GAIA: Delving into Gradient-based Attribution Abnormality for Out-of-distribution Detection

Detecting out-of-distribution (OOD) examples is crucial to guarantee the reliability and safety of deep neural networks in real-world settings. In this paper, we offer an innovative perspective on quantifying the disparities between in-distribution (ID) and OOD data -- analyzing the uncertainty that arises when models attempt to explain their predictive decisions. This perspective is motivated by our observation that gradient-based attribution methods encounter challenges in assigning feature importance to OOD data, thereby yielding divergent explanation patterns. Consequently, we investigate how attribution gradients lead to uncertain explanation outcomes and introduce two forms of abnormalities for OOD detection: the zero-deflation abnormality and the channel-wise average abnormality. We then propose GAIA, a simple and effective approach that incorporates Gradient Abnormality Inspection and Aggregation. The effectiveness of GAIA is validated on both commonly utilized (CIFAR) and large-scale (ImageNet-1k) benchmarks. Specifically, GAIA reduces the average FPR95 by 23.10% on CIFAR10 and by 45.41% on CIFAR100 compared to advanced post-hoc methods.Comment: Accepted by NeurIPS202

Shoggoth: Towards Efficient Edge-Cloud Collaborative Real-Time Video Inference via Adaptive Online Learning

This paper proposes Shoggoth, an efficient edge-cloud collaborative architecture, for boosting inference performance on real-time video of changing scenes. Shoggoth uses online knowledge distillation to improve the accuracy of models suffering from data drift and offloads the labeling process to the cloud, alleviating constrained resources of edge devices. At the edge, we design adaptive training using small batches to adapt models under limited computing power, and adaptive sampling of training frames for robustness and reducing bandwidth. The evaluations on the realistic dataset show 15%-20% model accuracy improvement compared to the edge-only strategy and fewer network costs than the cloud-only strategy.Comment: Accepted by 60th ACM/IEEE Design Automation Conference (DAC2023

Negative Feedback Regulation of Wnt4 Signaling by EAF1 and EAF2/U19

Previous studies indicated that EAF (ELL-associated factor) family members, EAF1 and EAF2/U19, play a role in cancer and embryogenesis. For example, EAF2/U19 may serve as a tumor suppressor in prostate cancer. At the same time, EAF2/U19 is a downstream factor in the non-canonical Wnt 4 signaling pathway required for eye development in Xenopus laevis, and along with EAF1, contributes to convergence and extension movements in zebrafish embryos through Wnt maintenance. Here, we used zebrafish embryos and mammalian cells to show that both EAF1 and EAF2/U19 were up-regulated by Wnt4 (Wnt4a). Furthermore, we found that EAF1 and EAF2/U19 suppressed Wnt4 expression by directly binding to the Wnt4 promoter as seen in chromatin immunoprecipitation assays. These findings indicate that an auto-regulatory negative feedback loop occurs between Wnt4 and the EAF family, which is conserved between zebrafish and mammalian. The rescue experiments in zebrafish embryos showed that early embryonic development required the maintenance of the appropriate levels of Wnt4a through the feedback loop. Others have demonstrated that the tumor suppressors p63, p73 and WT1 positively regulate Wnt4 expression while p21 has the opposite effect, suggesting that maintenance of appropriate Wnt4 expression may also be critical for adult tissue homeostasis and prevention against tumor initiation. Thus, the auto-regulatory negative feedback loop that controls expression of Wnt4 and EAF proteins may play an important role in both embryonic development and tumor suppression. Our findings provide the first convincing line of evidence that EAF and Wnt4 form an auto-regulatory negative feedback loop in vivo

EdgeMA: Model Adaptation System for Real-Time Video Analytics on Edge Devices

Real-time video analytics on edge devices for changing scenes remains a difficult task. As edge devices are usually resource-constrained, edge deep neural networks (DNNs) have fewer weights and shallower architectures than general DNNs. As a result, they only perform well in limited scenarios and are sensitive to data drift. In this paper, we introduce EdgeMA, a practical and efficient video analytics system designed to adapt models to shifts in real-world video streams over time, addressing the data drift problem. EdgeMA extracts the gray level co-occurrence matrix based statistical texture feature and uses the Random Forest classifier to detect the domain shift. Moreover, we have incorporated a method of model adaptation based on importance weighting, specifically designed to update models to cope with the label distribution shift. Through rigorous evaluation of EdgeMA on a real-world dataset, our results illustrate that EdgeMA significantly improves inference accuracy.Comment: Accepted by 30th International Conference on Neural Information Processing (ICONIP 2023

Precision Control of Amphoteric Doping in Cu x Bi2Se3 Nanoplates.

