Miracle-dz/UniFAR-FTUs huggingface dataset:
Drinking Motives, Alcohol Misuse, and Internalizing and Externalizing Psychopathology across College: A Cross-Lagged Panel Study

Abstract Background: Drinking motives are strong proximal predictors of alcohol use behaviors and may represent a mediational mechanism by which different individual predispositions toward internalizing or externalizing psychopathology lead to the development of alcohol misuse. However, whether the association is due to a causal relationship or a shared etiology (i.e., confounding) is difficult to determine and may change across developmental periods. Methods: This study leveraged a cross-lagged panel design to disentangle the nature of the relationships between self-report measures of drinking motives, alcohol misuse, and internalizing and externalizing psychopathology in a 4-year longitudinal sample of college students (N = 9,889). Results: Results pointed to a putative causal effect of drinking motives on early binge drinking frequency, but the direction of effect later reversed, reflecting a possible developmental shift during college. On the other hand, the relationships between drinking motives and internalizing/externalizing psychopathology appeared to be driven by shared etiology rather than direct causal mechanisms. Conclusions: These findings highlight the distinct and important role of drinking motives in the etiology of alcohol misuse and have implications for the application of tailored prevention and treatment strategies.
Comment on "Gauss-Bonnet inflation"

Recently, an interesting inflationary scenario, named Gauss-Bonnet inflation, was proposed by Kanti et al. [Phys. Rev. D 92, 041302 (2015); Phys. Rev. D 92, 083524 (2015)]. In the model, there is no inflaton potential, but the inflaton couples to the Guass-Bonnet term. In the case of quadratic coupling, they find inflation occurs with a graceful exit. The scenario is attractive because of the natural setup. However, we show there exists a gradient instability in the tensor perturbations in this inflationary model. We further prove the no-go theorem for Gauss-Bonnet inflation without an inflaton potential.
Filtration of environmentally sourced aquatic media impacts laboratory‐colonised Aedes albopictus early development and adult bacteriome composition

Microorganisms form close associations with metazoan hosts forming symbiotic communities, known as microbiomes, that modulate host physiological processes. Mosquitoes are of special interest in exploring microbe‐modulated host processes due to their oversized impact on human health. However, most mosquito work is done under controlled laboratory conditions where natural microbiomes are not present and inferences from these studies may not extend to natural populations. Here we attempt to assemble a wild‐resembling bacteriome under laboratory conditions in an established laboratory colony of Aedes albopictus using aquatic media from environmentally‐exposed and differentially filtered larval habitats. While we did not successfully replicate a wild bacteriome using these filtrations, we show that these manipulations alter the bacteriomes of mosquitoes, generating a unique composition not seen in wild populations collected from and near our source water or in our laboratory colony. We also demonstrate that our filtration regimens impact larval development times, as well as impact adult survival on different carbohydrate diets.
Feature Selection in Spectral Clustering

Spectral clustering is a powerful technique in clustering specially when the structure of data is not linear and classical clustering methods lead to fail. In this paper, we propose a spectral clustering algorithm with a feature selection schema based on extracted features of Kernel PCA. In the proposed algorithm, selecting appropriate vectors is dependent upon entropy of clusters on these vectors and weighting method is influenced by sum of the existence gap between clusters and entropy of the vectors. Tuning the parameters has a great effect on the results of spectral clustering techniques. In the ideal case, comparing our method with NJW and Kernel K-Means indicate the successful of the proposed algorithm.
Comprehensive immunoprofiling and systematic adjuvant comparisons for identifying suitable vaccine: Adjuvant pairings

ABSTRACT Adjuvants are critical components of vaccines that enhance the host immune response to the vaccine antigen, however, only a small number of adjuvants are used in vaccines approved for human use. This is in part due to the slow process of novel adjuvants advancing from preclinical models to human studies, and modest mechanistic insights obtained using standard immunological methods to justify selection of a particular adjuvant for clinical evaluation. Here, we discuss several aspects of current adjuvant research and strategies to better assess the complex pathways triggered by adjuvant candidates that can increase adjuvanticity and vaccine efficacy while minimizing reactogenicity. We propose a more systematic use of broad immunoprofiling, coupled with data integration using computational and mathematical modeling. This comprehensive evaluation of the host immune response will facilitate the selection of the most appropriate adjuvant for a vaccine, ultimately leading to the expeditious evaluation of novel adjuvants for vaccines against emerging infectious diseases, which will prove especially valuable during a pandemic where speed is of the essence when developing vaccines.
Predication and specification in the syntax of cleft sentences

