arXiv subjects

Min Wu

Publications and source records attributed to Min Wu.

At least 181 records · Page 10Linked to original sources

arXiv2022

RadioSES: mmWave-Based Audioradio Speech Enhancement and Separation System

Speech enhancement and separation have been a long-standing problem, especially with the recent advances using a single microphone. Although microphones perform well in constrained settings, their performance for speech separation decreases in noisy conditions. In this work, we propose RadioSES, an audioradio speech enhancement and separation system that overcomes inherent problems in audio-only systems. By fusing a complementary radio modality, RadioSES can estimate the number of speakers, solve source association problem, separate and enhance noisy mixture speeches, and improve both intelligibility and perceptual quality. We perform millimeter-wave sensing to detect and localize speakers, and introduce an audioradio deep learning framework to fuse the separate radio features with the mixed audio features. Extensive experiments using commercial off-the-shelf devices show that RadioSES outperforms a variety of state-of-the-art baselines, with consistent performance gains in different environmental settings. Compared with the audiovisual methods, RadioSES provides similar improvements (e.g., ~3 dB gains in SiSDR), along with the benefits of lower computational complexity and being less privacy concerning.

eess.AS↗

arXiv2022

On-chip ultra-compact hexagonal boron nitride topological ring-resonator in visible region

Ultra-compact topological ring-resonators with chirality are important devices for quantum optics. However, there are limited demonstrations of chiral resonators, especially in the visible region. We proposed a topological photonic ring-resonator based on hexagonal boron nitride (hBN) valley photonic crystal (VPC). The spin-valley locking effect in VPC allows achieving robust unidirectional transmission of edge states in the visible region (600 nm-650 nm). As a result, a high quality factor (679.3) with a free spectral range of 15.2 nm in the visible region can be achieved in a hBN all-pass filter with a compact size. In addition, we investigated the transmission properties of hBN ring-resonators with different shapes and combinations, confirming the flexibility of designing topological ring-resonators based on this principle. This design can be readily integrated with quantum photonic chips for broad applications.

physics.optics↗

arXiv2022

Magnetotransport due to conductivity fluctuations in non-magnetic ZrTe2 nanoplates

Transition metal dichalcogenides with nontrivial band structures exhibit various fascinating physical properties and have sparked intensively research interest. Here, we performed systematic magnetotransport measurements on mechanical exfoliation prepared ZrTe2 nanoplates. We revealed that the negative longitudinal magnetoresistivity observed at high field region in the presence of parallel electric and magnetic fields could stem from the conductivity fluctuations due to the excess Zr in the nanoplates. In addition, the parametric plot, the planar Hall resistivity as function of the in-plane anisotropic magnetoresistivity, has an ellipse-shaped pattern with shifted orbital center, which further strengthen the evidence for the conductivity fluctuations. Our work provides some useful insights into transport phenomena in topological materials.

cond-mat.mes-hall↗

arXiv2022

Separable-HoverNet and Instance-YOLO for Colon Nuclei Identification and Counting

Nuclear segmentation, classification and quantification within Haematoxylin & Eosin stained histology images enables the extraction of interpretable cell-based features that can be used in downstream explainable models in computational pathology (CPath). However, automatic recognition of different nuclei is faced with a major challenge in that there are several different types of nuclei, some of them exhibiting large intraclass variability. In this work, we propose an approach that combine Separable-HoverNet and Instance-YOLOv5 to indentify colon nuclei small and unbalanced. Our approach can achieve mPQ+ 0.389 on the Segmentation and Classification-Preliminary Test Dataset and r2 0.599 on the Cellular Composition-Preliminary Test Dataset on ISBI 2022 CoNIC Challenge.

eess.IV↗

arXiv2022

Dimensions of certain sets of continued fractions with non-decreasing partial quotients

