Miracle-dz/UniFAR-FTUs huggingface dataset:
Wasserstein Metric-Based Location Spoofing Attack Detection in WiFi Positioning Systems

WiFi positioning systems (WPS) have been introduced as parts of 5G location services (LCS) to provide fast positioning results of user devices in urban areas. However, they are prominently threatened by location spoofing attacks. To end this, we present a Wasserstein metric-based attack detection scheme to counter the location spoofing attacks in the WPS. The Wasserstein metric is used to measure the similarity of each two hotspots by their signal’s frequency offset distribution features. Then, we apply the clustering method to find the fake hotspots which are generated by the same device. When applied with WPS, the proposed method can prevent location spoofing by filtering out the fake hotspots set by attackers. We set up experimental tests by commercial WiFi devices, which show that our method can detect fake devices with 99% accuracy. Finally, the real-world test shows our method can effectively secure the positioning results against location spoofing attacks.
Running Title : Thermal niche multidimensionality 3 4

7 1Departamento de Biología y Geología, Física y Química inorgánica. Universidad Rey 8 Juan Carlos, C/ Tulipán s/n, Móstoles, Madrid, 28933 Spain 9 2Department of Biogeography and Global Change, Museo Nacional de Ciencias 10 Naturales (MNCN CSIC), C/José Gutiérrez Abascal, 2, Madrid, 28006 Spain 11 3Laboratorio de Zoología y Ecología Acuática – LAZOEA, Universidad de los Andes, 12 Bogotá, Colombia 13 4Facultad de Medicina Veterinaria y Zootecnia. Universidad Central del Ecuador, 14 Ecuador 15 Corresponding author contact information: Joaquín Calatayud; +34 601395450; 16 j.calatayud. ortega@gmail.com 17 18 Abstract 19 Understanding the consequences of climate change requires understanding how 20 temperature controls species' responses across key biological aspects, as well as the 21 coordination of thermal responses across these aspects. We study the role of temperature 22 in determining the species' diel, seasonal, and geographical occurrence, using dung 23 beetles as a model system. We found that temperature has relatively low –but not 24 negligible– effects in the three spatiotemporal scales, once accounting for alternative 25 factors. More importantly, the estimated thermal responses were largely incongruent 26 across scales. This shows that species have multidimensional thermal niches, entailing 27 that adjustments to fulfil temperature requirements for one biological aspect, such as 28 seasonal ontogenetic cycles, may result in detrimental effects on other aspects, like diel 29 activity. These trade-offs can expose individuals to inadequate temperatures, reducing 30 populations’ performance. Paradoxically, the relatively weak effects of temperature we 31 found may have serious consequences for species' responses to warming if temperature 32 regulates essential aspects of species’ biology in divergent ways. 33
A Survey of Internet of Things node’s transactions Secure through Blockchain Technology

Internet of Things is widely used in various sectors in recent years, due to its efficiency, scalability, feasibility, etc. Thus, need such type of network infrastructure for reducing the human effort from unwanted work like household chores, information gathers or repeated work, etc. That is possible from the Internet of Things. Internet of Things is a collection of smart nodes that collect the data and produce a result by systematically analyzed them. Therefore it can reduce the dependability from humans and expect the best result in any worst condition. Now the Internet of Things is a rapidly growing industry in the world and also there are lots of vulnerability have occurred in recent years. Thus, need to secure Internet of Things nodes during communicating between each other device as well as saving or processing information, without precaution from the attacks, it cannot safe, sensitive information from the attacker, because enhancing of technology there will be increased computational overhead. Therefore, need security measures like Confidentiality, Integrity, Authentication, Non-repudiation, Availability, etc. in any transactions of IoT devices. And such security measures can provide now the only Blockchain technology. And it can safe from the attacker's intention. Blockchain is a decentralized, distributed ledger technology that overcomes the data’s tamper and provides the availability from its decentralization environment. This technology can use in the IoT environment to protect them from malicious, unauthorized user/node and make it able to take action against them. In this study, introduced the concept of a secure IoT node’s transaction through Blockchain technology.
Seasonal variations of foliar element distributions of silver birch in different habitats