This paper reviews the differences between predicational and specificational copular sentences in the realm of (pseudo-)cleft constructions, and proposes an analysis which treats the it of specificational it -clefts as a pro-predicate that inverts with its subject in the course of the syntactic derivation. In contrastive-focus it -clefts, the sentence-final relative clause is a right-dislocated headless relative dependent on a formal licensing relationship with the operator inside the relative clause and a content-licensing relationship with the focus. This dual licensing dependency explains the restrictions on the distribution of which as the relative clause operator in contrastive-focus it -clefts. Continuous-topic it -clefts are structurally assimilated to pseudorelative constructions, which accounts for the restrictions on the realisation of the left periphery of their relative clause. Keywords: it-cleft; predication; specification; headless relative; asyndetic specification
Pervasive RNA folding is crucial for narnavirus genome maintenance

Significance Artificial RNA replicons could serve as a platform to study the origin of life as well as to promote new technological advancement. To engineer an RNA replicon, understanding the structure–function relationship of the sequence is critical. To gain a deeper insight into it, we focused on the virus family Narnaviridae that have a simple single-stranded RNA genome. By using a combined approach of computational RNA structure analysis and in vivo examination of each structural element, we found that not only typical terminal RNA structures but also pervasive internal RNA folding of the replicon is critical for the maintenance of the RNA in vivo. This finding implies that taking account of pervasive folding status is important for RNA replicon design.
Improving drug safety with adverse event detection using natural language processing

ABSTRACT Introduction Pharmacovigilance (PV) involves monitoring and aggregating adverse event information from a variety of data sources, including health records, biomedical literature, spontaneous adverse event reports, product labels, and patient-generated content like social media posts, but the most pertinent details in these sources are typically available in narrative free-text formats. Natural language processing (NLP) techniques can be used to extract clinically relevant information from PV texts to inform decision-making. Areas covered We conducted a non-systematic literature review by querying the PubMed database to examine the uses of NLP in drug safety and distilled the findings to present our expert opinion on the topic. Expert opinion New NLP techniques and approaches continue to be applied for drug safety use cases; however, systems that are fully deployed and in use in a clinical environment remain vanishingly rare. To see high-performing NLP techniques implemented in the real setting will require long-term engagement with end users and other stakeholders and revised workflows in fully formulated business plans for the targeted use cases. Additionally, we found little to no evidence of extracted information placed into standardized data models, which should be a way to make implementations more portable and adaptable.
High-resolution daily profiles of tissue adrenal steroids by portable automated collection

Rhythms are intrinsic to endocrine systems, and disruption of these hormone oscillations occurs at very early stages of the disease. Because adrenal hormones are secreted with both circadian and ultradian periods, conventional single–time point measurements provide limited information about rhythmicity and, crucially, do not provide information during sleep, when many hormones fluctuate from nadir to peak concentrations. If blood sampling is attempted overnight, then this necessitates admission to a clinical research unit, can be stressful, and disturbs sleep. To overcome this problem and to measure free hormones within their target tissues, we used microdialysis, an ambulatory fraction collector, and liquid chromatography–tandem mass spectrometry to obtain high-resolution profiles of tissue adrenal steroids over 24 hours in 214 healthy volunteers. For validation, we compared tissue against plasma measurements in a further seven healthy volunteers. Sample collection from subcutaneous tissue was safe, well tolerated, and allowed most normal activities to continue. In addition to cortisol, we identified daily and ultradian variation in free cortisone, corticosterone, 18-hydroxycortisol, aldosterone, tetrahydrocortisol and allo-tetrahydrocortisol, and the presence of dehydroepiandrosterone sulfate. We used mathematical and computational methods to quantify the interindividual variability of hormones at different times of the day and develop “dynamic markers” of normality in healthy individuals stratified by sex, age, and body mass index. Our results provide insight into the dynamics of adrenal steroids in tissue in real-world settings and may serve as a normative reference for biomarkers of endocrine disorders (ULTRADIAN, NCT02934399). Description A wearable microdialysis device allows LC-MS/MS analysis to quantify adrenal steroid hormone variability over 24 hours in healthy ambulatory adults. Editor’s summary Adrenal hormones are highly dynamic but commonly measured only at limited time points. Upton et al. present a flexible waist belt for minimally invasive, dense monitoring of diurnal variability in adrenal steroid hormones in humans. The well-tolerated belt includes a catheter that is inserted into subcutaneous fat, with microdialysis samples collected every 20 minutes for later analysis by LC-MS/MS. Profiling in mainly healthy individuals revealed inter-individual variation in adrenal steroid hormones over 24 hours and coordinated aspects of the hypothalamus-pituitary-adrenal (HPA) axis. Such high-resolution monitoring has the potential to advance the study of hormones in disease and chronobiology more generally. —Catherine Charneski
RSMT: Real-time Stylized Motion Transition for Characters

