Miracle-dz/UniFAR-FTUs huggingface dataset:
No evidence of associations among body composition and symptoms in persons with multiple sclerosis.

OBJECTIVE To examine the association among body composition, defined as the percentage of body fat mass (BF) and lean mass (LM), bone mineral content (BMC) and bone mineral density (BMD) in the human body and the presentation of symptoms (i.e., fatigue, pain, depression, anxiety, and poor sleep quality) in persons with multiple sclerosis (MS). DESIGN The present study is a secondary analysis of baseline data from a physical activity intervention for persons with MS. The research question was informed by the theory of unpleasant symptoms, namely an examination of the relationship between body composition (i.e., physiologic antecedent) and symptoms in this sample of 77 participants with MS. The association between body composition and symptoms was examined using partial correlation analyses controlling for age, sex, and disability status. RESULTS Participants were 49 ± 9 years of age on average and diagnosed with MS for an average of 12 ± 8 years. Most participants identified as female (74%), reported having a relapsing-remitting disease course (78%), and moderate MS-related disability based on a median (interquartile range) Patient Determined Disease Steps score of 3.0 (3.0). Partial correlation analyses indicate there were no significant correlations observed among body composition (%BF, %LM, BMC, and BMD) and any symptoms (fatigue, pain, depression, anxiety, and sleep quality). CONCLUSION Additional research that examines other physiologic factors as well as psychological and situational factors based on the theory of unpleasant symptoms that might account for symptoms in persons with MS is necessary. This will inform behavioral interventions and rehabilitation programs that target theory-based factors such as correlates of symptoms in MS. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
Essentials of Physical Medicine and Rehabilitation: Review and Self-Assessment

This comprehensive review presents over 700 multiple-choice questions and answers covering the full-range of physical medicine and rehabilitation. It goes beyond standard review texts in providing detailed explanations of the answers with many accompanying illustrations. Can be used as a companion to the text Essentials of Physical Medicine and Rehabilitation, or independently as a review for exams.
The Independent and Interactive Associations of Bilingualism and Sex on Cognitive Performance in Hispanics/Latinos of the Hispanic Community Health Study/Study of Latinos.

Sixty percent of Hispanics/Latinos are bilingual which research suggests may confer certain cognitive advantages. Female sex confers cognitive advantages in verbal learning and memory compared to male sex, regardless of race or ethnicity. Understanding the independent and interactive associations of bilingualism and sex with cognition may aid in predicting cognitive aging in Hispanics/Latinos. We examined baseline (2008-2011) data from the Hispanic Community Health Study/Study of Latinos, a multicenter, prospective community-based study. Our analyses included 6,110 males and females ≥45 years old who self-reported birth and parents' origin outside of the continental US, Spanish as their first language, and were evaluated in Spanish. Bilingualism was assessed along a Likert scale (1 = only Spanish to 4 = English>Spanish) for language proficiency (reading/spoken) and patterns of use (thinking/socializing). Cognitive testing included verbal learning, memory, fluency, and Digit Symbol Substitution (DSS). Linear regression models adjusted for relevant confounders, the complex survey design, and sampling weights. Participants' self-reported language proficiency was Spanish better than English, while patterns of use suggested more Spanish than English. Higher language proficiency was associated with higher performance on all cognitive indices while higher patterns of use associated with higher fluency and DSS scores (p-values < 0.01). Female sex was associated with higher performance on all cognitive indices (p-values < 0.05). There were no significant interactions with bilingualism (regardless of metric) by sex on cognition. For Hispanics/Latinos residing in the continental US and reporting birth and parents' origin elsewhere, bilingualism and female sex have independent cognitive benefits that are important to consider when evaluating cognitive performance.
Increasing incidence of Epstein‐Barr virus–related nasopharyngeal carcinoma in the United States