Novel information on silver birch (Betula pendula Roth) foliar element contents and their seasonal, between-habitat and leaf level variations are provided by applying fine-scaled element mapping with micro X-ray fluorescence. In the monthly leaf samples collected from May to October from six different habitats, pairwise scatter plots and Spearman’s rank correlations showed statistically significant positive correlations between Si, Al and Fe, and covariations between also many other pairs of elements. Of the ten elements studied, seven showed statistically significant changes in their average levels between May and June. The contents of P, S and K decreased in most habitats during the later season, whereas Ca and in some habitats also Mn and Zn increased. Comparing habitats, trees in the limestone habitat had relatively low content of Mg, strongly increasing levels of P until the late season, and high content of Ca and Fe. Other habitats also revealed distinctive particularities in their foliar elements, such as a high relative content of S and a low content of Ca at the seashore. Mn was high in three habitats, possibly due to bedrock characteristics. Except for P, the contents of all elements diverged between the midrib and other leaf areas. Zn content was particularly high in the leaf veins. Mn levels were highest at the leaf margins, indicating a possible sequestration mechanism for this potentially harmful element. Si may help to alleviate the metallic toxicities of Al and Fe. Because the growing season studied was dry, some trees developed symptoms of drought stress. The injured leaf parts had reduced levels of P, S and K, suggesting translocation of these nutrients before permanent damage.
Determinants of Response and Mechanisms of Resistance of CAR T-cell Therapy in Multiple Myeloma.

B-cell maturation antigen (BCMA)-specific chimeric antigen receptor (CAR) T cells have substantial therapeutic potential in multiple myeloma (MM), but most patients eventually relapse. Determinants of response and mechanisms of resistance are most likely multifactorial and include MM-related factors, premanufacturing T-cell characteristics, CAR T-cell-related features, and several components of the immunosuppressive microenvironment. Efforts to improve the potency and safety of CAR T-cell therapy include optimizing CAR design, combinatorial approaches to enhance persistence and activity, treatment of less heavily pretreated patients, and dual-antigen targeting to prevent antigen escape. We expect that these rationally designed strategies will contribute to further improvement in the clinical outcome of patients with MM. SIGNIFICANCE: Although BCMA-specific CAR T-cell therapies are highly effective in heavily pretreated patients with MM, there has been, until now, no indication of a plateau in the survival curves. In this review, we provide an overview of the determinants of response and the mechanisms that contribute to the development of treatment failure after initial remission (acquired resistance). A better understanding of these mechanisms, underlying lack of disease response, and acquired resistance may lead to further improvements in the effectiveness of CAR T-cell therapy.
Creating a Low-Light, Motion-Blurred Image Processing Pipeline

When processing images, there is a consistent trade-off between blur and noise. Given that noise is high-frequency content, denoising by nature blurs an image. By contrast, deblurring introduces noise into the image. In low-light conditions, the situation is compounded by the need to brighten an image. The process of brightening the image itself introduces additional noise. Taking images while in motion, especially of wildlife, though not necessarily of course, introduces additional blur. Past projects have considered one issue or the other, but few have attempted to tackle the basket of issues in an end-to-end manner. We thus set out to create a pipeline that takes these dark, blurry images and processes them into sharp, bright images.
Shock compression of precompressed deuterium

Here we report quasi-isentropic dynamic compression and thermodynamic characterization of solid, precompressed deuterium over an ultra fast time scale (< 100 ps) and a microscopic length scale (< 1 im). We further report a fast transition in dynamically compressed solid deuterium that is consistent with the ramp to shock transition, with a time scale of less than 10 ps. These results suggest that high density dynamic compression of hydrogen may be possible on microscopic length scales.
Allelic variation in HLA‐DRB1 is associated with development of antidrug antibodies in cancer patients treated with atezolizumab that are neutralizing in vitro

