DPF-Nutrition: Food Nutrition Estimation via Depth Prediction and Fusion

A reasonable and balanced diet is essential for maintaining good health. With the advancements in deep learning, automated nutrition estimation method based on food images offers a promising solution for monitoring daily nutritional intake and promoting dietary health. While monocular image-based nutrition estimation is convenient, efficient, and economical, the challenge of limited accuracy remains a significant concern. To tackle this issue, we proposed DPF-Nutrition, an end-to-end nutrition estimation method using monocular images. In DPF-Nutrition, we introduced a depth prediction module to generate depth maps, thereby improving the accuracy of food portion estimation. Additionally, we designed an RGB-D fusion module that combined monocular images with the predicted depth information, resulting in better performance for nutrition estimation. To the best of our knowledge, this was the pioneering effort that integrated depth prediction and RGB-D fusion techniques in food nutrition estimation. Comprehensive experiments performed on Nutrition5k evaluated the effectiveness and efficiency of DPF-Nutrition

Solid dispersion-based pellet for colon delivery of tacrolimus through time- and pH-dependent layer coating: preparation, in vitro and in vivo studies

The intent of the present investigation is to develop and evaluate colon-specific coated tacrolimus solid dispersion pellet (SDP) that retards drug release in the stomach and small intestine but progressively releases in the colon. Tacrolimus-SDP was prepared by extrusion-spheronization technology and optimized by the micromeritic properties including flowability, friability, yields and dissolution rate. Subsequently, the pH-dependent layer (Eudragit L30D55) and time-dependent layer (Eudragit NE30D and L30D55) were coated on the SDP to form tacrolimus colon-specific pellets (CSP) using a fluidized bed coater. Under in vitro gradient pH environment, tacrolimus only released from CSP after changing pH to 6.8 and then quickly released in the phosphate buffer solution of pH 7.2. The Cmax of CSP was 195.68 ± 3.14 ng/mL at Tmax 4.5 ± 0.24 h where as in case of SDP, the Cmax was 646.16 ± 8.15 ng/mL at Tmax 0.5 ± 0.03 h, indicating the ability of CSP targeted to colon. The highest area under the curve was achieved 2479.58 ± 183.33 ng·h/mL for SDP, which was 2.27-fold higher than tacrolimus suspension. However, the best biodistribution performance was achieved from CSP. In conclusion, SDP combining of pH- and time-dependent approaches was suitable for targeted delivery of tacrolimus to colon

A phase field model for mass transport with semi-permeable interfaces

In this paper, a thermal-dynamical consistent model for mass transfer across permeable moving interfaces is proposed by using the energy variation method. We consider a restricted diffusion problem where the flux across the interface depends on its conductance and the difference of the concentration on each side. The diffusive interface phase-field framework used here has several advantages over the sharp interface method. First of all, explicit tracking of the interface is no longer necessary. Secondly, the interfacial condition can be incorporated with a variable diffusion coefficient. A detailed asymptotic analysis confirms the diffusive interface model converges to the existing sharp interface model as the interface thickness goes to zero. A decoupled energy stable numerical scheme is developed to solve this system efficiently. Numerical simulations first illustrate the consistency of theoretical results on the sharp interface limit. Then a convergence study and energy decay test are conducted to ensure the efficiency and stability of the numerical scheme. To illustrate the effectiveness of our phase-field approach, several examples are provided, including a study of a two-phase mass transfer problem where drops with deformable interfaces are suspended in a moving fluid.Comment: 20 pages, 15 figure

Learning Continuous Grasping Function with a Dexterous Hand from Human Demonstrations

We propose to learn to generate grasping motion for manipulation with a dexterous hand using implicit functions. With continuous time inputs, the model can generate a continuous and smooth grasping plan. We name the proposed model Continuous Grasping Function (CGF). CGF is learned via generative modeling with a Conditional Variational Autoencoder using 3D human demonstrations. We will first convert the large-scale human-object interaction trajectories to robot demonstrations via motion retargeting, and then use these demonstrations to train CGF. During inference, we perform sampling with CGF to generate different grasping plans in the simulator and select the successful ones to transfer to the real robot. By training on diverse human data, our CGF allows generalization to manipulate multiple objects. Compared to previous planning algorithms, CGF is more efficient and achieves significant improvement on success rate when transferred to grasping with the real Allegro Hand. Our project page is at https://jianglongye.com/cgf .Comment: Project page: https://jianglongye.com/cg

LQG Control Over SWIPT-enabled Wireless Communication Network

In this paper, we consider using simultaneous wireless information and power transfer (SWIPT) to recharge the sensor in the LQG control, which provides a new approach to prolonging the network lifetime. We analyze the stability of the proposed system model and show that there exist two critical values for the power splitting ratio {\alpha}. Then, we propose an optimization problem to derive the optimal value of {\alpha}. This problem is non-convex but its numerical solution can be derived by our proposed algorithm efficiently. Moreover, we provide the feasible condition of the proposed optimization problem. Finally, simulation results are presented to verify and illustrate the main theoretical results

