. However, users of these models can only control the prefix of sentences or certain global aspects of generated text. It is challenging to simultaneously achieve fine-grained controllability and preserve the state-of-the-art unconditional text generation capability. In this paper, we first propose a new task named "Outline to Story" (O2S) as a test bed for fine-grained controllable generation of long text, which generates a multi-paragraph story from cascaded events, i.e. a sequence of outline events that guide subsequent paragraph generation. We then create dedicate datasets for future benchmarks, built by state-of-the-art keyword extraction techniques. Finally, we propose an extremely simple yet strong baseline method for the O2S task, which fine tunes pre-trained language models on augmented sequences of outline-story pairs with simple language modeling objective. Our method does not introduce any new parameters or perform any architecture modification, except several special tokens as delimiters to build augmented sequences. Extensive experiments on various datasets demonstrate state-of-the-art conditional story generation performance with our model, achieving better fine-grained controllability and user flexibility. Our paper is among the first ones by our knowledge to propose a model and to create datasets for the task of "outline to story". Our work also instantiates research interest of fine-grained controllable generation of open-domain long text, where controlling inputs are represented by short text.
A new parsimonious method for classifying Cancer Tissue-of-Origin Based on DNA Methylation 450K data

DNA methylation is a well-studied genetic modification that regulates gene transcription of Eukaryotes. Its alternations have been recognized as a significant component of cancer development. In this study, we use the DNA methylation 450k data from The Cancer Genome Atlas to evaluate the efficacy of DNA methylation data on cancer classification for 30 cancer types. We propose a new method for gene selection in high dimensional data(over 450 thousand). Variance filtering is first introduced for dimension reduction and Recursive feature elimination (RFE) is then used for feature selection. We address the problem of selecting a small subsets of genes from large number of methylated sites, and our parsimonious model is demonstrated to be efficient, achieving an accuracy over 91%, outperforming other studies which use DNA micro-arrays and RNA-seq Data . The performance of 20 models, which are based on 4 estimators (Random Forest, Decision Tree, Extra Tree and Support Vector Machine) and 5 classifiers (k-Nearest Neighbours, Support Vector Machine, XGboost, Light GBM and Multi-Layer Perceptron), is compared and robustness of the RFE algorithm is examined. Results suggest that the combined model of extra tree plus catboost classifier offers the best performance in cancer identification, with an overall validation accuracy of 91% , 92.3%, 93.3% and 93.5% for 20, 30, 40 and 50 features respectively. The biological functions in cancer development of 50 selected genes is also explored through enrichment analysis and the results show that 12 out of 16 of our top features have already been identified to be specific with cancer and we also propose some more genes to be tested for future studies. Therefore, our method may be utilzed as an auxiliary diagnostic method to determine the actual clinicopathological status of a specific cancer.
Accurate Determination of Concentrations by Localized Proton Magnetic Resonance Spectroscopy

Abstract Data processing is proposed for accurate quantification of localized proton spectra. Localized proton NMR spectra from phantoms and 30 healthy volunteers were acquired. Scalar coupled spin systems were simulated, allowing for the J‐modulation and second order effects. Curve fitting was carried out by a linear combination of the simulated multiplets. The error in curve fitting is approximately 5 times lower than that attained using the standard protocol proposed by the manufacturer. The curve‐fitting protocol eliminates the operator‐dependent tasks, facilitating and accelerating the quantification of the major metabolite concentrations.
Substrate effect on excitonic shift and radiative lifetime of two-dimensional materials

Substrates have strong effects on optoelectronic properties of two-dimensional (2D) materials, which have emerged as promising platforms for exotic physical phenomena and outstanding applications. To reliably interpret experimental results and predict such effects at 2D interfaces, theoretical methods accurately describing electron correlation and electron-hole interaction such as first-principles many-body perturbation theory are necessary. In our previous work (2020 Phys. Rev. B 102 205113), we developed the reciprocal-space linear interpolation method that can take into account the effects of substrate screening for arbitrarily lattice-mismatched interfaces at the GW level of approximation. In this work, we apply this method to examine the substrate effect on excitonic excitation and recombination of 2D materials by solving the Bethe–Salpeter equation. We predict the nonrigid shift of 1s and 2s excitonic peaks due to substrate screening, in excellent agreements with experiments. We then reveal its underlying physical mechanism through 2D hydrogen model and the linear relation between quasiparticle gaps and exciton binding energies when varying the substrate screening. At the end, we calculate the exciton radiative lifetime of monolayer hexagonal boron nitride with various substrates at zero and room temperature, as well as the one of WS2 where we obtain good agreement with experimental lifetime. Our work answers important questions of substrate effects on excitonic properties of 2D interfaces.
Polytropic spheres modelling dark matter haloes of dwarf galaxies