The treatment of diseases with biologic agents can result in the formation of antidrug antibodies (ADA). Although drivers for ADA formation are unknown, a role for antigen presentation is likely, and variation in human leukocyte antigen (HLA) genes has been shown to be associated with occurrence of ADA for several biologics. Here, we performed an HLA‐wide association study in 1982 patients treated with the anti‐PD‐L1 antibody atezolizumab across eight clinical trials. On average, 29.8% of patients were ADA‐positive (N = 591, range of 13.5%–38.4% per study) and 14.6% of patients were positive for ADA that were neutralizing in vitro (neutralizing antibodies [NAb], N = 278, range of 6.4%–21.9% per study). In a meta‐analysis of logistic regression coefficients, we found statistically significant associations between HLA class II alleles and ADA status. The top‐associated alleles were HLA‐DRB1*01:01 in a comparison of ADA‐positive versus ADA‐negative patients (p = 3.4 × 10−5, odds ratio [OR] 1.96, 95% confidence interval [95% CI] 1.64–2.28) and HLA‐DQA1*01:01 when comparing NAb‐positive with ADA‐negative patients (p = 2.8 × 10−7, OR 2.31, 95% CI 1.98–2.66). Both alleles occur together on a common HLA haplotype, and analyses considering only NAb‐negative, ADA‐positive patients did not yield significant results, suggesting that the genetic association is mainly driven by NAb status. In conclusion, our study showed that HLA class II genotype is associated with the risk of developing ADA, and specifically NAb, in patients treated with atezolizumab, but the effect estimates suggest that immunogenetic factors are not sufficient as clinically meaningful predictors.
Motor Function and White Matter Connectivity in Children Treated with Therapeutic Hypothermia for Neonatal Encephalopathy

Therapeutic hypothermia reduces the incidence of severe motor disability, such as cerebral palsy, following neonatal hypoxic-ischemic encephalopathy. However, cooled children without cerebral palsy at school-age demonstrate motor deficits and altered white matter connectivity. In this study, we used diffusion-weighted imaging to investigate the relationship between white matter connectivity and motor performance, measured using the Movement Assessment Battery for Children-2, in school-age children treated with therapeutic hypothermia for neonatal hypoxic ischaemic encephalopathy at birth, who did not develop cerebral palsy (cases), and matched controls. Analysis of tract-level microstructure (33 cases, 36 controls) revealed correlations between total motor scores and fractional anisotropy, in cases but not controls, in the anterior thalamic radiation bilaterally, the inferior fronto-occipital fasciculus bilaterally and both the hippocampal and cingulate gyrus parts of the left cingulum. Analysis of structural brain networks (22 cases, 32 controls), in which edges were determined by probabilistic tractography and weighted by fractional anisotropy, revealed correlations between total motor scores and several whole-brain network metrics in cases but not controls. We then investigated edge-level association with motor function using the network-based statistic. This revealed subnetworks which exhibited group differences in the association between motor outcome and edge weights, for total motor scores as well as for balance and manual dexterity domain scores. All three of these subnetworks comprised numerous frontal lobe regions known to be associated with motor function, including the superior frontal gyrus and middle frontal gyrus. These findings demonstrate an association between impaired motor function and brain organisation in case children.
American Older Adults in COVID-19 Times: Vulnerability Types, Aging Attitudes, and Emotional Responses