Styled online in-between motion generation has important application scenarios in computer animation and games. Its core challenge lies in the need to satisfy four critical requirements simultaneously: generation speed, motion quality, style diversity, and synthesis controllability. While the first two challenges demand a delicate balance between simple fast models and learning capacity for generation quality, the latter two are rarely investigated together in existing methods, which largely focus on either control without style or uncontrolled stylized motions. To this end, we propose a Real-time Stylized Motion Transition method (RSMT) to achieve all aforementioned goals. Our method consists of two critical, independent components: a general motion manifold model and a style motion sampler. The former acts as a high-quality motion source and the latter synthesizes styled motions on the fly under control signals. Since both components can be trained separately on different datasets, our method provides great flexibility, requires less data, and generalizes well when no/few samples are available for unseen styles. Through exhaustive evaluation, our method proves to be fast, high-quality, versatile, and controllable. The code and data are available at https://github.com/yuyujunjun/RSMT-Realtime-Stylized-Motion-Transition.
Facial Expression Re-targeting from a Single Character

Video retargeting for digital face animation is used in virtual reality, social media, gaming, movies, and video conference, aiming to animate avatars' facial expressions based on videos of human faces. The standard method to represent facial expressions for 3D characters is by blendshapes, a vector of weights representing the avatar's neutral shape and its variations under facial expressions, e.g., smile, puff, blinking. Datasets of paired frames with blendshape vectors are rare, and labeling can be laborious, time-consuming, and subjective. In this work, we developed an approach that handles the lack of appropriate datasets. Instead, we used a synthetic dataset of only one character. To generalize various characters, we re-represented each frame to face landmarks. We developed a unique deep-learning architecture that groups landmarks for each facial organ and connects them to relevant blendshape weights. Additionally, we incorporated complementary methods for facial expressions that landmarks did not represent well and gave special attention to eye expressions. We have demonstrated the superiority of our approach to previous research in qualitative and quantitative metrics. Our approach achieved a higher MOS of 68% and a lower MSE of 44.2% when tested on videos with various users and expressions.
Leveraging User-Wise SVD for Accelerated Convergence in Iterative ELAA-MIMO Detections

Numerous low-complexity iterative algorithms have been proposed to offer the performance of linear multiple-input multiple-output (MIMO) detectors bypassing the channel matrix inverse. These algorithms exhibit fast convergence in well-conditioned MIMO channels. However, in the emerging MIMO paradigm utilizing extremely large aperture arrays (ELAA), the wireless channel may become ill-conditioned because of spatial non-stationarity, which results in a considerably slower convergence rate for these algorithms. In this paper, we propose a novel ELAA-MIMO detection scheme that leverages user-wise singular value decomposition (UW-SVD) to accelerate the convergence of these iterative algorithms. By applying UW-SVD, the MIMO signal model can be converted into an equivalent form featuring a better-conditioned transfer function. Then, existing iterative algorithms can be utilized to recover the transmitted signal from the converted signal model with accelerated convergence towards zero-forcing performance. Our simulation results indicate that proposed UW-SVD scheme can significantly accelerate the convergence of the iterative algorithms in spatially non-stationary ELAA channels. Moreover, the computational complexity of the UW-SVD is comparatively minor in relation to the inherent complexity of the iterative algorithms.
Sample Complexity for Quadratic Bandits: Hessian Dependent Bounds and Optimal Algorithms

In stochastic zeroth-order optimization, a problem of practical relevance is understanding how to fully exploit the local geometry of the underlying objective function. We consider a fundamental setting in which the objective function is quadratic, and provide the first tight characterization of the optimal Hessian-dependent sample complexity. Our contribution is twofold. First, from an information-theoretic point of view, we prove tight lower bounds on Hessian-dependent complexities by introducing a concept called energy allocation, which captures the interaction between the searching algorithm and the geometry of objective functions. A matching upper bound is obtained by solving the optimal energy spectrum. Then, algorithmically, we show the existence of a Hessian-independent algorithm that universally achieves the asymptotic optimal sample complexities for all Hessian instances. The optimal sample complexities achieved by our algorithm remain valid for heavy-tailed noise distributions, which are enabled by a truncation method.
ProtoGate: Prototype-based Neural Networks with Local Feature Selection for Tabular Biomedical Data

Tabular biomedical data poses challenges in machine learning because it is often high-dimensional and typically low-sample-size. Previous research has attempted to address these challenges via feature selection approaches, which can lead to unstable performance on real-world data. This suggests that current methods lack appropriate inductive biases that capture patterns common to different samples. In this paper, we propose ProtoGate, a prototype-based neural model that introduces an inductive bias by attending to both homogeneity and heterogeneity across samples. ProtoGate selects features in a global-to-local manner and leverages them to produce explainable predictions via an interpretable prototype-based model. We conduct comprehensive experiments to evaluate the performance of ProtoGate on synthetic and real-world datasets. Our results show that exploiting the homogeneous and heterogeneous patterns in the data can improve prediction accuracy while prototypes imbue interpretability.
Generalizable Metric Network for Cross-domain Person Re-identification

