Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the Security Awareness of the Python and JavaScript Open Source Communities

Software security is undoubtedly a major concern in today's soft-ware engineering. Although the level of awareness of security issues is often high, practical experiences show that neither preventive actions nor reactions to possible issues are always addressed properly in reality. By analyzing large quantities of commits in the open-source communities, we can categorize the vulnerabilities mitigated by the developers and study their distribution, resolution time, etc. to learn and improve security management processes and practices. With the help of the Software Heritage Graph Dataset, we inves-tigated the commits of two of the most popular script languages - Python and] avaScript - projects collected from public repositories and identified those that mitigate a certain vulnerability in the code (i.e. vulnerability resolution commits). On the one hand, we identi-fied the types of vulnerabilities (in terms of CWE groups) referred to in commit messages and compared their numbers within the two communities. On the other hand, we examined the average time elapsing between the publish date of a vulnerability and the first reference to it in a commit. We found that there is a large intersection in the vulnerability types mitigated by the two communities, but most prevalent vulner-abilities are specific to language. Moreover, neither the] avaScript nor the Python community reacts very fast to appearing security vulnerabilities in general with only a couple of exceptions for cer-tain CWE groups.
Phylogenetic Relationships and Divergence Timing of Crocodylus Based on Morphology and the Fossil Record

Abstract Maximum parsimony analysis of 164 morphological characters supports the monophyly of crown-genus Crocodylus relative to other extant lineages. Crocodylus cataphractus is the basalmost living species, and monophyletic New World and Indopacific assemblages are supported. The Indian Mugger (Crocodylus palustris) is closely related to the Indopacific assemblage. Most fossils assigned to Crocodylus do not belong to the crown genus. Only one extinct fossil included in the analysis, Crocodylus palaeindicus (= C. sivalensis), falls within the crown-group Crocodylus and is of uncertain affinities within the group; Adams consensus trees draw C. palaeindicus toward the root of non–cataphractus Crocodylus, and the Nile crocodile (C. niloticus) is the sister taxon to the New World assemblage. The oldest fossil diagnostically belonging to the clade is from the Late Miocene, which is congruent with previous protein distance estimates of a Late Tertiary divergence among living species. Fossils and molecules never actually clashed regarding divergence timing, because very different meanings were applied to the name Crocodylus by paleontologists and neontologists.
Response of tree-line populations of foxtail pine (Pinus balfouriana) to climate variation over the last 1000 years

La densite du peuplement ainsi que les taux de recrutement et de mortalite du pin de Balfour (Pinus balfouriana Grev. & Balf.) dans les forets a la limite des arbres, au cours des 1000 dernieres annees, ont ete reconstitues. La mortalite et le recrutement n'ont pas ete correles durant cette periode. Dans la majorite des sites, la variance du recrutement n'etait pas significativement plus grande que la variance de mortalite. Le recrutement et la densite du peuplement ont presente une correlation, significative et inverse, avec la temperature estivale au cours des 1000 dernieres annees. Les taux de mortalite n'ont pas ete correles avec le climat. La correlation inverse entre le recrutement et le climat indique que le bilan hydrique peut moderer les effets de la temperature sur les forets a la limite des arbres. Cette hypothese est appuyee par une correlation positive significative entre l'etablissement des semis et la couverture de neige hivernale au cours des 50 dernieres annees. Malgre le fait que la limite des arbres a grandement oscille dans ces sites durant la periode reconstituee, les populations pres de cette limite n'ont pas ete fortement affectees par les variations climatiques. Ceci suggere que des gradients abrupts de vulnerabilite aux changements climatiques peuvent exister a la limite des arbres dans la Sierra Nevada.
Crop yield prediction integrating genotype and weather variables using deep learning

