Miracle-dz/UniFAR-FTUs huggingface dataset:
Interlaboratory Study on Quantitative Methods of Analysis of C10−C13 Polychloro-n-alkanes

Seven laboratories participated in an international interlaboratory comparison exercise to compare the quantitative methods used for measuring C10−C13 polychloro-n-alkanes (PCAs). Participants were supplied with two solutions (PCA-1, PCA-70) containing PCAs of “known” but unstated concentrations, and two real world samples (fish extracts FE1 and FE2) each consisting of a cleaned up extract (lipid-free) of a fish tissue known to contain PCAs. A well-characterized commercial formulation, PCA-60, of stated concentration was also supplied and was used as the external standard for the exercise. Participants having other commercially available C10−C13 PCA mixtures were encouraged to use them as external standards for the study, and the choice of the quantitative method employed was left to participants, though all were based on high-resolution gas chromatography with detection by electron capture negative ion mass spectrometry (plus electron capture detection in one case). The results of the study met with mixe.
Epitaxial Relationships of Pentacene Polymorphs on KCI (001)

Epitaxial relationships of pentacene polymorphs on KCI (001) were investigated by X-ray diffractometry and adsorption energy calculation. The in-plane and out-of-plane structures of the deposited thin films were investigated using a four-axes X-ray diffractmeter. Two polymorphs, bulk and thin film phases, appeared and the in-plane orientations were determined for each polymorph. The adsorption energies of clusters including four pentacene molecules were successfully calculated as a function of rotation angle and the results were well consistent with the experimental data.
On Mixing of Markov Chains: Coupling, Spectral Independence, and Entropy Factorization

For general spin systems, we prove that a contractive coupling for any local Markov chain implies optimal bounds on the mixing time and the modified log-Sobolev constant for a large class of Markov chains including the Glauber dynamics, arbitrary heat-bath block dynamics, and the Swendsen-Wang dynamics. This reveals a novel connection between probabilistic techniques for bounding the convergence to stationarity and analytic tools for analyzing the decay of relative entropy. As a corollary of our general results, we obtain $O(n\log{n})$ mixing time and $\Omega(1/n)$ modified log-Sobolev constant of the Glauber dynamics for sampling random $q$-colorings of an $n$-vertex graph with constant maximum degree $\Delta$ when $q>(11/6 - \epsilon_0)\Delta$ for some fixed $\epsilon_0>0$. We also obtain $O(\log{n})$ mixing time and $\Omega(1)$ modified log-Sobolev constant of the Swendsen-Wang dynamics for the ferromagnetic Ising model on an $n$-vertex graph of constant maximum degree when the parameters of the system lie in the tree uniqueness region. At the heart of our results are new techniques for establishing spectral independence of the spin system and block factorization of the relative entropy. On one hand we prove that a contractive coupling of a local Markov chain implies spectral independence of the Gibbs distribution. On the other hand we show that spectral independence implies factorization of entropy for arbitrary blocks, establishing optimal bounds on the modified log-Sobolev constant of the corresponding block dynamics.
PVStereo: Pyramid Voting Module for End-to-End Self-Supervised Stereo Matching

Supervised learning with deep convolutional neural networks (DCNNs) has seen huge adoption in stereo matching. However, the acquisition of large-scale datasets with well-labeled ground truth is cumbersome and labor-intensive, making supervised learning-based approaches often hard to implement in practice. To overcome this drawback, we propose a robust and effective self-supervised stereo matching approach, consisting of a pyramid voting module (PVM) and a novel DCNN architecture, referred to as OptStereo. Specifically, our OptStereo first builds multi-scale cost volumes, and then adopts a recurrent unit to iteratively update disparity estimations at high resolution; while our PVM can generate reliable semi-dense disparity images, which can be employed to supervise OptStereo training. Furthermore, we publish the HKUST-Drive dataset, a large-scale synthetic stereo dataset, collected under different illumination and weather conditions for research purposes. Extensive experimental results demonstrate the effectiveness and efficiency of our self-supervised stereo matching approach on the KITTI Stereo benchmarks and our HKUST-Drive dataset. PVStereo, our best-performing implementation, greatly outperforms all other state-of-the-art self-supervised stereo matching approaches. Our project page is available at sites.google. com/view/pvstereo.
Modelling Animal Biodiversity Using Acoustic Monitoring and Deep Learning