Let $[a_1(x),a_2(x),a_3(x),\cdots]$ be the continued fraction expansion of $x\in (0,1)$. This paper is concerned with certain sets of continued fractions with non-decreasing partial quotients. As a main result, we obtain the Hausdorff dimension of the set \[\left\{x\in(0,1): a_1(x)\leq a_2(x)\leq \cdots,\ \limsup\limits_{n\to\infty}\frac{\log a_n(x)}{ψ(n)}=1\right\}\] for any $ψ:\mathbb{N}\rightarrow\mathbb{R}^+$ satisfying $ψ(n)\to\infty$ as $n\to\infty$.

math.NT↗

arXiv2021

An Eulerian nonlinear elastic model for compressible and fluidic tissue with radially symmetric growth

Cell proliferation, apoptosis, and myosin-dependent contraction can generate elastic stress and strain in living tissues, which may be dissipated by internal rearrangement through cell topological transition and cytoskeletal reorganization. Moreover, cells and tissues can change their sizes in response to mechanical cues. The present work demonstrates the role of tissue compressibility and internal rearranging activities on its size and mechanics regulation in the context of differential growth induced by a field of growth-promoting chemical factors. We develop a mathematical model based on finite elasticity and growth theory and the reference map techniques to describe the coupled tissue growth and mechanics in the Eulerian frame. We incorporate the tissue rearrangement by introducing a rearranging rate to the reference map evolution, leading to elastic-energy dissipation when tissue growth and deformation are in radial symmetry. By linearizing the model, we show that the stress follows the Maxwell-type viscoelastic relaxation. The rearrangement rate, which we call tissue fluidity, sets the stress relaxation time, and the ratio between the shear modulus and the fluidity sets the tissue viscosity. By nonlinear simulation of growing tissue spheroids and discs with graded growth rates along the radius, we find that the tissue compressibility and fluidity influence their equilibrium size. By comparing the nonlinear simulations with the linear analytical solutions, we show the size change as a nonlinear effect due to the advection of the tissue density flow, which only occurs when both tissue compressibility and fluidity are small. We apply the model to study tumor spheroid growth and epithelial disc growth when a reaction-diffusion process determines the growth-promoting factor field.

physics.bio-ph↗

arXiv2021

On upper and lower fast Knintchine spectra of continued fractions

Let $ψ:\mathbb{N}\to \mathbb{R}^+$ be a function satisfying $ϕ(n)/n\to \infty$ as $n \to \infty$. We investigate from a multifractal analysis point of view the growth rate of the sums $\sum^n_{k=1}\log a_k(x)$ relative to $ψ(n)$, where $[a_1(x),a_2(x), a_3(x)\cdots]$ denotes the continued fraction expansion of $x\in (0,1)$. The upper (resp. lower) fast Khintchine spectrum is defined by the Hausdorff dimension of the set of all points $x$ for which the upper (resp. lower) limit of $\frac{1}{ψ(n)}\sum^n_{k=1}\log a_k(x)$ is $1$. The precise formulas of these two spectra are completely determined, which strengthens a result of Liao and Rams (2016).

math.DS↗

arXiv2021

Multi-Source Video Domain Adaptation with Temporal Attentive Moment Alignment

Multi-Source Domain Adaptation (MSDA) is a more practical domain adaptation scenario in real-world scenarios. It relaxes the assumption in conventional Unsupervised Domain Adaptation (UDA) that source data are sampled from a single domain and match a uniform data distribution. MSDA is more difficult due to the existence of different domain shifts between distinct domain pairs. When considering videos, the negative transfer would be provoked by spatial-temporal features and can be formulated into a more challenging Multi-Source Video Domain Adaptation (MSVDA) problem. In this paper, we address the MSVDA problem by proposing a novel Temporal Attentive Moment Alignment Network (TAMAN) which aims for effective feature transfer by dynamically aligning both spatial and temporal feature moments. TAMAN further constructs robust global temporal features by attending to dominant domain-invariant local temporal features with high local classification confidence and low disparity between global and local feature discrepancies. To facilitate future research on the MSVDA problem, we introduce comprehensive benchmarks, covering extensive MSVDA scenarios. Empirical results demonstrate a superior performance of the proposed TAMAN across multiple MSVDA benchmarks.

