CVRecon: Rethinking 3D Geometric Feature Learning For Neural Reconstruction

Recent advances in neural reconstruction using posed image sequences have made remarkable progress. However, due to the lack of depth information, existing volumetric-based techniques simply duplicate 2D image features of the object surface along the entire camera ray. We contend this duplication introduces noise in empty and occluded spaces, posing challenges for producing high-quality 3D geometry. Drawing inspiration from traditional multi-view stereo methods, we propose an end-to-end 3D neural reconstruction framework CVRecon, designed to exploit the rich geometric embedding in the cost volumes to facilitate 3D geometric feature learning. Furthermore, we present Ray-contextual Compensated Cost Volume (RCCV), a novel 3D geometric feature representation that encodes view-dependent information with improved integrity and robustness. Through comprehensive experiments, we demonstrate that our approach significantly improves the reconstruction quality in various metrics and recovers clear fine details of the 3D geometries. Our extensive ablation studies provide insights into the development of effective 3D geometric feature learning schemes. Project page: https://cvrecon.ziyue.cool

Disentangling Object Motion and Occlusion for Unsupervised Multi-frame Monocular Depth

Conventional self-supervised monocular depth prediction methods are based on a static environment assumption, which leads to accuracy degradation in dynamic scenes due to the mismatch and occlusion problems introduced by object motions. Existing dynamic-object-focused methods only partially solved the mismatch problem at the training loss level. In this paper, we accordingly propose a novel multi-frame monocular depth prediction method to solve these problems at both the prediction and supervision loss levels. Our method, called DynamicDepth, is a new framework trained via a self-supervised cycle consistent learning scheme. A Dynamic Object Motion Disentanglement (DOMD) module is proposed to disentangle object motions to solve the mismatch problem. Moreover, novel occlusion-aware Cost Volume and Re-projection Loss are designed to alleviate the occlusion effects of object motions. Extensive analyses and experiments on the Cityscapes and KITTI datasets show that our method significantly outperforms the state-of-the-art monocular depth prediction methods, especially in the areas of dynamic objects. Our code will be made publicly available

Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADCs is particularly important since a good codebook design can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices $K$. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of $K$ is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for $K$ and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of $K$ are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of $K$ and the design of the ADC quantizer; 2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol.Comment: Submitted to IEEE for possible publicatio

Fas (CD95) induces rapid, TLR4/IRAK4-dependent release of pro-inflammatory HMGB1 from macrophages

Although Fas (CD95) is recognized as a death receptor that induces apoptosis, recent studies indicate that the Fas/FasL system can induce pro-inflammatory cytokine production by macrophages independent of conventional caspase-mediated apoptotic signaling. The precise mechanism(s) by which Fas activates macrophage inflammation is unknown. We hypothesized that Fas stimulates rapid release of high mobility group box 1 (HMGB1) that acts in an autocrine and/or paracrine manner to stimulate pro-inflammatory cytokine production via a Toll-like receptor-4 (TLR4)/Interleukin-1 receptor associated kinase-4 (IRAK4)-dependent mechanism. Following Fas activation, HMGB1 was released within 1 hr from viable RAW267.4 cells and primary murine peritoneal macrophages. HMGB1 release was more rapid following Fas activation compared to LPS stimulation. Neutralization of HMGB1 with an inhibitory anti-HMGB1 monoclonal antibody strongly inhibited Fas-induced production of tumor necrosis factor (TNF) and macrophage inflammatory protein-2 (MIP-2). Both Fas-induced HMGB1 release and associated pro-inflammatory cytokine production were significantly decreased from Tlr4-/- and Irak4-/- macrophages, but not Tlr2-/- macrophages. These findings reveal a novel mechanism underlying Fas-mediated pro-inflammatory physiological responses in macrophages. We conclude that Fas activation induces rapid, TLR4/IRAK4-dependent release of HMGB1 that contributes to Fas-mediated pro-inflammatory cytokine production by viable macrophages

DUA-DA: Distillation-based Unbiased Alignment for Domain Adaptive Object Detection

Though feature-alignment based Domain Adaptive Object Detection (DAOD) have achieved remarkable progress, they ignore the source bias issue, i.e. the aligned features are more favorable towards the source domain, leading to a sub-optimal adaptation. Furthermore, the presence of domain shift between the source and target domains exacerbates the problem of inconsistent classification and localization in general detection pipelines. To overcome these challenges, we propose a novel Distillation-based Unbiased Alignment (DUA) framework for DAOD, which can distill the source features towards a more balanced position via a pre-trained teacher model during the training process, alleviating the problem of source bias effectively. In addition, we design a Target-Relevant Object Localization Network (TROLN), which can mine target-related knowledge to produce two classification-free metrics (IoU and centerness). Accordingly, we implement a Domain-aware Consistency Enhancing (DCE) strategy that utilizes these two metrics to further refine classification confidences, achieving a harmonization between classification and localization in cross-domain scenarios. Extensive experiments have been conducted to manifest the effectiveness of this method, which consistently improves the strong baseline by large margins, outperforming existing alignment-based works.Comment: 10pages,5 figure

A novel GLP-1/GIP dual agonist is more effective than liraglutide in reducing inflammation and enhancing GDNF release in the MPTP mouse model of Parkinson's disease