Accurate prediction of crop yield supported by scientific and domain-relevant insights, is useful to improve agricultural breeding, provide monitoring across diverse climatic conditions and thereby protect against climatic challenges to crop production. We used performance records from Uniform Soybean Tests (UST) in North America to build a Long Short Term Memory (LSTM)—Recurrent Neural Network based model that leveraged pedigree relatedness measures along with weekly weather parameters to dissect and predict genotype response in multiple-environments. Our proposed models outperformed other competing machine learning models such as Support Vector Regression with Radial Basis Function kernel (SVR-RBF), least absolute shrinkage and selection operator (LASSO) regression and the data-driven USDA model for yield prediction. Additionally, for providing interpretability of the important time-windows in the growing season, we developed a temporal attention mechanism for LSTM models. The outputs of such interpretable models could provide valuable insights to plant breeders.
The bipyridyl herbicide paraquat-induced toxicity in human neuroblastoma SH-SY5Y cells: relevance to dopaminergic pathogenesis

The Bipyridyl Herbicide Paraquat-Induced Toxicity in Human Neuroblastoma SH-SY5Y Cells: Relevance to Dopaminergic Pathogenesis. (August 2005) Wonsuk Yang, B.S., Yonsei University; M.S., Yonsei University Chair of Advisory Committee: Dr. Evelyn Tiffany-Castiglioni Paraquat (PQ) is a cationic non-selective bipyridyl herbicide widely used in agriculture to control weeds and grasses. Epidemiologic studies indicate that exposure to pesticides can be a risk factor in the incidence of Parkinson`s disease (PD). A strong correlation has been reported between exposure to paraquat and PD incidence in Canada, Taiwan, and United States. This correlation is supported by animal studies showing that paraquat produces toxicity in dopaminergic neurons of the rat and mouse brain. However, it is unclear how paraquat triggers toxicity in dopaminergic neurons. Based on the previous reports, it was hypothesized that paraquat may induce oxidative stress and proteasomal dysfunction-mediated toxicity in dopaminergic neurons. To explore this possibility, dopaminergic SH-SY5Y human neuroblastoma cells were treated with paraquat, and several biomarkers of oxidative stress or proteasomal dysfunction were investigated. First, a specific dopamine transporter inhibitor GBR12909 significantly protected SY5Y cells against the toxicity of paraquat, indicating that paraquat exerts its toxicity by a mechanism involving the dopamine transporter (DAT). Second, paraquat
Resource-sharing Policy in Multi-tenant Scientiﬁc Workﬂow-as-a-Service Cloud Platform

Increased adoption of scientiﬁc workﬂows in the community has urged for the development of platforms that provide workﬂow executions as a service. As a result, Workﬂow-as-a-Service (WaaS) concept emerged to address the future design of Workﬂow Management Systems (WMS). A traditional WMS is usually designed to execute a single workﬂow in a dedicated process while WaaS cloud platforms enhance the process by exploiting multiple workﬂows execution in a multi-tenant environment. In this paper, we explore resource-sharing policy to improve system utilization and to fulﬁl various Quality of Service requirements from multiple users in WaaS cloud platforms. We propose an E lastic B udget-constrained resource P rovisioning and S cheduling algorithm for M ultiple workﬂows that can reduce the computational overhead by encouraging resource-sharing to minimize workﬂows’ makespan while meeting user-deﬁned budget. Our experiments show that the algorithm achieves higher performance in terms of minimizing the makespan compared to the state-of-the-art budget-constraint scheduling algorithm.
The speciation and subtyping of campylobacter isolates from sewage plants and waste water from a connected poultry abattoir using molecular techniques