The incidence of nasopharyngeal carcinoma (NPC) has been historically low in the United States. Although etiological factors differ by histological subtype, Epstein‐Barr virus is accepted as the primary risk factor for nonkeratinizing NPC. In light of the changing epidemiology of viral‐associated cancers, it is important to evaluate the temporal incidence of NPC in the United States.
Gravitational waves from supernova mass loss and natal kicks in close binaries

Some fraction of compact binaries that merge within a Hubble time may have formed from two massive stars in isolation. For this isolated-binary formation channel, binaries need to survive two supernova (SN) explosions in addition to surviving common-envelope evolution. For the SN explosions, both the mass loss and natal kicks change the orbital characteristics, producing either a bound or unbound binary. We show that gravitational waves (GWs) may be produced not only from the core-collapse SN process, but also from the SN mass loss and SN natal kick during the pre-SN to post-SN binary transition. We model the dynamical evolution of a binary at the time of the second SN explosion with an equation of motion that accounts for the finite time-scales of the SN mass loss and the SN natal kick. From the dynamical evolution of the binary, we calculate the GW burst signals associated with the SN natal kicks. We find that such GW bursts may be of interest to future mid-band GW detectors like DECIGO. We also find that the energy radiated away from the GWs emitted due to the SN mass loss and natal kick may be a significant fraction, ${\gtrsim }10{\,{\rm {per\, cent}}}$, of the post-SN binary’s orbital energy. For unbound post-SN binaries, the energy radiated away in GWs tends to be higher than that of bound binaries.
Conditional emergence of classical domain and branching of quantum histories

We outline the Minimalistic Measurement Scheme (MMS) compatible with regular unitary evolution of a closed quantum system. Within this approach, a part of the system becomes informationally isolated (restricted) which leads to a natural emergence of the classical domain. This measurement scenario is a simpler alternative to environment-induced decoherence. In its basic version, MMS involves two ancilla qubits, $A$ and $X$, entangled with each other and with the System $S$. Informational or thermodynamic cost of measurement is represented by $X$-qubit being isolated, i.e. becoming unavailable for future interactions with the rest of the system. Conditional upon this isolation, $A$-qubit, that plays the role of an Apparatus, becomes classical and records the outcome of the measurement. The procedure may be used to perform von Neumann-style projective measurements or generalized ones, that corresponds to Positive-Operator Value Measure (POVM). By repeating the same generalized measurement multiple times with different $A$- and $X$-qubits, one asymptotically approaches the wave function collapse in the basis determined by the premeasurement process. We present a simple result for the total information extracted after $N$ such weak measurements. Building upon MMS, we propose a construction that maps a history of a quantum system onto a set of $A$-qubits. It resembles the Consistent History (CH) formulation of Quantum Mechanics (QM), but is distinct from it, and is built entirely within the conventional QM. In particular, consistency postulate of CH formalism is not automatically satisfied, but rather is an emerging property. Namely, each measurement event corresponds to the branching of mutually exclusive classical realities whose probabilities are additive. In a general case, however, the superposition between different histories is determined by the history density matrix.
Sparse Canonical Correlation Analysis via Concave Minimization

A new approach to the sparse Canonical Correlation Analysis (sCCA)is proposed with the aim of discovering interpretable associations in very high-dimensional multi-view, i.e.observations of multiple sets of variables on the same subjects, problems. Inspired by the sparse PCA approach of Journee et al. (2010), we also show that the sparse CCA formulation, while non-convex, is equivalent to a maximization program of a convex objective over a compact set for which we propose a first-order gradient method. This result helps us reduce the search space drastically to the boundaries of the set. Consequently, we propose a two-step algorithm, where we first infer the sparsity pattern of the canonical directions using our fast algorithm, then we shrink each view, i.e. observations of a set of covariates, to contain observations on the sets of covariates selected in the previous step, and compute their canonical directions via any CCA algorithm. We also introduceDirected Sparse CCA, which is able to find associations which are aligned with a specified experiment design, andMulti-View sCCA which is used to discover associations between multiple sets of covariates. Our simulations establish the superior convergence properties and computational efficiency of our algorithm as well as accuracy in terms of the canonical correlation and its ability to recover the supports of the canonical directions. We study the associations between metabolomics, trasncriptomics and microbiomics in a multi-omic study usingMuLe, which is an R-package that implements our approach, in order to form hypotheses on mechanisms of adaptations of Drosophila Melanogaster to high doses of environmental toxicants, specifically Atrazine, which is a commonly used chemical fertilizer.
Suppressing parametric instabilities in LIGO using low-noise acoustic mode dampers