Person Re-identification (Re-ID) is a crucial technique for public security and has made significant progress in supervised settings. However, the cross-domain (i.e., domain generalization) scene presents a challenge in Re-ID tasks due to unseen test domains and domain-shift between the training and test sets. To tackle this challenge, most existing methods aim to learn domain-invariant or robust features for all domains. In this paper, we observe that the data-distribution gap between the training and test sets is smaller in the sample-pair space than in the sample-instance space. Based on this observation, we propose a Generalizable Metric Network (GMN) to further explore sample similarity in the sample-pair space. Specifically, we add a Metric Network (M-Net) after the main network and train it on positive and negative sample-pair features, which is then employed during the test stage. Additionally, we introduce the Dropout-based Perturbation (DP) module to enhance the generalization capability of the metric network by enriching the sample-pair diversity. Moreover, we develop a Pair-Identity Center (PIC) loss to enhance the model's discrimination by ensuring that sample-pair features with the same pair-identity are consistent. We validate the effectiveness of our proposed method through a lot of experiments on multiple benchmark datasets and confirm the value of each module in our GMN.
Should I Stop or Should I Go: Early Stopping with Heterogeneous Populations

Randomized experiments often need to be stopped prematurely due to the treatment having an unintended harmful effect. Existing methods that determine when to stop an experiment early are typically applied to the data in aggregate and do not account for treatment effect heterogeneity. In this paper, we study the early stopping of experiments for harm on heterogeneous populations. We first establish that current methods often fail to stop experiments when the treatment harms a minority group of participants. We then use causal machine learning to develop CLASH, the first broadly-applicable method for heterogeneous early stopping. We demonstrate CLASH's performance on simulated and real data and show that it yields effective early stopping for both clinical trials and A/B tests.
Exploring New Frontiers in Agricultural NLP: Investigating the Potential of Large Language Models for Food Applications

This paper explores new frontiers in agricultural natural language processing by investigating the effectiveness of using food-related text corpora for pretraining transformer-based language models. In particular, we focus on the task of semantic matching, which involves establishing mappings between food descriptions and nutrition data. To accomplish this, we fine-tune a pre-trained transformer-based language model, AgriBERT, on this task, utilizing an external source of knowledge, such as the FoodOn ontology. To advance the field of agricultural NLP, we propose two new avenues of exploration: (1) utilizing GPT-based models as a baseline and (2) leveraging ChatGPT as an external source of knowledge. ChatGPT has shown to be a strong baseline in many NLP tasks, and we believe it has the potential to improve our model in the task of semantic matching and enhance our model's understanding of food-related concepts and relationships. Additionally, we experiment with other applications, such as cuisine prediction based on food ingredients, and expand the scope of our research to include other NLP tasks beyond semantic matching. Overall, this paper provides promising avenues for future research in this field, with potential implications for improving the performance of agricultural NLP applications.
Tailstorm: A Secure and Fair Blockchain for Cash Transactions

Proof-of-work (PoW) cryptocurrencies rely on a balance of security and fairness in order to maintain a sustainable ecosystem of miners and users. Users demand fast and consistent transaction confirmation, and in exchange drive the adoption and valuation of the cryptocurrency. Miners provide the confirmations, however, they primarily seek rewards. In unfair systems, miners can amplify their rewards by consolidating mining power. Centralization however, undermines the security guarantees of the system and might discourage users. In this paper we present Tailstorm, a cryptocurrency that strikes this balance. Tailstorm merges multiple recent protocol improvements addressing security, confirmation latency, and throughput with a novel incentive mechanism improving fairness. We implement a parallel proof-of-work consensus mechanism with $k$ PoWs per block to obtain state-of-the-art consistency guarantees. Inspired by Bobtail and Storm, we structure the individual PoWs in a tree which, by including a list of transactions with each PoW, reduces confirmation latency and improves throughput. Our proposed incentive mechanism discounts rewards based on the depth of this tree. Thereby, it effectively punishes information withholding, the core attack strategy used to reap an unfair share of rewards. We back our claims with a comprehensive analysis. We present a generic system model which allows us to specify Bitcoin, $B_k$, and Tailstorm from a joint set of assumptions. We provide an analytical bound for the fairness of Tailstorm and Bitcoin in honest networks and we confirm the results through simulation. We evaluate the effectiveness of dishonest behaviour through reinforcement learning. Our attack search reproduces known optimal strategies against Bitcoin, uncovers new ones against $B_k$, and confirms that Tailstorm's reward discounting makes it more resilient to incentive layer attacks.