Dynamic Routing and Knowledge Re-Learning for Data-Free Black-Box Attack.

Deep learning models have emerged as strong and efficient tools that can be applied to a broad spectrum of complex learning problems and many real-world applications. However, more and more works show that deep models are vulnerable to adversarial examples. Compared to vanilla attack settings, this paper advocates a more practical setting of data-free black-box attack, for which the attackers can completely not access the structures and parameters of the target model, as well as the intermediate features and any training data associated with the model.

Building an Open-Vocabulary Video CLIP Model With Better Architectures, Optimization and Data.

Despite significant results achieved by Contrastive Language-Image Pretraining (CLIP) in zero-shot image recognition, limited effort has been made exploring its potential for zero-shot video recognition. This paper presents Open-VCLIP++, a simple yet effective framework that adapts CLIP to a strong zero-shot video classifier, capable of identifying novel actions and events during testing. Open-VCLIP++ minimally modifies CLIP to capture spatial-temporal relationships in videos, thereby creating a specialized video classifier while striving for generalization.

Adaptive Cross-Modal Transferable Adversarial Attacks From Images to Videos.

The cross-model transferability of adversarial examples makes black-box attacks to be practical. However, it typically requires access to the input of the same modality as black-box models to attain reliable transferability. Unfortunately, the collection of datasets may be difficult in security-critical scenarios.

Towards Transferable Adversarial Attacks on Image and Video Transformers.

The transferability of adversarial examples across different convolutional neural networks (CNNs) makes it feasible to perform black-box attacks, resulting in security threats for CNNs. However, fewer endeavors have been made to investigate transferable attacks for vision transformers (ViTs), which achieve superior performance on various computer vision tasks. Unlike CNNs, ViTs establish relationships between patches extracted from inputs by the self-attention module.

Generalized Meta-FDMixup: Cross-Domain Few-Shot Learning Guided by Labeled Target Data.

The vanilla Few-shot Learning (FSL) learns to build a classifier for a new concept from one or very few target examples, with the general assumption that source and target classes are sampled from the same domain. Recently, the task of Cross-Domain Few-Shot Learning (CD-FSL) aims at tackling the FSL where there is a huge domain shift between the source and target datasets. Extensive efforts on CD-FSL have been made via either directly extending the meta-learning paradigm of vanilla FSL methods, or employing massive unlabeled target data to help learn models.

Ultrafast non-volatile flash memory based on van der Waals heterostructures.

Flash memory has become a ubiquitous solid-state memory device widely used in portable digital devices, computers and enterprise applications. The development of the information age has demanded improvements in memory speed and retention performance. Here we demonstrate an ultrafast non-volatile flash memory based on MoS/hBN/multilayer graphene van der Waals heterostructures, which achieves an ultrafast writing/erasing speed of 20 ns through two-triangle-barrier modified Fowler-Nordheim tunnelling.

A Steep-Slope MoS/Graphene Dirac-Source Field-Effect Transistor with a Large Drive Current.

In the continuous transistor feature size scaling down, the scaling of the supply voltage is stagnant because of the subthreshold swing (SS) limit. A transistor with a new mechanism is needed to break through the thermionic limit of SS and hold the large drive current at the same time. Here, by adopting the recently proposed Dirac-source field-effect transistor (DSFET) technology, we experimentally demonstrate a MoS/graphene (1.

LncRNA XIST sponges miR-199a-3p to modulate the Sp1/LRRK2 signal pathway to accelerate Parkinson's disease progression.

and models of Parkinson's disease were established to investigate the effects of the lncRNA XIST/miR-199a-3p/Sp1/LRRK2 axis. The binding between XIST and miR-199a-3p as well as miR-199a-3p and Sp1 were examined by luciferase reporter assay and confirmed by RNA immunoprecipitation analysis. Following the Parkinson's disease animal behavioural assessment by suspension and swim tests, the brain tissue injuries were evaluated by hematoxylin and eosin, TdT-mediated dUTP-biotin nick end labelling, and tyrosine hydroxylase stainings.

A Study of Multi-Task and Region-Wise Deep Learning for Food Ingredient Recognition.

Food recognition has captured numerous research attention for its importance for health-related applications. The existing approaches mostly focus on the categorization of food according to dish names, while ignoring the underlying ingredient composition. In reality, two dishes with the same name do not necessarily share the exact list of ingredients.

Effects of early-life zinc deficiency on learning and memory in offspring and the changes in DNA methylation patterns.

Objective: To investigate the effect of maternal zinc deficiency on learning and memory in offspring and the changes in DNA methylation patterns.

Methods: Pregnant rats were divided into zinc adequate (ZA), zinc deficient (ZD), and paired fed (PF) groups. Serum zinc contents and AKP activity in mother rats and offspring at P21 (end of lactation) and P60 (weaned, adult) were detected.

A Dynamic Frame Selection Framework for Fast Video Recognition.

