Miracle-dz/UniFAR-FTUs huggingface dataset:
Unitary signings and induced subgraphs of Cayley graphs of $\mathbb{Z}_2^{n}$

Let $G$ be a Cayley graph of the elementary abelian $2$-group $\mathbb{Z}_2^{n}$ with respect to a set $S$ of size $d$. We prove that for any such $G, S$ and $d$, the maximum degree of any induced subgraph of $G$ on any set of more than half the vertices is at least $\sqrt d$. This is deduced from the recent breakthrough result of Huang who proved the above for the $n$-hypercube $Q^n$, in which the set of generators $S$ is the set of all vectors of Hamming weight $1$. Motivated by his method we define and study unitary signings of adjacency matrices of graphs, and compare them to the orthogonal signings of Huang. As a byproduct, we answer a recent question of Belardo et. al. about the spectrum of signed $5$-regular graphs.
Convolutional Kernel Networks for Graph-Structured Data

We introduce a family of multilayer graph kernels and establish new links between graph convolutional neural networks and kernel methods. Our approach generalizes convolutional kernel networks to graph-structured data, by representing graphs as a sequence of kernel feature maps, where each node carries information about local graph substructures. On the one hand, the kernel point of view offers an unsupervised, expressive, and easy-to-regularize data representation, which is useful when limited samples are available. On the other hand, our model can also be trained end-to-end on large-scale data, leading to new types of graph convolutional neural networks. We show that our method achieves competitive performance on several graph classification benchmarks, while offering simple model interpretation. Our code is freely available at this https URL.
Gaussian Graphical Model Exploration and Selection in High Dimension Low Sample Size Setting

Gaussian graphical models (GGM) are often used to describe the conditional correlations between the components of a random vector. In this article, we compare two families of GGM inference methods: the nodewise approach and the penalised likelihood maximisation. We demonstrate on synthetic data that, when the sample size is small, the two methods produce graphs with either too few or too many edges when compared to the real one. As a result, we propose a composite procedure that explores a family of graphs with a nodewise numerical scheme and selects a candidate among them with an overall likelihood criterion. We demonstrate that, when the number of observations is small, this selection method yields graphs closer to the truth and corresponding to distributions with better KL divergence with regards to the real distribution than the other two. Finally, we show the interest of our algorithm on two concrete cases: first on brain imaging data, then on biological nephrology data. In both cases our results are more in line with current knowledge in each field.
A Comparative Study of Sequence Classification Models for Privacy Policy Coverage Analysis

Privacy policies are legal documents that describe how a website will collect, use, and distribute a user's data. Unfortunately, such documents are often overly complicated and filled with legal jargon; making it difficult for users to fully grasp what exactly is being collected and why. Our solution to this problem is to provide users with a coverage analysis of a given website's privacy policy using a wide range of classical machine learning and deep learning techniques. Given a website's privacy policy, the classifier identifies the associated data practice for each logical segment. These data practices/labels are taken directly from the OPP-115 corpus. For example, the data practice "Data Retention" refers to how long a website stores a user's information. The coverage analysis allows users to determine how many of the ten possible data practices are covered, along with identifying the sections that correspond to the data practices of particular interest.
Bessel regression model: Robustness to analyze bounded data

We would like to thank Prof. Fernando Quintana (Pontificia Universidad Catolica de Chile - Santiago, Chile) for detailed discussions and suggestions on an earlier version of this paper. The first and second authors gratefully acknowledge the financial support from FAPEMIG (Brazil). The first and third authors acknowledge the support from CNPq (Brazil).
Retraction Mechanism of Soft Torus Robot With a Hydrostatic Skeleton

Soft robots have attracted much attention in recent years owing to their high adaptability. Long articulated soft robots enable diverse operations, and tip-extending robots that navigate their environment through growth are highly effective in robotic search applications. Robots that extend from the tip can lengthen their body without friction from the environment. However, the flexibility of the thin membrane inhibits the retraction motion of the tip due to buckling. Two methods have been proposed to resolve this issue; increasing the pressure of the internal fluid to reinforce rigidity, and mounting an actuator at the tip. The disadvantage of the former is that the increase is limited by the membrane pressure resistance, while the second method leads to robot complexity. In this letter, we present a tip-retraction mechanism with a hydrostatic skeleton that can prevent buckling and takes advantage of the friction from the external environment. Water is used as the internal fluid to increase ground pressure with the environment, which is different from the conventional methods that use pneumatic. We explore the failure pattern of the retraction motion and propose solutions by using a hydrostatic skeleton robot. Additionally, we develop a prototype robot that successfully retracts by using the proposed methodology. Our solution can contribute to the advancement of mechanical design in the soft robotics field with applications to soft snakes and manipulators.
A Computational Investigation on Denominalization

