Miracle-dz/UniFAR-FTUs huggingface dataset:
Cluster-Level Contrastive Learning for Emotion Recognition in Conversations

A key challenge for Emotion Recognition in Conversations (ERC) is to distinguish semantically similar emotions. Some works utilise Supervised Contrastive Learning (SCL) which uses categorical emotion labels as supervision signals and contrasts in high-dimensional semantic space. However, categorical labels fail to provide quantitative information between emotions. ERC is also not equally dependent on all embedded features in the semantic space, which makes the high-dimensional SCL inefficient. To address these issues, we propose a novel low-dimensional Supervised Cluster-level Contrastive Learning (SCCL) method, which first reduces the high-dimensional SCL space to a three-dimensional affect representation space Valence-Arousal-Dominance (VAD), then performs cluster-level contrastive learning to incorporate measurable emotion prototypes. To help modelling the dialogue and enriching the context, we leverage the pre-trained knowledge adapters to infuse linguistic and factual knowledge. Experiments show that our method achieves new state-of-the-art results with <inline-formula><tex-math notation="LaTeX">$69.81\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>69</mml:mn><mml:mo>. </mml:mo><mml:mn>81</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq1-3243463.gif"/></alternatives></inline-formula> on IEMOCAP, <inline-formula><tex-math notation="LaTeX">$65.7\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>65</mml:mn><mml:mo>. </mml:mo><mml:mn>7</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq2-3243463.gif"/></alternatives></inline-formula> on MELD, and <inline-formula><tex-math notation="LaTeX">$62.51\%$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>62</mml:mn><mml:mo>. </mml:mo><mml:mn>51</mml:mn><mml:mo>%</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="yang-ieq3-3243463.gif"/></alternatives></inline-formula> on DailyDialog datasets. The analysis also proves that the VAD space is not only suitable for ERC but also interpretable, with VAD prototypes enhancing its performance and stabilising the training of SCCL. In addition, the pre-trained knowledge adapters benefit the performance of the utterance encoder and SCCL. Our code is available at: <uri>https://github.com/SteveKGYang/SCCL</uri>
Causally-Interpretable Random-Effects Meta-Analysis

Recent work has made important contributions in the development of causally-interpretable meta-analysis. These methods transport treatment effects estimated in a collection of randomized trials to a target population of interest. Ideally, estimates targeted toward a specific population are more interpretable and relevant to policy-makers and clinicians. However, between-study heterogeneity not arising from differences in the distribution of treatment effect modifiers can raise difficulties in synthesizing estimates across trials. The existence of such heterogeneity, including variations in treatment modality, also complicates the interpretation of transported estimates as a generic effect in the target population. We propose a conceptual framework and estimation procedures that attempt to account for such heterogeneity, and develop inferential techniques that aim to capture the accompanying excess variability in causal estimates. This framework also seeks to clarify the kind of treatment effects that are amenable to the techniques of generalizability and transportability.
Multivariate Bayesian dynamic modeling for causal prediction

Bayesian dynamic modeling and forecasting is developed in the setting of sequential time series analysis for causal inference. Causal evaluation of sequentially observed time series data from control and treated units focuses on the impacts of interventions using synthetic control constructs. Methodological contributions include the development of multivariate dynamic models for time-varying effects across multiple treated units and explicit foci on sequential learning of effects of interventions. Analysis explores the utility of dimension reduction of multiple potential synthetic control variables. These methodological advances are evaluated in a detailed case study in commercial forecasting. This involves in-study evaluation of interventions in a supermarket promotions experiment, with coupled predictive analyses in selected regions of a large-scale commercial system. Generalization of causal predictive inferences from experimental settings to broader populations is a central concern, and one that can be impacted by cross-series dependencies.
ClueGAIN: Application of Transfer Learning On Generative Adversarial Imputation Nets (GAIN)

