BaNa: a noise resilient fundamental frequency detection algorithm for speech and music

Fundamental frequency (F0) is one of the essential features in many acoustic related applications. Although numerous F0 detection algorithms have been developed, the detection accuracy in noisy environments still needs improvement. We present a hybrid noise resilient F0 detection algorithm named BaNa that combines the approaches of harmonic ratios and Cepstrum analysis. A Viterbi algorithm with a cost function is used to identify the F0 value among several F0 candidates. Speech and music databases with eight different types of additive noise are used to evaluate the performance of the BaNa algorithm and several classic and state-of-the-art F0 detection algorithms. Results show that for almost all types of noise and signal-to-noise ratio (SNR) values investigated, BaNa achieves the lowest Gross Pitch Error (GPE) rate among all the algorithms. Moreover, for the 0 dB SNR scenarios, the BaNa algorithm is shown to achieve 20% to 35% GPE rate for speech and 12% to 39% GPE rate for music. We also describe implementation issues that must be addressed to run the BaNa algorithm as a real-time application on a smartphone platform.Peer ReviewedPostprint (author's final draft

SPFL: A Self-purified Federated Learning Method Against Poisoning Attacks

While Federated learning (FL) is attractive for pulling privacy-preserving distributed training data, the credibility of participating clients and non-inspectable data pose new security threats, of which poisoning attacks are particularly rampant and hard to defend without compromising privacy, performance or other desirable properties of FL. To tackle this problem, we propose a self-purified FL (SPFL) method that enables benign clients to exploit trusted historical features of locally purified model to supervise the training of aggregated model in each iteration. The purification is performed by an attention-guided self-knowledge distillation where the teacher and student models are optimized locally for task loss, distillation loss and attention-based loss simultaneously. SPFL imposes no restriction on the communication protocol and aggregator at the server. It can work in tandem with any existing secure aggregation algorithms and protocols for augmented security and privacy guarantee. We experimentally demonstrate that SPFL outperforms state-of-the-art FL defenses against various poisoning attacks. The attack success rate of SPFL trained model is at most 3$\%$ above that of a clean model, even if the poisoning attack is launched in every iteration with all but one malicious clients in the system. Meantime, it improves the model quality on normal inputs compared to FedAvg, either under attack or in the absence of an attack

One-pot synthesis of 2-alkyl cycloketones on bifunctional Pd/ZrO₂ catalyst

2-Alkyl cycloketones are essential chemicals and intermediates for synthetic perfumes and pesticides, which are conventionally produced by multistep process including aldol condensation, separation and hydrogenation. In present work, a batch one-pot cascade approach using aldehydes and cycloketones as the raw materials, and a bifunctional Pd/ZrO2 catalyst was developed for the synthesis of 2-alkyl cycloketones, e.g., cyclohexanone and cycloheptanone. Very high aldehydes (except for paraldehyde with large steric hindrance) conversion and high yields for 2-alkyl cycloketones (e.g., 99 % of conversion for n-butanal and 76 wt.% of yield for 2-butyl cyclohexanone) were obtained at mild temperature of 140 °C. After 10 cycles of reuse, Pd/ZrO2 catalyst showed slight deactivation (ca. 5 % conversion and 10 % yield losses), due to the coke on the catalyst. However, the performance of the catalyst was completely recovered after an oxidative regeneration

In-situ crack and keyhole pore detection in laser directed energy deposition through acoustic signal and deep learning

Cracks and keyhole pores are detrimental defects in alloys produced by laser directed energy deposition (LDED). Laser-material interaction sound may hold information about underlying complex physical events such as crack propagation and pores formation. However, due to the noisy environment and intricate signal content, acoustic-based monitoring in LDED has received little attention. This paper proposes a novel acoustic-based in-situ defect detection strategy in LDED. The key contribution of this study is to develop an in-situ acoustic signal denoising, feature extraction, and sound classification pipeline that incorporates convolutional neural networks (CNN) for online defect prediction. Microscope images are used to identify locations of the cracks and keyhole pores within a part. The defect locations are spatiotemporally registered with acoustic signal. Various acoustic features corresponding to defect-free regions, cracks, and keyhole pores are extracted and analysed in time-domain, frequency-domain, and time-frequency representations. The CNN model is trained to predict defect occurrences using the Mel-Frequency Cepstral Coefficients (MFCCs) of the lasermaterial interaction sound. The CNN model is compared to various classic machine learning models trained on the denoised acoustic dataset and raw acoustic dataset. The validation results shows that the CNN model trained on the denoised dataset outperforms others with the highest overall accuracy (89%), keyhole pore prediction accuracy (93%), and AUC-ROC score (98%). Furthermore, the trained CNN model can be deployed into an in-house developed software platform for online quality monitoring. The proposed strategy is the first study to use acoustic signals with deep learning for insitu defect detection in LDED process.Comment: 36 Pages, 16 Figures, accepted at journal Additive Manufacturin

