Miracle-dz/UniFAR-FTUs huggingface dataset:
Privacy Lost and Found: An Investigation at Scale of Web Privacy Policy Availability

Legal jurisdictions around the world require organisations to post privacy policies on their websites. However, in spite of laws such as GDPR and CCPA reinforcing this requirement, organisations sometimes do not comply, and a variety of semi-compliant failure modes exist. To investigate the landscape of web privacy policies, we crawl the privacy policies from 7 million organisation websites with the goal of identifying when policies are unavailable. We conduct a large-scale investigation of the availability of privacy policies and identify potential reasons for unavailability such as dead links, documents with empty content, documents that consist solely of placeholder text, and documents unavailable in the specific languages offered by their respective websites. We estimate the frequencies of these failure modes and the overall unavailability of privacy policies on the web and find that privacy policies URLs are only available in 34% of websites. Further, 1.37% of these URLs are broken links and 1.23% of the valid links lead to pages without a policy. Further, to enable investigation of privacy policies at scale, we use the capture-recapture technique to estimate the total number of English language privacy policies on the web and the distribution of these documents across top level domains and sectors of commerce. We estimate the lower bound on the number of English language privacy policies to be around 3 million. Finally, we release the CoLIPPs Corpus containing around 600k policies and their metadata consisting of policy URL, length, readability, sector of commerce, and policy crawl date.
Reynolds number induced growth of the large-scale rolls in plane Couette flow using resolvent analysis

Abstract In direct numerical simulations (DNS) of turbulent Couette flow, the observation has been made that the long streamwise rolls increase in length with the Reynolds number (Lee & Moser, J. Fluid Mech., vol. 842, 2018, pp. 128–145). To understand this, we employ both linear stability theory and its extension to resolvent analysis. For this, we emphasise the high Reynolds number ($Re \rightarrow \infty$) and small streamwise wavenumbers ($\alpha \rightarrow 0$) limit, imposing the distinguished limit $Re_{\alpha }=Re \, \alpha = O(1)$. We find that in case of linear stability theory, $Re_{\alpha }$ acts as a global invariant in the resulting eigenvalue problem, while in case of resolvent analysis, $Re_{\alpha }$ acts as a local invariant in the behaviour of the energy of the system characterised through the first singular value $\sigma _1$ of the resolvent operator within the investigated asymptotic limit. In order to obtain constant streamwise structures for increasing Reynolds numbers, the respective streamwise wavenumber has to decrease, which verifies the observations from DNS studies of an increasing length of the streamwise structures with the Reynolds number. In linear stability theory, a parameter reduction is achieved for the above asymptotic limit, resulting in the modified Orr–Sommerfeld and Squire equations being dependent only on $Re_{\alpha }$. The behaviour of both the coherent structures obtained from linear stability theory and resolvent analysis are compared with each other and show similar behaviours over $Re_{\alpha }$.
Fractal Ledger: A New Approach towards Scalable Blockchain with Graph-Chain Structure

Scalability is often considered the "Achilles' heel" of blockchain technology. With a traditional chain-based structure, blocks cannot be generated concurrently, thus limiting the throughput and slowing transaction confirmation. Recently emerged graph-based blockchains generally excel in on-chain performance but are weaker in structural flexibility, which is essential for off-chain or cross-chain scaling. In this paper, we propose Fractal Ledger, a novel blockchain model to scale out the ledger in a fractal manner. Fractal Ledger is comprised of fractal units that synthesize both chain and graph structure. Through conversions and interactions of fractal units, Fractal Ledger can share the advantages of both types of blockchains and offer scalability in structure and storage. We introduce the deduction mechanisms for flexible structure scalability and a three-directional folding scheme of lossless on-chain data compression for storage scalability. Our experimental results reveal the effectiveness of Fractal Ledger in enhancing structure and storage scalability.
eProcessor: European, Extendable, Energy-Efficient, Extreme-Scale, Extensible, Processor Ecosystem

The eProcessor project aims at creating a RISC-V full stack ecosystem. The eProcessor architecture combines a high-performance out-of-order core with energy-efficient accelerators for vector processing and artificial intelligence with reduced-precision functional units. The design of this architecture follows a hardware/software co-design approach with relevant application use cases from the high-performance computing, bioinformatics and artificial intelligence domains. Two eProcessor prototypes will be developed based on two fabricated eProcessor ASICs integrated into a computer-on-module.
Correspondences, integral structures, and compatibilities in p -adic cohomology.