Language has been a dynamic system and word meanings always have been changed over times. Every time a novel concept or sense is introduced, we need to assign it a word to express it. Also, some changes have happened because the result of a change can be more desirable for humans, or cognitively easier to be used by humans. Finding the patterns of these changes is interesting and can reveal some facts about human cognitive evolution. As we have enough resources for studying this problem, it is a good idea to work on the problem through computational modeling, and that can make the work easier and possible to be studied on large scale. In this work, we want to study the nouns which have been used as verbs after some years of their emergence as nouns and find some commonalities among these nouns. In other words, we are interested in finding what potential requirements are essential for this change.
Cloth in the Wind: A Case Study of Physical Measurement Through Simulation

For many of the physical phenomena around us, we have developed sophisticated models explaining their behavior. Nevertheless, measuring physical properties from visual observations is challenging due to the high number of causally underlying physical parameters -- including material properties and external forces. In this paper, we propose to measure latent physical properties for cloth in the wind without ever having seen a real example before. Our solution is an iterative refinement procedure with simulation at its core. The algorithm gradually updates the physical model parameters by running a simulation of the observed phenomenon and comparing the current simulation to a real-world observation. The correspondence is measured using an embedding function that maps physically similar examples to nearby points. We consider a case study of cloth in the wind, with curling flags as our leading example -- a seemingly simple phenomena but physically highly involved. Based on the physics of cloth and its visual manifestation, we propose an instantiation of the embedding function. For this mapping, modeled as a deep network, we introduce a spectral layer that decomposes a video volume into its temporal spectral power and corresponding frequencies. Our experiments demonstrate that the proposed method compares favorably to prior work on the task of measuring cloth material properties and external wind force from a real-world video.
Hamiltonian analysis of unimodular gravity and its quantization in the connection representation

We perform the Hamiltonian analysis of unimodular gravity in terms of the connection representation. The unimodular condition is imposed straightforwardly into the action with a Lagrange multiplier. After classifying constraints into first class and second class, the canonical quantization is carried out. We consider the difference of the corresponding physical states between unimodular gravity and general relativity.
Inhibition of PI3Kδ Enhances Poly I:C-Induced Antiviral Responses and Inhibits Replication of Human Metapneumovirus in Murine Lungs and Human Bronchial Epithelial Cells

Viral infections of the airway can exacerbate respiratory diseases, such as asthma or chronic obstructive pulmonary disease (COPD), and accelerate disease progression. Phosphoinositide 3-kinase (PI3K)δ, a class 1A PI3K, has been studied as a potential target for achieving anti-oncogenic and anti-inflammatory effects. However, the role of PI3Kδ in antiviral responses is poorly understood. Using a synthetic double-stranded RNA poly I:C and a selective PI3Kδ inhibitor IC87114, we investigated the role of PI3Kδ signaling in poly I:C-induced expression of the T lymphocyte-inhibitory molecule programmed death 1 ligand 1 (PD-L1), inflammatory responses and antiviral interferon (IFN) responses. C57BL/6N mice were treated with IC87114 or vehicle by intratracheal (i.t.) instillation followed by i.t. administration of poly I:C. Poly I:C increased PD-L1 expression on epithelial cells, lymphocytes, macrophages, and neutrophils in the lungs and IC87114 suppressed poly I:C-induced PD-L1 expression on epithelial cells and neutrophils possibly via inhibition of the Akt/mTOR signaling pathway. IC87114 also attenuated poly I:C-induced increases in numbers of total cells, macrophages, neutrophils and lymphocytes, as well as levels of KC, IL-6 and MIP-1β in bronchoalveolar lavage fluid. Gene expression of IFNβ, IFNλ2 and IFN-stimulated genes (ISGs) were upregulated in response to poly I:C and a further increase in gene expression was observed following IC87114 treatment. In addition, IC87114 enhanced poly I:C-induced phosphorylation of IRF3. We assessed the effects of IC87114 on human primary bronchial epithelial cells (PBECs). IC87114 decreased poly I:C-induced PD-L1 expression on PBECs and secretion of IL-6 and IL-8 into culture supernatants. IC87114 further enhanced poly I:C- induced increases in the concentrations of IFNβ and IFNλ1/3 in culture supernatants as well as upregulated gene expression of ISGs in PBECs. Similar results were obtained in PBECs transfected with siRNA targeting the PIK3CD gene encoding PI3K p110δ, and stimulated with poly I:C. In human metapneumovirus (hMPV) infection of PBECs, IC87114 suppressed hMPV-induced PD-L1 expression and reduced viral replication without changing the production levels of IFNβ and IFNλ1/3 in culture supernatants. These data suggest that IC87114 may promote virus elimination and clearance through PD-L1 downregulation and enhanced antiviral IFN responses, preventing prolonged lung inflammation, which exacerbates asthma and COPD.
Phylogeny of the genus Pinnixa White, 1846 (Crustacea, Brachyura, Pinnotheridae) and allies inferred from mitochondrial and nuclear molecular markers, with generic reassignment of twenty-one species

