Related papers: SCN as a Local Probe of Protein Structural Dynamic…
Semiconductor spin noise spectroscopy (SNS) has emerged as a unique experimental tool that utilizes spin fluctuations to provide profound insight into undisturbed spin dynamics in doped semiconductors and semiconductor nanostructures. The…
The application of surface plasmon resonance (SPR) has transformed the field of study of interactions between a ligand immobilized on the surface of a sensor chip, designated as $L_S$, and an analyte in solution, referred to as $A$. This…
Any time variation in the fine-structure constant alters the ionization history of the universe and therefore changes the pattern of cosmic microwave background fluctuations. We calculate the changes in the spectrum of these fluctuations as…
Protein structural classification (PSC) is a supervised problem of assigning proteins into pre-defined structural (e.g., CATH or SCOPe) classes based on the proteins' sequence or 3D structural features. We recently proposed PSC approaches…
Convolutional layers have long served as the primary workhorse for image classification. Recently, an alternative to convolution was proposed using the Sharpened Cosine Similarity (SCS), which in theory may serve as a better feature…
Millimeter-wave (mmWave) sensing enables privacy-preserving, always-on edge perception, but its measurements are often sparse, temporally irregular, and corrupted by high-frequency noise. Existing mmWave pipelines predominantly rely on…
Conditional t-SNE (ct-SNE) is a recent extension to t-SNE that allows removal of known cluster information from the embedding, to obtain a visualization revealing structure beyond label information. This is useful, for example, when one…
The microscopic arrangement of atoms and molecules is the determining factor in how materials behave and perform. Beyond the long-range periodicity, the local disorder with local structures deviating from the average lattice structure plays…
Semi-supervised change detection (SSCD) aims to detect changes between bi-temporal remote sensing images by utilizing limited labeled data and abundant unlabeled data. Existing methods struggle in complex scenarios, exhibiting poor…
The laser spectroscopy is a fundamental approach for the realisation of traceable optical frequency standards. In the $1.55\,\mu$m wavelength band, widely used in telecommunications, the acetylene is the typical and the most widespread…
Among wurtzite-type ferroelectrics, scandium-doped aluminum nitride (ScAlN) has emerged as a leading candidate for CMOS-compatible low-voltage memory, combining strong spontaneous polarization with process compatibility. A remarkable…
Texture is one of the most-studied visual attribute for image characterization since the 1960s. However, most hand-crafted descriptors are monochromatic, focusing on the gray scale images and discarding the color information. In this…
Although two-stage object detectors have continuously advanced the state-of-the-art performance in recent years, the training process itself is far from crystal. In this work, we first point out the inconsistency problem between the fixed…
We report a comprehensive Cu $K$-edge RIXS investigation of $\rm La_{2-x}Sr_xCuO_4$ (LSCO) for 0$\leq$x$\leq$0.35, stripe-ordered $\rm La_{1.875}Ba_{0.125}CuO_4$ (LBCO), and $\rm La_{2}Cu_{0.96}Ni_{0.04}O_4$ (LCNO) crystals. The RIXS…
Current self-supervised denoising methods for paired noisy images typically involve mapping one noisy image through the network to the other noisy image. However, after measuring the spectral bias of such methods using our proposed Image…
A protein residue network (PRN) is a network of interacting amino acids within a protein. We describe characteristics of a sparser, highly central and more volatile sub-network of a PRN called the short-cut network (SCN), as a protein folds…
We study a large data set of protein structure ensembles of very diverse sizes determined by nuclear magnetic resonance. By examining the distance-dependent correlations in the displacement of residues pairs and conducting finite size…
Contrastive learning, especially self-supervised contrastive learning (SSCL), has achieved great success in extracting powerful features from unlabeled data. In this work, we contribute to the theoretical understanding of SSCL and uncover…
Structure from Motion (SfM) using imagery that involves extreme appearance changes is yet a challenging task due to a loss of feature repeatability. Using feature correspondences obtained by matching densely extracted convolutional neural…
Super-luminous supernovae (SLSNe) are rare events defined as being significantly more luminous than normal terminal stellar explosions. The source of the extra powering needed to achieve such luminosities is still unclear. Discoveries in…