Miracle-dz/UniFAR-FTUs huggingface dataset:
Lunar Simulation in the Lunar Dust Adhesion Bell Jar

The Lunar Dust Adhesion Bell Jar has been assembled at the NASA Glenn Research Center to provide a high fidelity lunar simulation facility to test the interactions of lunar dust and lunar dust simulant with candidate aerospace materials and coatings. It has a sophisticated design which enables it to treat dust in a way that will remove adsorbed gases and create a chemically reactive surface. It can simulate the vacuum, thermal, and radiation environments of the Moon, including proximate areas of illuminated heat and extremely cold shadow. It is expected to be a valuable tool in the development of dust repellant and cleaning technologies for lunar surface systems.
Evaluating the influence of laser wavelength and detection stage geometry on optical detection efficiency in a single-particle mass spectrometer

Abstract. Single-particle mass spectrometry (SPMS) is a useful tool for the online study of aerosols with the ability to measure size-resolved chemical composition with a temporal resolution relevant to atmospheric processes. In SPMS, optical particle detection is used for the effective temporal alignment of an ablation laser pulse with the presence of a particle in the ion source, and it gives the option of aerodynamic sizing by measuring the offset of particle arrival times between two detection stages. The efficiency of the optical detection stage has a strong influence on the overall instrument performance. A custom detection laser system consisting of a high-powered fibre-coupled Nd:YAG solid-state laser with a collimated beam was implemented in the detection stage of a laser ablation aerosol particle time-of-flight (LAAP-TOF) single-particle mass spectrometer without major modifications to instrument geometry. The use of a collimated laser beam permitted the construction of a numerical model that predicts the effects of detection laser wavelength, output power, beam focussing characteristics, light collection angle, particle size, and refractive index on the effective detection radius (R) of the detection laser beam. We compare the model predictions with an ambient data set acquired during the Ice in Clouds Experiment – Dust (ICE-D) project. The new laser system resulted in an order-of-magnitude improvement in instrument sensitivity to spherical particles in the size range 500–800 nm compared to a focussed 405 nm laser diode system. The model demonstrates that the limit of detection in terms of particle size is determined by the scattering cross section (Csca) as predicted by Mie theory. In addition, if light is collected over a narrow collection angle, oscillations in the magnitude of Csca with respect to particle diameter result in a variation in R, resulting in large particle-size-dependent variation in detection efficiency across the particle transmission range. This detection bias is imposed on the aerodynamic size distributions measured by the instrument and accounts for some of the detection bias towards sea salt particles in the ambient data set.
Detection of Toxoplasma gondii by PCR and mouse bioassay in commercial cuts of pork from experimentally infected pigs

The distribution of T. gondii in commercial cuts of pork (ham, tenderloin, spareribs and arm picnic) by PCR and bioassay from experimentally infected pigs, was evaluated. Eighteen mixed breed pigs were divided into two groups (G). The G1 animals (n=10) were infected with 4 x10 oocysts of the T. gondii VEG strain and the G2 animals (n=8) were used as control. Pigs of both groups were slaughtered at 59 day after infection, and meat samples were collected for bioassay and PCR. All animals from G1 were positive by at least one or both tests, and all control animals were negative. T. gondii was identified in pork by mouse bioassay and PCR in 27/40 (67.5%) and in 9/40 (22.5%) of the evaluated samples, respectively. There were no statistical differences in the distribution of tissue cysts from commercial cuts of pork by bioassay (P>0.05). However, statistical differences were observed when mouse bioassay and PCR were compared (P<0.01).
Sound and Complete Bidirectional Typechecking for Higher-Rank Polymorphism with Existentials

ANONYMOUS AUTHOR(S) Bidirectional typechecking, in which terms either synthesize a type or are checked against a known type, has become popular for its scalability, its error reporting, and its ease of implementation. Following principles from proof theory, bidirectional typing can be applied to many type constructs. The principles underlying a bidirectional approach to indexed types (generalized algebraic datatypes) are less clear. Building on prooftheoretic treatments of equality, we give a declarative specification of typing based on focalization. This approach permits declarative rules for coverage of pattern matching, as well as support for first-class existential types using a focalized subtyping judgment. We use refinement types to avoid explicitly passing equality proofs in our term syntax, making our calculus close to languages such as Haskell and OCaml. We also extend the declarative specification with an explicit rules for deducing when a type is principal, permitting us to give a complete declarative specification for a rich type system with significant type inference. We also give a set of algorithmic typing rules, and prove that it is sound and complete with respect to the declarative system. The proof requires a number of technical innovations, including proving soundness and completeness in a mutually recursive fashion.
Dating with Scambots: Understanding the Ecosystem of Fraudulent Dating Applications