ABSTRACT We used mitochondrial 16S-NADH1 complex, mitochondrial 12S, and nuclear histone 3 genes to infer a molecular-based phylogeny, which allowed us to study phylogenetic relationships between species of Pinnixa White, 1846 sensu lato and other closely related pinnotherids. Polyphyly of Pinnixa s.l. was confirmed by maximum likelihood analyses. By our restricted definition, the genus Pinnixa sensu stricto is represented in these analyses only by its type species, Pinnixa cylindrica (Say, 1818). As a result of these molecular analyses, in combination with morphological studies, twelve species are reassigned to existing genera: Pinnixa faba (Dana, 1851), Pinnixa franciscana Rathbun, 1918, Pinnixa littoralis Holmes, 1894, Pinnixa schmitti Rathbun, 1918, and Pinnixa tubicola Holmes, 1894 are placed in the genus Scleroplax Rathbun, 1893, whereas Laminapinnixa miamiensis McDermott, 2014, Laminapinnixa faxoni (Rathbun, 1918), Pinnixa abbotti Glassell, 1935, Pinnixa arenicola Rathbun, 1922, Pinnixa floridana Rathbun, 1918, Pinnixa leptosynaptae Wass, 1968 and Laminapinnixa vanderhorsti Rathbun, 1924 are reassigned to Glassella Campos & Wicksten, 1997, the last two strictly on morphological bases. In addition, three new genera are erected to receive nine species: Rathbunixa n. gen. includes members of the Pinnixa pearsei Wass, 1955 – Pinnixa sayana Stimpson, 1860 complex, Pinnixa affinis Rathbun, 1918, and species in the Pinnixa californiensis Rathbun, 1894 – Pinnixa occidentalis Rathbun, 1893 complex; Tubicolixa n. gen. includes Pinnixa chaetopterana Stimpson, 1860 and the Pinnixa brevipollex Rathbun, 1898 – Pinnixa rapax Bouvier, 1917 complex; and Sayixa n. gen. is established for Pinnixa monodactyla (Say, 1818).
A Benchmark for Systematic Generalization in Grounded Language Understanding

Human language users easily interpret expressions that describe unfamiliar situations composed from familiar parts ("greet the pink brontosaurus by the ferris wheel"). Modern neural networks, by contrast, struggle to interpret compositions unseen in training. In this paper, we introduce a new benchmark, gSCAN, for evaluating compositional generalization in models of situated language understanding. We take inspiration from standard models of meaning composition in formal linguistics. Going beyond an earlier related benchmark that focused on syntactic aspects of generalization, gSCAN defines a language grounded in the states of a grid world. This allows us to build novel generalization tasks that probe the acquisition of linguistically motivated rules. For example, agents must understand how adjectives such as 'small' are interpreted relative to the current world state or how adverbs such as 'cautiously' combine with new verbs. We test a strong multi-modal baseline model and a state-of-the-art compositional method finding that, in most cases, they fail dramatically when generalization requires systematic compositional rules.
Text classification with word embedding regularization and soft similarity measure