Copper-doped Bi2Se3 (Cu x Bi2Se3) is of considerable interest for tailoring its electronic properties and inducing exotic charge correlations while retaining the unique Dirac surface states. However, the copper dopants in Cu x Bi2Se3 display complex electronic behaviors and may function as either electron donors or acceptors depending on their concentration and atomic sites within the Bi2Se3 crystal lattice. Thus, a precise understanding and control of the doping concentration and sites is of both fundamental and practical significance. Herein, we report a solution-based one-pot synthesis of Cu x Bi2Se3 nanoplates with systematically tunable Cu doping concentrations and doping sites. Our studies reveal a gradual evolution from intercalative sites to substitutional sites with increasing Cu concentrations. The Cu atoms at intercalative sites function as electron donors while those at the substitutional sites function as electron acceptors, producing distinct effects on the electronic properties of the resulting materials. We further show that Cu0.18Bi2Se3 exhibits superconducting behavior, which is not present in Bi2Se3, highlighting the essential role of Cu doping in tailoring exotic quantum properties. This study establishes an efficient methodology for precise synthesis of Cu x Bi2Se3 with tailored doping concentrations, doping sites, and electronic properties

Early patterning of cloned mouse embryos contributes to post-implantation development

AbstractSeveral research groups have suggested that the embryonic–abembryonic (Em–Ab) axis in the mouse can be predicted by the first cleavage plane of the early embryo. Currently, it is not known whether this early patterning occurs in cloned embryos produced by nuclear transfer and whether it affects development to term. In this work, the relationship between the first cleavage plane and the Em–Ab axis was determined by the labeling of one blastomere in cloned mouse embryos at the 2-cell stage, followed by ex-vivo tracking until the blastocyst stage. The results demonstrate that approximately half of the cloned blastocysts had an Em–Ab axis perpendicular to the initial cleavage plane of the 2-cell stage. These embryos were classified as “orthogonal” and the remainder as “deviant”. Additionally, we report here that cloned embryos were significantly more often orthogonal than their naturally fertilized counterparts and overexpressed Sox2. Orthogonal cloned embryos demonstrated a higher rate of post-implantation embryonic development than deviant embryos, but cloned pups did not all survive. These results reveal that the angular relationship between the Em–Ab axis and the first cleavage plane can influence later development and they support the hypothesis that proper early patterning of mammalian embryos is required after nuclear transfer

Kctd9 Deficiency Impairs Natural Killer Cell Development and Effector Function

We previously showed that potassium channel tetramerization domain containing 9 (KCTD9) is aberrantly expressed in natural killer (NK) cells in patients with hepatitis B virus-associated acute-on-chronic liver failure and mice with experimental fulminant hepatitis. However, the mechanism underlying the regulation of NK cell function and fulminant hepatitis progression by KCTD9 is unknown. Here, we investigated the role of Kctd9 in regulation of early development, maturation, and function of NK cells using Kctd9-knockout mice. Compared to wild-type mice, Kctd9-deficient mice exhibited impaired NK cell lineage commitment, as evidenced by selective reduction in the refined NK progenitors, and incomplete NK cell maturation, as manifested by a higher proportion of CD11b− NK cells and a lower percentage of CD11b+ NK cells with high proliferative potential. Moreover, Kctd9-depleted NK cells displayed insufficient IFN-γ production, degranulation, and granzyme B production in response to cytokine stimulation, and attenuated cytotoxicity to tumor cells in vitro. The defect in NK cells was further supported by ameliorated liver damage and improved survival in Kctd9-deficient mice following murine hepatitis virus strain-3 (MHV-3) infection, which otherwise leads to immune-mediated fulminant hepatitis, a phenotype homologous to that caused by NK cell depletion in wild-type mice. Further investigation to identify the underlying mechanism revealed that Kctd9 deficiency hindered the expression of transcription factors, including Ets1, Nfil3, Eomes, and Id2 in NK cells. Collectively, our data reveal that Kctd9 acts as a novel regulator for NK cell commitment, maturation, and effector function

KwaiYiiMath: Technical Report

Recent advancements in large language models (LLMs) have demonstrated remarkable abilities in handling a variety of natural language processing (NLP) downstream tasks, even on mathematical tasks requiring multi-step reasoning. In this report, we introduce the KwaiYiiMath which enhances the mathematical reasoning abilities of KwaiYiiBase1, by applying Supervised Fine-Tuning (SFT) and Reinforced Learning from Human Feedback (RLHF), including on both English and Chinese mathematical tasks. Meanwhile, we also constructed a small-scale Chinese primary school mathematics test set (named KMath), consisting of 188 examples to evaluate the correctness of the problem-solving process generated by the models. Empirical studies demonstrate that KwaiYiiMath can achieve state-of-the-art (SOTA) performance on GSM8k, CMath, and KMath compared with the similar size models, respectively.Comment: technical report. arXiv admin note: text overlap with arXiv:2306.16636 by other author

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN