Contaminated Confessions Revisited

A second wave of false confessions is cresting. In the first twenty-one years of post-conviction DNA testing, 250 innocent people were exonerated, forty of which had falsely confessed. Those false confessions attracted sustained public attention from judges, law enforcement, policymakers, and the media. Those exonerations not only showed that false confessions can happen, but did more by shedding light on the problem of confession contamination, in which details of the crime are disclosed to suspects during the interrogation process. As a result, false confessions can appear deceptively rich, detailed, and accurate. In just the last five years, there has been a new surge in false confessions — a set of twenty-six more false confessions among DNA exonerations. All but two of these most recent confessions included crime scene details corroborated by crime scene information. Illustrating the power of contaminated false confessions, in nine of the cases, defendants were convicted despite DNA tests that excluded them at the time. As a result, this second wave of false confessions should cause even more alarm than the first. In the vast majority of cases there is no evidence to test using DNA. Unless a scientific framework is adopted to regulate interrogations, including by requiring recording of entire interrogations, overhauling interrogation methods, providing for judicial review of reliability at trial, and informing jurors with expert testimony, the insidious problems of confession contamination will persist

Data De-Duplication with Adaptive Chunking and Accelerated Modification Identifying

The data de-duplication system not only pursues the high de-duplication rate, which refers to the aggregate reduction in storage requirements gained from de-duplication, but also the de-duplication speed. To solve the problem of random parameter-setting brought by Content Defined Chunking (CDC), a self-adaptive data chunking algorithm is proposed. The algorithm improves the de-duplication rate by conducting pre-processing de-duplication to the samples of the classified files and then selecting the appropriate algorithm parameters. Meanwhile, FastCDC, a kind of content-based fast data chunking algorithm, is adopted to solve the problem of low de-duplication speed of CDC. By introducing de-duplication factor and acceleration factor, FastCDC can significantly boost de-duplication speed while not sacrificing the de-duplication rate through adjusting these two parameters. The experimental results demonstrate that our proposed method can improve the de-duplication rate by about 5 %, while FastCDC can obtain the increase of de-duplication speed by 50 % to 200 % only at the expense of less than 3 % de-duplication rate loss

Unlearnable Clusters: Towards Label-agnostic Unlearnable Examples

There is a growing interest in developing unlearnable examples (UEs) against visual privacy leaks on the Internet. UEs are training samples added with invisible but unlearnable noise, which have been found can prevent unauthorized training of machine learning models. UEs typically are generated via a bilevel optimization framework with a surrogate model to remove (minimize) errors from the original samples, and then applied to protect the data against unknown target models. However, existing UE generation methods all rely on an ideal assumption called label-consistency, where the hackers and protectors are assumed to hold the same label for a given sample. In this work, we propose and promote a more practical label-agnostic setting, where the hackers may exploit the protected data quite differently from the protectors. E.g., a m-class unlearnable dataset held by the protector may be exploited by the hacker as a n-class dataset. Existing UE generation methods are rendered ineffective in this challenging setting. To tackle this challenge, we present a novel technique called Unlearnable Clusters (UCs) to generate label-agnostic unlearnable examples with cluster-wise perturbations. Furthermore, we propose to leverage VisionandLanguage Pre-trained Models (VLPMs) like CLIP as the surrogate model to improve the transferability of the crafted UCs to diverse domains. We empirically verify the effectiveness of our proposed approach under a variety of settings with different datasets, target models, and even commercial platforms Microsoft Azure and Baidu PaddlePaddle. Code is available at \url{https://github.com/jiamingzhang94/Unlearnable-Clusters}.Comment: CVPR202

Differential expression profiling between the relative normal and dystrophic muscle tissues from the same LGMD patient