For centuries researchers have used sound to monitor and study wildlife. Traditionally, conservationists have identified species by ear; however, it is now common to deploy audio recording technology to monitor animal and ecosystem sounds. Animals use sound for communication, mating, navigation and territorial defence. Animal sounds provide valuable information and help conservationists to quantify biodiversity. Acoustic monitoring has grown in popularity due to the availability of diverse sensor types which include camera traps, portable acoustic sensors, passive acoustic sensors, and even smartphones. Passive acoustic sensors are easy to deploy and can be left running for long durations to provide insights on habitat and the sounds made by animals and illegal activity. While this technology brings enormous benefits, the amount of data that is generated makes processing a time-consuming process for conservationists. Consequently, there is interest among conservationists to automatically process acoustic data to help speed up biodiversity assessments. Processing these large data sources and extracting relevant sounds from background noise introduces significant challenges. In this paper we outline an approach for achieving this using state of the art in machine learning to automatically extract features from time-series audio signals and modelling deep learning models to classify different bird species based on the sounds they make. The acquired bird songs are processed using mel-frequency cepstrum (MFC) to extract features which are later classified using a multilayer perceptron (MLP). Our proposed method achieved promising results with 0.74 sensitivity, 0.92 specificity and an accuracy of 0.74.
Trapping tetracycline-loaded nanoparticles into polycaprolactone fiber networks for periodontal regeneration therapy

The controlled delivery of antibiotics, anti-inflammatory agents, or chemotherapeutic agents to the periodontal site is a recognized strategy to improve the efficiency of regenerative processes of hard tissues. A novel approach based on the trapping of tetracycline hydrochloride–loaded particles in polycaprolactone nanofibers was used to guide the regeneration processes of periodontal tissue at the gum interface. Chitosan nanoparticles loaded with different levels of tetracycline hydrochloride (up to 5% wt) were prepared by solution nebulization induced by electrical forces (i.e. electrospraying). The fine tuning of process parameters allows to obtain nanoparticles with tailored sizes ranging from 0.485 ± 0.147 µm to 0.639 ± 0.154 µm. The tetracycline hydrochloride release profile had a predominant burst effect for the first 70% of release followed by a relatively slow release over 24 h, which is promising for oral drug delivery. We also demonstrated that trapping tetracycline hydrochloride–loaded particles with submicrometer diameters into a polycaprolactone fiber network contributed to slowing the release of tetracycline hydrochloride from the nanoparticles, thus providing a more prolonged release in the periodontal pocket during clinical therapy. Preliminary studies on human mesenchymal stem cells confirm the viability of cells up to 5 days after culture, and thereby, validate the use of nanoparticle-/nanofiber-integrated systems in periodontal therapies.
Intramolecular and intermolecular crosslinked poly(vinyl alcohol)–borate complexes for the sustained release of fertilizers and enzymes

