Towards Adversarially Robust Continual Learning

Recent studies show that models trained by continual learning can achieve the comparable performances as the standard supervised learning and the learning flexibility of continual learning models enables their wide applications in the real world. Deep learning models, however, are shown to be vulnerable to adversarial attacks. Though there are many studies on the model robustness in the context of standard supervised learning, protecting continual learning from adversarial attacks has not yet been investigated. To fill in this research gap, we are the first to study adversarial robustness in continual learning and propose a novel method called \textbf{T}ask-\textbf{A}ware \textbf{B}oundary \textbf{A}ugmentation (TABA) to boost the robustness of continual learning models. With extensive experiments on CIFAR-10 and CIFAR-100, we show the efficacy of adversarial training and TABA in defending adversarial attacks.Comment: ICASSP 202

Diffusion-weighted MRI and intravoxel incoherent motion model for diagnosis of pediatric solid abdominal tumors

Background Pediatric retroperitoneal tumors in the renal bed are often large and heterogeneous, and their diagnosis based on conventional imaging alone is not possible. More advanced imaging methods, such as diffusion‐weighted (DW) MRI and the use of intravoxel incoherent motion (IVIM), have the potential to provide additional biomarkers that could facilitate their noninvasive diagnosis. Purpose To assess the use of an IVIM model for diagnosis of childhood malignant abdominal tumors and discrimination of benign from malignant lesions. Study Type Retrospective. Population Forty‐two pediatric patients with abdominal lesions (n = 32 malignant, n = 10 benign), verified by histopathology. Field Strength/Sequence 1.5T MRI system and a DW‐MRI sequence with six b‐values (0, 50, 100, 150, 600, 1000 s/mm2). Assessment Parameter maps of apparent diffusion coefficient (ADC), and IVIM maps of slow diffusion coefficient (D), fast diffusion coefficient (D*), and perfusion fraction (f) were computed using a segmented fitting model. Histograms were constructed for whole‐tumor regions of each parameter. Statistical Tests Comparison of histogram parameters of and their diagnostic performance was determined using Kruskal–Wallis, Mann–Whitney U, and receiver‐operating characteristic (ROC) analysis. Results IVIM parameters D* and f were significantly higher in neuroblastoma compared to Wilms' tumors (P < 0.05). The ROC analysis showed that the best diagnostic performance was achieved with D* 90th percentile (area under the curve [AUC] = 0.935; P = 0.002; cutoff value = 32,376 × 10−6 mm2/s) and f mean values (AUC = 1.00; P < 0.001; cutoff value = 14.7) in discriminating between neuroblastoma (n = 11) and Wilms' tumors (n = 8). Discrimination between tumor types was not possible with IVIM D or ADC parameters. Malignant tumors revealed significantly lower ADC, D, and higher D* values than in benign lesions (all P < 0.05). Data Conclusion IVIM perfusion parameters could distinguish between malignant childhood tumor types, providing potential imaging biomarkers for their diagnosis. Level of Evidence: 4 Technical Efficacy: Stage

SinSR: Diffusion-Based Image Super-Resolution in a Single Step

While super-resolution (SR) methods based on diffusion models exhibit promising results, their practical application is hindered by the substantial number of required inference steps. Recent methods utilize degraded images in the initial state, thereby shortening the Markov chain. Nevertheless, these solutions either rely on a precise formulation of the degradation process or still necessitate a relatively lengthy generation path (e.g., 15 iterations). To enhance inference speed, we propose a simple yet effective method for achieving single-step SR generation, named SinSR. Specifically, we first derive a deterministic sampling process from the most recent state-of-the-art (SOTA) method for accelerating diffusion-based SR. This allows the mapping between the input random noise and the generated high-resolution image to be obtained in a reduced and acceptable number of inference steps during training. We show that this deterministic mapping can be distilled into a student model that performs SR within only one inference step. Additionally, we propose a novel consistency-preserving loss to simultaneously leverage the ground-truth image during the distillation process, ensuring that the performance of the student model is not solely bound by the feature manifold of the teacher model, resulting in further performance improvement. Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method can achieve comparable or even superior performance compared to both previous SOTA methods and the teacher model, in just one sampling step, resulting in a remarkable up to x10 speedup for inference. Our code will be released at https://github.com/wyf0912/SinS

Internet and gaming addiction: a systematic literature review of neuroimaging studies

In the past decade, research has accumulated suggesting that excessive Internet use can lead to the development of a behavioral addiction. Internet addiction has been considered as a serious threat to mental health and the excessive use of the Internet has been linked to a variety of negative psychosocial consequences. The aim of this review is to identify all empirical studies to date that used neuroimaging techniques to shed light upon the emerging mental health problem of Internet and gaming addiction from a neuroscientific perspective. Neuroimaging studies offer an advantage over traditional survey and behavioral research because with this method, it is possible to distinguish particular brain areas that are involved in the development and maintenance of addiction. A systematic literature search was conducted, identifying 18 studies. These studies provide compelling evidence for the similarities between different types of addictions, notably substance-related addictions and Internet and gaming addiction, on a variety of levels. On the molecular level, Internet addiction is characterized by an overall reward deficiency that entails decreased dopaminergic activity. On the level of neural circuitry, Internet and gaming addiction led to neuroadaptation and structural changes that occur as a consequence of prolonged increased activity in brain areas associated with addiction. On a behavioral level, Internet and gaming addicts appear to be constricted with regards to their cognitive functioning in various domains. The paper shows that understanding the neuronal correlates associated with the development of Internet and gaming addiction will promote future research and will pave the way for the development of addiction treatment approaches

