Miracle-dz/UniFAR-FTUs huggingface dataset:
In addition to being expressed in peripheral cell types including immunocytes , neutrophils and rat islet cells

Robarts Research Institute, Molecular Brain Research Group, University of Western Ontario, 100 Perth Drive, London, Ontario, Canada, N6A 5K8 Department of Physiology, University of Toronto, 1 King’s College Circle, Toronto, Ontario, Canada, M5S 1A8 Division of Neurology, Department of Medicine, University of Toronto, Suite RFE 3-805, 190 Elizabeth Street, Toronto, Ontario, Canada, M5G 2C4 Toronto Western Hospital Research Institute, 399 Bathurst Street, Toronto, Ontario, Canada, M5T 2S8 Laboratory of Molecular Biology, Department of Synthetic Chemistry and Biological Chemistry, Graduate School of Engineering, Kyoto University, Kyoto 615-8510, Japan
A Richly Annotated Corpus for Different Tasks in Automated Fact-Checking

Automated fact-checking based on machine learning is a promising approach to identify false information distributed on the web. In order to achieve satisfactory performance, machine learning methods require a large corpus with reliable annotations for the different tasks in the fact-checking process. Having analyzed existing fact-checking corpora, we found that none of them meets these criteria in full. They are either too small in size, do not provide detailed annotations, or are limited to a single domain. Motivated by this gap, we present a new substantially sized mixed-domain corpus with annotations of good quality for the core fact-checking tasks: document retrieval, evidence extraction, stance detection, and claim validation. To aid future corpus construction, we describe our methodology for corpus creation and annotation, and demonstrate that it results in substantial inter-annotator agreement. As baselines for future research, we perform experiments on our corpus with a number of model architectures that reach high performance in similar problem settings. Finally, to support the development of future models, we provide a detailed error analysis for each of the tasks. Our results show that the realistic, multi-domain setting defined by our data poses new challenges for the existing models, providing opportunities for considerable improvement by future systems.
Optimizing class partitioning in multi-class classification using a descriptive control language.

Many of the best statistical classification algorithms are binary classifiers, that is they can only distinguish between one of two classes. The number of possible ways of generalizing binary classification to multi-class increases exponentially with the number of classes. There is some indication that the best method of doing so will depend on the dataset. As such, we are particularly interested in data-driven solution design, whether based on prior considerations or on empirical examination of the data. Here we demonstrate how a recursive control language can be used to describe a multitude of different partitioning strategies in multi-class classification, including those in most common use. We use it both to manually construct new partitioning configurations as well as to examine those that have been automatically designed. Eight different strategies are tested on eight different datasets using both support vector machines (SVM) as well as logistic regression as the base binary classifiers. Numerical tests suggest that a one-size-fits-all solution consisting of one-versus-one is appropriate for most datasets however one dataset benefitted from the techniques applied in this paper. The best solution exploited a property of the dataset to produce an uncertainty coefficient 36\% higher (0.016 absolute gain) than one-vs.-one. Adaptive solutions that empirically examined the data also produced gains over one-vs.-one while also being faster.
Continuous spikes and waves during sleep in perinatal stroke: a multicentric case control study

Objective: the main one is to determine associated factors to develop a continuous spikes and waves during slow sleep (CSWS) syndrome after a focal perinatal stroke. Then we discuss the impact of CSWS and others releavant factors on neurocognitive outcome in each group. Methods: we performed a multicentric case-control study and enrolled 1) patients born at term >37SA with a perinatal stroke (from 20 gestational weeks to 28e day post-natal) 2) one or more brain MRI and 4) 2 or more overnight EEG recordings. Anoxie encephalopathy and bilateral brain injury were exclusion criteria. We determined two groups: stroke-CSWS (cases) and stroke-no CSWS (controls), first group was defined by clinical features consistent with a sleep potentiate epileptiform activity more than 85% during non-REM sleep. Results: A sample of 26 patients with perinatal stroke met the inclusion criteria during a 15 year period: 16 stroke-CSWS and 10 stroke-no CSWS. Both groups were comparable in terms of epidemiologic features, degree of cerebral palsy and stroke presentation. Child presenting a prolonged hemicorporal focal seizure with or not fever after 18 months were more able to develop a CSWS (87.5 % vs 0%, p <0.001). Presence yet in awake EEGs of fronto-central spike or spike waves with a bilateral synchrony trend, especially when no improvement was seen with recommended antiepileptic drug and a first association with benzodiazepines, was a strong marker for an evolution towards CSWS (62.5% vs 0%, p=0.002%). Subcortical lesion with thalamus involvement was not statistically higher in CSWS patients but cortical sparing was more frequent in CSWS (43.7% vs 0%, p=0.02). Superficial arterial ischemic MCA was less frequent in CSWS (6.3% vs 50%, p=0.02). Global cognitive outcome was poor in CSWS patients but not in all cases with 25% poor, 38% favorable and 37% difficult issue although 75% of no CSWS had a favorable outcome regarding schooling and neuropsychometric data. The only two factors who seemed associated with a poor global cognitive outcome were presence of CSWS and/or regression (33.3% vs 60%, p=0.01) Conclusions: A combined factor associating anatomical (MRI) and functional (sleep EEGs at a critical age) data should reach pertinence to predict epilepsy and cognition outcome after perinatal stroke. Further studies should investigate this opportunity.
On the equivalence of all models for (∞,2)$(\infty,2)$ ‐categories