Type 2 diabetes mellitus (T2DM) is one of the risk factors for Parkinson's disease (PD). Insulin desensitisation has been observed in the brains of patients, which may promote neurodegeneration. Incretins are a family of growth factors that can re-sensitise insulin signalling. We have previously shown that mimetics of glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP) have neuroprotective effects in the 1-methyl-4-phenyl-1,2,3,6-tetrahydropypridine (MPTP) mouse model of PD. Recently, dual GLP-1/GIP receptor agonists have been developed. We therefore tested the novel dual agonist DA3-CH in comparison with the best GLP-1 analogue currently on the market, liraglutide (both drugs 25nmol/kg ip once-daily for 7 days) in the MPTP mouse model of PD (25 mg/kg ip once-daily for 7 days). In the Rotarod and grip strength assessment, DA3-CH was superior to liraglutide in reversing the MPTP–induced motor impairment. Dopamine synthesis as indicated by levels of tyrosine hydroxylase was much reduced by MPTP in the substantia nigra and striatum, and DA3-CH reversed this while liragutide only partially reversed this. The chronic inflammation response as shown in increased levels of activated microglia and astrocytes was reduced by both drugs. Importantly, expression levels of the neuroprotective growth factor Glial Derived Neurotrophic Factor (GDNF) was much enhanced by both DA3-CH and liragutide. The results demonstrate that the combination of GLP-1 and GIP receptor activation is superior to single GLP-1 receptor activation alone. Therefore, new dual agonists may be a promising treatment for PD. The GLP-1 receptor agonist exendin-4 has already shown disease modifying effects in clinical trials in PD patients

Loss of Asxl1 leads to myelodysplastic syndrome-like disease in mice

ASXL1 is mutated/deleted with high frequencies in multiple forms of myeloid malignancies, and its alterations are associated with poor prognosis. De novo ASXL1 mutations cause Bohring-Opitz syndrome characterized by multiple congenital malformations. We show that Asxl1 deletion in mice led to developmental abnormalities including dwarfism, anophthalmia, and 80% embryonic lethality. Surviving Asxl1(-/-) mice lived for up to 42 days and developed features of myelodysplastic syndrome (MDS), including dysplastic neutrophils and multiple lineage cytopenia. Asxl1(-/-) mice had a reduced hematopoietic stem cell (HSC) pool, and Asxl1(-/-) HSCs exhibited decreased hematopoietic repopulating capacity, with skewed cell differentiation favoring granulocytic lineage. Asxl1(+/-) mice also developed mild MDS-like disease, which could progress to MDS/myeloproliferative neoplasm, demonstrating a haploinsufficient effect of Asxl1 in the pathogenesis of myeloid malignancies. Asxl1 loss led to an increased apoptosis and mitosis in Lineage(-)c-Kit(+) (Lin(-)c-Kit(+)) cells, consistent with human MDS. Furthermore, Asxl1(-/-) Lin(-)c-Kit(+) cells exhibited decreased global levels of H3K27me3 and H3K4me3 and altered expression of genes regulating apoptosis (Bcl2, Bcl2l12, Bcl2l13). Collectively, we report a novel ASXL1 murine model that recapitulates human myeloid malignancies, implying that Asxl1 functions as a tumor suppressor to maintain hematopoietic cell homeostasis. Future work is necessary to clarify the contribution of microenvironment to the hematopoietic phenotypes observed in the constitutional Asxl1(-/-) mice

Two novel dual GLP-1/GIP receptor agonists are neuroprotective in the MPTP mouse model of Parkinson's disease

Type 2 diabetes mellitus (T2DM) is a risk factors for developing Parkinson's disease (PD). Insulin desensitization is observed in the brains of PD patients, which may be an underlying mechanism that promotes neurodegeneration. Incretin hormones are growth factors that can re-sensitize insulin signalling. We have previously shown that analogues of the incretins GLP-1 or GIP have neuroprotective effects in the MPTP mouse model of PD. Novel dual GLP-1/GIP receptor agonists have been developed as treatments for T2DM. We have tested 3 novel dual receptor agonists DA-JC1, DA-JC4 and DA-CH5 in comparison with the GLP-1 analogue liraglutide (all drugs at 25 nmol/kg ip once-daily for 6 days) in the MPTP mouse model of PD (4 × 25 mg/kg ip). In the Rotarod and grip strength assessment, DA-CH5 performed best in reversing the MPTP–induced motor impairment. Dopamine synthesis as indicated by levels of tyrosine hydroxylase was much reduced by MPTP in the substantia nigra and striatum, and DA-CH5 was the best drug to reverse this. Pro-inflammatory cytokines were best reduced by DA-CH5, while expression levels of the neuroprotective growth factor Glial-Derived Neurotrophic Factor (GDNF) was most increased by DA-JC4. Synapses were protected best by DA-JC4 and DA-CH5. Both DA-JC1 and liraglutide showed inferior effects. These results show that a combination of GLP-1 and GIP receptor activation is more efficient compared to single GLP-1 receptor activation. We conclude that dual agonists are a promising novel treatment for PD. The GLP-1 mimetic exendin-4 has previously shown disease modifying effects in two clinical trials in Parkinson patients

Mixed halide perovskites for spectrally stable and high-efficiency blue light-emitting diodes.

Bright and efficient blue emission is key to further development of metal halide perovskite light-emitting diodes. Although modifying bromide/chloride composition is straightforward to achieve blue emission, practical implementation of this strategy has been challenging due to poor colour stability and severe photoluminescence quenching. Both detrimental effects become increasingly prominent in perovskites with the high chloride content needed to produce blue emission. Here, we solve these critical challenges in mixed halide perovskites and demonstrate spectrally stable blue perovskite light-emitting diodes over a wide range of emission wavelengths from 490 to 451 nanometres. The emission colour is directly tuned by modifying the halide composition. Particularly, our blue and deep-blue light-emitting diodes based on three-dimensional perovskites show high EQE values of 11.0% and 5.5% with emission peaks at 477 and 467 nm, respectively. These achievements are enabled by a vapour-assisted crystallization technique, which largely mitigates local compositional heterogeneity and ion migration