BACKGROUND: Limb-girdle muscular dystrophy (LGMD) is a group of heterogeneous muscular disorders with autosomal dominant and recessive inheritance, in which the pelvic or shoulder girdle musculature is predominantly or primarily involved. Although analysis of the defective proteins has shed some light onto their functions implicated in the etiology of LGMD, our understanding of the molecular mechanisms underlying muscular dystrophy remains incomplete. METHODS: To give insight into the molecular mechanisms of AR-LGMD, we have examined the differentially expressed gene profiling between the relative normal and pathological skeletal muscles from the same AR-LGMD patient with the differential display RT-PCR approach. The research subjects came from a Chinese AR-LGMD family with three affected sisters. RESULTS: In this report, we have identified 31 known genes and 12 unknown ESTs, which were differentially expressed between the relative normal and dystrophic muscle from the same LGMD patient. The expression of many genes encoding structural proteins of skeletal muscle fibers (such as titin, myosin heavy and light chains, and nebulin) were dramatically down-regulated in dystrophic muscles compared to the relative normal muscles. The genes, reticulocalbin 1, kinectin 1, fatty acid desaturase 1, insulin-like growth factor binding protein 5 (IGFBP5), Nedd4 family interacting protein 1 (NDFIP1), SMARCA2 (SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 2), encoding the proteins involved in signal transduction and gene expression regulation were up-regulated in the dystrophic muscles. CONCLUSION: The functional analysis of these expression-altered genes in the pathogenesis of LGMD could provide additional information for understanding possible molecular mechanisms of LGMD development

Learning from Heterogeneity: A Dynamic Learning Framework for Hypergraphs

Graph neural network (GNN) has gained increasing popularity in recent years owing to its capability and flexibility in modeling complex graph structure data. Among all graph learning methods, hypergraph learning is a technique for exploring the implicit higher-order correlations when training the embedding space of the graph. In this paper, we propose a hypergraph learning framework named LFH that is capable of dynamic hyperedge construction and attentive embedding update utilizing the heterogeneity attributes of the graph. Specifically, in our framework, the high-quality features are first generated by the pairwise fusion strategy that utilizes explicit graph structure information when generating initial node embedding. Afterwards, a hypergraph is constructed through the dynamic grouping of implicit hyperedges, followed by the type-specific hypergraph learning process. To evaluate the effectiveness of our proposed framework, we conduct comprehensive experiments on several popular datasets with eleven state-of-the-art models on both node classification and link prediction tasks, which fall into categories of homogeneous pairwise graph learning, heterogeneous pairwise graph learning, and hypergraph learning. The experiment results demonstrate a significant performance gain (average 12.5% in node classification and 13.3% in link prediction) compared with recent state-of-the-art methods

On the Importance of Spatial Relations for Few-shot Action Recognition

Deep learning has achieved great success in video recognition, yet still struggles to recognize novel actions when faced with only a few examples. To tackle this challenge, few-shot action recognition methods have been proposed to transfer knowledge from a source dataset to a novel target dataset with only one or a few labeled videos. However, existing methods mainly focus on modeling the temporal relations between the query and support videos while ignoring the spatial relations. In this paper, we find that the spatial misalignment between objects also occurs in videos, notably more common than the temporal inconsistency. We are thus motivated to investigate the importance of spatial relations and propose a more accurate few-shot action recognition method that leverages both spatial and temporal information. Particularly, a novel Spatial Alignment Cross Transformer (SA-CT) which learns to re-adjust the spatial relations and incorporates the temporal information is contributed. Experiments reveal that, even without using any temporal information, the performance of SA-CT is comparable to temporal based methods on 3/4 benchmarks. To further incorporate the temporal information, we propose a simple yet effective Temporal Mixer module. The Temporal Mixer enhances the video representation and improves the performance of the full SA-CT model, achieving very competitive results. In this work, we also exploit large-scale pretrained models for few-shot action recognition, providing useful insights for this research direction

Multi‐Channel Lanthanide Nanocomposites for Customized Synergistic Treatment of Orthotopic Multi‐Tumor Cases

Simultaneous photothermal ablation of multiple tumors is limited by unpredictable photo-induced apoptosis, caused by individual intratumoral differences. Here, a multi-channel lanthanide nanocomposite was used to achieve tailored synergistic treatment of multiple subcutaneous orthotopic tumors under non-uniform whole-body infrared irradiation prescription. The nanocomposite reduces intratumoral glutathione by simultaneously activating the fluorescence and photothermal channels. The fluorescence provides individual information on different tumors, allowing customized prescriptions to be made. This enables optimal induction of hyperthermia and dosage of chemo drugs, to ensure treatment efficacy, while avoiding overtherapy. With an accessional therapeutic laser system, customized synergistic treatment of subcutaneous orthotopic cancer cases with multiple tumors is possible with both high efficacy and minimized side effects