Many studies have attempted to solve the problem of missing data using various approaches. Among them, Generative Adversarial Imputation Nets (GAIN) was first used to impute data with Generative Adversarial Nets (GAN) and good results were obtained. Subsequent studies have attempted to combine various approaches to address some of its limitations. ClueGAIN is first proposed in this study, which introduces transfer learning into GAIN to solve the problem of poor imputation performance in high missing rate data sets. ClueGAIN can also be used to measure the similarity between data sets to explore their potential connections.
Leveraging Demonstrations to Improve Online Learning: Quality Matters

We investigate the extent to which offline demonstration data can improve online learning. It is natural to expect some improvement, but the question is how, and by how much? We show that the degree of improvement must depend on the quality of the demonstration data. To generate portable insights, we focus on Thompson sampling (TS) applied to a multi-armed bandit as a prototypical online learning algorithm and model. The demonstration data is generated by an expert with a given competence level, a notion we introduce. We propose an informed TS algorithm that utilizes the demonstration data in a coherent way through Bayes' rule and derive a prior-dependent Bayesian regret bound. This offers insight into how pretraining can greatly improve online performance and how the degree of improvement increases with the expert's competence level. We also develop a practical, approximate informed TS algorithm through Bayesian bootstrapping and show substantial empirical regret reduction through experiments.
Towards Efficient Trajectory Generation for Ground Robots beyond 2D Environment

With the development of robotics, ground robots are no longer limited to planar motion. Passive height variation due to complex terrain and active height control provided by special structures on robots require a more general navigation planning framework beyond 2D. Existing methods rarely considers both simultaneously, limiting the capabilities and applications of ground robots. In this paper, we proposed an optimization-based planning framework for ground robots considering both active and passive height changes on the z-axis. The proposed planner first constructs a penalty field for chassis motion constraints defined in $\mathbb{R}^{3}$ such that the optimal solution space of the trajectory is continuous, resulting in a high-quality smooth chassis trajectory. Also, by constructing custom constraints in the z-axis direction, it is possible to plan trajectories for different types of ground robots which have z-axis degree of freedom. We performed simulations and real-world experiments to verify the efficiency and trajectory quality of our algorithm.
Heterophily-Aware Graph Attention Network

Graph Neural Networks (GNNs) have shown remarkable success in graph representation learning. Unfortunately, current weight assignment schemes in standard GNNs, such as the calculation based on node degrees or pair-wise representations, can hardly be effective in processing the networks with heterophily, in which the connected nodes usually possess different labels or features. Existing heterophilic GNNs tend to ignore the modeling of heterophily of each edge, which is also a vital part in tackling the heterophily problem. In this paper, we firstly propose a heterophily-aware attention scheme and reveal the benefits of modeling the edge heterophily, i.e., if a GNN assigns different weights to edges according to different heterophilic types, it can learn effective local attention patterns, which enable nodes to acquire appropriate information from distinct neighbors. Then, we propose a novel Heterophily-Aware Graph Attention Network (HA-GAT) by fully exploring and utilizing the local distribution as the underlying heterophily, to handle the networks with different homophily ratios. To demonstrate the effectiveness of the proposed HA-GAT, we analyze the proposed heterophily-aware attention scheme and local distribution exploration, by seeking for an interpretation from their mechanism. Extensive results demonstrate that our HA-GAT achieves state-of-the-art performances on eight datasets with different homophily ratios in both the supervised and semi-supervised node classification tasks.
The θ-formulation of the two-dimensional elastica: buckling and boundary layer theory

The equations of a planar elastica under pressure can be rewritten in a useful form by parameterizing the variables in terms of the local orientation angle, θ, instead of the arc length. This ‘θ-formulation’ lends itself to a particularly easy boundary layer analysis in the limit of weak bending stiffness. Within this parameterization, boundary layers are located at inflection points, where θ is extremum, and they connect regions of low and large curvature. A simple composite solution is derived without resorting to elliptic functions and integrals. This approximation can be used as an elementary building block to describe complex shapes. Applying this theory to the study of an elastic ring under uniform pressure and subject to a set of point forces, we discover a snapping instability. This instability is confirmed by numerical simulations. Finally, we carry out experiments and find good agreement of the theory with the experimental shape of the deformed elastica.
Tool interoperability for model-based systems engineering

