Miracle-dz/UniFAR-FTUs huggingface dataset:
epithelial ovarian cancer risk wide analysis of common copy number variation and - Genome

: 250 words; Main Text: 4,626 words (4,000 Limit); 3 ABSTRACT Background Germline DNA copy number variation (CNV) is a ubiquitous source of genetic variation and remains largely unexplored in association with epithelial ovarian cancer (EOC) risk. Methods CNV was quantified in the DNA of approximately 3500 cases and controls genotyped with the Illumina 610k and HumanOmni2.5M arrays. We performed a genome ‐ wide association study of common (>1%) CNV regions (CNVRs) with EOC and high-grade serous (HGSOC) risk and, using The Cancer Genome Atlas (TCGA), performed in silico analyses of tumor-gene expression. Results Three CNVRs were associated (P<0.01) with EOC risk: two large (~100kb) regions within the 610k set and one small (<5kb) region with the higher resolution 2.5M data. Large CNVRs included a duplication at LILRA6 (OR=2.57, P=0.001) and a deletion at CYP2A7 (OR=1.90, P=0.007) that was strongly associated with HGSOC risk (OR=3.02, P=8.98x10 -5 ). Somatic CYP2A7 alterations correlated with EGLN2 expression in tumors (P=2.94x10 -47 ). An intronic ERBB4/HER4 deletion was associated with reduced EOC risk (OR=0.33, P=9.5x10 -2 ) and somatic deletions correlated with ERBB4 downregulation (P=7.05x10 -5 ). Five CNVRs were associated with HGSOC including two reduced-risk deletions: one at 1p36.33 (OR=0.28, P=0.001) that correlated with lower CDKIIA expression in TCGA tumors (P=2.7x10 -7 ), and another at 8p21.2 (OR=0.52, P=0.002) that was present somatically where it correlated with lower GNRH1 expression (P=5.9x10 -5 ). Conclusions Though CNV appears to not EOC of two genotyping platforms. Four case ‐ control studies from used the Illumina 610 quad Beadchip Array© (“610k”). An independent sample of cases and controls from was genotyped on the Illumina Beadchip© Both GWAS sets included patients with incident, pathologically and false positive CNV calls can also limit statistical power. Though multiple detection algorithms are often used to increase sensitivity, we opted to use PennCNV alone which called ~90% of all variants detected using four algorithms in a previous study (10). We controlled for false positives at multiple stages in our analytic pipeline, including stringent QC control of logRatio, BAF, and sample outliers which excluded approximately 25% of samples. Future studies should include technical validation of CNV such as quantitative PCR and this may also allow more permissive QC criterion to be used to increase sample size. Considering these limitations, we reported EOC risk associations that reached a P<0.01 threshold and did not adjust for multiple testing. As a discovery study, it was preferential to reduce type II error – and avoid missing possibly important findings – at the expense of increased type I error.
. The Densest $k$-Subhypergraph Problem. SIAM Journal on Discrete Mathematics , 32 (2),

The densest k -subgraph (D k S) problem and its corresponding minimization problem smallest p -edge subgraph (S p ES) have come to play a central role in approximation algorithms. This is due both to their practical importance and to their usefulness as a tool for solving and establishing approximation bounds for other problems. These two problems are not well understood, and it is widely believed that they do not admit a subpolynomial approximation ratio (although the best-known hardness results do not rule this out). In this paper we generalize both D k S and S p ES from graphs to hypergraphs. We consider the densest k -subhypergraph (D k SH) problem (given a hypergraph ( V,E ), ﬁnd a subset W ⊆ V of k vertices so as to maximize the number of hyperedges contained in W ), and deﬁne the minimum p -union (M p U) problem (given a hypergraph, choose p of the hyperedges so as to minimize the number of vertices in their union). We focus in particular on the case where all hyperedges have size 3, as this is the simplest nongraph setting. For this case we provide an O ( n 4(4 −√ 3) / 13+ (cid:15) ) < O ( n 0 . 697831+ (cid:15) )-approximation (for arbitrary constant (cid:15) > 0) for D k SH and an ˜ O ( n 2 / 5 )-approximation for M p U. We also give an O ( √ m )-approximation for M p U in general hypergraphs. Finally, we examine the interesting special case of interval hypergraphs (instances where the vertices are a subset of the natural numbers and the hyperedges are intervals of the line) and prove that both problems admit an exact polynomial-time solution on these instances.
New damage index based on least squares distance for damage diagnosis in steel girder of bridge's deck