In this work, we are focusing on a new and yet uncovered way for malicious apps to gain profit. They claim to be dating apps. However, their sole purpose is to lure users into purchasing premium/VIP services to start conversations with other (likely fake female) accounts in the app. We call these apps as fraudulent dating apps. This paper performs a systematic study to understand the whole ecosystem of fraudulent dating apps. Specifically, we have proposed a three-phase method to detect them and subsequently comprehend their characteristics via analyzing the existing account profiles. Our observation reveals that most of the accounts are not managed by real persons, but by chatbots based on predefined conversation templates. We also analyze the business model of these apps and reveal that multiple parties are actually involved in the ecosystem, including producers who develop apps, publishers who publish apps to gain profit, and the distribution network that is responsible for distributing apps to end users. Finally, we analyze the impact of them to users (i.e., victims) and estimate the overall revenue. Our work is the first systematic study on fraudulent dating apps, and the results demonstrate the urge for a solution to protect users.
Can you hear an axon growing ?

Axons are linear structures of nerve cells that can range from a few tens of micrometers up to meters in length. In addition to external cues, the length of an axon is also regulated by unknown internal mechanisms. Molecular motors have been suggested to generate oscillations with an axonlength dependent frequency that could be used to measure an axon’s extension. Here, we present a mechanism for determining the axon length that couples the mechanical properties of an axon to the spectral decomposition of the oscillatory signal.
Graph theory approaches to functional network organization in brain disorders: A critique for a brave new small-world

Over the past two decades, resting-state functional connectivity (RSFC) methods have provided new insights into the network organization of the human brain. Studies of brain disorders such as Alzheimer’s disease or depression have adapted tools from graph theory to characterize differences between healthy and patient populations. Here, we conducted a review of clinical network neuroscience, summarizing methodological details from 106 RSFC studies. Although this approach is prevalent and promising, our review identified four challenges. First, the composition of networks varied remarkably in terms of region parcellation and edge definition, which are fundamental to graph analyses. Second, many studies equated the number of connections across graphs, but this is conceptually problematic in clinical populations and may induce spurious group differences. Third, few graph metrics were reported in common, precluding meta-analyses. Fourth, some studies tested hypotheses at one level of the graph without a clear neurobiological rationale or considering how findings at one level (e.g., global topology) are contextualized by another (e.g., modular structure). Based on these themes, we conducted network simulations to demonstrate the impact of specific methodological decisions on case-control comparisons. Finally, we offer suggestions for promoting convergence across clinical studies in order to facilitate progress in this important field.
Resistant Gram-Negative Bacteria and Diagnostic Point-of-Care Options for the Field Setting during Military Operations

The spread of multidrug-resistant bacteria in resource-poor settings affects the military medical service in case of deployments of soldiers to war and crisis zones. Patients with war injuries are prone to colonization or infection with multidrug-resistant bacteria. Resistant Gram-negative bacteria play a dominant role in military wound infections. Problematic hygiene conditions on deployment facilitate exposition of soldiers with subsequent colonization. Although colonizing strains are frequently cleared from their hosts after returning from deployment, transmission to close contacts of the soldiers in the home country cannot be excluded and therapeutic options are reduced if colonization progresses to invasive infection. Since sophisticated culture-based diagnostic approaches are typically not available in the field setting on deployment, molecular rapid diagnostic test systems are an option for transmission control if the locally prevalent molecular resistance mechanisms are known. Efforts for global resistance surveillance can contribute to better understanding of resistance distribution and spread at deployment sites. This review summarizes experience of the military medical services with multidrug resistance on deployment and with the influx of resistant strains to the home country and discusses potential use of available molecular rapid test systems as an option for the field setting.
The Effects of Adding Noise During Backpropagation Training on a Generalization Performance

We study the effects of adding noise to the inputs, outputs, weight connections, and weight changes of multilayer feedforward neural networks during backpropagation training. We rigorously derive and analyze the objective functions that are minimized by the noise-affected training processes. We show that input noise and weight noise encourage the neural-network output to be a smooth function of the input or its weights, respectively. In the weak-noise limit, noise added to the output of the neural networks only changes the objective function by a constant. Hence, it cannot improve generalization. Input noise introduces penalty terms in the objective function that are related to, but distinct from, those found in the regularization approaches. Simulations have been performed on a regression and a classification problem to further substantiate our analysis. Input noise is found to be effective in improving the generalization performance for both problems. However, weight noise is found to be effective in improving the generalization performance only for the classification problem. Other forms of noise have practically no effect on generalization.
Image Classification in Arabic: Exploring Direct English to Arabic Translations