Supervisory control design of cyber-physical systems has many challenges. Model-based systems engineering can address these, with solutions originating from various disciplines. We discuss several tools, each state-of-the-art in its own discipline, offering functionality such as specification, synthesis, and verification. Integrating such mono-disciplinary tools in a multi-disciplinary workflow is a major challenge. We present Analytics as a Service, built on the Arrowhead framework, to connect these tools and make them interoperable. A seamless integration of the tools has been established through a service-oriented architecture: The engineer can easily access the functionality of the tools from a single interface, as translation steps between equivalent models for the respective tools are automated.
Memory Conductance Switching in a Ni–Ti–O Compound Thin Film

Nonvolatile conductance switching phenomena in a nickel-titanium oxide compound thin film were investigated for memory device applications. On/off switching ratios as high as ∼104 were observed. Whereas the low-resistance state (LRS) showed good retention and endurance properties, the high resistance state (HRS) showed unstable switching properties. The temperature dependence of the LRS and HRS revealed that the switching mechanism is fundamentally based on the repeated process of the electroforming and the rupture of conducting filaments. The authors suggest that relatively weaker chemical binding between the Ti and O causes the unstable electrical conduction in the HRS.
Skeletal muscle area predicts the outcomes of non-small-cell lung cancer after trimodality therapy

Abstract OBJECTIVES Sarcopenia correlates with poor prognosis in various malignancies. However, the prognostic significance of sarcopenia remains to be determined in patients with non-small-cell lung cancer who undergo surgery after receiving neoadjuvant chemoradiotherapy (NACRT). METHODS We retrospectively reviewed the patients with stage II/III non-small-cell lung cancer who underwent surgery following NACRT. The paravertebral skeletal muscle area (SMA) (cm2) at the 12th thoracic vertebra level was measured. We calculated the SMA index (SMAI) as SMA/squared height (cm2/m2). Patients were divided into low and high SMAI groups, and the association of SMAI with clinicopathological factors and prognosis was assessed. RESULTS The patients’ [men, 86 (81.1%)] median age was 63 (21–76) years. There were 106 patients including 2 (1.9%), 10 (9.4%), 74 (69.8%), 19 (17.9%) and 1 (0.9%) patients with stage IIA, IIB, IIIA, IIIB and IIIC, respectively. Of the patients, 39 (36.8%) and 67 (63.2%) were classified in the low and the high SMAI groups, respectively. Kaplan–Meier analysis showed that the low group had a significantly shorter overall survival and disease-free survival than the high group. Multivariable analysis identified low SMAI as an independent poor prognostic factor for overall survival. CONCLUSIONS Pre-NACRT SMAI correlates with poor prognosis; therefore, assessing sarcopenia based on pre-NACRT SMAI may help determine optimal treatment strategies and suitable nutritional and exercise interventions.
CHALLENGES IN THE MANAGEMENT OF HIGH-RISK MULTIPLE MYELOMA

JULY 2022 BLOOD CANCER DISCOVERY | 273 Summary: The multiple myeloma treatment landscape has changed dramatically. This change, paralleled by an increase in scientific knowledge, has resulted in significant improvement in survival. However, heterogeneity remains in clinical outcomes, with a proportion of patients not benefiting from current approaches and continuing to have a poor prognosis. A significant proportion of the variability in outcome can be predicted on the basis of clinical and biochemical parameters and tumor-acquired genetic variants, allowing for risk stratification and a more personalized approach to therapy. This article discusses the principles that can enable the rational and effective development of therapeutic approaches for high-risk multiple myeloma. PERSPECTIVE
Cold atmospheric pressure air plasma jet for medical applications