Summary In this study the distribution of phenotypes of campylobacter strains in sewage and surface waters was investigated by subtyping and by speciation of isolates from various aquatic environments. These environments included two municipal sewage plants (SPA and SPB) and waste water from a poultry abattoir (WWA). Both the sewage plants SPA and SPB collected domestic and industrial waste, and SPA received drain water from WWA. SPB received no waste water from any meat-processing plant. The isolates were speciated by PCR and subtyped by PCR/RFLP based on the flagellin PCR products. From all three reservoirs, no Campylobacter lari was isolated, and approximately 80% of the isolates could be identified as C. jejuni and the rest belonged to the C. coli species. The PCR/RFLP typing technique has a high discrimination level and was reproducible between two separate laboratories. The 182 isolates tested yielded 22 distinct Dde I profiles. The results indicate that strains with profiles found in poultry are also detectable in waste water presumed to be solely from domestic and human sources. In addition some strains were unique to the known poultry-related sources, suggesting that avian-specific strains, non-pathogenic to man, may exist in the environment. In contrast some strains were unique to human waste indicating the potential importance of non-poultry sources of infection. No seasonality was observed in the profile distribution. So, at least in the Netherlands, it is unlikely that infections caused by contaminated surface waters contribute to the seasonality of human campylobacteriosis.
Balanced Truncation of $k$-Positive Systems

This papers shows that balanced truncation of single-input-single-output systems with $k$-positive Hankel operators is totally positive for any approximation of order up to $k$. The results generalize the fact that balanced truncation of a relaxation system are known to be totally positive for any order ($k=\infty$) and extends our preliminary work on first order approximation of internally positive systems to externally (input-output) positive systems ($k=1$).
Was there COVID-19 back in 2012? Challenge for AI in Diagnosis with Similar Indications

Purpose: Since the recent COVID-19 outbreak, there has been an avalanche of research papers applying deep learning based image processing to chest radiographs for detection of the disease. To test the performance of the two top models for CXR COVID-19 diagnosis on external datasets to assess model generalizability. Methods: In this paper, we present our argument regarding the efficiency and applicability of existing deep learning models for COVID-19 diagnosis. We provide results from two popular models - COVID-Net and CoroNet evaluated on three publicly available datasets and an additional institutional dataset collected from EMORY Hospital between January and May 2020, containing patients tested for COVID-19 infection using RT-PCR. Results: There is a large false positive rate (FPR) for COVID-Net on both ChexPert (55.3%) and MIMIC-CXR (23.4%) dataset. On the EMORY Dataset, COVID-Net has 61.4% sensitivity, 0.54 F1-score and 0.49 precision value. The FPR of the CoroNet model is significantly lower across all the datasets as compared to COVID-Net - EMORY(9.1%), ChexPert (1.3%), ChestX-ray14 (0.02%), MIMIC-CXR (0.06%). Conclusion: The models reported good to excellent performance on their internal datasets, however we observed from our testing that their performance dramatically worsened on external data. This is likely from several causes including overfitting models due to lack of appropriate control patients and ground truth labels. The fourth institutional dataset was labeled using RT-PCR, which could be positive without radiographic findings and vice versa. Therefore, a fusion model of both clinical and radiographic data may have better performance and generalization.
Multi-Agent Reinforcement Learning for Cooperative Coded Caching via Homotopy Optimization

Introducing cooperative coded caching into small cell networks is a promising approach to reducing traffic loads. By encoding content via maximum distance separable (MDS) codes, coded fragments can be collectively cached at small-cell base stations (SBSs) to enhance caching efficiency. However, content popularity is usually time-varying and unknown in practice. As a result, cached content is anticipated to be intelligently updated by taking into account limited caching storage and interactive impacts among SBSs. In response to these challenges, we propose a multi-agent deep reinforcement learning (DRL) framework to intelligently update cached content in dynamic environments. With the goal of minimizing long-term expected fronthaul traffic loads, we first model dynamic coded caching as a cooperative multi-agent Markov decision process. Owing to the use of MDS coding, the resulting decision-making falls into a class of constrained reinforcement learning problems with continuous decision variables. To deal with this difficulty, we custom-build a novel DRL algorithm by embedding homotopy optimization into a deep deterministic policy gradient formalism. Next, to empower the caching framework with an effective trade-off between complexity and performance, we propose centralized, and partially and fully decentralized caching controls by applying the derived DRL approach. Simulation results demonstrate the superior performance of the proposed multi-agent framework.
Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization

Adversarial imitation learning alternates between learning a discriminator -- which tells apart expert's demonstrations from generated ones -- and a generator's policy to produce trajectories that can fool this discriminator. This alternated optimization is known to be delicate in practice since it compounds unstable adversarial training with brittle and sample-inefficient reinforcement learning. We propose to remove the burden of the policy optimization steps by leveraging a novel discriminator formulation. Specifically, our discriminator is explicitly conditioned on two policies: the one from the previous generator's iteration and a learnable policy. When optimized, this discriminator directly learns the optimal generator's policy. Consequently, our discriminator's update solves the generator's optimization problem for free: learning a policy that imitates the expert does not require an additional optimization loop. This formulation effectively cuts by half the implementation and computational burden of adversarial imitation learning algorithms by removing the reinforcement learning phase altogether. We show on a variety of tasks that our simpler approach is competitive to prevalent imitation learning methods.
3DMotion-Net: Learning Continuous Flow Function for 3D Motion Prediction

This paper deals with predicting future 3D motions of 3D object scans from the previous two consecutive frames. Previous methods mostly focus on sparse motion prediction in the form of skeletons. While in this paper, we focus on predicting dense 3D motions in the form of 3D point clouds. To approach this problem, we propose a self-supervised approach that leverages the power of the deep neural network to learn a continuous flow function of 3D point clouds that can predict temporally consistent future motions and naturally bring out the correspondences among consecutive point clouds at the same time. More specifically, in our approach, to eliminate the unsolved and challenging process of defining a discrete point convolution on 3D point cloud sequences to encode spatial and temporal information, we introduce a learnable latent code to represent the temporal-aware shape descriptor, which is optimized during the model training. Moreover, a temporally consistent motion Morpher is proposed to learn a continuous flow field which deforms a 3D scan from the current frame to the next frame. We perform extensive experiments on D-FAUST, SCAPE, and TOSCA benchmark data sets. The results demonstrate that our approach is capable of handling temporally inconsistent input and produces consistent future 3D motion while requiring no ground truth supervision.
Cycle and circuit chip-firing on graphs

Motivated by the notion of chip-firing on the dual graph of a planar graph, we consider `cycle chip-firing' on an arbitrary graph $G$. The chip-firing rule is governed by ${\mathcal L}^*(G)$, the dual Laplacian of $G$ determined by choosing a basis for the lattice of integral flows on $G$. We show that any graph admits such a basis so that ${\mathcal L}^*(G)$ is an $M$-matrix, leading to a firing rule on these basis elements that is avalanche finite. This follows from a more general result on bases of integral lattices that may be of independent interest. Our results provide a notion of $z$-superstable cycle configurations that are in bijection with the set of spanning trees of $G$. We show that for planar graphs, as well as for the graphs $K_5$ and $K_{3,3}$, one can find such an M-basis consisting of circuits of the underlying graph. We consider the question for arbitrary graphs and address some open questions.
Testing selection indices for sustainable hill sheep production – lamb growth and carcass traits

Two selection indexes, one intended for lamb producers and finishers and one for store lamb producers, were derived using genetic parameters for carcass and maternal characteristics from Conington et al. (2001) and economic values from Conington et al. (2004). This paper summarizes responses to selection for lamb traits only, after 5 years of selection (1998 to 2003) on two farms using these selection indexes. The index for lamb producers and finishers, evaluated on farm 1, with a flock size of 680 ewes, includes economic weightings for maternal traits as well as carcass weight, fat and conformation grades, whereas the index for store lamb producers, evaluated on farm 2, with a flock size of 580 ewes, only includes economic values for maternal traits and lamb growth to weaning. Three selection lines of Scottish Blackface sheep per farm were created with the first lambs born to each line in 1999. These lines were selection (S), control (C) and industry (I); they were of equal size on each farm. Five top- and five average-performing ram lambs were selected each year for the S and C lines respectively using a multi-trait best linear unbiased prediction (BLUP) implementation of the indexes. The I-line used four mature rams bought from industry, selected on appearance only, i.e. adherence to breed ‘type’. Results showed that 5 years after the implementation of the index, the S line had significantly higher index scores than the C or I lines on both farms. The means (s.d.) for the average index scores in 2003 are 114 (328), 119 (371) and 451 (328) (farm 1), and −8 (146), −11 (130), and 250 (129) (farm 2) for the C, I and S lines, respectively, giving predicted net differences (S-C) of £3·38 (farm 1) and £2·58 (farm 2) per ewe. Phenotypic responses showed significant S v. C differences in weaning weight on both farms. As predicted from previous analyses, no changes in carcass quality traits were seen at farm 1 although S-line carcass weights tended to be heavier than those from the C or I lines. The results show that genetic improvement using multi-trait selection indices has been successful and it is a viable, long-term strategy to improve levels of production for hill sheep in extensive environments.
Probing the structure of the initial state of heavy-ion collisions with 
pT
-dependent flow fluctuations

