Large Convolutional Model Tuning via Filter Subspace

Efficient fine-tuning methods are critical to address the high computational and parameter complexity while adapting large pre-trained models to downstream tasks. Our study is inspired by prior research that represents each convolution filter as a linear combination of a small set of filter subspace elements, referred to as filter atoms. In this paper, we propose to fine-tune pre-trained models by adjusting only filter atoms, which are responsible for spatial-only convolution, while preserving spatially-invariant channel combination knowledge in atom coefficients. In this way, we bring a new filter subspace view for model tuning. Furthermore, each filter atom can be recursively decomposed as a combination of another set of atoms, which naturally expands the number of tunable parameters in the filter subspace. By only adapting filter atoms constructed by a small number of parameters, while maintaining the rest of model parameters constant, the proposed approach is highly parameter-efficient. It effectively preserves the capabilities of pre-trained models and prevents overfitting to downstream tasks. Extensive experiments show that such a simple scheme surpasses previous tuning baselines for both discriminate and generative tasks

LDMNet: Low Dimensional Manifold Regularized Neural Networks

Deep neural networks have proved very successful on archetypal tasks for which large training sets are available, but when the training data are scarce, their performance suffers from overfitting. Many existing methods of reducing overfitting are data-independent, and their efficacy is often limited when the training set is very small. Data-dependent regularizations are mostly motivated by the observation that data of interest lie close to a manifold, which is typically hard to parametrize explicitly and often requires human input of tangent vectors. These methods typically only focus on the geometry of the input data, and do not necessarily encourage the networks to produce geometrically meaningful features. To resolve this, we propose a new framework, the Low-Dimensional-Manifold-regularized neural Network (LDMNet), which incorporates a feature regularization method that focuses on the geometry of both the input data and the output features. In LDMNet, we regularize the network by encouraging the combination of the input data and the output features to sample a collection of low dimensional manifolds, which are searched efficiently without explicit parametrization. To achieve this, we directly use the manifold dimension as a regularization term in a variational functional. The resulting Euler-Lagrange equation is a Laplace-Beltrami equation over a point cloud, which is solved by the point integral method without increasing the computational complexity. We demonstrate two benefits of LDMNet in the experiments. First, we show that LDMNet significantly outperforms widely-used network regularizers such as weight decay and DropOut. Second, we show that LDMNet can be designed to extract common features of an object imaged via different modalities, which proves to be very useful in real-world applications such as cross-spectral face recognition

Shear viscosity coefficient of magnetized QCD medium with anomalous magnetic moments near chiral phase transition

We study the properties of the shear viscosity coefficient of quark matter near the chiral phase transition at finite temperature and chemical potential, and the kinds of high temperature, high density and strong magnetic field background might be generated by high-energy heavy ion collisions. The strong magnetic field induces anisotropy, that is, the quantization of Landau energy levels in phase space. If the magnetic field is strong enough, it will interfere with significant QCD phenomena, such as the generation of dynamic quark mass, which may affect the transport properties of quark matter. The inclusion of the anomalous magnetic moments (AMM) of the quarks at finite density into the NJL model gives rise to additional spin polarization magnetic effects. As the inclusion of AMM of the quarks leads to inverse magnetic catalysis around the transition temperature, we will systematically study the thermodynamic phase transition characteristics of shear viscosity coefficient in QCD media near the phase boundary. The shear viscosity coefficient of the dissipative fluid system can be decomposed into five different components as the strong magnetic field exists. The influences of the order of chiral phase transition and the critical endpoint on dissipative phenomena in such a magnetized medium are quantitatively investigated. It is found that ${\eta}_{1}$, ${\eta}_{2}$, ${\eta}_{3}$, and ${\eta}_{4}$ all increase with temperature. For first-order phase transitions, ${\eta}_{1}$, ${\eta}_{2}$, ${\eta}_{3}$, and ${\eta}_{4}$ exhibit discontinuous characteristics.Comment: 22 pages, 10 figure

Calcium and Calmodulin Involve in Mycorrhizal and Root Development in Trifoliate Orange Colonized by Rhizophagus intraradices