Image classification is an ongoing research challenge. Most of the current research focuses on image classification in English with very little research in Arabic. Expanding image classification to Arabic has several applications and benefits. This paper investigates the accuracy of direct translations of English labels that are available in ImageNet, a database of images labeled in English that is commonly used in computer vision research, to Arabic. A dataset comprised of 2,887 labeled images was constructed by randomly selecting images from ImageNet. All of the labels were translated to Arabic using an online translation service. The accuracy of each translation was evaluated by a human judge. Results indicated that 65.6% of the generated Arabic labels were accurate with the highest results achieved when the labels consisted of only one word. This study makes three important contributions to the image classification literature: (1) it determines a baseline level of accuracy for image classification in Arabic algorithms; (2) it provides 1,910,935 images classified with accurate Arabic labels (based on accurately labeling 1,895 images that consist of 1,643 unique synsets); and (3) it measures the accuracy of translations of image labels in ImageNet to Arabic.
Envision of an integrated information system for project-driven production in construction

Construction frequently appears at the bottom of productivity charts with decreasing indexes of productivity over the years. Lack of innovation and delayed adoption, informal processes or insufficient rigor and consistency in process execution, insufficient knowledge transfer from project to project, weak project monitoring, little cross- functional cooperation, little collaboration with suppliers, conservative company culture, and a shortage of young talent and people development are usual issues. Whereas work has been carried out on information technology and automation in construction their application is isolated without an interconnected information flow. This paper suggests a framework to address production issues on construction by implementing an integrated automatic supervisory control and data acquisition for management and operations. The system is divided into planning, monitoring, controlling, and executing groups clustering technologies to track both the project product and production. This research stands on the four pillars of manufacturing knowledge and lean production (production processes, production management, equipment/tool design, and automated systems and control). The framework offers benefits such as increased information flow, detection and prevention of overburdening equipment or labor (Muri) and production unevenness (Mura), reduction of waste (Muda), evidential and continuous process standardization and improvement, reuse and abstraction of project information across endeavors.
Occlusion-Aware Method for Temporally Consistent Superpixels

A wide variety of computer vision applications rely on superpixel or supervoxel algorithms as a preprocessing step. This underlines the overall importance that these approaches have gained in recent years. However, most methods show a lack of temporal consistency or fail in producing temporally stable superpixels. In this paper, we present an approach to generate temporally consistent superpixels for video content. Our method is formulated as a contour-evolving expectation-maximization framework, which utilizes an efficient label propagation scheme to encourage the preservation of superpixel shapes and their relative positioning over time. By explicitly detecting the occlusion of superpixels and the disocclusion of new image regions, our framework is able to terminate and create superpixels whose corresponding image region becomes hidden or newly appears. Additionally, the occluded parts of superpixels are incorporated in the further optimization. This increases the compliance of the superpixel flow with the optical flow present in the scene. Using established benchmark suites, we show that our approach produces highly competitive results in comparison to state-of-the-art streaming-capable supervoxel and superpixel algorithms for video content. This is further shown by comparing the streaming-capable approaches as basis for the task of interactive video segmentation where the proposed approach provides the lowest overall misclassification rate.
Avoiding Latent Variable Collapse With Generative Skip Models

Variational autoencoders learn distributions of high-dimensional data. They model data with a deep latent-variable model and then fit the model by maximizing a lower bound of the log marginal likelihood. VAEs can capture complex distributions, but they can also suffer from an issue known as "latent variable collapse," especially if the likelihood model is powerful. Specifically, the lower bound involves an approximate posterior of the latent variables; this posterior "collapses" when it is set equal to the prior, i.e., when the approximate posterior is independent of the data. While VAEs learn good generative models, latent variable collapse prevents them from learning useful representations. In this paper, we propose a simple new way to avoid latent variable collapse by including skip connections in our generative model; these connections enforce strong links between the latent variables and the likelihood function. We study generative skip models both theoretically and empirically. Theoretically, we prove that skip models increase the mutual information between the observations and the inferred latent variables. Empirically, we study images (MNIST and Omniglot) and text (Yahoo). Compared to existing VAE architectures, we show that generative skip models maintain similar predictive performance but lead to less collapse and provide more meaningful representations of the data.
Identification of Human B-1 Helper T Cells With a Th1-Like Memory Phenotype and High Integrin CD49d Expression