An increase in the number of incidents involving steel bridges in recent years has made it necessary to analyze their safety. So far, researchers have proposed several methods to determine the dynamic behavior of steel bridges, distinguish changes in their behavior, extract dynamic specification, and detect damage in them. In this study, an applied algorithm and a new damage index based on the time–frequency matrix and least squares distance have been proposed and used to determine the damage and detect its location. The performance of the suggested index to identify the damage in the numerical model of two steel beams with 31 damage scenarios was investigated. In addition, the LS‐Index was assessed for damage diagnosis in the analytical model of the College Bridge. The finite element model of the College Bridge was created based on extensive in situ measurements and laboratory tests, and 10 different damage scenarios were considered. The results showed the ability of the proposed index to identify damage in the girder of the bridge. The obtained results suggested that the index can also report the damage simultaneously. The performance of the proposed method was also investigated using noise‐polluted signals with three different signal‐to‐noise ratios. Based on the calculations, the index had a good performance. To diagnose damage, there is no need to measure the input loading. Besides, it is not necessary to create a numerical model. Owing to the applicability, accuracy, and high ability, the damage index can be used to detect damage in the steel girders of bridge decks.
Analytical prediction of optimal split ratio of consequent-pole permanent magnet machines

For consequent-pole permanent magnet (CPM) machines, the permanent magnet (PM)-arc ratio and split ratio have great influence on the output torque. This study establishes general relationship of the output torque/torque density with PM-arc ratio and split ratio considering the effects of slot/pole number combinations and the tooth-tips. In addition, both the optimal PM-arc ratio and split ratio for maximising the torque density are analytically derived and confirmed by finite-element analysis. It is found that the optimal PM-arc ratio is determined by the ratio of the airgap length to the PM thickness. It is demonstrated that the optimal split ratio depends on the ratio of average airgap flux density below one slot to maximum flux density in the stator iron k B γ and the number of slots for the slots per pole q ≤ 1 . 5 or the number of poles for q > 1 . 5 . Moreover, it is found that the optimal split ratio decreases with the ratio k B γ , which depends on the PM thickness and PM-arc ratio, and it is reduced when the effect of the stator tooth-tips is taken into consideration. The experimental results on the 12-slot/10-pole CPM machine are given to verify these analyses.
s -HANKEL HYPERMATRICES AND 2 × 2 DETERMINANTAL IDEALS

We introduce the concept of an s -Hankel hypermatrix, which generalizes both Hankel matrices and generic hypermatrices. We study two determinantal ideals associated to an s -Hankel hypermatrix: the ideal I ⟨ s;t ⟩ generated by certain 2 (cid:2) 2 slice minors, and the ideal e I ⟨ s;t ⟩ generated by certain 2 (cid:2) 2 generalized minors. We describe the structure of these two ideals, with particular attention to the primary decomposition of I ⟨ s;t ⟩ , and provide the explicit list of minimal primes for large values of s . Finally we give some geometrical interpretations and generalize a theorem of Watanabe.
RetrieverTTS: Modeling Decomposed Factors for Text-Based Speech Insertion

This paper proposes a new"decompose-and-edit"paradigm for the text-based speech insertion task that facilitates arbitrary-length speech insertion and even full sentence generation. In the proposed paradigm, global and local factors in speech are explicitly decomposed and separately manipulated to achieve high speaker similarity and continuous prosody. Specifically, we proposed to represent the global factors by multiple tokens, which are extracted by cross-attention operation and then injected back by link-attention operation. Due to the rich representation of global factors, we manage to achieve high speaker similarity in a zero-shot manner. In addition, we introduce a prosody smoothing task to make the local prosody factor context-aware and therefore achieve satisfactory prosody continuity. We further achieve high voice quality with an adversarial training stage. In the subjective test, our method achieves state-of-the-art performance in both naturalness and similarity. Audio samples can be found at https://ydcustc.github. io/retrieverTTS-demo/.
Turbulent magnetic helicity fluxes in solar convective zone

