On the Effectiveness of ASR Representations in Real-world Noisy Speech Emotion Recognition

This paper proposes an efficient attempt to noisy speech emotion recognition (NSER). Conventional NSER approaches have proven effective in mitigating the impact of artificial noise sources, such as white Gaussian noise, but are limited to non-stationary noises in real-world environments due to their complexity and uncertainty. To overcome this limitation, we introduce a new method for NSER by adopting the automatic speech recognition (ASR) model as a noise-robust feature extractor to eliminate non-vocal information in noisy speech. We first obtain intermediate layer information from the ASR model as a feature representation for emotional speech and then apply this representation for the downstream NSER task. Our experimental results show that 1) the proposed method achieves better NSER performance compared with the conventional noise reduction method, 2) outperforms self-supervised learning approaches, and 3) even outperforms text-based approaches using ASR transcription or the ground truth transcription of noisy speech.Comment: Submitted to ICASSP 202

A worldwide bibliometric analysis of malignant peripheral nerve sheath tumors from 2000 to 2022

BackgroundCurrently, malignant peripheral nerve sheath tumors (MPNST) are the subject of intense research interest. However, bibliometric studies have not been conducted in this field. The purpose of the study was to identify historical trends and presents a bibliometric analysis of the MPNST literature from 2000 to 2022.MethodsFor the bibliometric analysis, publications were retrieved from the Web of Science database based on the following search terms: [TI = (MPNST) OR TI= (malignant peripheral nerve sheath tumors) AND PY = (2000–2022)]. The following information was collected for each document: the publication trends and geographical distribution, important authors and collaboration, keyword distribution and evaluation, most popular journals, and most influential articles.ResultsWe included 1400 documents for bibliometric analysis, covering five categories: 824 articles, 17 proceedings papers, 68 letters, 402 meeting abstracts, and 89 reviews. Corrections, editorials, book chapters, data papers, publications with expressed concerns, and retractions were excluded from our research.ConclusionSince 2000, the number of publications on MPNST has continuously increased. Among all countries that contributed to the MPNST research, the USA, Japan, and China were the three most productive countries. The journal Modern Pathology has the most publications on MPNST, while those in the Cancer Research journal were the most frequently cited. The University of Texas MD Anderson Cancer Center may be a good partner to collaborate with. Recent research trends in MPNST have focused on tumorigenesis, clinical management, and predictive biomarkers

Patient-derived organoids as a platform for drug screening in metastatic colorectal cancer

Introduction: Most advanced colorectal cancers are aggressive, and there is a lack of effective methods for selecting appropriate anticancer regimens. Patient-derived organoids (PDOs) have emerged as preclinical platforms for modeling clinical responses to cancer therapy.Methods: In this study, we successfully constructed a living biobank with 42 organoids derived from primary and metastatic lesions of metastatic colorectal cancer patients. Tumor tissue was obtained from patients undergoing surgical resection of the primary or metastatic lesion and then used to establish PDOs. Immunohistochemistry (IHC) and drug sensitivity assays were performed to analyze the properties of these organoids.Results: The mCRC organoids were successfully established with an 80% success rate. The PDOs maintained the genetic and phenotypic heterogeneity of their parental tumors. The IC50 values of5-fluorouracil (5-FU), oxaliplatin, and irinotecan (CPT11) were determined for mCRC organoids using drug sensitivity assays. The in vitro chemosensitivity data revealed the potential value of PDOs for clinical applications in predicting chemotherapy response and clinical outcomes in mCRC patients.Discussion: In summary, the PDO model is an effective platform for in vitro assessment of patient-specific drug sensitivity, which can guide personalized treatment decisions for patients with end-stage CRC

Unsupervised discovery of solid-state lithium ion conductors

Partial funding for Open Access provided by the UMD Libraries' Open Access Publishing Fund.Although machine learning has gained great interest in the discovery of functional materials, the advancement of reliable models is impeded by the scarcity of available materials property data. Here we propose and demonstrate a distinctive approach for materials discovery using unsupervised learning, which does not require labeled data and thus alleviates the data scarcity challenge. Using solid-state Li-ion conductors as a model problem, unsupervised materials discovery utilizes a limited quantity of conductivity data to prioritize a candidate list from a wide range of Li-containing materials for further accurate screening. Our unsupervised learning scheme discovers 16 new fast Li-conductors with conductivities of 10−4–10−1 S cm−1 predicted in ab initio molecular dynamics simulations. These compounds have structures and chemistries distinct to known systems, demonstrating the capability of unsupervised learning for discovering materials over a wide materials space with limited property data

First Principle Computational Study of Fast Ionic Conductors

Fast ionic conductors have great potential to enable novel technologies in energy storage and conversion. However, it is not yet understood why only a few materials can deliver exceptionally higher ionic conductivity than typical solids or how on can design fast ion conductors following simple principles. In this dissertation, I applied first principles computational method to understanding the fast ionic diffusion within fast ionic conductors and I demonstrated a conceptually simple framework for guiding the design of super-ionic conductor materials. I studied Na0.5Bi0.5TiO3 (NBT) as the model material for oxygen ionic conductor. The structure-property relationship of the NBT materials is established. Based on the newly gained materials understanding, our first principles computation predicted that Na and K were promising dopants to increase oxygen ionic conductivity. The newly designed NBT materials with A-site Na and K substituted A sites exhibited a many-fold increase in the ionic conductivity at 900K comparing to that in the experimental compound. We demonstrated that the concerted migration mechanism with low energy barrier is the universal mechanism of fast ionic diffusion in a broad range of ionic conducting materials. Our theory provides a conceptually simple framework for guiding the design of super-ionic conductor materials, that is, inserting mobile ions into high-energy sites to activate concerted ion conduction with lower migration barriers. We demonstrated this strategy by designing a number of novel fast Li-ion conducting materials to activate concerted migration with reduced diffusion barrier. We identified the common features of crystal structural framework for lithium SICs. Based on the determined attributes, we performed a high-throughput screening of all lithium-containing oxide and sulfide compounds. The screening revealed several crystal structures that are potential to be fast ion conductors. Through aliovalent doping, we modified the Li content of these structures which resulting in different Li sublattice within the structure and we found a number of lithium super- ionic conductors that are predicted to have Li+ conductivities greater than 0.1 mS/cm at 300K