Human B-1 cells have been proposed to be CD20+CD27+CD43+CD1c− B cells found in the umbilical cord and adult peripheral blood, but their regulatory mechanisms have not been well elucidated. Previously, we reported that mouse CD49dhigh CD4+ T cells could enhance the secretion of natural antibodies by B-1 cells. In this study, we aimed to investigate the presence and helper functions of the human equivalents of murine CD49dhigh CD4+ T cells. Here, we showed that human CD49dhigh CD4+ T cells found in the peritoneal cavity (PEC), spleen, and peripheral blood can enhance the production of IgM antibodies by B-1 cells. As revealed in mouse, CD49dhigh CD4+ T cells were more abundant in the PEC and showed a higher tendency to form conjugates with B cells than CD49dlow CD4+ T cells. Moreover, CD49dhigh CD4+ T cells showed a Th1-like memory phenotype, characterized by high expression of CD44 and CXCR3; low expression of CD62L and CCR7; rapid production of IFN-γ, tumor necrosis factor-α, and IL-2 upon stimulation with phorbol myristate acetate and ionomycin; and rapid proliferation upon stimulation with anti-CD3 and anti-CD28 antibodies. These cells also expressed high levels of PD-1, ICOS, and CD5, suggesting that they are undergoing chronic stimulation. Remarkably, CD49dhigh CD4+ T cells specifically helped B-1 cells, but not follicular memory B cells (CD27+ CD43−CD1c−) or marginal zone B cells (CD27+CD43−CD1c+), produce IgM and IgG antibodies. In parallel, the titer of human anti-blood group A IgM was positively correlated with the frequency of CD49dhigh CD4+ T cells. In conclusion, we identified human CD49dhigh CD4+ T cells with a Th1-like memory phenotype that secrete Th1 proinflammatory cytokines and help B-1 cells secrete antibodies, thereby aiding in primary defense. We suggest that these CD49dhigh CD4+ T cells are a unique type of B-cell helper T cells distinct from follicular helper T cells.
An O(log1.5n log log n) Approximation Algorithm for Mean Isoperimetry and Robust k-means

Given a weighted graph $G=(V,E)$, and $U\subseteq V$, the normalized cut value for $U$ is defined as the sum of the weights of edges exiting $U$ divided by the number of vertices in $U$. The \textsc{mean isoperimetry problem} for weighted graphs is a generalization of the sparsest cut problem in which, given a parameter $k$ alongside a weighted graph $G=(V,E)$, the objective is to find $k$ disjoint nonempty subsets of $V$ for which the average normalized cut value of the parts is as small as possible. In this paper, we present an $O(\log^{1.5}n \log\log n)$ approximation algorithm for the \textsc{mean isoperimetry problem}. Our approach is based on developing an exact dynamic programming algorithm for this problem on weighted trees. We also show how our algorithm can be utilized to approximately solve a robust version of the $k$-means clustering problem in $\mathbb{R}^d$, in which there can be some ``outlier'' points, lying outside the $k$ parts. For this, the standard $k$-means objective function is modified to take into account the effect of the outliers by adding some penalty function $g$ for them. We show that for a relatively general class of penalty functions $g$, this problem admits an $O(\log^{1.5}n \log\log n)$-approximation algorithm, whose running time is polynomial in $n$, $k$, and $d$.
A Faster Algorithm for Minimum-Cost Bipartite Matching in Minor-Free Graphs

We give an $\tilde{O}(n^{7/5} \log (nC))$-time algorithm to compute a minimum-cost maximum cardinality matching (optimal matching) in $K_h$-minor free graphs with $h=O(1)$ and integer edge weights having magnitude at most $C$. This improves upon the $\tilde{O}(n^{10/7}\log{C})$ algorithm of Cohen et al. [SODA 2017] and the $O(n^{3/2}\log (nC))$ algorithm of Gabow and Tarjan [SIAM J. Comput. 1989]. For a graph with $m$ edges and $n$ vertices, the well-known Hungarian Algorithm computes a shortest augmenting path in each phase in $O(m)$ time, yielding an optimal matching in $O(mn)$ time. The Hopcroft-Karp [SIAM J. Comput. 1973], and Gabow-Tarjan [SIAM J. Comput. 1989] algorithms compute, in each phase, a maximal set of vertex-disjoint shortest augmenting paths (for appropriately defined costs) in $O(m)$ time. This reduces the number of phases from $n$ to $O(\sqrt{n})$ and the total execution time to $O(m\sqrt{n})$. In order to obtain our speed-up, we relax the conditions on the augmenting paths and iteratively compute, in each phase, a set of carefully selected augmenting paths that are not restricted to be shortest or vertex-disjoint. As a result, our algorithm computes substantially more augmenting paths in each phase, reducing the number of phases from $O(\sqrt{n})$ to $O(n^{2/5})$. By using small vertex separators, the execution of each phase takes $\tilde{O}(m)$ time on average. For planar graphs, we combine our algorithm with efficient shortest path data structures to obtain a minimum-cost perfect matching in $\tilde{O}(n^{6/5} \log{(nC)})$ time. This improves upon the recent $\tilde{O}(n^{4/3}\log{(nC)})$ time algorithm by Asathulla et al. [SODA 2018].