Context. Dwarf galaxies and their dark matter (DM) haloes have velocity curves of a different character than those in large galaxies. These velocity curves are modelled by a simple pseudo-isothermal model containing only two parameters, which do not give us insight into the physics of the DM halo. Aims. We seek to obtain some insight into the physical conditions in DM haloes of dwarf galaxies by using a simple physically based model of DM haloes. Methods. To treat the diversity of the dwarf galaxy velocity profiles in a unifying framework, we applied polytropic spheres characterised by the polytropic index n and the relativistic parameter σ as a model of dwarf-galaxy DM haloes and matched the velocity of circular geodesics of the polytropes to the velocity curves observed in the dwarf galaxies from the LITTLE THINGS ensemble. Results. We introduce three classes of the LITTLE THINGS dwarf galaxies in relation to the polytrope models due to the different character of the velocity profile. The first class corresponds to polytropes that have n < 1 with linearly increasing velocity along the whole profile, the second class has 1 < n < 2 and the velocity profile becomes flat in the external region, the third class has n > 2, and the velocity profile reaches a maximum and demonstrates a decline in the external region. The σ parameter has to be strongly non-relativistic (σ < 10−8) for all dwarf galaxy models; this parameter varies for the models of each class, but these variations have negligible influence on the character of the velocity profile. Conclusions. Our results indicate a possibility that at least two different kinds of DM are behind the composition of DM haloes. The matches of the observational velocity curves are of the same quality as those obtained by the pseudo-isothermal, core-like models of dwarf galaxy DM haloes.
EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets

Heavily overparameterized language models such as BERT, XLNet and T5 have achieved impressive success in many NLP tasks. However, their high model complexity requires enormous computation resources and extremely long training time for both pre-training and fine-tuning. Many works have studied model compression on large NLP models, but only focusing on reducing inference time while still requiring an expensive training process. Other works use extremely large batch sizes to shorten the pre-training time, at the expense of higher computational resource demands. In this paper, inspired by the Early-Bird Lottery Tickets recently studied for computer vision tasks, we propose EarlyBERT, a general computationally-efficient training algorithm applicable to both pre-training and fine-tuning of large-scale language models. By slimming the self-attention and fully-connected sub-layers inside a transformer, we are the first to identify structured winning tickets in the early stage of BERT training. We apply those tickets towards efficient BERT training, and conduct comprehensive pre-training and fine-tuning experiments on GLUE and SQuAD downstream tasks. Our results show that EarlyBERT achieves comparable performance to standard BERT, with 35 45% less training time. Code is available at https://github.com/VITA-Group/EarlyBERT.
Temperature effect on transport performance by inorganic nanofiltration membranes

The effect of temperature on nanofiltration performance was examined using three inorganic membranes with a molecular-weight cutoff of approximately 200, 600, and 2000, respectively. The inorganic porous membranes were prepared from silica-zirconia colloidal sols and used in nanofiltration experiments for neutral solutes over a temperature range of 20 to 60°C. The rejection of solutes decreased with an increase in temperature for the membranes, while the permeate volume flux increased. Three transport coefficients - reflection coefficient, solute permeability, and water permeability - were obtained using the Spiegler-Kedem equation, which accounts for the contribution of convection and diffusion to solute flux. As a result, the reflection coefficient corresponding to the fraction of solutes reflected by the membrane in convective flow was almost constant, irrespective of experimental temperature. Solute permeabilities, however, increased with temperature. The dependency was larger for larger solutes and membranes with smaller pore diameters. Therefore, the hindered diffusion of solutes through micropores was indicative of an activated process. Moreover, pure water permeability, after correction for the temperature effect on viscosity, also increased with experimental temperature.
A NEW PHENOLIC HYDROXYL MODIFIED POLYSTYRENE ADSORBENT FOR THE REMOVAL OF PHENOLIC COMPOUNDS FROM THEIR AQUEOUS SOLUTIONS

A new phenolic hydroxyl modified polystyrene adsorbent (AM-1) was synthesized for the removal of phenolic compounds from their aqueous solutions. This new adsorbent can be used directly without the wetting pretreatment. A comparison of the sorption properties of the AM-1 and the commercial Amberlite XAD-4 toward four phenolic compounds, phenol, p-cresol, p-chlorophenol, and p-nitrophenol, was made. The capacities of equilibrium adsorption of AM-1 for all four phenolic compounds were around two times higher than that of Amberlite XAD-4, which may be contributed to the presence of phenol hydroxyl group on the surface and the unique pore structure of AM-1. Freundlich isotherm equations and the values of isosteric adsorption enthalpies for the four phenolic compounds indicated a physical adsorption mechanism on both adsorbents. Column studies showed that both the breakthrough adsorption capacity and the total adsorption capacity of phenol on AM-1 were higher than on Amberlite XAD-4 while less acetone was needed for desorbing phenol from AM-1 than from Amberlite XAD-4.
LARGE ENVIRONMENTAL CHAMBER: AMMONIA RECOVERY CALIBRATION

The ability to accurately measure ammonia emissions from farms is an important issue both in terms of establishing emissions regulations and for effective evaluation of mitigation techniques. To address this issue, experimental trials were carried out to determine the ability to quantitatively recover ammonia released within a large environmental chamber designed to house six dairy cows or manure processing technologies. Ammonia was released over 30 min periods at a range of values (0.1 to 0.7 g/min) from three positions within the chamber. Chamber temperature was maintained at 22.2  C. Air flow was maintained at one of three setpoints; 10.5, 14.0, or 21.0 air exchanges per h. The amount of ammonia recovered from each release was determined by calculating the average increase in ammonia concentration during the release and stabilization periods, and multiplying by the measured volumetric air flow. Over 86 trials, the recoveries averaged 105.1  0.8% of the amounts released as determined by the weight change of the release cylinder. Small increases in measured recoveries were associated with increased air flow and increased amounts of ammonia released. Results from this study indicate that increases in ammonia within the chamber, including short–term increases, can be quantitatively recovered.