CLIP Brings Better Features to Visual Aesthetics Learners

The success of pre-training approaches on a variety of downstream tasks has revitalized the field of computer vision. Image aesthetics assessment (IAA) is one of the ideal application scenarios for such methods due to subjective and expensive labeling procedure. In this work, an unified and flexible two-phase \textbf{C}LIP-based \textbf{S}emi-supervised \textbf{K}nowledge \textbf{D}istillation paradigm is proposed, namely \textbf{\textit{CSKD}}. Specifically, we first integrate and leverage a multi-source unlabeled dataset to align rich features between a given visual encoder and an off-the-shelf CLIP image encoder via feature alignment loss. Notably, the given visual encoder is not limited by size or structure and, once well-trained, it can seamlessly serve as a better visual aesthetic learner for both student and teacher. In the second phase, the unlabeled data is also utilized in semi-supervised IAA learning to further boost student model performance when applied in latency-sensitive production scenarios. By analyzing the attention distance and entropy before and after feature alignment, we notice an alleviation of feature collapse issue, which in turn showcase the necessity of feature alignment instead of training directly based on CLIP image encoder. Extensive experiments indicate the superiority of CSKD, which achieves state-of-the-art performance on multiple widely used IAA benchmarks

Dynamic Handover: Throw and Catch with Bimanual Hands

Humans throw and catch objects all the time. However, such a seemingly common skill introduces a lot of challenges for robots to achieve: The robots need to operate such dynamic actions at high-speed, collaborate precisely, and interact with diverse objects. In this paper, we design a system with two multi-finger hands attached to robot arms to solve this problem. We train our system using Multi-Agent Reinforcement Learning in simulation and perform Sim2Real transfer to deploy on the real robots. To overcome the Sim2Real gap, we provide multiple novel algorithm designs including learning a trajectory prediction model for the object. Such a model can help the robot catcher has a real-time estimation of where the object will be heading, and then react accordingly. We conduct our experiments with multiple objects in the real-world system, and show significant improvements over multiple baselines. Our project page is available at \url{https://binghao-huang.github.io/dynamic_handover/}.Comment: Accepted at CoRL 2023. https://binghao-huang.github.io/dynamic_handover

DSHGT: Dual-Supervisors Heterogeneous Graph Transformer -- A pioneer study of using heterogeneous graph learning for detecting software vulnerabilities

Vulnerability detection is a critical problem in software security and attracts growing attention both from academia and industry. Traditionally, software security is safeguarded by designated rule-based detectors that heavily rely on empirical expertise, requiring tremendous effort from software experts to generate rule repositories for large code corpus. Recent advances in deep learning, especially Graph Neural Networks (GNN), have uncovered the feasibility of automatic detection of a wide range of software vulnerabilities. However, prior learning-based works only break programs down into a sequence of word tokens for extracting contextual features of codes, or apply GNN largely on homogeneous graph representation (e.g., AST) without discerning complex types of underlying program entities (e.g., methods, variables). In this work, we are one of the first to explore heterogeneous graph representation in the form of Code Property Graph and adapt a well-known heterogeneous graph network with a dual-supervisor structure for the corresponding graph learning task. Using the prototype built, we have conducted extensive experiments on both synthetic datasets and real-world projects. Compared with the state-of-the-art baselines, the results demonstrate promising effectiveness in this research direction in terms of vulnerability detection performance (average F1 improvements over 10\% in real-world projects) and transferability from C/C++ to other programming languages (average F1 improvements over 11%)

Peroxisome proliferator-activated receptor alpha mediates enhancement of gene expression of cerebroside sulfotransferase in several murine organs

Sulfatides, 3-O-sulfogalactosylceramides, are known to have multifunctional properties. These molecules are distributed in various tissues of mammals, where they are synthesized from galactosylceramides by sulfation at C3 of the galactosyl residue. Although this reaction is specifically catalyzed by cerebroside sulfotransferase (CST), the mechanisms underlying the transcriptional regulation of this enzyme are not understood. With respect to this issue, we previously found potential sequences of peroxisome proliferator-activated receptor (PPAR) response element on upstream regions of the mouse CST gene and presumed the possible regulation by the nuclear receptor PPAR alpha. To confirm this hypothesis, we treated wild-type and Ppara-null mice with the specific PPAR alpha agonist fenofibrate and examined the amounts of sulfatides and CST gene expression in various tissues. Fenofibrate treatment increased sulfatides and CST mRNA levels in the kidney, heart, liver, and small intestine in a PPAR alpha-dependent manner. However, these effects of fenofibrate were absent in the brain or colon. Fenofibrate treatment did not affect the mRNA level of arylsulfatase A, which is the key enzyme for catalyzing desulfation of sulfatides, in any of these six tissues. Analyses of the DNA-binding activity and conventional gene expression targets of PPAR alpha has demonstrated that fenofibrate treatment activated PPAR alpha in the kidney, heart, liver, and small intestine but did not affect the brain or colon. These findings suggest that PPAR alpha activation induces CST gene expression and enhances sulfatide synthesis in mice, which suggests that PPAR alpha is a possible transcriptional regulator for the mouse CST gene.ArticleGLYCOCONJUGATE JOURNAL. 30(6):553-560 (2013)journal articl