Since the seminal work of Mikolov et al., word embeddings have become the preferred word representations for many natural language processing tasks. Document similarity measures extracted from word embeddings, such as the soft cosine measure (SCM) and the Word Mover's Distance (WMD), were reported to achieve state-of-the-art performance on semantic text similarity and text classification. Despite the strong performance of the WMD on text classification and semantic text similarity, its super-cubic average time complexity is impractical. The SCM has quadratic worst-case time complexity, but its performance on text classification has never been compared with the WMD. Recently, two word embedding regularization techniques were shown to reduce storage and memory costs, and to improve training speed, document processing speed, and task performance on word analogy, word similarity, and semantic text similarity. However, the effect of these techniques on text classification has not yet been studied. In our work, we investigate the individual and joint effect of the two word embedding regularization techniques on the document processing speed and the task performance of the SCM and the WMD on text classification. For evaluation, we use the $k$NN classifier and six standard datasets: BBCSPORT, TWITTER, OHSUMED, REUTERS-21578, AMAZON, and 20NEWS. We show 39% average $k$NN test error reduction with regularized word embeddings compared to non-regularized word embeddings. We describe a practical procedure for deriving such regularized embeddings through Cholesky factorization. We also show that the SCM with regularized word embeddings significantly outperforms the WMD on text classification and is over 10,000 times faster.
An overview of pulmonary infections due to rapidly growing mycobacteria in South Asia and impressions from a subtropical region

Background: Rapidly growing mycobacteria (RGM) comprise nearly half of the validated species of nontuberculous mycobacteria (NTM) and have been reported to have a higher incidence in Asia as compared to Europe and America. There is limited information on RGM infections from South Asia. Hence, the present study aimed to ascertain the incidence of pulmonary infections due to RGM in Delhi and to review the status of available information on the prevalence of RGM in South Asia, a region endemic for tuberculosis. Methods: We analyzed 933 mycobacterial isolates obtained from pulmonary samples in Delhi and performed species identification by polymerase chain reaction (PCR)-restriction analysis (restriction fragment length polymorphism) and line probe assay. Drug susceptibility testing (DST) was performed by broth microdilution method. We also reviewed reports available on pulmonary infections in South Asia, attributed to RGM. Results: Of the 933 mycobacterial isolates studied, NTM were identified in 152 (16.3%). Of these, 65/152 (42.8%) were RGM comprising Mycobacterium fortuitum (34/65; 52.3%), Mycobacterium abscessus (25/65; 38.5%), Mycobacterium chelonae (3/65; 4.61%), Mycobacterium mucogenicum (2/65; 3.1%), and Mycobacterium smegmatis (1/65; 1.5%). On applying the American Thoracic Society/Infectious Diseases Society of America guidelines, 11/25 (44%) M. abscessus, 3/3 (100%) M. chelonae, and both isolates of M. mucogenicum were found to be clinically relevant. DST revealed that maximum susceptibility of the RGM was seen to linezolid, clarithromycin, and amikacin. Conclusions: Of the RGM isolated in the present study, 16/65 (24.6%) were found to be clinically relevant. Hence, it is important to recognize these organisms as potential pathogens to identify patients with RGM disease to initiate appropriate therapy.
A Case Involving Hypnosis/Guided Imagery as an Adjunct Treatment of a Fractured Wrist

A 26year-old football player fell on his . left wrist when he.attempted a diving catch on artificial turf in the end zone in an NFL preseason game August 13, 1994. He experienced immediate wrist pain but remained in the game for another play, in which he attempted to catch another pass. The player was examined briefly on the sidelines, and more extensively in the locker room at halftime. The wrist was taped heavily, and the receiver did not re-enter the game. Following the game his complaints were minimal. He was placed in an adhesive tape/cast wadding compression wrap. The next morning the player presented with exquisite tenderness on the radial side of the injured wrist. He had decreased motion secondary to pain. Most of the pain was localized in the "anatomical snuff-box" of the wrist. The player had mild to moderate swelling. He was treated with ice and placed in a wrist immobilizer.
Women Are at a Higher Risk of Chronic Metabolic Diseases Compared to Men With Increasing Body Mass Index in China