Correspondences, Integral Structures, and Compatibilities in p-adic Cohomology by Bryden R. Cais Chair: Brian D. Conrad Fix a discrete valuation ring R with fraction field K of characteristic zero and residue field k. In this thesis, we study the Hodge filtration of the first de Rham cohomology of a smooth, proper, and geometrically connected curve XK over K and the endomorphisms of it induced by correspondences on the curve. Our inves- tigations proceed along two courses: in one direction, we equip the Hodge filtration with a canonical integral structure, i.e. a short exact sequence of free R-modules that is functorial in finite K-morphisms of XK and recovers the Hodge filtration of H dR(XK/K) after extending scalars to K. In the other direction, we suppose that k has characteristic p > 0 and employ techniques from rigid and formal geometry to study the Hodge filtration of H dR(XK/K)—together with its canonical integral structure—via p-adic cohomology. We give two different constructions of a canonical integral structure on the Hodge filtration of H dR(XK/K). One construction uses certain models of XK over R and Grothendieck duality theory, while the other uses the Neron model of the Jacobian of XK and the theory of rigidified extensions of Grothendieck and Mazur-Messing. We will prove under mild hypotheses that these different constructions yield the same integral structure. When k has characteristic p > 0, we can attach several different p-adic cohomol- ogy theories to XK . We will study the comparison maps between these theories, and will be especially concerned with their interaction with endomorphisms induced by correspondences on XK and Frobenius. A significant impetus for writing this thesis comes from Gross’ beautiful paper [22] on Galois representations. In that paper, Gross’ main theorem is conditional on certain compatibilities between p-adic cohomology theories. As a consequence of our work, we resolve these compatibility issues. Our work concerning canonical integral structures moreover provides a reference for several results that play a key role in [22] but that we have been unable to find in the literature.
When TikTok meets celebrity: an investigation of how celebrity attachment influences visit intention

ABSTRACT Considering the rapid development of TikTok short videos combining celebrities and tourist destinations, this study aims to examine how attachment to TikTok celebrities enhances visit intention. Presence theory (Sense of Presence and Telepresence) was applied to construct the theoretical framework. 276 survey data were collected from respondents who had watched TikTok-Chengdu celebrity travel videos but had not visited Chengdu, China. Results indicated that celebrity attachment positively predicts sense of presence and telepresence. Furthermore, sense of presence and telepresence positively predict tourist enjoyment, and telepresence has a greater impact. Moreover, tourist enjoyment positively predicts their intention to visit Chengdu. Theoretical and managerial contributions are presented.
Jointly modeling deep mutational scans identifies shifted mutational effects among SARS-CoV-2 spike homologs

Deep mutational scanning (DMS) is a high-throughput experimental technique that measures the effects of thousands of mutations to a protein. These experiments can be performed on multiple homologs of a protein or on the same protein selected under multiple conditions. It is often of biological interest to identify mutations with shifted effects across homologs or conditions. However, it is challenging to determine if observed shifts arise from biological signal or experimental noise. Here, we describe a method for jointly inferring mutational effects across multiple DMS experiments while also identifying mutations that have shifted in their effects among experiments. A key aspect of our method is to regularize the inferred shifts, so that they are nonzero only when strongly supported by the data. We apply this method to DMS experiments that measure how mutations to spike proteins from SARS-CoV-2 variants (Delta, Omicron BA.1, and Omicron BA.2) affect cell entry. Most mutational effects are conserved between these spike homologs, but a fraction have markedly shifted. We experimentally validate a subset of the mutations inferred to have shifted effects, and confirm differences of >1,000-fold in the impact of the same mutation on spike-mediated viral infection across spikes from different SARS-CoV-2 variants. Overall, our work establishes a general approach for comparing sets of DMS experiments to identify biologically important shifts in mutational effects. Significance Statement Amino-acid mutations to a protein have effects that can shift as the protein evolves or is put under new selective pressure. The effects of amino-acid mutations to a specific protein under a defined selective pressure can be measured by deep mutational scanning experiments. Here, we devise an approach to quantify shifts in mutational effects between experiments performed on different homologs (i.e. variants) of the same protein, or on the same protein selected under different conditions. We use this approach to compare experiments performed on three homologs of SARS-CoV-2 spike, identifying mutations that have shifted in their effect on spike-mediated viral infection by >1,000 fold across SARS-CoV-2 variants.
ddRAD-seq-derived SNPs reveal novel association signatures for fruit-related traits in peach