Discrepancies and error evaluation metrics for machine learning interatomic potentials

Abstract Machine learning interatomic potentials (MLIPs) are a promising technique for atomic modeling. While small errors are widely reported for MLIPs, an open concern is whether MLIPs can accurately reproduce atomistic dynamics and related physical properties in molecular dynamics (MD) simulations. In this study, we examine the state-of-the-art MLIPs and uncover several discrepancies related to atom dynamics, defects, and rare events (REs), compared to ab initio methods. We find that low averaged errors by current MLIP testing are insufficient, and develop quantitative metrics that better indicate the accurate prediction of atomic dynamics by MLIPs. The MLIPs optimized by the RE-based evaluation metrics are demonstrated to have improved prediction in multiple properties. The identified errors, the evaluation metrics, and the proposed process of developing such metrics are general to MLIPs, thus providing valuable guidance for future testing and improvements of accurate and reliable MLIPs for atomistic modeling

Statistical variances of diffusional properties from ab initio molecular dynamics simulations

Ionic diffusion: Mapping the uncertainty The calculation of ionic diffusivity with ab initio molecular dynamics is often plagued by poor statistics; how accurate are the results? Due to computational limits only small systems and timescales can be modeled, limiting the number of diffusion events sampled. Here, Yifei Mo and colleagues at the University of Maryland outline best practice to obtain ionic diffusivity, as well as how to obtain statistical errors with this approach. They show linear behavior in diffusivity only happens for intermediate time intervals of the simulations. Moreover, variance of diffusivity is related to the total mean squared displacement of all ions, the statistical error reducing as the displacement increases. Accurate ionic diffusion calculations can only be performed for super-ionic conductors, or at high temperature, with implications for the reliability of calculations of diffusivity in other materials

Origin of Outstanding Stability in the Lithium Solid Electrolyte Materials: Insights from Thermodynamic Analyses Based on First-Principles Calculations

First-principles calculations were performed to investigate the electrochemical stability of lithium solid electrolyte materials in all-solid-state Li-ion batteries. The common solid electrolytes were found to have a limited electrochemical window. Our results suggest that the outstanding stability of the solid electrolyte materials is not thermodynamically intrinsic but is originated from kinetic stabilizations. The sluggish kinetics of the decomposition reactions cause a high overpotential leading to a nominally wide electrochemical window observed in many experiments. The decomposition products, similar to the solid-electrolyte-interphases, mitigate the extreme chemical potential from the electrodes and protect the solid electrolyte from further decompositions. With the aid of the first-principles calculations, we revealed the passivation mechanism of these decomposition interphases and quantified the extensions of the electrochemical window from the interphases. We also found that the artificial coating layers applied at the solid electrolyte and electrode interfaces have a similar effect of passivating the solid electrolyte. Our newly gained understanding provided general principles for developing solid electrolyte materials with enhanced stability and for engineering interfaces in all-solid-state Li-ion batteries

Infection of post-harvest peaches by Monilinia fructicola accelerates sucrose decomposition and stimulates the Embden–Meyerhof–Parnas pathway

Fruit fungal infection: Effects on sugar metabolism A study by researchers in China provides insights into changes to sugar metabolism during fungal infection in post-harvest peaches. Fungal infections and associated disease development can alter sugar metabolism in post-harvest fruits, leading to rapid decay and a short shelf life. However, little is known about the mechanisms behind these fungal infections. Xingfeng Shao and Yingying Wei at Ningbo University and co-workers examined the effect of brown rot, caused by a fungus called Monilinia fructicola, on sugar metabolism in two peach cultivars kept under chilled conditions. As the disease progressed, the fruit increased its energy supply by decomposing sucrose and generating more glucose. The team uncovered the major enzymes responsible for this sucrose decomposition. The increased glucose stimulated the Embden-Meyerhof-Parnas pathway, which in turn helped the peaches fight infection

Polarization properties of calibration reflector system in the polarization-modulated space laser communication

S-polarization light and p-polarization light are mutually cross orthogonal, which can be used as signal light for emitting and receiving in the polarization-modulated space laser communication, respectively. Due to the retro reflection characteristics of the corner cube retroreflector (CCR), it is widely used as a calibration reflector system in the polarization-modulated space laser communication. The polarization states of the incident light will be change owing to the total internal reflection (TIR) of uncoated rear surface, in addition, each of the six propagation trips will in general produce a different output polarization. For the calibration reflector system in the polarization-modulated space laser communication, the polarization state of the received light, especially the intensity ratio of the p-polarization component, needs to be clarified. In this paper, a framework is presented to calculate polarization by ray tracing through CCR with arbitrary input polarization states and incident angles. On this basis, the relationships between intensity ratio of the p-polarization component in the received light of each propagation trip and the incident light with different polarization states at normal incidence as well as the circular polarized light at incident angles within +/- 15 degrees analyzed. Theoretical analysis and experiments have guiding significance for the development of the polarization-modulated space laser communication