By flowing atmospheric pressure air through a direct current powered microhollow cathode discharge, we were able to generate a 2cm long plasma jet. With increasing flow rate, the flow becomes turbulent and temperatures of the jet are reduced to values close to room temperature. Utilizing the jet, yeast grown on agar can be eradicated with a treatment of only a few seconds. Conversely, animal studies show no skin damage even with exposures ten times longer than needed for pathogen extermination. This cold plasma jet provides an effective mode of treatment for yeast infections of the skin.
INTERVAL ASYMPTOTIC ESTIMATOR FOR CHLORINE MONITORING IN DRINKING WATER DISTRIBUTION SYSTEMS

Abstract Asymptotic and interval observer for quality monitoring in drinking water distribution systems is derived in this paper. It produces robust interval bounds on the estimated state variables of the water quality. Solving two differential equations generates the bounds; hence the numerical efficiency is sufficient for on-line monitoring of the water quality. The observer is applied to example water network and tight bounds-estimates are obtained.
The SET domain protein PsKMT3 regulates histone H3K36 trimethylation and modulates effector gene expression in the soybean pathogen Phytophthora sojae

Abstract Plant pathogens secrete effector proteins to overcome host immunity and promote colonization. In oomycete plant pathogens, the expression of many effector genes is altered upon infection; however, the regulatory mechanisms are unclear. In this study, we identified a su(var)3–9, enhancer of zeste, and trithorax (SET) domain protein‐encoding gene, PsKMT3, that was highly induced at early infection stages in Phytophthora sojae. Deletion of PsKMT3 led to asexual development and pathogenicity defects. Chromatin immunoprecipitation followed by sequencing (ChIP‐seq) and western blot analyses demonstrated that histone H3K36 trimethylation (H3K36me3) was significantly reduced genome‐wide in mutants. RNA‐seq analysis identified 374 genes encoding secreted proteins that were differentially expressed in pskmt3 at the mycelium stage. The significantly altered genes encompassed the RxLR (Arg‐x‐Lys‐Arg) effector gene family, including the essential effector genes Avh23, Avh181, Avh240, and Avh241. Transcriptome analysis at early infection stages showed misregulation of effector gene expression waves in pskmt3. H3K36me3 was directly and indirectly associated with RxLR effector gene activation. Our results reveal a role of a SET domain protein in regulating effector gene expression and modulating histone methylation in P. sojae.
Memory Effect on the Survival of Deinococcus radiodurans after Exposure in Near Space

Earth’s near space is an extreme environment with high radiation and extreme cold. Which factors affect the survival of microbes in near space is related to many studies, such as studies of radioresistance mechanisms, panspermia hypothesis, long-distance microbial transfer, and developing extraterrestrial habitats. ABSTRACT Near space (20 to 100 km in altitude) is an extreme environment with high radiation and extreme cold, making it diﬃcult for organisms to survive. However, many studies had shown that there were still microbes living in this extremely harsh environment. It was particularly important to study which factors affected the survival of microorganisms living in near space after exposure to irradiation, as this was related to many studies, such as studies of radioresistance mechanisms, panspermia hypothesis, long-distance microbial transfer, and developing extraterrestrial habitats. Survival after radiation was probably influenced by the growth condition before radiation, which is called the memory effect. In this research, we used different growth conditions to affect the growth of Deinococcus radiodurans and lyophilized bacteria in exponential phase to maintain the physiological state at this stage. Then high-altitude scientific balloon exposure experiments were carried out by using the Chinese Academy of Sciences Balloon-Borne Astrobiology Platform (CAS-BAP) at Dachaidan, Qinghai, China (37°44′N, 95°21′E). The aim was to investigate which factors influence survival after near-space exposure. The results suggested that there was a memory effect on the survival of D. radiodurans after exposure. If the differences in growth rate were caused by differences in nutrition, the survival rate and growth rate were positively correlated. Moreover, the addition of paraquat and Mn2+ during the growth phase can also increase survival. This finding may help to deepen the understanding of the mechanics of radiation protection and provide relevant evidence for many studies, such as of long-distance transfer of microorganisms in near space. IMPORTANCE Earth’s near space is an extreme environment with high radiation and extreme cold. Which factors affect the survival of microbes in near space is related to many studies, such as studies of radioresistance mechanisms, panspermia hypothesis, long-distance microbial transfer, and developing extraterrestrial habitats. We performed several exposure experiments with Deinococcus radiodurans in near space to investigate which factors influence the survival rate after near-space exposure; that is, there was a relationship between survival after radiation and the growth condition before radiation. The results suggested that there was a memory effect on the survival of D. radiodurans after exposure. This finding may help to deepen the understanding of the mechanism of radiation protection and provide relevant evidence for many studies, such as of long-distance transfer of microorganisms in near space.
Cardiometabolic HFpEF: NASH of the Heart

