On the Detection of Adaptive Adversarial Attacks in Speaker Verification Systems

Speaker verification systems have been widely used in smart phones and Internet of things devices to identify legitimate users. In recent work, it has been shown that adversarial attacks, such as FAKEBOB, can work effectively against speaker verification systems. The goal of this paper is to design a detector that can distinguish an original audio from an audio contaminated by adversarial attacks. Specifically, our designed detector, called MEH-FEST, calculates the minimum energy in high frequencies from the short-time Fourier transform of an audio and uses it as a detection metric. Through both analysis and experiments, we show that our proposed detector is easy to implement, fast to process an input audio, and effective in determining whether an audio is corrupted by FAKEBOB attacks. The experimental results indicate that the detector is extremely effective: with near zero false positive and false negative rates for detecting FAKEBOB attacks in Gaussian mixture model (GMM) and i-vector speaker verification systems. Moreover, adaptive adversarial attacks against our proposed detector and their countermeasures are discussed and studied, showing the game between attackers and defenders

Compression and denoising of time-resolved light transport

Exploiting temporal information of light propagation captured at ultra-fast frame rates has enabled applications such as reconstruction of complex hidden geometry and vision through scattering media. However, these applications require high-dimensional and high-resolution transport data, which introduces significant performance and storage constraints. Additionally, due to different sources of noise in both captured and synthesized data, the signal becomes significantly degraded over time, compromising the quality of the results. In this work, we tackle these issues by proposing a method that extracts meaningful sets of features to accurately represent time-resolved light transport data. Our method reduces the size of time-resolved transport data up to a factor of 32, while significantly mitigating variance in both temporal and spatial dimensions

Universal Murray's law for optimised fluid transport in synthetic structures

Materials following Murray's law are of significant interest due to their unique porous structure and optimal mass transfer ability. However, it is challenging to construct such biomimetic hierarchical channels with perfectly cylindrical pores in synthetic systems following the existing theory. Achieving superior mass transport capacity revealed by Murray's law in nanostructured materials has thus far remained out of reach. We propose a Universal Murray's law applicable to a wide range of hierarchical structures, shapes and generalised transfer processes. We experimentally demonstrate optimal flow of various fluids in hierarchically planar and tubular graphene aerogel structures to validate the proposed law. By adjusting the macroscopic pores in such aerogel-based gas sensors, we also show a significantly improved sensor response dynamic. Our work provides a solid framework for designing synthetic Murray materials with arbitrarily shaped channels for superior mass transfer capabilities, with future implications in catalysis, sensing and energy applications.Comment: 19 pages, 4 figure

xPath: Human-AI Diagnosis in Pathology with Multi-Criteria Analyses and Explanation by Hierarchically Traceable Evidence

Data-driven AI promises support for pathologists to discover sparse tumor patterns in high-resolution histological images. However, from a pathologist's point of view, existing AI suffers from three limitations: (i) a lack of comprehensiveness where most AI algorithms only rely on a single criterion; (ii) a lack of explainability where AI models tend to work as 'black boxes' with little transparency; and (iii) a lack of integrability where it is unclear how AI can become part of pathologists' existing workflow. Based on a formative study with pathologists, we propose two designs for a human-AI collaborative tool: (i) presenting joint analyses of multiple criteria at the top level while (ii) revealing hierarchically traceable evidence on-demand to explain each criterion. We instantiate such designs in xPath -- a brain tumor grading tool where a pathologist can follow a top-down workflow to oversee AI's findings. We conducted a technical evaluation and work sessions with twelve medical professionals in pathology across three medical centers. We report quantitative and qualitative feedback, discuss recurring themes on how our participants interacted with xPath, and provide initial insights for future physician-AI collaborative tools.Comment: 31 pages, 11 figure