The connection between initial-state geometry and anisotropic flow can be quantified through a well-established mapping between $p_T$-integrated flow harmonics and cumulants of the initial transverse energy distribution. In this paper we successfully extend this mapping to also include $p_T$-differential flow. In doing so, we find that subleading principal components of anisotropic flow can reveal previously unobserved details of the hydrodynamic response, in both the linear and the nonlinear regimes. Most importantly, we show that they provide novel information on the small-scale structures present in the initial stage of relativistic heavy-ion collisions.
Control-Aware Representations for Model-based Reinforcement Learning

A major challenge in modern reinforcement learning (RL) is efficient control of dynamical systems from high-dimensional sensory observations. Learning controllable embedding (LCE) is a promising approach that addresses this challenge by embedding the observations into a lower-dimensional latent space, estimating the latent dynamics, and utilizing it to perform control in the latent space. Two important questions in this area are how to learn a representation that is amenable to the control problem at hand, and how to achieve an end-to-end framework for representation learning and control. In this paper, we take a few steps towards addressing these questions. We first formulate a LCE model to learn representations that are suitable to be used by a policy iteration style algorithm in the latent space. We call this model control-aware representation learning (CARL). We derive a loss function for CARL that has close connection to the prediction, consistency, and curvature (PCC) principle for representation learning. We derive three implementations of CARL. In the offline implementation, we replace the locally-linear control algorithm (e.g.,~iLQR) used by the existing LCE methods with a RL algorithm, namely model-based soft actor-critic, and show that it results in significant improvement. In online CARL, we interleave representation learning and control, and demonstrate further gain in performance. Finally, we propose value-guided CARL, a variation in which we optimize a weighted version of the CARL loss function, where the weights depend on the TD-error of the current policy. We evaluate the proposed algorithms by extensive experiments on benchmark tasks and compare them with several LCE baselines.
Continuous Submodular Function Maximization

Continuous submodular functions are a category of generally non-convex/non-concave functions with a wide spectrum of applications. The celebrated property of this class of functions - continuous submodularity - enables both exact minimization and approximate maximization in poly. time. Continuous submodularity is obtained by generalizing the notion of submodularity from discrete domains to continuous domains. It intuitively captures a repulsive effect amongst different dimensions of the defined multivariate function. In this paper, we systematically study continuous submodularity and a class of non-convex optimization problems: continuous submodular function maximization. We start by a thorough characterization of the class of continuous submodular functions, and show that continuous submodularity is equivalent to a weak version of the diminishing returns (DR) property. Thus we also derive a subclass of continuous submodular functions, termed continuous DR-submodular functions, which enjoys the full DR property. Then we present operations that preserve continuous (DR-)submodularity, thus yielding general rules for composing new submodular functions. We establish intriguing properties for the problem of constrained DR-submodular maximization, such as the local-global relation. We identify several applications of continuous submodular optimization, ranging from influence maximization, MAP inference for DPPs to provable mean field inference. For these applications, continuous submodularity formalizes valuable domain knowledge relevant for optimizing this class of objectives. We present inapproximability results and provable algorithms for two problem settings: constrained monotone DR-submodular maximization and constrained non-monotone DR-submodular maximization. Finally, we extensively evaluate the effectiveness of the proposed algorithms.