cs.CV↗

arXiv2021

Unusual Magnetic Properties in Layered Magnetic Topological Insulator EuSn2As2

EuSn2As2 with layered rhombohedral crystal structure is proposed to be a candidate of intrinsic antiferromagnetic (AFM) topological insulator. Here, we have investigated systematic magnetoresistance (MR) and magnetization measurements on the high quality EuSn2As2 single crystal with the magnetic field both parallel and perpendicular to (00l) plane. Both the kink of magnetic susceptibility and longitudinal resistivity reveal that EuSn2An2 undergoes an AFM transition at TN = 21 K. At T = 2 K, the magnetization exhibits two successive plateaus of ~ 5.6 μB/Eu and ~ 6.6 μB/Eu at the corresponding critical magnetic fields. Combined with the negative longitudinal MR and abnormal Hall resistance, we demonstrate that EuSn2An2 undergoes complicated magnetic transitions from an AFM state to a canted ferromagnetic (FM) state at Hc and then to a polarized FM state at Hs as the magnetic field increase.

cond-mat.mtrl-sci↗

arXiv2021

Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, with around 50 million patients worldwide. Accessible and non-invasive methods of diagnosing and characterising AD are therefore urgently required. Electroencephalography (EEG) fulfils these criteria and is often used when studying AD. Several features derived from EEG were shown to predict AD with high accuracy, e.g. signal complexity and synchronisation. However, the dynamics of how the brain transitions between stable states have not been properly studied in the case of AD and EEG data. Energy landscape analysis is a method that can be used to quantify these dynamics. This work presents the first application of this method to both AD and EEG. Energy landscape assigns energy value to each possible state, i.e. pattern of activations across brain regions. The energy is inversely proportional to the probability of occurrence. By studying the features of energy landscapes of 20 AD patients and 20 healthy age-matched counterparts, significant differences were found. The dynamics of AD patients' brain networks were shown to be more constrained - with more local minima, less variation in basin size, and smaller basins. We show that energy landscapes can predict AD with high accuracy, performing significantly better than baseline models.

q-bio.NC↗

arXiv2021

Time-Series Representation Learning via Temporal and Contextual Contrasting

Learning decent representations from unlabeled time-series data with temporal dynamics is a very challenging task. In this paper, we propose an unsupervised Time-Series representation learning framework via Temporal and Contextual Contrasting (TS-TCC), to learn time-series representation from unlabeled data. First, the raw time-series data are transformed into two different yet correlated views by using weak and strong augmentations. Second, we propose a novel temporal contrasting module to learn robust temporal representations by designing a tough cross-view prediction task. Last, to further learn discriminative representations, we propose a contextual contrasting module built upon the contexts from the temporal contrasting module. It attempts to maximize the similarity among different contexts of the same sample while minimizing similarity among contexts of different samples. Experiments have been carried out on three real-world time-series datasets. The results manifest that training a linear classifier on top of the features learned by our proposed TS-TCC performs comparably with the supervised training. Additionally, our proposed TS-TCC shows high efficiency in few-labeled data and transfer learning scenarios. The code is publicly available at https://github.com/emadeldeen24/TS-TCC.

cs.LG↗

arXiv2021

Tailoring Dzyaloshinskii-Moriya interaction in a transition metal dichalcogenide by dual-intercalation