Background: Chronic non-communicable diseases are the major causes of mortality in the world. However, few studies have investigated the association between multi-categories BMI and chronic diseases from perspective of sex stratification. This study aimed to investigate the risk of chronic diseases at different BMI levels, and to further explore whether BMI-health risk associations differ by sex. Methods: In total, 21,134 participants aged 19–65 years (60.4% men) from the Tianjin People's Hospital, Tianjin Union Medical Center-Health Management Center were recruited for this cross-sectional study. Sex-specific percentiles of BMI were calculated and divided into 11 categories according to the 2000 CDC growth charts. Health-related indicators, such as hyperglycemia, hypertension, non-alcoholic fatty liver diseases (NAFLD), hyperuricemia, etc., were used as dependent variables in this study. Statistical differences were tested by unpaired Mann–Whitney U-test and chi-squared test. Logistic regression models were used to examine the associations between BMI and health-related indicators. Results: The risk of hyperglycemia (OR: 1.67, 95%CI: 1.23–2.29), NAFLD (OR: 2.22, 95%CI: 1.74–2.85), hypertriglyceridemia (OR: 1.65, 95%CI: 1.28–2.12), and hyperuricemia (OR: 1.39, 95%CI: 1.12–1.72) in men began to increase significantly when BMI was in the range of 22.59–23.89 kg/m2. However, in women, the risk of hyperglycemia (OR: 3.02, 95%CI: 1.25-8.98) and hyperuricemia (OR: 1.94, 95%CI: 1.26–3.05) began to increase significantly when BMI was in the range of 22.76–23.62 kg/m2, and the risk of NAFLD (OR: 5.48, 95%CI: 2.49–14.47) began to increase significantly when BMI was in the range of 21.08–21.97 kg/m2. Besides, at the same BMI level, the risk of diseases in women were significantly higher than that in men, especially when BMI > 25 kg/m2. Conclusion: In the Chinese population, the risk of chronic diseases in women were significantly higher than that in men at the same BMI level, especially when BMI was >25 kg/m2. In addition, the risk of chronic diseases began to increase significantly when BMI was >21.97 kg/m2 in women and 23.89 kg/m2 in men. The results indicated that women should be more alert to the risk of chronic diseases caused by the increase of BMI than men.
A Computational Model of the Endothelial to Mesenchymal Transition

Endothelial cells (ECs) form the lining of lymph and blood vessels. Changes in tissue requirements or wounds may cause ECs to behave as tip or stalk cells. Alternatively, they may differentiate into mesenchymal cells (MCs). These processes are known as EC activation and endothelial-to-mesenchymal transition (EndMT), respectively. EndMT, Tip, and Stalk EC behaviors all require SNAI1, SNAI2, and Matrix metallopeptidase (MMP) function. However, only EndMT inhibits the expression of VE-cadherin, PECAM1, and VEGFR2, and also leads to EC detachment. Physiologically, EndMT is involved in heart valve development, while a defective EndMT regulation is involved in the physiopathology of cardiovascular malformations, congenital heart disease, systemic and organ fibrosis, pulmonary arterial hypertension, and atherosclerosis. Therefore, the control of EndMT has many promising potential applications in regenerative medicine. Despite the fact that many molecular components involved in EC activation and EndMT have been characterized, the system-level molecular mechanisms involved in this process have not been elucidated. Toward this end, hereby we present Boolean network model of the molecular involved in the regulation of EC activation and EndMT. The simulated dynamic behavior of our model reaches fixed and cyclic patterns of activation that correspond to the expected EC and MC cell types and behaviors, recovering most of the specific effects of simple gain and loss-of-function mutations as well as the conditions associated with the progression of several diseases. Therefore, our model constitutes a theoretical framework that can be used to generate hypotheses and guide experimental inquiry to comprehend the regulatory mechanisms behind EndMT. Our main findings include that both the extracellular microevironment and the pattern of molecular activity within the cell regulate EndMT. EndMT requires a lack of VEGFA and sufficient oxygen in the extracellular microenvironment as well as no FLI1 and GATA2 activity within the cell. Additionally Tip cells cannot undergo EndMT directly. Furthermore, the specific conditions that are sufficient to trigger EndMT depend on the specific pattern of molecular activation within the cell.