Low-rank Adaptation Method for Wav2vec2-based Fake Audio Detection

Self-supervised speech models are a rapidly developing research topic in fake audio detection. Many pre-trained models can serve as feature extractors, learning richer and higher-level speech features. However,when fine-tuning pre-trained models, there is often a challenge of excessively long training times and high memory consumption, and complete fine-tuning is also very expensive. To alleviate this problem, we apply low-rank adaptation(LoRA) to the wav2vec2 model, freezing the pre-trained model weights and injecting a trainable rank-decomposition matrix into each layer of the transformer architecture, greatly reducing the number of trainable parameters for downstream tasks. Compared with fine-tuning with Adam on the wav2vec2 model containing 317M training parameters, LoRA achieved similar performance by reducing the number of trainable parameters by 198 times.Comment: 6page

The anti-resection activity of the X protein encoded by Hepatitis Virus B

Chronic infection of hepatitis virus B (HBV) is associated with an increased incidence of hepatocellular carcinoma (HCC). HBV encodes an oncoprotein (HBx) that is crucial for viral replication and interferes with multiple cellular activities including gene expression, histone modifications and genomic stability. To date, it remains unclear how disruption of these activities contributes to hepatocarcinogenesis. Here, we report that HBV exhibits a novel anti‐resection activity by disrupting DNA end resection, thus impairing the initial steps of homologous recombination (HR). This anti‐resection activity occurs in primary human hepatocytes (PHHs) undergoing a natural viral infection‐replication cycle, as well as in cells with integrated HBV genomes. Among the seven HBV‐encoded proteins, we identified HBx as the sole viral factor that inhibits resection. By disrupting an evolutionarily conserved Cullin4A‐DDB1‐RING type of E3 ligase, CRL4WDR70, via its H‐box, we show that HBx inhibits H2B monoubiquitylation at lysine 120 (uH2B) at double strand breaks, thus reducing the efficiency of long‐range resection. We further show that directly impairing H2B monoubiquitylation elicited tumorigenesis upon engraftment of deficient cells in athymic mice, confirming that the impairment of CRL4WDR70 function by HBx is sufficient to promote carcinogenesis. Finally, we demonstrated that lack of H2B monoubiquitylation is manifest in human HBV‐associated HCC (HBVHCC) when compared with HBV‐free HCC, implying corresponding defects of epigenetic regulation and end resection. We conclude that the anti‐resection activity of HBx induces an HR defect and genome instability and contributes to tumorigenesis of host hepatocytes

ADD 2023: the Second Audio Deepfake Detection Challenge

Audio deepfake detection is an emerging topic in the artificial intelligence community. The second Audio Deepfake Detection Challenge (ADD 2023) aims to spur researchers around the world to build new innovative technologies that can further accelerate and foster research on detecting and analyzing deepfake speech utterances. Different from previous challenges (e.g. ADD 2022), ADD 2023 focuses on surpassing the constraints of binary real/fake classification, and actually localizing the manipulated intervals in a partially fake speech as well as pinpointing the source responsible for generating any fake audio. Furthermore, ADD 2023 includes more rounds of evaluation for the fake audio game sub-challenge. The ADD 2023 challenge includes three subchallenges: audio fake game (FG), manipulation region location (RL) and deepfake algorithm recognition (AR). This paper describes the datasets, evaluation metrics, and protocols. Some findings are also reported in audio deepfake detection tasks

A Cephalometric Evaluation of Soft Tissue Profile Changes After Premolar Extractions