Therapeutic agents or agricultural fertilizers captured in polymer colloids (PCs) give rise to interesting applications, which are typically related to sustained release. We synthesized crosslinked polymer structures with poly(vinyl alcohol) (PVA) and borax precursors. Fourier transform infrared spectroscopy showed that a polymer–boron ion complex was formed with the crosslinking reaction at the OH site of PVA; thereby, PCs were formed. Field-emission scanning electron microscopy showed that a uniform mesoporous two-dimensional structure formed via intermolecular and intramolecular crosslinking. Trypsin enzyme and phosphate fertilizer were trapped in these PCs independently to study sustained release. Fertilizer-incorporated PCs were mixed with soil samples, in which seeds of fenugreek were sown, and the plant growth was monitored a duration of 15 days. The fertilizer release, studied with UV–visible spectroscopy, showed a sustained signature of the fertilizer (at 690 nm) in the water extracts of soil, with much healthier plant growth compared to the control. For the trypsin-incorporated PC samples, the released enzyme was made to interact with bovine serum albumin protein to monitor the released percentage with UV absorption spectroscopy. A systematic increase in the enzyme signature (at 280 nm) was observed for a duration of 60 min; this indicated the potential of PC for sustained drug release. The swelling calculations predicted that the mechanism involved was composed of pseudo-swelling behavior. We envisaged that the hydroxyl groups of the PC broke in water and formed a complex with water. This complex slowly dissolved in water to release the entrapped molecules. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci, 2011
Colonoscopy screening and surveillance guidelines

The Colonoscopy Screening and Surveillance Guidelines were developed by the Japan Gastroenterological Endoscopy Society as basic guidelines based on the scientific methods. The importance of endoscopic screening and surveillance for both detection and post‐treatment follow‐up of colorectal cancer has been recognized as essential to reduce disease mortality. There is limited high‐level evidence in this field; therefore, we had to focus on the consensus of experts. These clinical practice guidelines consist of 20 clinical questions and eight background knowledge topics that have been determined as the current guiding principles.
M ar 2 02 1 MarCo : a new dependence structure to model match outcomes in football

The approaches commonly used to model the number of goals in a football match are characterised by strong assumptions about the dependence between the number of goals scored by the two competing teams and about their marginal distribution. In this work, we argue that the assumptions traditionally made are not always based on solid arguments and sometimes they can be hardly justified. In light of this, we propose a modification of the Dixon and Coles (1997) model by relaxing the assumption of Poisson-distributed marginal variables and by introducing an innovative dependence structure. Specifically, we define the joint distribution of the number of goals scored during a match by means of thoroughly chosen marginal (Mar-) and conditional distributions (-Co). The resulting Mar-Co model is able to balance flexibility and conceptual simplicity. A real data application involving five European leagues suggests that the introduction of the novel dependence structure allows to capture and interpret fundamental league-specific dynamics. In terms of betting performance, the newly introduced Mar-Co model does not perform worse than the Dixon and Coles one in a traditional framework (i.e. 1-X-2 bet) and it outperforms the competing model when a more comprehensive dependence structure is needed (i.e. Under/Over 2.5 bet).
Projection-Based QLP Algorithm for Efficiently Computing Low-Rank Approximation of Matrices

Matrices with low numerical rank are omnipresent in many signal processing and data analysis applications. The pivoted QLP (p-QLP) algorithm constructs a highly accurate approximation to an input low-rank matrix. However, it is computationally prohibitive for large matrices. In this paper, we introduce a new algorithm termed Projection-based Partial QLP (PbP-QLP) that efficiently approximates the p-QLP with high accuracy. Fundamental in our work is the exploitation of randomization and in contrast to the p-QLP, PbP-QLP does not use the pivoting strategy. As such, PbP-QLP can harness modern computer architectures, even better than competing randomized algorithms. The efficiency and effectiveness of our proposed PbP-QLP algorithm are investigated through various classes of synthetic and real-world data matrices.
Long-term measurements of atmospheric peroxyacetylnitrate (PAN) at rural sites in Ontario and Nova Scotia; seasonal variations and long-range transport