Neuropathology of COVID-19 (neuro-COVID): clinicopathological update

Coronavirus disease 2019 (COVID-19) is emerging as the greatest public health crisis in the early 21st century. Its causative agent, Severe Acute Respiratory Syndrome coronavirus 2 (SARS-CoV-2), is an enveloped single-stranded positive-sense ribonucleic acid virus that enters cells via the angiotensin converting enzyme 2 receptor or several other receptors. While COVID-19 primarily affects the respiratory system, other organs including the brain can be involved. In Western clinical studies, relatively mild neurological dysfunction such as anosmia and dysgeusia is frequent (~70-84%) while severe neurologic disorders such as stroke (~1-6%) and meningoencephalitis are less common. It is unclear how much SARS-CoV-2 infection contributes to the incidence of stroke given co-morbidities in the affected patient population. Rarely, clinically-defined cases of acute disseminated encephalomyelitis, Guillain-Barré syndrome and acute necrotizing encephalopathy have been reported in COVID-19 patients. Common neuropathological findings in the 184 patients reviewed include microglial activation (42.9%) with microglial nodules in a subset (33.3%), lymphoid inflammation (37.5%), acute hypoxic-ischemic changes (29.9%), astrogliosis (27.7%), acute/subacute brain infarcts (21.2%), spontaneous hemorrhage (15.8%), and microthrombi (15.2%). In our institutional cases, we also note occasional anterior pituitary infarcts. COVID-19 coagulopathy, sepsis, and acute respiratory distress likely contribute to a number of these findings. When present, central nervous system lymphoid inflammation is often minimal to mild, is detected best by immunohistochemistry and, in one study, indistinguishable from control sepsis cases. Some cases evince microglial nodules or neuronophagy, strongly supporting viral meningoencephalitis, with a proclivity for involvement of the medulla oblongata. The virus is detectable by reverse transcriptase polymerase chain reaction, immunohistochemistry, or electron microscopy in human cerebrum, cerebellum, cranial nerves, olfactory bulb, as well as in the olfactory epithelium; neurons and endothelium can also be infected. Review of the extant cases has limitations including selection bias and limited clinical information in some cases. Much remains to be learned about the effects of direct viral infection of brain cells and whether SARS-CoV-2 persists long-term contributing to chronic symptomatology

Epidemiological characteristics, clinical presentations, and prognoses of pediatric brain tumors: Experiences of national center for children’s health

BackgroundWe aimed to describe the epidemiological characteristics, clinical presentations, and prognoses in a national health center for children.MethodsFrom January 2015 to December 2020, 484 patients aged 0-16 years, who were diagnosed with brain tumors and received neurosurgery treatment, were enrolled in the study. Pathology was based on the World Health Organization 2021 nervous system tumor classification, and tumor behaviors were classified according to the International Classification of Diseases for Oncology, third edition.ResultsAmong the 484 patients with brain tumors, the median age at diagnosis was 4.62 [2.19, 8.17] years (benign tumors 4.07 [1.64, 7.13] vs. malignant tumors 5.36 [2.78, 8.84], p=0.008). The overall male-to-female ratio was 1.33:1(benign 1.09:1 vs. malignant 1.62:1, p=0.029). Nausea, vomiting, and headache were the most frequent initial symptoms. The three most frequent tumor types were embryonal tumors (ET, 22.8%), circumscribed astrocytic gliomas (20.0%), and pediatric-type diffuse gliomas (11.0%). The most common tumor locations were the cerebellum and fourth ventricle (38.67%), the sellar region (22.9%) and ventricles (10.6%). Males took up a higher proportion than females in choroid plexus tumors (63.6%), ET (61.1%), ependymal tumors (68.6%), and germ cell tumors (GCTs, 78.1%). Patients were followed for 1 to 82 months. The overall 5-year survival rate was 77.5%, with survival rates of 91.0% for benign tumors and 64.6% for malignant tumors.ConclusionBrain tumors presented particularly sex-, age-, and regional-dependent epidemiological characteristics. Our results were consistent with previous reports and might reflect the real epidemiological status in China