A pleasing profile and esthetic harmony are among the most important goals for successful orthodontic treatment. The balance of the facial structure is affected not only by orthodontic treatment but also by growth. The literature has suggested that soft tissue changes with growth do not directly follow the underlying skeletal structures. They are sex- and age specific. The nose and chin continue to grow, with the nose growing relatively more forward than the chin. The length and thickness of the lips increase, with the largest incremental change occurring during the adolescent growth period. The literature has also suggested that there is conflicting and contradictory information on the soft tissue changes associated with tooth movement. The controversy rests with soft tissue changes following either extraction or nonextraction mechanics. Some studies have indicated the soft tissue profile improved or was within the desired esthetic range after extraction of premolars. Other studies suggested that orthodontic treatment involving the extraction of premolars cause undesirable retrusion of the lips along with unfavorable profile changes. Interest in the literature has also centered on the prediction of a ratio between upper lip and upper incisor retractions after treatment. Many ratios have been developed but with different reference planes. All the above studies have used lateral cephalometric radiographs that were taken prior to, and after orthodontic treatment. In most of these studies, they have differences in malocclusion status, gender, extraction pattern, and stage of growth. It is important to use a sample consisting of the same gender patients with one type of malocclusion, same extraction pattern and little growth left for the evaluation of soft tissue profile changes after extractions. The null hypotheses of this study are: 1) profile will not change after premolar extraction; 2) there is no correlation between the retraction of the upper lip and the upper incisors. In the present study, every effort was made to standardize the sample and to control other dependent variables in order to evaluate cephalometric changes in soft tissue profile after different premolar extraction patterns. The sample consisted of 104 pre and post-treatment lateral cephalometric radiographs from 52 Caucasian postadolescent female patients who were at least 16 years old before treatment. Two treatment groups are: 1) patients (n = 26, class II division 1) had extractions of 2 upper first premolars (2UFPE); 2) patients (n = 26, class I) had extractions of 4 first premolars (4FPE). All the patients were selected from a Milwaukee based orthodontic practice. Combinations of 31 soft and hard tissue measurements were chosen in the study. Statistical comparisons were made between the pre- and post-treatment measurements in each group and between two groups. Correlations between every two measurements and ratios between lip and incisor retraction were also calculated

Bayesian Optimization via Exact Penalty

Constrained optimization problems pose challenges when the objective function and constraints are nonconvex and their evaluation requires expensive black-box simulations. Recently, hybrid optimization methods that integrate statistical surrogate modeling with numerical optimization algorithms have shown great promise, as they inherit the properties of global convergence from statistical surrogate modeling and fast local convergence from numerical optimization algorithms. However, the computational efficiency is not satisfied by practical needs under limited budgets and in the presence of equality constraints. In this article, we propose a novel hybrid optimization method, called exact penalty Bayesian optimization (EPBO), which employs Bayesian optimization within the exact penalty framework. We model the composite penalty function by a weighted sum of Gaussian processes, where the qualitative components of the constraint violations are smoothed by their predictive means. The proposed method features (i) closed-form acquisition functions, (ii) robustness to initial designs, (iii) the capability to start from infeasible points, and (iv) effective handling of equality constraints. We demonstrate the superiority of EPBO to state-of-the-art competitors using a suite of benchmark synthetic test problems and two real-world engineering design problems.</p

High Sensitive Immunoelectrochemical Measurement of Lung Cancer Tumor Marker ProGRP Based on TiO₂-Au Nanocomposite

Progastrin-releasing peptide (ProGRP), which is known to be highly specific and sensitive to small cell lung cancer (SCLC), has been proven to be a valuable substitute for neuron-specific enolase in SCLC diagnostics and monitoring, especially in its early stages. The detection of ProGRP levels also facilitates a selection of therapeutic treatments. For the fabrication of our proposed biosensor, titanium (IV) oxide microparticles were first used, followed by dispersing gold nanoparticles into chitosan and immobilizing them onto a carbon paste electrode (CPE) surface. The developed immunosensor exhibits a much higher biosensing performance in comparison with current methods, when it comes to the detection of ProGRP. Therefore, the proposed CPE/TiO2/(CS+AuNPs)/anti-ProGRP/BSA/ProGRP is excellent for the development of a compact diagnostics apparatus