Self-doping effect in confined copper selenide semiconducting quantum dots for efficient photoelectrocatalytic oxygen evolution

Self-doping can not only suppress the photogenerated charge recombination of semiconducting quantum dots by self-introducing trapping states within the bandgap, but also provide high-density catalytic active sites as the consequence of abundant non-saturated bonds associated with the defects. Here, we successfully prepared semiconducting copper selenide (CuSe) confined quantum dots with abundant vacancies and systematically investigated their photoelectrochemical characteristics. Photoluminescence characterizations reveal that the presence of vacancies reduces the emission intensity dramatically, indicating a low recombination rate of photogenerated charge carriers due to the self-introduced trapping states within the bandgap. In addition, the ultra-low charge transfer resistance measured by electrochemical impedance spectroscopy implies the efficient charge transfer of CuSe semiconducting quantum dots-based photoelectrocatalysts, which is guaranteed by the high conductivity of their confined structure as revealed by room-temperature electrical transport measurements. Such high conductivity and low photogenerated charge carriers recombination rate, combined with high-density active sites and confined structure, guaranteeing the remarkable photoelectrocatalytic performance and stability as manifested by photoelectrocatalysis characterizations. This work promotes the development of semiconducting quantum dots-based photoelectrocatalysis and demonstrates CuSe semiconducting quantum confined catalysts as an advanced photoelectrocatalysts for oxygen evolution reaction

Limits on the Weak Equivalence Principle and Photon Mass with FRB 121102 Subpulses

Fast radio bursts (FRBs) are short-duration (~millisecond) radio transients with cosmological origin. The simple sharp features of the FRB signal have been utilized to probe two fundamental laws of physics, namely, testing Einstein\u27s weak equivalence principle and constraining the rest mass of the photon. Recently, Hessels et al. found that after correcting for dispersive delay, some of the bursts in FRB 121102 have complex time–frequency structures that include subpulses with a time–frequency downward drifting property. Using the delay time between subpulses in FRB 121102, here we show that the parameterized post-Newtonian parameter γ is the same for photons with different energies to the level of ... (see full abstract in article)

Case report of a Li-Fraumeni syndrome-like phenotype with a de novo mutation in <i>CHEK2</i>

BACKGROUND: Cases of multiple tumors are rarely reported in China. In our study, a 57-year-old female patient had concurrent squamous cell carcinoma, mucoepidermoid carcinoma, brain cancer, bone cancer, and thyroid cancer, which has rarely been reported to date. METHODS: To determine the relationship among these multiple cancers, available DNA samples from the thyroid, lung, and skin tumors and from normal thyroid tissue were sequenced using whole exome sequencing. RESULTS: The notable discrepancies of somatic mutations among the 3 tumor tissues indicated that they arose independently, rather than metastasizing from 1 tumor. A novel deleterious germline mutation (chr22:29091846, G->A, p.H371Y) was identified in CHEK2, a Li–Fraumeni syndrome causal gene. Examining the status of this novel mutation in the patient's healthy siblings revealed its de novo origin. CONCLUSION: Our study reports the first case of Li–Fraumeni syndrome-like in Chinese patients and demonstrates the important contribution of de novo mutations in this type of rare disease

Detection and analysis of human papillomavirus (HPV) DNA in breast cancer patients by an effective method of HPV capture

Despite an increase in the number of molecular epidemiological studies conducted in recent years to evaluate the association between human papillomavirus (HPV) and the risk of breast carcinoma, these studies remain inconclusive. Here we aim to detect HPV DNA in various tissues from patients with breast carcinoma using the method of HPV capture combined with massive paralleled sequencing (MPS). To validate the confidence of our methods, 15 cervical cancer samples were tested by PCR and the new method. Results showed that there was 100% consistence between the two methods.DNA from peripheral blood, tumor tissue, adjacent lymph nodes and adjacent normal tissue were collected from seven malignant breast cancer patients, and HPV type 16(HPV16) was detected in 1/7, 1/7, 1/7and 1/7 of patients respectively. Peripheral blood, tumor tissue and adjacent normal tissue were also collected from two patients with benign breast tumor, and 1/2, 2/2 and 2/2 was detected to have HPV16 DNA respectively. MPS metrics including mapping ratio, coverage, depth and SNVs were provided to characterize HPV in samples. The average coverage was 69% and 61.2% for malignant and benign samples respectively. 126 SNVs were identified in all 9 samples. The maximum number of SNVs was located in the gene of E2 and E4 among all samples. Our study not only provided an efficient method to capture HPV DNA, but detected the SNVS, coverage, SNV type and depth. The finding has provided further clue of association between HPV16 and breast cancer