The atmospheric behaviour of PAN at locations well-removed from major urban areas has been studied over extended periods using highly sensitive, automated gas chromatographic techniques at two sites in eastern Canada; Longwoods in southern Ontario (Februaryactober 1983), and Kejimkujik in Nova Scotia (May 1984 onwards). In addition, data have also been obtained from more limited measurement programs in northern Ontario at Chalk River (November-December 1983) and North Bay (January-February 1984). The variations in PAN mixing ratios observed at these sites have been examined with reference to available data for other atmospheric components, such as O 3 , HNO 3 , particulate nitrate and sulphate. Air mass back-trajectory calculations have been performed for Kejimkujik to relate observed periods of elevated PAN mixing ratio to specific source regions for precursor NMHC and NO x . For the sites studied, periods of enhanced PAN mixing ratio are observed to be possible throughout the yearly cycle, indicating that significant photochemical activity can occur even during wintertime. This contrasts with the available data for ozone, where photochemical formation in polluted air masses appears to be suppressed in winter. Seasonal patterns of PAN behaviour are probably sitedependent, but the observations suggest that the highest PAN maxima are favoured during fall and late winter/early spring. The long-term data from Kejimkujik also reveal an underlying seasonal variation in the typical background value of PAN observed at this site; this background value maximizes in late winter/early spring and minimizes in summer. In general, the magnitude of the observed PAN maxima appears to decrease with distance from the source region of influence. PAN accounts for a significant fraction of the observed oxidized nitrogen species at all the sites studied, and appears to make an increasing contribution as the transport time from the source region increases. In background tropospheric air, the contribution of PAN may be as high as 80-90%. DOI: 10.1111/j.1600-0889.1988. tb00113.x
The Semi-Supervised iNaturalist-Aves Challenge at FGVC7 Workshop

This document describes the details and the motivation behind a new dataset we collected for the semi-supervised recognition challenge~\cite{semi-aves} at the FGVC7 workshop at CVPR 2020. The dataset contains 1000 species of birds sampled from the iNat-2018 dataset for a total of nearly 150k images. From this collection, we sample a subset of classes and their labels, while adding the images from the remaining classes to the unlabeled set of images. The presence of out-of-domain data (novel classes), high class-imbalance, and fine-grained similarity between classes poses significant challenges for existing semi-supervised recognition techniques in the literature. The dataset is available here: \url{https://github.com/cvl-umass/semi-inat-2020}
Learning to Optimize DAG Scheduling in Heterogeneous Environment

Scheduling job flows efficiently and rapidly on distributed computing clusters is one of huge challenges for daily operation of data centers. In a practical scenario, a single job consists of numerous stages with complex dependency relation represented as a Directed Acyclic Graph (DAG) structure. Nowa-days a data center usually equips with a cluster of heterogeneous computing servers which are different in the hardware/software configuration. From both the cost saving and environmental friendliness, the data centers could benefit a lot from optimizing the job scheduling problems in the heterogeneous environment. Thus the problem has attracted more and more attention from both the industry and academy. In this paper, we propose a task-duplication based learning algorithm, namely LACHESIS22The second of the Three Fates in ancient Greek mythology, who deter-mines destiny. , aiming to optimize the problem. In the proposed approach, it first perceives the topological dependencies between jobs using a reinforcement learning framework and a specially designed graph neural network (GNN) to select the most promising task to be executed. Then the task is assigned to a specific executor with the consideration of duplicating all its precedent tasks according to an expert-designed rules. We have conducted extensive experiments over standard workloads to evaluate the proposed solution. The experimental results suggest that LACHESIS can achieve at most 26.7% reduction of makespan and 35.2% improvement of speedup ratio over seven strong baseline algorithms, including the state-of-the-art heuristics methods and a variety of deep reinforcement learning based algorithms.
Augmented Environment Representations with Complete Object Models

While 2D occupancy maps commonly used in mobile robotics enable safe navigation in indoor environments, in order for robots to understand and interact with their environment and its inhabitants representing 3D geometry and semantic environment information is required. Semantic information is crucial in effective interpretation of the meanings humans attribute to different parts of a space, while 3D geometry is important for safety and high-level understanding. We propose a pipeline that can generate a multi-layer representation of indoor environments for robotic applications. The proposed representation includes 3D metric-semantic layers, a 2D occupancy layer, and an object instance layer where known objects are replaced with an approximate model obtained through a novel model-matching approach. The metric-semantic layer and the object instance layer are combined to form an augmented representation of the environment. Experiments show that the proposed shape matching method outperforms a state-of-the-art deep learning method when tasked to complete unseen parts of objects in the scene. The pipeline performance translates well from simulation to real world as shown by F1-score analysis, with semantic segmentation accuracy using Mask R-CNN acting as the major bottleneck. Finally, we also demonstrate on a real robotic platform how the multi-layer map can be used to improve navigation safety.
Online monitoring of hiPSC expansion and hepatic differentiation in 3D culture by dielectric spectroscopy

