112 research outputs found
Deep Cross-Modal Audio-Visual Generation
Cross-modal audio-visual perception has been a long-lasting topic in
psychology and neurology, and various studies have discovered strong
correlations in human perception of auditory and visual stimuli. Despite works
in computational multimodal modeling, the problem of cross-modal audio-visual
generation has not been systematically studied in the literature. In this
paper, we make the first attempt to solve this cross-modal generation problem
leveraging the power of deep generative adversarial training. Specifically, we
use conditional generative adversarial networks to achieve cross-modal
audio-visual generation of musical performances. We explore different encoding
methods for audio and visual signals, and work on two scenarios:
instrument-oriented generation and pose-oriented generation. Being the first to
explore this new problem, we compose two new datasets with pairs of images and
sounds of musical performances of different instruments. Our experiments using
both classification and human evaluations demonstrate that our model has the
ability to generate one modality, i.e., audio/visual, from the other modality,
i.e., visual/audio, to a good extent. Our experiments on various design choices
along with the datasets will facilitate future research in this new problem
space
Hierarchical Cross-Modal Talking Face Generationwith Dynamic Pixel-Wise Loss
We devise a cascade GAN approach to generate talking face video, which is
robust to different face shapes, view angles, facial characteristics, and noisy
audio conditions. Instead of learning a direct mapping from audio to video
frames, we propose first to transfer audio to high-level structure, i.e., the
facial landmarks, and then to generate video frames conditioned on the
landmarks. Compared to a direct audio-to-image approach, our cascade approach
avoids fitting spurious correlations between audiovisual signals that are
irrelevant to the speech content. We, humans, are sensitive to temporal
discontinuities and subtle artifacts in video. To avoid those pixel jittering
problems and to enforce the network to focus on audiovisual-correlated regions,
we propose a novel dynamically adjustable pixel-wise loss with an attention
mechanism. Furthermore, to generate a sharper image with well-synchronized
facial movements, we propose a novel regression-based discriminator structure,
which considers sequence-level information along with frame-level information.
Thoughtful experiments on several datasets and real-world samples demonstrate
significantly better results obtained by our method than the state-of-the-art
methods in both quantitative and qualitative comparisons
Proteasome activator 28A: A clinical biomarker and pharmaceutical target in acute cerebral infarction therapy
Purpose: To determine the dynamic changes in serum levels of PA28α in patients with acute cerebral infarction (ACI), and to investigate its correlation with infarct size and neurological deficit of the disease.
Methods: A total of 100 ACI patients and 100 healthy volunteers were recruited from The First Affiliated Hospital of Xinxiang Medical University as case and control groups, respectively. Their serum levels of PA28α were determined by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). The potential of PA28α in predicting the incidence of ACI was assessed by plotting ROC curves. Multivariate logistic regression analysis was conducted to investigate the risk factors of ACI. In addition, an ACI model in rats was established, and ACI rats were classified into 1, 3, 5, 7 and 14 day subgroups based on the duration post-ACI. Rats in the sham group served as control.
Results: Serum level of PA28α was significantly higher in ACI patients than in controls. Moreover, the serum level of PA28α at admission was positively correlated to the NIHSS score and infarct volume of ACI patients. The level of PA28α in ACI rats gradually increased post-ACI, reaching a peak on day 7. The number of apoptotic brain cells in ACI rats gradually decreased after ACI. In addition, PA28α level was negatively correlated to the number of apoptotic brain cells in ACI rats (R2 = 0.5148, p < 0.001).
Conclusion: The serum level of PA28α is elevated in ACI patients, and is positively correlated to infarct volume and neurological deficit of the disease. The dynamic change in brain cell apoptosis post-ACI is negatively correlated to the serum level of PA28α. These findings may provide theoretical basis for the diagnosis and treatment of ACI
MasterRTL: A Pre-Synthesis PPA Estimation Framework for Any RTL Design
In modern VLSI design flow, the register-transfer level (RTL) stage is a
critical point, where designers define precise design behavior with hardware
description languages (HDLs) like Verilog. Since the RTL design is in the
format of HDL code, the standard way to evaluate its quality requires
time-consuming subsequent synthesis steps with EDA tools. This time-consuming
process significantly impedes design optimization at the early RTL stage.
Despite the emergence of some recent ML-based solutions, they fail to maintain
high accuracy for any given RTL design. In this work, we propose an innovative
pre-synthesis PPA estimation framework named MasterRTL. It first converts the
HDL code to a new bit-level design representation named the simple operator
graph (SOG). By only adopting single-bit simple operators, this SOG proves to
be a general representation that unifies different design types and styles. The
SOG is also more similar to the target gate-level netlist, reducing the gap
between RTL representation and netlist. In addition to the new SOG
representation, MasterRTL proposes new ML methods for the RTL-stage modeling of
timing, power, and area separately. Compared with state-of-the-art solutions,
the experiment on a comprehensive dataset with 90 different designs shows
accuracy improvement by 0.33, 0.22, and 0.15 in correlation for total negative
slack (TNS), worst negative slack (WNS), and power, respectively.Comment: To be published in the Proceedings of 42nd IEEE/ACM International
Conference on Computer-Aided Design (ICCAD), 202
Effects of Chinese Medicine Tong xinluo on Diabetic Nephropathy via Inhibiting TGF- β
Diabetic nephropathy (DN) is a major cause of chronic kidney failure and characterized by interstitial and glomeruli fibrosis. Epithelial-to-mesenchymal transition (EMT) plays an important role in the pathogenesis of DN. Tong xinluo (TXL), a Chinese herbal compound, has been used in China with established therapeutic efficacy in patients with DN. To investigate the molecular mechanism of TXL improving DN, KK-Ay mice were selected as models for the evaluation of pathogenesis and treatment in DN. In vitro, TGF-β1 was used to induce EMT. Western blot (WB), immunofluorescence staining, and real-time polymerase chain reaction (RT-PCR) were applied to detect the changes of EMT markers in vivo and in vitro, respectively. Results showed the expressions of TGF-β1 and its downstream proteins smad3/p-smad3 were greatly reduced in TXL group; meantime, TXL restored the expression of smad7. As a result, the expressions of collagen IV (Col IV) and fibronectin (FN) were significantly decreased in TXL group. In vivo, 24 h-UAER (24-hour urine albumin excretion ratio) and BUN (blood urea nitrogen) were decreased and Ccr (creatinine clearance ratio) was increased in TXL group compared with DN group. In summary, the present study demonstrates that TXL successfully inhibits TGF-β1-induced epithelial-to-mesenchymal transition in DN, which may account for the therapeutic efficacy in TXL-mediated renoprotection
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