Dzyaloshinskii-Moriya interaction (DMI) is vital to form various chiral spin textures, novel behaviors of magnons and permits their potential applications in energy-efficient spintronic devices. Here, we realize a sizable bulk DMI in a transition metal dichalcogenide (TMD) 2H-TaS2 by intercalating Fe atoms, which form the chiral supercells with broken spatial inversion symmetry and also act as the source of magnetic orderings. Using a newly developed protonic gate technology, gate-controlled protons intercalation could further change the carrier density and intensely tune DMI via the Ruderman-Kittel-Kasuya-Yosida mechanism. The resultant giant topological Hall resistivity of 1.4 uohm.cm at -5.2V (about 460% of the zero-bias value) is larger than most of the known magnetic materials. Theoretical analysis indicates that such a large topological Hall effect originates from the two-dimensional Bloch-type chiral spin textures stabilized by DMI, while the large anomalous Hall effect comes from the gapped Dirac nodal lines by spin-orbit interaction. Dual-intercalation in 2HTaS2 provides a model system to reveal the nature of DMI in the large family of TMDs and a promising way of gate tuning of DMI, which further enables an electrical control of the chiral spin textures and related electromagnetic phenomena.

cond-mat.mes-hall↗

arXiv2021

Feasibility Study on Intra-Grid Location Estimation Using Power ENF Signals

The Electric Network Frequency (ENF) is a signature of power distribution networks that can be captured by multimedia recordings made in areas where there is electrical activity. This has led to an emergence of several forensic applications based on the use of the ENF signature. Examples of such applications include estimating or verifying the time-of-recording of a media signal and inferring the power grid associated with the location in which the media signal was recorded. In this paper, we carry out a feasibility study to examine the possibility of using embedded ENF traces to pinpoint the location-of-recording of a signal within a power grid. In this study, we demonstrate that it is possible to pinpoint the location-of-recording to a certain geographical resolution using power signal recordings containing strong ENF traces. To this purpose, a high-passed version of an ENF signal is extracted and it is demonstrated that the correlation between two such signals, extracted from recordings made in different geographical locations within the same grid, decreases as the distance between the recording locations increases. We harness this property of correlation in the ENF signals to propose trilateration based localization methods, which pinpoint the unknown location of a recording while using some known recording locations as anchor locations. We also discuss the challenges that need to be overcome in order to extend this work to using ENF traces in noisier audio/video recordings for such fine localization purposes.

eess.SP↗

arXiv2021

On the exponent of convergence of Engel series

For $x\in (0,1)$, let $\langle d_1(x),d_2(x),d_3(x),\cdots \rangle$ be the Engel series expansion of $x$. Denote by $λ(x)$ the exponent of convergence of the sequence $\{d_n(x)\}$, namely \begin{equation*} λ(x)= \inf\left\{s \geq 0: \sum_{n \geq 1} d^{-s}_n(x)<\infty\right\}. \end{equation*} It follows from Erdős, Rényi and Szüsz (1958) that $λ(x) =0$ for Lebesgue almost all $x\in (0,1)$. This paper is concerned with the topological and fractal properties of the level set $\{x\in (0,1): λ(x) =α\}$ for $α\in [0,\infty]$. For the topological properties, it is proved that each level set is uncountable and dense in $(0,1)$. Furthermore, the level set is of the first Baire category for $α\in [0,\infty)$ but residual for $α=\infty$. For the fractal properties, we prove that the Hausdorff dimension of the level set is as follows: \[ \dim_{\rm H} \big\{x \in (0,1): λ(x) =α\big\}=\dim_{\rm H} \big\{x \in (0,1): λ(x) \geqα\big\}= \left\{ \begin{array}{ll} 1-α, & \hbox{$0\leq α\leq1$;} 0, & \hbox{$1<α\leq \infty$.} \end{array} \right. \]

math.NT↗

arXiv2021

Opportunistic Screening of Osteoporosis Using Plain Film Chest X-ray

Osteoporosis is a common chronic metabolic bone disease that is often under-diagnosed and under-treated due to the limited access to bone mineral density (BMD) examinations, Dual-energy X-ray Absorptiometry (DXA). In this paper, we propose a method to predict BMD from Chest X-ray (CXR), one of the most common, accessible, and low-cost medical image examinations. Our method first automatically detects Regions of Interest (ROIs) of local and global bone structures from the CXR. Then a multi-ROI model is developed to exploit both local and global information in the chest X-ray image for accurate BMD estimation. Our method is evaluated on 329 CXR cases with ground truth BMD measured by DXA. The model predicted BMD has a strong correlation with the gold standard DXA BMD (Pearson correlation coefficient 0.840). When applied for osteoporosis screening, it achieves a high classification performance (AUC 0.936). As the first effort in the field to use CXR scans to predict the spine BMD, the proposed algorithm holds strong potential in enabling early osteoporosis screening through routine chest X-rays and contributing to the enhancement of public health.