Combined action of helical motions of plasma (the kinetic α effect) and non-uniform (differential) rotation is a key dynamo mechanism of solar and galactic large-scale magnetic fields. Dynamics of magnetic helicity of small-scale fields is a crucial mechanism in a nonlinear dynamo saturation where turbulent magnetic helicity fluxes allow to avoid catastrophic quenching of the α effect. The convective zone of the Sun and solar-like stars as well as galactic discs are the source for production of turbulent magnetic helicity fluxes. In the framework of the mean-field approach and the spectral τ approximation, we derive turbulent magnetic helicity fluxes using the Coulomb gauge in a density-stratified turbulence. The turbulent magnetic helicity fluxes include non-gradient and gradient contributions. The non-gradient magnetic helicity flux is proportional to a nonlinear effective velocity (which vanishes in the absence of the density stratification) multiplied by small-scale magnetic helicity, while the gradient contributions describe turbulent magnetic diffusion of the small-scale magnetic helicity. In addition, the turbulent magnetic helicity fluxes contain source terms proportional to the kinetic α effect or its gradients, and also contributions caused by the large-scale shear (solar differential rotation). We have demonstrated that the turbulent magnetic helicity fluxes due to the kinetic α effect and its radial derivative in combination with the nonlinear magnetic diffusion of the small-scale magnetic helicity are dominant in the solar convective zone.
Probing parity-odd bispectra with anisotropies of GW V modes

It is well known that non-trivial squeezed tensor bispectra can lead to anisotropies in the inflationary stochastic gravitational wave (GW) background, providing us with an alternative and complementary window to primordial non-Gaussianities (NGs) with respect to the CMB. Previous works have highlighted the detection prospects of parity-even tensor NGs via the GW I-mode anisotropies. In this work we extend this by analysing for the first time the additional information carried by GW V-mode anisotropies due to squeezed NGs. We show that GW V modes allow us to probe parity-odd squeezed 〈 tts 〉 and 〈 ttt 〉 bispectra. These bispectra break parity at the non-linear level and can be introduced by allowing alternative symmetry breaking patterns during inflation, like those comprised in solid inflation. Considering a BBO-like experiment, we find that a non-zero detection of squeezed 〈 tts 〉 parity-odd bispectra in the V modes dipole is possible without requiring any short-scale enhancement of the GW power spectrum amplitude over the constraints set by the CMB. We also briefly discuss the role of V-CMB cross-correlations. Our work can be extended in several directions and motivates a systematic search for polarized GW anisotropies in the next generations of GW experiments.
Tensor Recovery Based on a Novel Non-Convex Function Minimax Logarithmic Concave Penalty Function

Non-convex relaxation methods have been widely used in tensor recovery problems, compared with convex relaxation methods, and can achieve better recovery results. In this paper, a new non-convex function, Minimax Logarithmic Concave Penalty (MLCP) function, is proposed, and some of its intrinsic properties are analyzed, among which it is interesting to find that the Logarithmic function is an upper bound of the MLCP function. The proposed function is generalized to tensor cases, yielding tensor MLCP and weighted tensor $L\gamma $ -norm. Consider that its explicit solution cannot be obtained when applying it directly to the tensor recovery problem. Therefore, the corresponding equivalence theorems to solve the such problem are given, namely, tensor equivalent MLCP theorem and equivalent weighted tensor $L\gamma $ -norm theorem. In addition, we propose two EMLCP-based models for classic tensor recovery problems, namely low-rank tensor completion (LRTC) and tensor robust principal component analysis (TRPCA), and design proximal alternate linearization minimization (PALM) algorithms to solve them individually. Furthermore, based on the Kurdyka-Åasiwicz property, it is proved that the solution sequence of the proposed algorithm has a finite length and converges to the critical point globally. Finally, extensive experiments show that the proposed algorithm achieves good results, and it is confirmed that the MLCP function is indeed better than the Logarithmic function in the minimization problem, which is consistent with the analysis of theoretical properties.
Involuntary Stabilization in Discrete-Event Physical Human–Robot Interaction