The goal of this paper is to provide the last equivalence needed in order to identify all known models for (∞,2)$(\infty,2)$ ‐categories. We do this by showing that Verity's model of saturated 2‐trivial complicial sets is equivalent to Lurie's model of ∞$\infty$ ‐bicategories, which, in turn, has been shown to be equivalent to all other known models for (∞,2)$(\infty,2)$ ‐categories. A key technical input is given by identifying the notion of ∞$\infty$ ‐bicategories with that of weak ∞$\infty$ ‐bicategories, a step which allows us to understand Lurie's model structure in terms of Cisinski–Olschok's theory. Several of our arguments use tools coming from a new theory of outer (co)‐Cartesian fibrations, further developed in a companion paper. In the last part of the paper, we construct a homotopically fully faithful scaled simplicial nerve functor for 2‐categories, give two equivalent descriptions of it, and show that the homotopy 2‐category of an ∞$\infty$ ‐bicategory retains enough information to detect thin 2‐simplices.
Highly sensitive CO gas sensor from defective graphene: role of van der Waals interactions

Layered materials, such as graphene, have attracted increasing interests since they can be extensively used in gas sensors, spintronic devices, and transparent electrodes. Although larger size of graphene sheets has been fabricated, in reality, the existence of the defects in layered materials is almost inevitable during the manufacturing process. Here, we performed the state-of-the-art density-functional theory calculations to study the interactions between CO molecule and the pristine and defective graphene layers, with the aim of designing a CO gas sensor with higher sensitivity. The van der Waals interactions predominate the binding between the CO gas and the sensor, and also significantly enhance the stability of the system. The defective graphene strongly interacts with CO, and thus enhances the sensitivity of the graphene and further tunes the electronic and magnetic properties of the entire system. Our computed results clearly demonstrate that the defective graphene could be a good sensor for gas molecules.
Root: shoot ratio and water use efficiency differ in cool season cereals grown in pure and mixed stands under low and high water levels

This study examined the effect of low and high water levels on the root: shoot ratio (R:S) and water use efficiency (WUE) response of four cool season cereals [wheat ( Triticum aestivum , cv. TAM III), barley ( Hordeum vulgare , cv. P919), rye ( Secale cereale , cv. Elbon), and oat ( Avena sativa , cv. Walker)]. The pot experiment was conducted in the green house of Dryland Agriculture Institute, West Texas A&M University Canyon, Texas, USA during winter 2009-10. Hypothesis that crop stands and water level have no significant effect on R:S and WUE of field crops was tested in the trial. Significant variations in the R:S and WUE were observed for crops combinations and water levels. The increase in plants competitions in the mixed stands not only reduced roots and shoot biomass but also declined the R:S and WUE. The increase in R:S under low water was observed for all four crop species due to the increase in the root biomass and the corresponding decrease in the shoot biomass.
A Rule for Gradient Estimator Selection, with an Application to Variational Inference

Stochastic gradient descent (SGD) is the workhorse of modern machine learning. Sometimes, there are many different potential gradient estimators that can be used. When so, choosing the one with the best tradeoff between cost and variance is important. This paper analyzes the convergence rates of SGD as a function of time, rather than iterations. This results in a simple rule to select the estimator that leads to the best optimization convergence guarantee. This choice is the same for different variants of SGD, and with different assumptions about the objective (e.g. convexity or smoothness). Inspired by this principle, we propose a technique to automatically select an estimator when a finite pool of estimators is given. Then, we extend to infinite pools of estimators, where each one is indexed by control variate weights. This is enabled by a reduction to a mixed-integer quadratic program. Empirically, automatically choosing an estimator performs comparably to the best estimator chosen with hindsight.
Tourism information system from integration viewpoint : a constructive case study