The Coronavirus Disease (COVID) pandemic has aroused challenges to emotional well-being of the individuals. With 1,582 respondents from the Health and Retirement Survey (HRS), this study investigates the heterogeneity in older adults' vulnerability and examines the relationship between vulnerability types, aging attitudes, and emotional responses. International Positive and Negative Affect Schedule Short-form (I-PANAS-SF) and Attitudes toward own aging (ATOT) were used to assess the emotional experiences and aging attitudes, and 14 kinds of pandemic-related deprivations evaluated vulnerability of individuals. Latent class analysis (LCA) was used to explore the vulnerability types, and weighted linear regressions examined the relationship between vulnerability, aging attitudes, and emotional responses. The results showed that the proportion for individuals with mild vulnerability (MV), healthcare use vulnerability (HV), and dual vulnerability in healthcare use and financial sustainment (DVs) was 67, 22, and 11%, respectively. Older adults aged below 65, Hispanics and non-Hispanic Blacks, and those not eligible for Medicaid were more likely to have HV or DVs. The relationship between vulnerability and positive emotions (PAs) was non-significant, yet individuals with HV (beta = 0.10, standard error [SE] = 0.16) or DVs (beta = 0.09, SE = 0.28) were likely to have more negative emotions (NAs) than their mildly vulnerable counterparts. Furthermore, aging attitudes moderated the relationship between vulnerability and emotions. The salutary effect of positive aging attitudes on emotional well-being was more significant among people with DVs than those with MV (beta = 0.20, SE = 0.04 for positive responses; beta = −0.15, SE = 0.04 for negative responses). Thus, we urge more attention for vulnerable older adults in a pandemic context. Meanwhile, encouraging positive aging attitudes might be helpful for older adults to have better emotional well-being, especially for those with DVs.
Isolation without taxation: near-zero-cost transitions for WebAssembly and SFI

Software sandboxing or software-based fault isolation (SFI) is a lightweight approach to building secure systems out of untrusted components. Mozilla, for example, uses SFI to harden the Firefox browser by sandboxing third-party libraries, and companies like Fastly and Cloudflare use SFI to safely co-locate untrusted tenants on their edge clouds. While there have been significant efforts to optimize and verify SFI enforcement, context switching in SFI systems remains largely unexplored: almost all SFI systems use heavyweight transitions that are not only error-prone but incur significant performance overhead from saving, clearing, and restoring registers when context switching. We identify a set of zero-cost conditions that characterize when sandboxed code has sufficient structured to guarantee security via lightweight zero-cost transitions (simple function calls). We modify the Lucet Wasm compiler and its runtime to use zero-cost transitions, eliminating the undue performance tax on systems that rely on Lucet for sandboxing (e.g., we speed up image and font rendering in Firefox by up to 29.7% and 10% respectively). To remove the Lucet compiler and its correct implementation of the Wasm specification from the trusted computing base, we (1) develop a static binary verifier, VeriZero, which (in seconds) checks that binaries produced by Lucet satisfy our zero-cost conditions, and (2) prove the soundness of VeriZero by developing a logical relation that captures when a compiled Wasm function is semantically well-behaved with respect to our zero-cost conditions. Finally, we show that our model is useful beyond Wasm by describing a new, purpose-built SFI system, SegmentZero32, that uses x86 segmentation and LLVM with mostly off-the-shelf passes to enforce our zero-cost conditions; our prototype performs on-par with the state-of-the-art Native Client SFI system.
The Matter of Chance: Auditing Web Search Results Related to the 2020 U.S. Presidential Primary Elections Across Six Search Engines

We examine how six search engines filter and rank information in relation to the queries on the U.S. 2020 presidential primary elections under the default—that is nonpersonalized—conditions. For that, we utilize an algorithmic auditing methodology that uses virtual agents to conduct large-scale analysis of algorithmic information curation in a controlled environment. Specifically, we look at the text search results for “us elections,” “donald trump,” “joe biden,” “bernie sanders” queries on Google, Baidu, Bing, DuckDuckGo, Yahoo, and Yandex, during the 2020 primaries. Our findings indicate substantial differences in the search results between search engines and multiple discrepancies within the results generated for different agents using the same search engine. It highlights that whether users see certain information is decided by chance due to the inherent randomization of search results. We also find that some search engines prioritize different categories of information sources with respect to specific candidates. These observations demonstrate that algorithmic curation of political information can create information inequalities between the search engine users even under nonpersonalized conditions. Such inequalities are particularly troubling considering that search results are highly trusted by the public and can shift the opinions of undecided voters as demonstrated by previous research.
Bias in Knowledge Graphs - an Empirical Study with Movie Recommendation and Different Language Editions of DBpedia