Robots are used by humans not only as tools but also to interactively assist and cooperate with humans, thereby forming physical human–robot interactions. In these interactions, there is a risk that a feedback loop causes unstable force interaction, in which force escalation exposes a human to danger. Previous studies have analyzed the stability of voluntary interaction but have neglected involuntary behavior in the interaction. In contrast to the previous studies, this study considered the involuntary behavior: a human’s force reproduction bias for discrete-event human–robot force interaction. We derived an asymptotic stability condition based on a mathematical bias model and found that the bias asymptotically stabilizes a human’s implicit equilibrium point far from the implicit equilibrium point and destabilizes the point near the point. The bias model, convergence of the interaction toward the implicit equilibrium point, and divergence around the point were consistently verified via behavioral experiments under three kinds of interactions using three different body parts: 1) a hand finger; 2) wrist; and 3) foot. Our results imply that humans implicitly secure a stable and close relationship between themselves and robots with their involuntary behavior.
Comparison of Speech Representations for the MOS Prediction System

Automatic methods to predict Mean Opinion Score (MOS) of listeners have been researched to assure the quality of Text-to-Speech systems. Many previous studies focus on architectural advances (e.g. MBNet, LDNet, etc.) to capture relations between spectral features and MOS in a more effective way and achieved high accuracy. However, the optimal representation in terms of generalization capability still largely remains unknown. To this end, we compare the performance of Self-Supervised Learning (SSL) features obtained by the wav2vec framework to that of spectral features such as magnitude of spectrogram and melspectrogram. Moreover, we propose to combine the SSL features and features which we believe to retain essential information to the automatic MOS to compensate each other for their drawbacks. We conduct comprehensive experiments on a large-scale listening test corpus collected from past Blizzard and Voice Conversion Challenges. We found that the wav2vec feature set showed the best generalization even though the given ground-truth was not always reliable. Furthermore, we found that the combinations performed the best and analyzed how they bridged the gap between spectral and the wav2vec feature sets.
DigiTac: A DIGIT-TacTip Hybrid Tactile Sensor for Comparing Low-Cost High-Resolution Robot Touch

Deep learning combined with high-resolution tactile sensing could lead to highly capable dexterous robots. However, progress is slow because of the specialist equipment and expertise. The DIGIT tactile sensor offers low-cost entry to high-resolution touch using GelSight-type sensors. Here we customize the DIGIT to have a 3D-printed sensing surface based on the TacTip family of soft biomimetic optical tactile sensors. The DIGIT-TacTip (DigiTac) enables direct comparison between these distinct tactile sensor types. For this comparison, we introduce a tactile robot system comprising a desktop arm, mounts and 3D-printed test objects. We use tactile servo control with a PoseNet deep learning model to compare the DIGIT, DigiTac and TacTip for edge- and surface-following over 3D-shapes. All three sensors performed similarly at pose prediction, but their constructions led to differing performances at servo control, offering guidance for researchers selecting or innovating tactile sensors. All hardware and software for reproducing this study will be openly released.
Learning from human perception to improve automatic speaker verification in style-mismatched conditions