Breeding for new peach cultivars with enhanced traits is a prime target in breeding programs. In this study, we used a discovery panel of 90 peach accessions in order to dissect the genetic architecture of 16 fruit-related traits. ddRAD-seq genotyping and the intersection between three variant callers yielded 13,045 high-confidence SNPs. These markers were subjected to an exhaustive association analysis by testing up to seven GWAS models. Blink was selected as the most adjusted, simultaneously balancing false positive and negative associations. Totally, we identified 16 association signals for six traits showing high broad-sense heritability: harvest date, fruit weight, flesh firmness, contents of flavonoids, anthocyanins and sorbitol. By assessing the allelic effect of significant markers on phenotypic attributes, nine SNP alleles were denoted favorable. A promising marker (SNC_034014.1_7012470) was found to be simultaneously associated with harvest date and fruit firmness conferring a positive allelic effect on both traits. We anticipate that this marker could be used to improve firmness in late harvested cultivars. Candidate causal genes were shortlisted when fulfilling the following criteria: i) position within the linkage disequilibrium block, ii) functional annotation and iii) expression pattern. A bibliographic review of previously reported QTLs mapping nearby the associated markers allowed us to benchmark the accuracy of our approach. Despite the moderate germplasm size, ddRAD-seq allowed us to produce an accurate representation of peach’s genome resulting in SNP markers suitable for empirical association studies. Together with candidate genes, they lay the foundation for further genetic dissection of peach key traits.
ATML1 Regulates the Differentiation of ER Body-containing Large Pavement Cells in Rosette Leaves of Brassicaceae Plants

Endoplasmic reticulum (ER)-derived organelles, ER bodies, participate in the defense against herbivores in Brassicaceae plants. ER bodies accumulate β-glucosidases, which hydrolyse specialized thioglucosides known as glucosinolates to generate bioactive substances. In Arabidopsis thaliana, the leaf ER (LER) bodies are formed in large pavement cells, which are found in the petioles, margins, and blades of rosette leaves. However, the regulatory mechanisms involved in establishing large pavement cells are unknown. Here, we show that the ARABIDOPSIS THALIANA MERISTEM L1 LAYER (ATML1) transcription factor regulates the formation of LER bodies in large pavement cells of rosette leaves. Overexpression of ATML1 enhanced the expression of LER body-related genes and the number of LER body-containing large pavement cells, whereas its knockout resulted in opposite effects. ATML1 enhances endoreduplication and cell size through LOSS OF GIANT CELLS FROM ORGANS (LGO). Although the overexpression and knockout of LGO affected the appearance of large pavement cells in Arabidopsis, the effect on LER body-related gene expression and LER body formation was weak. LER body-containing large pavement cells were also found in Eutrema salsugineum, another Brassicaceae species. Our results demonstrate that ATML1 establishes large pavement cells to induce LER body formation in Brassicaceae plants, contributing to the defense against herbivores.
Chondroitin and dermatan sulfate exposure induces a wound healing state in fibroblasts through Cux1-mediated SerpinB2 transcriptional repression

Mucopolysaccharidoses (MPS) are a group of syndromes characterized by the accumulation of sulfated glycosaminoglycans (sGAGs), leading to profound connective tissue alterations, including impaired endochondral ossification. The function of sGAGs involves determining the mechanical properties of the extracellular matrix and regulating growth factor signaling pathways, such as Fgf2. In this study, we investigated the deposition of chondroitin sulfate and dermatan sulfate, two major sGAGs, and their resemblance to wound healing states in human fibroblasts. Our findings indicate that this condition alters cell adhesion, providing a potential explanation for fibrosis-like changes observed in MPS patients. Furthermore, we elucidate the molecular pathway responsible for this effect, wherein increased Cathepsin L activation leads to the processing of the transcription factor Cux1 into a stable form capable of regulating the expression of target genes, including SERPINB2. The presence of similar changes in cell adhesion in human-induced pluripotent stem cell-derived mesenchymal cells further reinforces the significance of sGAGs in cell adhesion and sheds light on possible mechanisms underlying altered endochondral ossification in MPS patients.
An image encryption algorithm based on chaotic Lorenz system and novel primitive polynomial S-boxes

Nowadays, the chaotic cryptosystems are gaining more attention due to their efficiency, the assurance of robustness and high sensitivity corresponding to initial conditions. In literature, on one hand there are many encryption algorithms that only guarantee security while on the other hand there are schemes based on chaotic systems that only promise the uncertainty. Due to these limitations, each of these approaches cannot adequately encounter the challenges of current scenario. Here we take a unified approach and propose an image encryption algorithm based on Lorenz chaotic system and primitive irreducible polynomial S-boxes. First, we propose 16 different S-boxes based on projective general linear group and 16 primitive irreducible polynomials of Galois field of order 256, and then utilize these S-boxes with combination of chaotic map in image encryption scheme. Three chaotic sequences can be produced by the Lorenz chaotic system corresponding to variables $x$, $y$ and $z$. We construct a new pseudo random chaotic sequence $k_i$ based on $x$, $y$ and $z$. The plain image is encrypted by the use of chaotic sequence $k_i$ and XOR operation to get a ciphered image. To demonstrate the strength of presented image encryption, some renowned analyses as well as MATLAB simulations are performed.
Toward Quantifying the Climate Heat Engine: Solar Absorption and Terrestrial Emission Temperatures and Material Entropy Production