A pot experiment was made to study effects of ethylene glycol tetraacetic acid (EGTA, an inhibitor of Ca2+) and trifluoperazine (TFP, an inhibitor of calmodulin (CaM) on mycorrhizal colonization, growth performance, and chlorophyll, sucrose and glucose concentrations of four-month-old trifoliate orange (Poncirus trifoliata) seedlings under mycorrhization with Rhizophagus intraradices. Exogenous EGTA and TFP notably inhibited root mycorrhizal colonization, and the addition of EGTA also decreased soil hyphal length. In general, EGTA treatment decreased but TFP increased easily extractable glomalin-related soil protein (EE-GRSP) and total GRSP (T-GRSP) concentrations. In addition, EGTA and TFP applications generally significantly inhibited growth performance (height, stem diameter, leaf number, and shoot and root biomass), root traits (total length, surface area, volume, and number of 1st, 2nd and 3rd order lateral root), and chlorophyll a,b and a+b concentrations, the mycorrhizal inoculation generally reversed the negative effects and markedly increased these variables, irrespective of whether the seedlings were applied by inhibitors or not. EGTA and TFP treatments generally inhibited sucrose and glucose levels of leaf and root, except that TFP application notably increased root glucose in AM and non-AM seedlings. AMF inoculation resulted in carbohydrate modification: decrease in leaf sucrose, increase in root sucrose and leaf glucose, as well increase in root glucose under no-inhibitor and EGTA conditions and decrease in root glucose under TFP. It suggests that Ca2+ and CaM were involved in mycorrhizal and root development in trifoliate orange

Experiment Research on Deformation Mechanism of CNT Film Material

Nanometer composite usually has multilevel structure, and deformation mechanism of its multilevel structure is the hot spot at present. The paper studies deformation mechanism of multilevel structure of CNT film material under tension loading and its influence on film mechanical properties by jointing multiscale experiment methods such as tensile test, digital image correlation, SEM observation, and in situ micro-Raman spectroscopy. The result shows that, during film loading process, the deformation of CNTs inside the film endures elastic elongation and glide successively, with very small axial elongation, which is about 7% of film deformation; the deformation of CNT bundle network structure endures deformation mechanism such as CNT bundle extension, rotation, and glide, and this structure deformation occupies about 93% of film deformation that large structure deformation makes CNT film have good toughness; during film loading process, the formation of CNT bundle long chain and glide mechanism in the chains help to improve film strength and toughness

Exploiting Polarized Material Cues for Robust Car Detection

Car detection is an important task that serves as a crucial prerequisite for many automated driving functions. The large variations in lighting/weather conditions and vehicle densities of the scenes pose significant challenges to existing car detection algorithms to meet the highly accurate perception demand for safety, due to the unstable/limited color information, which impedes the extraction of meaningful/discriminative features of cars. In this work, we present a novel learning-based car detection method that leverages trichromatic linear polarization as an additional cue to disambiguate such challenging cases. A key observation is that polarization, characteristic of the light wave, can robustly describe intrinsic physical properties of the scene objects in various imaging conditions and is strongly linked to the nature of materials for cars (e.g., metal and glass) and their surrounding environment (e.g., soil and trees), thereby providing reliable and discriminative features for robust car detection in challenging scenes. To exploit polarization cues, we first construct a pixel-aligned RGB-Polarization car detection dataset, which we subsequently employ to train a novel multimodal fusion network. Our car detection network dynamically integrates RGB and polarization features in a request-and-complement manner and can explore the intrinsic material properties of cars across all learning samples. We extensively validate our method and demonstrate that it outperforms state-of-the-art detection methods. Experimental results show that polarization is a powerful cue for car detection.Comment: Accepted by AAAI 202

N-(2-Chloro­phen­yl)-2-(4,6-dimethyl­pyrimidin-2-ylsulfan­yl)acetamide

In the title compound, C14H14ClN3OS, the 4,6-dimethyl­pyrimidine ring and the chloro­benzene ring subtend a dihedral angle of 80.0 (2)°. The length of the Csp 2—S bond is significantly shorter than that of the Csp 3—S bond. The crystal structure is stabilized by inter­molecular N—H⋯O, C—H⋯O and C—H⋯N hydrogen bonding, and C—H⋯π inter­actions