Foregut microbiome in development of esophageal adenocarcinoma

Esophageal adenocarcinoma (EA), the type of cancer linked to heartburn due to gastroesophageal reflux diseases (GERD), has increased six fold in the past 30 years. This cannot currently be explained by the usual environmental or by host genetic factors. EA is the end result of a sequence of GERD-related diseases, preceded by reflux esophagitis (RE) and Barrett&#x2019;s esophagus (BE). Preliminary studies by Pei and colleagues at NYU on elderly male veterans identified two types of microbiotas in the esophagus. Patients who carry the type II microbiota are &#x3e;15 fold likely to have esophagitis and BE than those harboring the type I microbiota. In a small scale study, we also found that 3 of 3 cases of EA harbored the type II biota. The findings have opened a new approach to understanding the recent surge in the incidence of EA. &#xd;&#xa;&#xd;&#xa;Our long-term goal is to identify the cause of GERD sequence. The hypothesis to be tested is that changes in the foregut microbiome are associated with EA and its precursors, RE and BE in GERD sequence. We will conduct a case control study to demonstrate the microbiome disease association in every stage of GERD sequence, as well as analyze the trend in changes in the microbiome along disease progression toward EA, by two specific aims. Aim 1 is to conduct a comprehensive population survey of the foregut microbiome and demonstrate its association with GERD sequence. Furthermore, spatial relationship between the esophageal microbiota and upstream (mouth) and downstream (stomach) foregut microbiotas as well as temporal stability of the microbiome-disease association will also be examined. Aim 2 is to define the distal esophageal metagenome and demonstrate its association with GERD sequence. Detailed analyses will include pathway-disease and gene-disease associations. Archaea, fungi and viruses, if identified, also will be correlated with the diseases. A significant association between the foregut microbiome and GERD sequence, if demonstrated, will be the first step for eventually testing whether an abnormal microbiome is required for the development of the sequence of phenotypic changes toward EA. If EA and its precursors represent a microecological disease, treating the cause of GERD might become possible, for example, by normalizing the microbiota through use of antibiotics, probiotics, or prebiotics. Causative therapy of GERD could prevent its progression and reverse the current trend of increasing incidence of EA

Channel Equalization and Beamforming for Quaternion-Valued Wireless Communication Systems

Quaternion-valued wireless communication systems have been studied in the past. Although progress has been made in this promising area, a crucial missing link is lack of effective and efficient quaternion-valued signal processing algorithms for channel equalization and beamforming. With most recent developments in quaternion-valued signal processing, in this work, we fill the gap to solve the problem by studying two quaternion-valued adaptive algorithms: one is the reference signal based quaternion-valued least mean square (QLMS) algorithm and the other one is the quaternion-valued constant modulus algorithm (QCMA). The quaternion-valued Wiener solution for possible block-based calculation is also derived. Simulation results are provided to show the working of the system

Multispectral Palmprint Recognition Using a Quaternion Matrix

Palmprints have been widely studied for biometric recognition for many years. Traditionally, a white light source is used for illumination. Recently, multispectral imaging has drawn attention because of its high recognition accuracy. Multispectral palmprint systems can provide more discriminant information under different illuminations in a short time, thus they can achieve better recognition accuracy. Previously, multispectral palmprint images were taken as a kind of multi-modal biometrics, and the fusion scheme on the image level or matching score level was used. However, some spectral information will be lost during image level or matching score level fusion. In this study, we propose a new method for multispectral images based on a quaternion model which could fully utilize the multispectral information. Firstly, multispectral palmprint images captured under red, green, blue and near-infrared (NIR) illuminations were represented by a quaternion matrix, then principal component analysis (PCA) and discrete wavelet transform (DWT) were applied respectively on the matrix to extract palmprint features. After that, Euclidean distance was used to measure the dissimilarity between different features. Finally, the sum of two distances and the nearest neighborhood classifier were employed for recognition decision. Experimental results showed that using the quaternion matrix can achieve a higher recognition rate. Given 3000 test samples from 500 palms, the recognition rate can be as high as 98.83%

Catestatin Enhances Neuropathic Pain Mediated by P2X4 Receptor of Dorsal Root Ganglia in a Rat Model of Chronic Constriction Injury

Background/Aims: Neuropathic pain (NPP) is the consequence of a number of central nervous system injuries or diseases. Previous studies have shown that NPP is mediated by P2X4 receptors that are expressed on satellite glial cells (SGCs) of dorsal root ganglia (DRG). Catestatin (CST), a neuroendocrine multifunctional peptide, may be involved in the pathogenesis of NPP. Here, we studied the mechanism through which CST affects NPP. Methods: We made rat models of chronic constriction injury (CCI) that simulate neuropathic pain. Rat behavioral changes were estimated by measuring the degree of hyperalgesia as assessed by the mechanical withdrawal threshold (MWT) and the thermal withdrawal latency (TWL). P2X4 mRNA expression was detected by quantitative real-time reverse transcription-polymerase chain reaction. P2X4 protein level and related signal pathways were assessed by western blot. Additionally, double-labeled immunofluorescence was employed to visualize the correspondence between the P2X4 receptor and glial fibrillary acidic protein. An enzyme-linked immunosorbent assay was performed to determine the concentration of CST and inflammatory factors. Results: CST led to lower MWT and TWL and increased P2X4 mRNA and protein expression on the SGCs of model rats. Further, CST upregulated the expression of phosphor-p38 and phosphor-ERK 1/2 on the SGCs of CCI rats. However, the expression level of phosphor-JNK and phosphor-p65 did not obviously change. Conclusion: Taken together, CST might boost NPP by enhancing the sensitivity of P2X4 receptors in the DRG of rats, which would provide us a novel perspective and research direction to explore new therapeutic targets for NPP