Interferometric gravitational-wave detectors like LIGO need to be able to measure changes in their arm lengths of order $10^{-18}~$m or smaller. This requires very high laser power in order to raise the signal above shot noise. One significant limitation to increased laser power is an opto-mechanical interaction between the laser field and the detector's test masses that can form an unstable feedback loop. Such parametric instabilities have long been studied as a limiting effect at high power, and were first observed to occur in LIGO in 2014. Since then, passive and active means have been used to avoid these instabilities, though at power levels well below the final design value. Here we report on the successful implementation of tuned, passive dampers to tame parametric instabilities in LIGO. These dampers are applied directly to all interferometer test masses to reduce the quality factors of their internal vibrational modes, while adding a negligible amount of noise to the gravitational-wave output. In accordance with our model, the measured mode quality factors have been reduced by at least a factor of ten with no visible increase in the interferometer's thermal noise level. We project that these dampers should remove most of the parametric instabilities in LIGO when operating at full power, while limiting the concomitant increase in thermal noise to approximately 1%.
A Mutual Bootstrapping Model for Automated Skin Lesion Segmentation and Classification

Automated skin lesion segmentation and classification are two most essential and related tasks in the computer-aided diagnosis of skin cancer. Despite their prevalence, deep learning models are usually designed for only one task, ignoring the potential benefits in jointly performing both tasks. In this paper, we propose the mutual bootstrapping deep convolutional neural networks (MB-DCNN) model for simultaneous skin lesion segmentation and classification. This model consists of a coarse segmentation network (coarse-SN), a mask-guided classification network (mask-CN), and an enhanced segmentation network (enhanced-SN). On one hand, the coarse-SN generates coarse lesion masks that provide a prior bootstrapping for mask-CN to help it locate and classify skin lesions accurately. On the other hand, the lesion localization maps produced by mask-CN are then fed into enhanced–SN, aiming to transfer the localization information learned by mask-CN to enhanced-SN for accurate lesion segmentation. In this way, both segmentation and classification networks mutually transfer knowledge between each other and facilitate each other in a bootstrapping way. Meanwhile, we also design a novel rank loss and jointly use it with the Dice loss in segmentation networks to address the issues caused by class imbalance and hard-easy pixel imbalance. We evaluate the proposed MB-DCNN model on the ISIC-2017 and PH2 datasets, and achieve a Jaccard index of 80.4% and 89.4% in skin lesion segmentation and an average AUC of 93.8% and 97.7% in skin lesion classification, which are superior to the performance of representative state-of-the-art skin lesion segmentation and classification methods. Our results suggest that it is possible to boost the performance of skin lesion segmentation and classification simultaneously via training a unified model to perform both tasks in a mutual bootstrapping way.
Multi-Task Music Representation Learning from Multi-Label Embeddings

This paper presents a novel approach to music representation learning. Triplet loss based networks have become popular for representation learning in various multimedia retrieval domains. Yet, one of the most crucial parts of this approach is the appropriate selection of triplets, which is indispensable, considering that the number of possible triplets grows cubically. We present an approach to harness multi-tag annotations for triplet selection, by using Latent Semantic Indexing to project the tags onto a high-dimensional space. From this we estimate tag-relatedness to select hard triplets. The approach is evaluated in a multi-task scenario for which we introduce four large multi-tag annotations for the Million Song Dataset for the music properties genres, styles, moods, and themes.
Intelligent Bearing Fault Diagnosis with Convolutional Long-Short-Term-Memory Recurrent Neural Network