As the rapid economic development of tourism industry in recent years, and gradually increasing user needs to tourism products and services, a variety of Tourism Information Systems (TISs) have been available. However, less attention is focused on these TISs from integration viewpoint, since currently most TISs are independent or isolated from the user’s perspective, tourists have to enter separately into different TISs in order to make a complete travel plan and get related tourism services. Advanced, Smart Tourism Information Systems (STISs) provide all tourism-related products and services in the unified system environment. This constructive case study proposes a framework for STIS including theoretical analyses of the core elements and components of the systems, and provides several potential development approaches to and recognized challenges as developing STISs. Interview-based scenarios with potential users are used to demonstrate the needs of independent travellers and in showing for which purposes the different systems should be integrated. The results show that the developed framework of STIS is promising and can be useful when integrating the systems as offering one-stop services for independent tourists. More research with a wider empirical sample is still required to generalize the results and test the framework in practice.
Randomly distributed unit sources to enhance optimization in tsunami waveform inversion

Abstract. In tsunami waveform inversion using the conventional Green's function technique, an optimal solution is sometimes difficult to obtain because of various factors. This study proposes a new method to both optimize the determination of the unknown parameters and introduce a global optimization method for tsunami waveform inversion. We utilize a genetic algorithm that further enhanced by a pattern search method to find an optimal distribution of unit source locations prior to the inversion. Unlike the conventional method that characterized by equidistant unit sources, our method generates a random spatial distribution of unit sources inside the inverse region. This leads to a better approximation of the initial profile of a tsunami. The method has been tested using an artificial tsunami source with real bathymetry data. Comparison results demonstrate that the proposed method has considerably outperformed the conventional one in terms of model accuracy.
3D tissue reconstruction with Kinect to evaluate neck lymphedema

Lymphedema is a condition of localized tissue swelling caused by a damaged lymphatic system. Therapy to these tissues is applied manually. Some of the methods are lymph drainage, compression therapy or bandaging. However, the therapy methods are still insufficiently evaluated. Especially, because of not having a reliable method to measure the change of such a soft and flexible tissue. In this research, our goal has been providing a 3d computer vision based method to measure the changes of the neck tissues. To do so, we used Kinect as a depth sensor and built our algorithms for the point cloud data acquired from this sensor. The resulting 3D models of the patient necks are used for comparing the models in time and measuring the volumetric changes accurately. Our discussions with the medical doctors validate that, when used in practice this approach would be able to give better indication on which therapy method is helping and how the tissue is changing in time.
Correcting for attenuation due to measurement error

I present a frequentist method for quantifying uncertainty when correcting correlations for attenuation due to measurement error. The method is conservative but has far better coverage properties than the methods currently used when sample sizes are small. I recommend the use of confidence curves in favor of confidence intervals when this method is used. I introduce the R package "attenuation" which can be used to calculate and visualize the methods described in this paper.
On the Stability of Fractal Differential Equations

In this paper, we give a review of fractal calculus which is an expansion of standard calculus. Fractal calculus is applied for functions which are not differentiable or integrable on totally disconnected fractal sets such as middle-$\mu$ Cantor sets. Analogues of the Lyapunov functions and features are given for asymptotic behaviors of fractal differential equations. Stability of fractal differentials in the sense of Lyapunov is defined. For the suggested fractal differential equations, sufficient conditions for the stability and uniform boundedness and convergence of the solutions are presented and proved. We present examples and graphs for more details of the results.
Relativistic stars in a cubic Galileon universe

We study relativistic stars in Hordenski theories that evade the gravitational wave constraints and exhibit the Vainshtein mechanism, focusing on a model based on the cubic Galileon Lagrangian. We derive the scalar field profile for static spherically symmetric objects in asymptotically de Sitter space-time with a linear time dependence. The exterior solution matches to the black hole solution found in the literature. Due to the Vainshtein mechanism, the stellar structure is indistinguishable from that of General Relativity with the same central density as long as the radius of the star is shorter than the Vainshtein radius. On the other hand, the scalar field is not suppressed beyond the Vainshtein radius. These solutions have an additional integration constant in addition to the mass of the star.
New integrable 1D models of superconductivity

In this paper we find new integrable one-dimensional lattice models of electrons. We describe all such nearest-neighbour integrable models with su(2)×su(2) symmetry by classifying solutions of the Yang–Baxter equation following the procedure first introduced in []. We find 12 R-matrices of difference form, some of which can be related to known models such as the XXX spin chain and the free Hubbard model, and some are new models. In addition, integrable generalizations of the Hubbard model are found by keeping the kinetic term of the Hamiltonian and adding all terms which preserve fermion number. We find that most of the new models cannot be diagonalized using the standard nested Bethe Ansatz.
Dynamic Time Warp Convolutional Networks