eess.IV↗

arXiv2021

Cross-domain Joint Dictionary Learning for ECG Inference from PPG

The inverse problem of inferring electrocardiogram (ECG) from photoplethysmogram (PPG) is an emerging research direction that combines the easy measurability of PPG and the rich clinical knowledge of ECG for long-term continuous cardiac monitoring. The prior art for reconstruction using a universal basis has limited fidelity for uncommon ECG waveform shapes due to the lack of rich representative power. In this paper, we design two dictionary learning frameworks, the cross-domain joint dictionary learning (XDJDL) and the label-consistent XDJDL (LC-XDJDL), to further improve the ECG inference quality and enrich the PPG-based diagnosis knowledge. Building on the K-SVD technique, our proposed joint dictionary learning frameworks aim to maximize the expressive power by optimizing simultaneously a pair of signal dictionaries for PPG and ECG with the transforms to relate their sparse codes and disease information. The proposed models are evaluated with 34,000+ ECG/PPG cycle pairs containing a variety of ECG morphologies and cardiovascular diseases. We demonstrate both visually and quantitatively that our proposed frameworks can achieve better inference performance than previous methods, suggesting an encouraging potential for ECG screening using PPG based on the proactive learned PPG-ECG relationship.

eess.SP↗

arXiv2020

Recent Advances in Network-based Methods for Disease Gene Prediction

Disease-gene association through Genome-wide association study (GWAS) is an arduous task for researchers. Investigating single nucleotide polymorphisms (SNPs) that correlate with specific diseases needs statistical analysis of associations. Considering the huge number of possible mutations, in addition to its high cost, another important drawback of GWAS analysis is the large number of false-positives. Thus, researchers search for more evidence to cross-check their results through different sources. To provide the researchers with alternative low-cost disease-gene association evidence, computational approaches come into play. Since molecular networks are able to capture complex interplay among molecules in diseases, they become one of the most extensively used data for disease-gene association prediction. In this survey, we aim to provide a comprehensive and an up-to-date review of network-based methods for disease gene prediction. We also conduct an empirical analysis on 14 state-of-the-art methods. To summarize, we first elucidate the task definition for disease gene prediction. Secondly, we categorize existing network-based efforts into network diffusion methods, traditional machine learning methods with handcrafted graph features and graph representation learning methods. Thirdly, an empirical analysis is conducted to evaluate the performance of the selected methods across seven diseases. We also provide distinguishing findings about the discussed methods based on our empirical analysis. Finally, we highlight potential research directions for future studies on disease gene prediction.

q-bio.QM↗

arXiv2020

Multi-View Collaborative Network Embedding

Real-world networks often exist with multiple views, where each view describes one type of interaction among a common set of nodes. For example, on a video-sharing network, while two user nodes are linked if they have common favorite videos in one view, they can also be linked in another view if they share common subscribers. Unlike traditional single-view networks, multiple views maintain different semantics to complement each other. In this paper, we propose MANE, a multi-view network embedding approach to learn low-dimensional representations. Similar to existing studies, MANE hinges on diversity and collaboration - while diversity enables views to maintain their individual semantics, collaboration enables views to work together. However, we also discover a novel form of second-order collaboration that has not been explored previously, and further unify it into our framework to attain superior node representations. Furthermore, as each view often has varying importance w.r.t. different nodes, we propose MANE+, an attention-based extension of MANE to model node-wise view importance. Finally, we conduct comprehensive experiments on three public, real-world multi-view networks, and the results demonstrate that our models consistently outperform state-of-the-art approaches.

cs.LG↗