1,059 research outputs found
Design and Synthesis of Inhibitors of Hypoxia Inducible Factor-1-mediated Functions
Hypoxia Inducible Factors (HIFs) are very important transcription factors that can respond to low oxygen concentrations in the cellular environment. Inhibition of HIFās transcriptional activity represents a promising approach to new anticancer compounds. Herein, we describe the design and synthesis of a series of HIF-1 inhibitors. Evaluation of these inhibitors using a cell-based luciferase assay led to the discovery compounds with sub-micromolar potency
Efficient Emotional Adaptation for Audio-Driven Talking-Head Generation
Audio-driven talking-head synthesis is a popular research topic for virtual
human-related applications. However, the inflexibility and inefficiency of
existing methods, which necessitate expensive end-to-end training to transfer
emotions from guidance videos to talking-head predictions, are significant
limitations. In this work, we propose the Emotional Adaptation for Audio-driven
Talking-head (EAT) method, which transforms emotion-agnostic talking-head
models into emotion-controllable ones in a cost-effective and efficient manner
through parameter-efficient adaptations. Our approach utilizes a pretrained
emotion-agnostic talking-head transformer and introduces three lightweight
adaptations (the Deep Emotional Prompts, Emotional Deformation Network, and
Emotional Adaptation Module) from different perspectives to enable precise and
realistic emotion controls. Our experiments demonstrate that our approach
achieves state-of-the-art performance on widely-used benchmarks, including LRW
and MEAD. Additionally, our parameter-efficient adaptations exhibit remarkable
generalization ability, even in scenarios where emotional training videos are
scarce or nonexistent. Project website: https://yuangan.github.io/eat/Comment: Accepted to ICCV 2023. Project page: https://yuangan.github.io/eat
Design of Decision Tree Structure with Improved BPNN Nodes for High-Accuracy Locomotion Mode Recognition Using a Single IMU
Smart wearable robotic system, such as exoskeleton assist device and powered lower limb prostheses can rapidly and accurately realize man–machine interaction through locomotion mode recognition system. However, previous locomotion mode recognition studies usually adopted more sensors for higher accuracy and effective intelligent algorithms to recognize multiple locomotion modes simultaneously. To reduce the burden of sensors on users and recognize more locomotion modes, we design a novel decision tree structure (DTS) based on using an improved backpropagation neural network (IBPNN) as judgment nodes named IBPNN-DTS, after analyzing the experimental locomotion mode data using the original values with a 200-ms time window for a single inertial measurement unit to hierarchically identify nine common locomotion modes (level walking at three kinds of speeds, ramp ascent/descent, stair ascent/descent, Sit, and Stand). In addition, we reduce the number of parameters in the IBPNN for structure optimization and adopted the artificial bee colony (ABC) algorithm to perform global search for initial weight and threshold value to eliminate system uncertainty because randomly generated initial values tend to result in a failure to converge or falling into local optima. Experimental results demonstrate that recognition accuracy of the IBPNN-DTS with ABC optimization (ABC-IBPNN-DTS) was up to 96.71% (97.29% for the IBPNN-DTS). Compared to IBPNN-DTS without optimization, the number of parameters in ABC-IBPNN-DTS shrank by 66% with only a 0.58% reduction in accuracy while the classification model kept high robustness
Compliant actuators that mimic biological muscle performance with applications in a highly biomimetic robotic arm
This paper endeavours to bridge the existing gap in muscular actuator design
for ligament-skeletal-inspired robots, thereby fostering the evolution of these
robotic systems. We introduce two novel compliant actuators, namely the
Internal Torsion Spring Compliant Actuator (ICA) and the External Spring
Compliant Actuator (ECA), and present a comparative analysis against the
previously conceived Magnet Integrated Soft Actuator (MISA) through
computational and experimental results. These actuators, employing a
motor-tendon system, emulate biological muscle-like forms, enhancing artificial
muscle technology. A robotic arm application inspired by the skeletal ligament
system is presented. Experiments demonstrate satisfactory power in tasks like
lifting dumbbells (peak power: 36W), playing table tennis (end-effector speed:
3.2 m/s), and door opening, without compromising biomimetic aesthetics.
Compared to other linear stiffness serial elastic actuators (SEAs), ECA and ICA
exhibit high power-to-volume (361 x 10^3 W/m) and power-to-mass (111.6 W/kg)
ratios respectively, endorsing the biomimetic design's promise in robotic
development
Simulating periodic systems on quantum computer
The variational quantum eigensolver (VQE) is one of the most appealing
quantum algorithms to simulate electronic structure properties of molecules on
near-term noisy intermediate-scale quantum devices. In this work, we generalize
the VQE algorithm for simulating extended systems. However, the numerical study
of an one-dimensional (1D) infinite hydrogen chain using existing VQE
algorithms shows a remarkable deviation of the ground state energy with respect
to the exact full configuration interaction (FCI) result. Here, we present two
schemes to improve the accuracy of quantum simulations for extended systems.
The first one is a modified VQE algorithm, which introduces an unitary
transformation of Hartree-Fock orbitals to avoid the complex Hamiltonian. The
second one is a Post-VQE approach combining VQE with the quantum subspace
expansion approach (VQE/QSE). Numerical benchmark calculations demonstrate that
both of two schemes provide an accurate enough description of the potential
energy curve of the 1D hydrogen chain. In addition, excited states computed
with the VQE/QSE approach also agree very well with FCI results
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