Where dealing with temporal sequences it is fair to assume that the same kind of deformations that motivated the development of the Dynamic Time Warp algorithm could be relevant also in the calculation of the dot product ("convolution") in a 1-D convolution layer. In this work a method is proposed for aligning the convolution filter and the input where they are locally out of phase utilising an algorithm similar to the Dynamic Time Warp. The proposed method enables embedding a non-parametric warping of temporal sequences for increasing similarity directly in deep networks and can expand on the generalisation capabilities and the capacity of standard 1-D convolution layer where local sequential deformations are present in the input. Experimental results demonstrate the proposed method exceeds or matches the standard 1-D convolution layer in terms of the maximum accuracy achieved on a number of time series classification tasks. In addition the impact of different hyperparameters settings is investigated given different datasets and the results support the conclusions of previous work done in relation to the choice of DTW parameter values. The proposed layer can be freely integrated with other typical layers to compose deep artificial neural networks of an arbitrary architecture that are trained using standard stochastic gradient descent.
Environmental stratification based on a 28 x 28 diallel of open-pollinated maize varieties

The objective of this study was to assess the representativeness of the test environments used by the maize breeding program of Embrapa in the first phase of genotype evaluation. Ear weight of 378 hybrids from a diallel of 28 open- pollinated varieties (OPVs) evaluated in ten environments were used. The following environments were evaluated: two growing seasons (1991-92 and 1992-93), at three locations (Sete Lagoas, MG, Londrina, PR, and Goiania-GO); in two growing seasons (1991/92 and 1993/94) in Aracaju-SE; and in two growing seasons (1992-93 and 1993-94), in Ponta Grossa-PR. The complex part of the interaction accounted for nearly 75% of the genotype by environment interaction (G x E). The environments of Londrina-91/92, Ponta Grossa-93/94 and Aracaju-93/94 differed from the others and also from each other, as shown by stratification analysis. The phenotypic correlation between genotype means in the pairwise grouped environments, interpreted as coefficient of genotypic determination, indicated that non-genetic causes were responsible for 64.40% of the mean phenotypic variances. The results confirmed the discrimination of three major environmental groups, representing the Northeast (Aracaju), Central Southeast (Sete Lagoas, Goiania and Londrina) and South (Ponta Grossa) regions.
Information Externalities in Bank Based Financing for Private Innovation - Can Specialization Overcome Asymmetries?

Investments in R&D have been identified as a cornerstone for growth and competitive advantages of firms and whole economies. We investigate the role that a firm s main bank plays for its investment in R&D. Existing literature suggests that the inherent information asymmetries of R&D projects make them hardly applicable for bank lending. We challenge this assumption by arguing that banks are heterogeneous with regard to their information processing capabilities. They can benefit from information externalities arising from industry and geographical specialization in their overall loan portfolio. We combine finance as well as innovation management and economics literature to develop this argument. We test our theoretical framework for more than 7,500 firm observations in Germany and their R&D expenditures over a five year time period. A unique database allows us to construct the overall corporate client portfolio of the main bank for each of these firms. Our results show trade-offs in the degree and nature of bank specialization. Industry specialization of a firm s main bank has a u-shaped effect on its R&D investment. Regional specialization, though, has an inverse ushaped effect. Recommendations on optimal firm-bank combinations for fostering R&D investment can be developed based on these results.
Machine Translation Evaluation using Bi-directional Entailment

In this paper, we propose a new metric for Machine Translation (MT) evaluation, based on bi-directional entailment. We show that machine generated translation can be evaluated by determining paraphrasing with a reference translation provided by a human translator. We hypothesize, and show through experiments, that paraphrasing can be detected by evaluating entailment relationship in the forward and backward direction. Unlike conventional metrics, like BLEU or METEOR, our approach uses deep learning to determine the semantic similarity between candidate and reference translation for generating scores rather than relying upon simple n-gram overlap. We use BERT's pre-trained implementation of transformer networks, fine-tuned on MNLI corpus, for natural language inferencing. We apply our evaluation metric on WMT'14 and WMT'17 dataset to evaluate systems participating in the translation task and find that our metric has a better correlation with the human annotated score compared to the other traditional metrics at system level.
Misdeeds Matter: Long-Term Stock Price Performance after the Filing of Class-Action Lawsuits

Consistent with theory, this study of shareholder litigation found a broad transformation in company characteristics and risk exposures and generally negative short- and long-term performance effects that differed substantially between two types of allegations. The findings have important implications for both regulator and institutional investor monitoring and decision-making strategies. Does shareholder litigation pay off for investors over the long term? How much does the type of allegation matter? We studied whether a disciplining effect occurs for sued companies and their managers and examined two different groups of allegations. Allegations of violations of the duty of loyalty affect individuals only, but the duty of care pertains to the corporate entity. In general, we observed a post-litigation transformation in company characteristics and risk exposures, which is consistent with theory. Although generally negative, short- and long-term performance effects differ substantially between types of allegations. We observed performance reversals only in companies with individual directors accused of insider trading. Effects are similar for companies with triggering events that precede the filing of a lawsuit. Our results have important implications for the decision-making and monitoring strategies of both regulators and institutional investors: whether to use litigation to exert control over managers.