AbstractA heat-engine analysis of a climate system requires the determination of the solar absorption temperature and the terrestrial emission temperature. These temperatures are entropically defined as the ratio of the energy exchanged to the entropy produced. The emission temperature, shown here to be greater than or equal to the effective emission temperature, is relatively well known. In contrast, the absorption temperature requires radiative transfer calculations for its determination and is poorly known.The maximum material (i.e., nonradiative) entropy production of a planet’s steady-state climate system is a function of the absorption and emission temperatures. Because a climate system does no work, the material entropy production measures the system’s activity. The sensitivity of this production to changes in the emission and absorption temperatures is quantified. If Earth’s albedo does not change, material entropy production would increase by about 5% per 1-K increase in absorption temperature. I.
Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. However, for complex, cluttered, and unseen environments with high uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors that could lead to catastrophic outcomes. Autonomous vehicles should ideally adapt to driving conditions; while this can be achieved through multiple routes, it would be beneficial as a first step to be able to characterize driveability in some quantified form. To this end, this paper aims to create a framework for investigating different factors that can impact driveability. Also, one of the main mechanisms to adapt autonomous driving systems to any driving condition is to be able to learn and generalize from representative scenarios. The machine learning algorithms that currently do so learn predominantly in a supervised manner and consequently need sufficient data for robust and efficient learning. Therefore, we also perform a comparative overview of 54 public driving datasets that enable learning and publish this dataset index at https://sites.google. com/view/driveability-survey-datasets. Specifically, we categorize the datasets according to the use cases and highlight the datasets that capture the complicated and hazardous driving conditions, which can be better used for training robust driving models. Furthermore, by discussions of what driving scenarios are not covered by the existing public datasets and what driveability factors need more investigation and data acquisition, this paper aims to encourage both targeted dataset collection and the proposal of novel driveability metrics that enhance the robustness of autonomous cars in adverse environments.
Multiple host targets of Pseudomonas effector protein HopM1 form a protein complex regulating apoplastic immunity and water homeostasis

Bacterial type III effector proteins injected into the host cell play a critical role in mediating bacterial interactions with plant and animal hosts. Notably, some bacterial effectors are reported to target sequence-unrelated host proteins with unknown functional relationships. The Pseudomonas syringae effector HopM1 is such an example; it interacts with and/or degrades several HopM1-interacting (MIN) Arabidopsis proteins, including HopM1-interacting protein 2 (MIN2/RAD23), HopM1-interacting protein 7 (MIN7/BIG5), HopM1-interacting protein 10 (MIN10/14-3-3ĸ), and HopM1-interacting protein 13 (MIN13/BIG2). In this study, we purified the MIN7 complex formed in planta and found that it contains MIN7, MIN10, MIN13, as well as a tetratricopeptide repeat protein named HLB1. Mutational analysis showed that, like MIN7, HLB1 is required for pathogen-associated molecular pattern (PAMP)-, effector-, and benzothiadiazole (BTH)-triggered immunity. HLB1 is recruited to the trans-Golgi network (TGN)/early endosome (EE) in a MIN7-dependent manner. Both min7 and hlb1 mutant leaves contained elevated water content in the leaf apoplast and artificial water infiltration into the leaf apoplast was sufficient to phenocopy immune-suppressing phenotype of HopM1. These results suggest that multiple HopM1-targeted MIN proteins form a protein complex with a dual role in modulating water level and immunity in the apoplast, which provides an explanation for the dual phenotypes of HopM1 during bacterial pathogenesis.
Stock market technical analysis using Japanese candlesticks and machine learning

One of the thorough methods used in the stock market to assess the situation and forecast stock price movements is the charting approach. One of the most common options is the Japanese candlestick. These candles offer fascinating and recurrent shapes when plotted in a time-series graph. Each candle, whether alone or in a group, may emit a powerful or weak trading signal. History is said to repeat itself in the stock market. We are interested in applying the apriori technique to uncover item sets that are frequently found with some pattern in order to establish association rules from the time series data of Japanese candlestick shapes. By doing this, the investor will increase his or her understanding of the upcoming trend and maximize their return. We are only concentrating on candlestick patterns, therefore as the dataset gets smaller, it will take less time to make predictions about the future. We used the NSE India ten-year dataset. As part of data pre-processing, candles are encoded. Following the completion of this experiment, it was discovered that using the apriori method on Japanese candlestick shapes produced several intriguing outcomes and eventually increased forecast accuracy.