Fault diagnosis is an important topic both in practice and research. There is an intense pressure on industrial plants to continue reducing unscheduled downtime, performance degradation, and safety hazards, which requires detecting and recovering of potential fault in its early stages. Intelligent fault diagnosis is a promising tool due to its ability in rapidly and efficiently processing collected signals and providing accurate diagnosis results. In the literature, although many studies have developed algorithms for detecting bearing fault, the results have generally been limited to relatively small train/test datasets and the input data has been manipulated (selective features used) in order to reach a high accuracy. In the following paper a Convolutional Long-Short-Term-Memory Recurrent Neural Network (CRNN) is proposed for intelligent fault diagnosis of bearings. The purpose is to introduce an algorithm which takes directly the raw time-series sensor data as the input and detects the health condition of the bearing as the output with a high accuracy and in a short period of time. The method can reach the highest accuracy to the best knowledge of author of the present paper voiding any sort of pre-processing or manipulation of the input data. The paper starts with a brief description of the new approach of diagnosis using a CRNN network, which the author plans to develop and implement for diagnosing bearing faults and concludes with the identification of the most significant advantages of the proposed method as well as a comparison between the proposed method and the other methods in the literature.
Efficient 3D Fully Convolutional Networks for Pulmonary Lobe Segmentation in CT Images

The human lung is a complex respiratory organ, consisting of five distinct anatomic compartments called lobes. Accurate and automatic segmentation of these pulmonary lobes from computed tomography (CT) images is of clinical importance for lung disease assessment and treatment planning. However, this task is challenging due to ambiguous lobar boundaries, anatomical variations and pathological deformations. In this paper, we propose a high-resolution and efficient 3D fully convolutional network to automatically segment the lobes. We refer to the network as Pulmonary Lobe Segmentation Network (PLS-Net), which is designed to efficiently exploit 3D spatial and contextual information from high-resolution volumetric CT images for effective volume-to-volume learning and inference. The PLS-Net is based on an asymmetric encoder-decoder architecture with three novel components: (i) 3D depthwise separable convolutions to improve the network efficiency by factorising each regular 3D convolution into two simpler operations; (ii) dilated residual dense blocks to efficiently expand the receptive field of the network and aggregate multi-scale contextual information for segmentation; and (iii) input reinforcement at each downsampled resolution to compensate for the loss of spatial information due to convolutional and downsampling operations. We evaluated the proposed PLS-Net on a multi-institutional dataset that consists of 210 CT images acquired from patients with a wide range of lung abnormalities. Experimental results show that our PLS-Net achieves state-of-the-art performance with better computational efficiency. Further experiments confirm the effectiveness of each novel component of the PLS-Net.
G-structures and superstrings from the worldsheet

$\mathcal{G}$-structures, where $\mathcal{G}$ is a Lie group, are a uniform characterisation of many differential geometric structures of interest in supersymmetric compactifications of string theories. Calabi-Yau $n$-folds are instances of torsion-free $SU(n)$-structures, while more general structures with non-zero torsion are required for heterotic flux compactifications. Exceptional geometries in dimensions $7$ and $8$ with $\mathcal{G}=G_2$ and $Spin(7)$ also feature prominently in this thesis. We discuss multiple connections between such geometries and the worldsheet theory describing strings on them, especially with respect to their chiral symmetry algebras.
BLOCCS: Block Sparse Canonical Correlation Analysis With Application To Interpretable Omics Integration