We introduce AdaFrame, a conditional computation framework that adaptively selects relevant frames on a per-input basis for fast video recognition. AdaFrame, which contains a Long Short-Term Memory augmented with a global memory to provide context information, operates as an agent to interact with video sequences aiming to search over time which frames to use. Trained with policy search methods, at each time step, AdaFrame computes a prediction, decides where to observe next, and estimates a utility, i.

A comparative study of the effectiveness and safety of combined procarbazine, lomustine, and vincristine as a therapeutic method for recurrent high-grade glioma: A protocol for systematic review and meta-analysis.

Background: Systematic evaluation of the effectiveness and safety of combined procarbazine, lomustine, and vincristine for treating recurrent high-grade glioma.

Methods: Electronic databases including PubMed, MEDLINE, EMBASE, Cochrane Library Central Register of Controlled Trials, WanFang, and China National Knowledge Infrastructure (CNKI) were used to search for studies related to the utilization of combined procarbazine, lomustine, and vincristine as a therapeutic method for recurrent high-grade glioma. Literature screening, extraction of data, and evaluation of high standard studies were conducted by 2 independent researchers.

Pose-Guided Person Image Synthesis in the Non-iconic Views.

Generating realistic images with the guidance of reference images and human poses is challenging. Despite the success of previous works on synthesizing person images in the iconic views, no efforts are made towards the task of poseguided image synthesis in the non-iconic views. Particularly, we find that previous models cannot handle such a complex task, where the person images are captured in the non-iconic views by commercially-available digital cameras.

Learning Layer-Skippable Inference Network.

The process of learning good representations for machine learning tasks can be very computationally expensive. Typically, we facilitate the same backbones learned on the training set to infer the labels of testing data. Interestingly, This learning and inference paradigm, however, is quite different from the typical inference scheme of human biological visual systems.

Two-dimensional materials for next-generation computing technologies.

Rapid digital technology advancement has resulted in a tremendous increase in computing tasks imposing stringent energy efficiency and area efficiency requirements on next-generation computing. To meet the growing data-driven demand, in-memory computing and transistor-based computing have emerged as potent technologies for the implementation of matrix and logic computing. However, to fulfil the future computing requirements new materials are urgently needed to complement the existing Si complementary metal-oxide-semiconductor technology and new technologies must be developed to enable further diversification of electronics and their applications.

Deep Ranking for Image Zero-Shot Multi-Label Classification.

During the past decade, both multi-label learning and zero-shot learning have attracted huge research attention, and significant progress has been made. Multi-label learning algorithms aim to predict multiple labels given one instance, while most existing zero-shot learning approaches target at predicting a single testing label for each unseen class via transferring knowledge from auxiliary seen classes to target unseen classes. However, relatively less effort has been made on predicting multiple labels in the zero-shot setting, which is nevertheless a quite challenging task.

Pixel2Mesh: 3D Mesh Model Generation via Image Guided Deformation.

In this paper, we propose an end-to-end deep learning architecture that generates 3D triangular meshes from single color images. Restricted by the nature of prevalent deep learning techniques, the majority of previous works represent 3D shapes in volumes or point clouds. However, it is non-trivial to convert these representations to compact and ready-to-use mesh models.

Leader-Based Multi-Scale Attention Deep Architecture for Person Re-Identification.

Person re-identification (re-id) aims to match people across non-overlapping camera views in a public space. This is a challenging problem because the people captured in surveillance videos often wear similar clothing. Consequently, the differences in their appearance are typically subtle and only detectable at particular locations and scales.

Object Detection from Scratch with Deep Supervision.

In this paper, we propose Deeply Supervised Object Detectors (DSOD), an object detection framework that can be trained from scratch. Recent advances in object detection heavily depend on the off-the-shelf models pre-trained on large-scale classification datasets like ImageNet and OpenImage. However, one problem is that adopting pre-trained models from classification to detection task may incur learning bias due to the different objective function and diverse distributions of object categories.

Vocabulary-Informed Zero-Shot and Open-Set Learning.

Despite significant progress in object categorization, in recent years, a number of important challenges remain; mainly, the ability to learn from limited labeled data and to recognize object classes within large, potentially open, set of labels. Zero-shot learning is one way of addressing these challenges, but it has only been shown to work with limited sized class vocabularies and typically requires separation between supervised and unsupervised classes, allowing former to inform the latter but not vice versa. We propose the notion of vocabulary-informed learning to alleviate the above mentioned challenges and address problems of supervised, zero-shot, generalized zero-shot and open set recognition using a unified framework.

Dense Dilated Network for Video Action Recognition.

The ability to recognize actions throughout a video is essential for surveillance, self-driving, and many other applications. Although many researchers have investigated deep neural networks to get a better result in video action recognition, these networks usually require a large number of well-labeled data to train. In this paper, we introduce a dense dilated network to collect action information from snippet-level to global-level.

Small footprint transistor architecture for photoswitching logic and in situ memory.

The need for continuous size downscaling of silicon transistors is driving the industrial development of strategies to enable further footprint reduction. The atomic thickness of two-dimensional materials allows the potential realization of high-area-efficiency transistor architectures. However, until now, the design of devices composed of two-dimensional materials has mimicked the basic architecture of silicon circuits.