Identifying the correct clinical and pathophysiological framework for complex syndromes is often challenging. With heart failure with preserved ejection fraction (HFpEF), we probably got off on the wrong foot. We have long considered patients with HFpEF as a subset of patients with heart failure who do not have reduced left ventricular ejection fraction. As our knowledge of HFpEF grows, we recognize the following: (1) HFpEF is a multisystemic syndrome with prominent extracardiac involvement; (2) HFpEF is not a single disease; a left ventricular ejection fraction >50% is rather a common feature of several distinct clinical phenotypes; and (3) the only drugs proven to be beneficial in HFpEF so far (sodium-glucose cotransporter 2 inhibitors [SGLT2is]) do not primarily target the heart (despite having an effect on the myocardium). These insights led to the recognition that several comorbidities produce different HFpEF phenotypes, each requiring tailored approaches and specific therapeutic targets. Considering 3 of the main comorbidities for HFpEF— metabolic stress/obesity, hypertension, and aging—the combination of obesity and hypertension arguably elicits the most prevalent HFpEF phenotype. This HFpEF phenotype has been defined as cardiometabolic HFpEF, a syndrome in which the combination of lipid accumulation and activation of maladaptive inflammatory pathways leads to structural and functional cell impairment, progressive development of fibrosis, and consequent organ dysfunction. Intriguingly, this description reminds us of another common syndrome: nonalcoholic steatohepatitis (NASH). More than 2 decades ago, it was hypothesized that NASH occurs as a consequence of 2 pathogenetic “hits”: lipid accumulation and inflammation (coupled with oxidative stress and fibrosis). On this basis, we submit that shared pathogenetic events are key to the development and progression of both NASH and cardiometabolic HFpEF.
Advancements and Technical Considerations for Extracellular Vesicle Isolation and Biomarker Identification in Glioblastoma

Extracellular vesicles (EVs) are membrane-bound particles released by all cells. Previous research has found that these microscopic vesicles contribute to intercellular signaling and communication. EVs carry a variety of cargo, including nucleic acids, proteins, metabolites, and lipids. The composition of EVs varies based on cell of origin. Therefore, EVs can serve as an important biomarker in the diagnosis and treatment of various cancers. EVs derived from glioblastoma (GBM) cells carry biomarkers, which could serve as the basis for a potential diagnostic strategy known as liquid biopsy. Multiple EV isolation techniques exist, including ultrafiltration, size exclusion chromatography, flow field-flow fractionation, sequential filtration, differential ultracentrifugation, and density-gradient ultracentrifugation. Recent and ongoing work aims to identify cellular markers to distinguish GBM-derived EVs from those released by noncancerous cells. Strategies include proteomic analysis of GBM EVs, identification of GBM-specific metabolites, and use of Food and Drug Administration-approved 5-aminolevulinic acid—an oral agent that causes fluorescence of GBM cells—to recognize GBM EVs in a patient's blood. In addition, accurately and precisely monitoring changes in EV cargo concentrations could help differentiate between pseudoprogression and GBM recurrence, thus preventing unnecessary surgical interventions.
Ionosphere-induced cross polarization of circularly polarized UHF propagation

To explore the possibility of more efficient spectrum use in the UHF satellite communication band, ionosphere-induced cross polarization of circularly polarized waves was investigated. The Poincare sphere is used to obtain a simple closed-form expression of cross polarization for propagation at an arbitrary angle with respect to the geomagnetic field. The predicted maximum cross polarization at 250 MHz is -25 dB. >