We introduce Block Sparse Canonical Correlation Analysis which estimates multiple pairs of canonical directions (together a "block") at once, resulting in significantly improved orthogonality of the sparse directions which, we demonstrate, translates to more interpretable solutions. Our approach builds on the sparse CCA method of (Solari, Brown, and Bickel 2019) in that we also express the bi-convex objective of our block formulation as a concave minimization problem over an orthogonal k-frame in a unit Euclidean ball, which in turn, due to concavity of the objective, is shrunk to a Stiefel manifold, which is optimized via gradient descent algorithm. Our simulations show that our method outperforms existing sCCA algorithms and implementations in terms of computational cost and stability, mainly due to the drastic shrinkage of our search space, and the correlation within and orthogonality between pairs of estimated canonical covariates. Finally, we apply our method, available as an R-package called BLOCCS, to multi-omic data on Lung Squamous Cell Carcinoma(LUSC) obtained via The Cancer Genome Atlas, and demonstrate its capability in capturing meaningful biological associations relevant to the hypothesis under study rather than spurious dominant variations.
Streaming PTAS for Constrained k-Means

We generalise the results of Bhattacharya et al. (Journal of Computing Systems, 62(1):93-115, 2018) for the list-$k$-means problem defined as -- for a (unknown) partition $X_1, . , X_k$ of the dataset $X \subseteq \mathbb{R}^d$, find a list of $k$-center sets (each element in the list is a set of $k$ centers) such that at least one of $k$-center sets $\{c_1, . , c_k\}$ in the list gives an $(1+\varepsilon)$-approximation with respect to the cost function $\min_{\textrm{permutation } \pi} \left[ \sum_{i=1}^{k} \sum_{x \in X_i} ||x - c_{\pi(i)}||^2 \right]$. The list-$k$-means problem is important for the constrained $k$-means problem since algorithms for the former can be converted to PTAS for various versions of the latter. Following are the consequences of our generalisations: - Streaming algorithm: Our $D^2$-sampling based algorithm running in a single iteration allows us to design a 2-pass, logspace streaming algorithm for the list-$k$-means problem. This can be converted to a 4-pass, logspace streaming PTAS for various constrained versions of the $k$-means problem. - Faster PTAS under stability: Our generalisation is also useful in $k$-means clustering scenarios where finding good centers becomes easy once good centers for a few "bad" clusters have been chosen. One such scenario is clustering under stability where the number of such bad clusters is a constant. Using the above idea, we significantly improve the running time of the known algorithm from $O(dn^3) (k \log{n})^{poly(\frac{1}{\beta}, \frac{1}{\varepsilon})}$ to $O \left(dn^3 k^{\tilde{O}_{\beta \varepsilon}(\frac{1}{\beta \varepsilon})} \right)$.
Multi-step Entity-centric Information Retrieval for Multi-Hop Question Answering

Multi-hop question answering (QA) requires an information retrieval (IR) system that can find multiple supporting evidence needed to answer the question, making the retrieval process very challenging. This paper introduces an IR technique that uses information of entities present in the initially retrieved evidence to learn to ‘hop’ to other relevant evidence. In a setting, with more than 5 million Wikipedia paragraphs, our approach leads to significant boost in retrieval performance. The retrieved evidence also increased the performance of an existing QA model (without any training) on the benchmark by 10.59 F1.
Effective Hamiltonian for nickelate oxides 
Nd1−xSrxNiO2

The authors combined the Heyd-Scuseria-Ernzerhof hybrid density functional first-principles calculation and the cluster exact diagonalization to study the strongly correlated electronic structures of the nickelate oxides Nd1\ensuremath{-}xSrxNiO2 and derive the effective one-band Hamiltonian model for the superconductivity.
Symmetry analysis in inflationary cosmology

We approach the cosmological inflation thought symmetries of differential equations. We consider the general inflaton field in a homogeneous Friedmann--Lema\^itre--Robertson--Walker spacetime and with the use of conformal transformations we are able to write the generic algebraic solution for the field equations. We put emphasis on the inflationary models and we show how we can construct new inflationary models from already known models by using symmetry transformations.