Hepatocyte‐like cells derived from human‐induced pluripotent stem cells (hiPSC‐HLC) are expected to have important applications in drug screening and regenerative medicine. However, hiPSC‐HLC are difficult to produce on a large‐scale to obtain relevant numbers for such applications. The aim of this study was to implement a novel integrated strategy for scalable production of hiPSC‐HLC and demonstrate the applicability of dielectric spectroscopy to monitor hiPSC expansion/differentiation processes. We cultured hiPSC as three‐dimensional (3D) aggregates in stirred‐tank bioreactors (STB) operated in perfusion with an in situ capacitance probe. Dissolved oxygen concentration and dilution rate were controlled along the process and after 5 days of cell expansion, the hepatic differentiation was integrated in sequential steps for 28 days. The hiPSC were able to grow as 3D aggregates and the expression of hepatic markers and albumin production after differentiation confirmed that hepatocyte differentiation improved when compared to 2D culture. These hiPSC‐HLC exhibited functional characteristics of hepatocytes including glycogen storage and drug metabolization capacity. Our results also show a good correlation between the cell permittivity measured online and the aggregate biovolume measured by standard offline methods, demonstrating for the first time the potential of dielectric spectroscopy to monitor hiPSC expansion and differentiation in STB.
Constrained Text Generation with Global Guidance - Case Study on CommonGen

This paper studies constrained text generation, which is to generate sentences under certain pre-conditions. We focus on CommonGen, the task of generating text based on a set of concepts, as a representative task of constrained text generation. Traditional methods mainly rely on supervised training to maximize the likelihood of target sentences.However, global constraints such as common sense and coverage cannot be incorporated into the likelihood objective of the autoregressive decoding process. In this paper, we consider using reinforcement learning to address the limitation, measuring global constraints including fluency, common sense and concept coverage with a comprehensive score, which serves as the reward for reinforcement learning. Besides, we design a guided decoding method at the word, fragment and sentence levels. Experiments demonstrate that our method significantly increases the concept coverage and outperforms existing models in various automatic evaluations.
Experiences and insights from using Github Classroom to support Project-Based Courses

This work presents an approach for using GitHub classroom as a shared, structured, and persistent repository to support project-based courses at the Software Engineering Undergraduate program at PUC Minas, in Brazil. We discuss the needs of the different stakeholders that guided the development of the approach. Results on the perceptions of professors and students show that the approach brings benefits. Besides the lessons learned, we present insights on improving the education of the next generation of software engineers by employing metrics to monitor skill development, verifying student work portfolios, and employing tooling support in project-based courses.
Robust Lower Bounds for Graph Problems in the Blackboard Model of Communication

We give lower bounds on the communication complexity of graph problems in the multi-party blackboard model. In this model, the edges of an $n$-vertex input graph are partitioned among $k$ parties, who communicate solely by writing messages on a shared blackboard that is visible to every party. We show that any non-trivial graph problem on $n$-vertex graphs has blackboard communication complexity $\Omega(n)$ bits, even if the edges of the input graph are randomly assigned to the $k$ parties. We say that a graph problem is non-trivial if the output cannot be computed in a model where every party holds at most one edge and no communication is allowed. Our lower bound thus holds for essentially all key graph problems relevant to distributed computing, including Maximal Independent Set (MIS), Maximal Matching, ($\Delta+1$)-coloring, and Dominating Set. In many cases, e.g., MIS, Maximal Matching, and $(\Delta+1)$-coloring, our lower bounds are optimal, up to poly-logarithmic factors.