Public knowledge graphs such as DBpedia and Wikidata have been recognized as interesting sources of background knowledge to build content-based recommender systems. They can be used to add information about the items to be recommended and links between those. While quite a few approaches for exploiting knowledge graphs have been proposed, most of them aim at optimizing the recommendation strategy while using a fixed knowledge graph. In this paper, we take a different approach, i.e., we fix the recommendation strategy and observe changes when using different underlying knowledge graphs. Particularly, we use different language editions of DBpedia. We show that the usage of different knowledge graphs does not only lead to differently biased recommender systems, but also to recommender systems that differ in performance for particular fields of recommendations.
Graph Learning: A Survey

Graphs are widely used as a popular representation of the network structure of connected data. Graph data can be found in a broad spectrum of application domains such as social systems, ecosystems, biological networks, knowledge graphs, and information systems. With the continuous penetration of artificial intelligence technologies, graph learning (i.e., machine learning on graphs) is gaining attention from both researchers and practitioners. Graph learning proves effective for many tasks, such as classification, link prediction, and matching. Generally, graph learning methods extract relevant features of graphs by taking advantage of machine learning algorithms. In this survey, we present a comprehensive overview on the state-of-the-art of graph learning. Special attention is paid to four categories of existing graph learning methods, including graph signal processing, matrix factorization, random walk, and deep learning. Major models and algorithms under these categories are reviewed, respectively. We examine graph learning applications in areas such as text, images, science, knowledge graphs, and combinatorial optimization. In addition, we discuss several promising research directions in this field.
Sparse Pose Trajectory Completion

We propose a method to learn, even using a dataset where objects appear only in sparsely sampled views (e.g. Pix3D), the ability to synthesize a pose trajectory for an arbitrary reference image. This is achieved with a cross-modal pose trajectory transfer mechanism. First, a domain transfer function is trained to predict, from an RGB image of the object, its 2D depth map. Then, a set of image views is generated by learning to simulate object rotation in the depth space. Finally, the generated poses are mapped from this latent space into a set of corresponding RGB images using a learned identity preserving transform. This results in a dense pose trajectory of the object in image space. For each object type (e.g., a specific Ikea chair model), a 3D CAD model is used to render a full pose trajectory of 2D depth maps. In the absence of dense pose sampling in image space, these latent space trajectories provide cross-modal guidance for learning. The learned pose trajectories can be transferred to unseen examples, effectively synthesizing all object views in image space. Our method is evaluated on the Pix3D and ShapeNet datasets, in the setting of novel view synthesis under sparse pose supervision, demonstrating substantial improvements over recent art.
Semi-supervised Long-tailed Recognition using Alternate Sampling

Main challenges in long-tailed recognition come from the imbalanced data distribution and sample scarcity in its tail classes. While techniques have been proposed to achieve a more balanced training loss and to improve tail classes data variations with synthesized samples, we resort to leverage readily available unlabeled data to boost recognition accuracy. The idea leads to a new recognition setting, namely semi-supervised long-tailed recognition. We argue this setting better resembles the real-world data collection and annotation process and hence can help close the gap to real-world scenarios. To address the semi-supervised long-tailed recognition problem, we present an alternate sampling framework combining the intuitions from successful methods in these two research areas. The classifier and feature embedding are learned separately and updated iteratively. The class-balanced sampling strategy has been implemented to train the classifier in a way not affected by the pseudo labels' quality on the unlabeled data. A consistency loss has been introduced to limit the impact from unlabeled data while leveraging them to update the feature embedding. We demonstrate significant accuracy improvements over other competitive methods on two datasets.