Our prior experiments show that humans and machines seem to employ different approaches to speaker discrimination, especially in the presence of speaking style variability. The experiments examined read versus conversational speech. Listeners focused on speaker-specific idiosyncrasies while"telling speakers together", and on relative distances in a shared acoustic space when"telling speakers apart". However, automatic speaker verification (ASV) systems use the same loss function irrespective of target or non-target trials. To improve ASV performance in the presence of style variability, insights learnt from human perception are used to design a new training loss function that we refer to as"CllrCE loss". CllrCE loss uses both speaker-specific idiosyncrasies and relative acoustic distances between speakers to train the ASV system. When using the UCLA speaker variability database, in the x-vector and conditioning setups, CllrCE loss results in significant relative improvements in EER by 1-66%, and minDCF by 1-31% and 1-56%, respectively, when compared to the x-vector baseline. Using the SITW evaluation tasks, which involve different conversational speech tasks, the proposed loss combined with self-attention conditioning results in significant relative improvements in EER by 2-5% and minDCF by 6-12% over baseline. In the SITW case, performance improvements were consistent only with conditioning.
Low Altitude 3-D Coverage Performance Analysis in Cell-Free Distributed Collaborative Massive MIMO Systems

To improve the poor performance of distributed operation and non-scalability of centralized operation in traditional cell-free massive MIMO, we propose a cell-free distributed collaborative (CFDC) massive multiple-input multiple-output (MIMO) system based on a novel two-layer model to take advantages of the distributed cloud-edge-end collaborative architecture in beyond 5G (B5G) internet of things (IoT) environment to provide strong flexibility and scalability. We further ultilize the proposed CFDC massive MIMO system to support the low altitude three-dimensional (3-D) coverage scenario with unmanned aerial vehicles (UAVs), while accounting for 3-D Rician channel estimation, user-centric association and different scalable receiving schemes. Since coexisted UAVs and ground users (GUEs) cause greater interference, we ultilize user-centric association strategy and minimum-mean-square error (MMSE) channel state information (CSI) estimation to obtain the estimated CSI of UAVs and GUEs. Under the CFDC scenarios, scalable receiving schemes as maximum ratio combing (MRC), partial zero-forcing (P-ZF) and partial minimum-mean-square error (P-MMSE) can be performed at edge servers and the closed-form expressions for uplink spectral efficiency (SE) are derived. Based on the derived expressions, we propose an efficient power control algorithm by solving a multi-objective optimization problem (MOOP) between maximizing the average SE of UAVs and GUEs simultaneously with Deep Q-Network (DQN). Numerical results verify the accuracy of the derived closed-form expressions and the effectiveness of the coexisted UAVs and GUEs transmission scheme in CFDC massive MIMO systems. The SE analysis under various system parameters offers numerous flexibilities for system optimization.
X-ray computed 𝜇-tomography for thecharacterization of optical fibers

In spite of their ubiquitous applications, the characterization of glass fibers by means of all-optical techniques is still facing some limitations. Recently, X-ray absorption has been proposed as a method for visualizing the inner structure of both standard and microstructure optical fibers. Here, we exploit X-ray absorption as nondestructive technique for the characterization of optical glass fibers. Starting from absorption contrast X-ray computed micro-tomography measurements, we obtain information about the spatial profile of the fiber refractive index at optical frequencies. We confirm the validity of our approach by comparing its results with complementary characterization techniques, based on electron spectroscopy or multiphoton microscopy.
NeuRIS: Neural Reconstruction of Indoor Scenes Using Normal Priors

Reconstructing 3D indoor scenes from 2D images is an important task in many computer vision and graphics applications. A main challenge in this task is that large texture-less areas in typical indoor scenes make existing methods struggle to produce satisfactory reconstruction results. We propose a new method, named NeuRIS, for high quality reconstruction of indoor scenes. The key idea of NeuRIS is to integrate estimated normal of indoor scenes as a prior in a neural rendering framework for reconstructing large texture-less shapes and, importantly, to do this in an adaptive manner to also enable the reconstruction of irregular shapes with fine details. Specifically, we evaluate the faithfulness of the normal priors on-the-fly by checking the multi-view consistency of reconstruction during the optimization process. Only the normal priors accepted as faithful will be utilized for 3D reconstruction, which typically happens in the regions of smooth shapes possibly with weak texture. However, for those regions with small objects or thin structures, for which the normal priors are usually unreliable, we will only rely on visual features of the input images, since such regions typically contain relatively rich visual features (e.g., shade changes and boundary contours). Extensive experiments show that NeuRIS significantly outperforms the state-of-the-art methods in terms of reconstruction quality.