Miracle-dz/UniFAR-FTUs huggingface dataset:
Testing effects of social rejection on aggressive and prosocial behavior: A meta-analysis.

Social rejection elicits profound feelings of distress. From an evolutionary perspective, the best way to alleviate this distress is to behave prosocially, minimizing the likelihood of further exclusion. Yet, examples ranging from the playground to the pub suggest rejection commonly elicits aggression. Opposing theoretical perspectives and discordant empirical results have left a basic question unanswered: does rejection more commonly elicit prosocial or aggressive behavior? We conducted three meta-analyses (one with studies measuring aggressive behavior; one with studies measuring prosocial behavior; and one with studies measuring both aggressive and prosocial behavior; N = 3864) to quantify: (1) the extent to which social rejection elicits prosocial or aggressive behavior and (2) potential moderating effects on these relations. Random-effects models revealed medium effects such that social rejection potentiated aggressive behavior (k = 19; d = 0.41, p < .0001) and attenuated prosocial behavior (k = 7; d = 0.59, p < .0001), an effect that remained consistent even when participants were given the option to behave prosocially or aggressively (k = 15; d = 0.71, p < .0001). These results cast doubt on the theory that rejection triggers prosocial behavior, and instead suggest it is a robust elicitor of aggression. Statement of Relevance: To our knowledge, these meta-analyses are the first to directly test whether social rejection elicits aggressive or prosocial behavior. By including a comprehensive collection of both published and unpublished research studies, and examining a wide variety of previously untested moderators, we show that social rejection robustly elicits aggressive behavior and inhibits prosocial behavior. Additionally, we demonstrate that aggressive behavior following social rejection is not simply a function of limited choices in response options. In fact, aggressive behavior was evoked even when the option to engage in prosocial behavior was provided. Furthermore, we conducted a comprehensive narrative review of the neural mechanisms underlying social rejection-elicited aggressive and prosocial behavior to supplement primary analyses. Overall, we believe that our work makes a critical theoretical contribution to the field.
Improving Body Satisfaction Through Fitness App Use: Explicating the Role of Social Comparison, Social Network Size, and Gender

ABSTRACT Body dissatisfaction is prevalent worldwide, fueling the highly lucrative diet and plastic surgery industry, with many adverse outcomes. Yet, limited attention has been dedicated to noninvasive interventions that effectively enhance body satisfaction. We argue that, by altering the target of the social comparison process, people can shift their focus from body appearance to physical activity level. The present study investigated whether social comparison in terms of physical activity level provided by fitness apps could affect users’ body satisfaction. A survey was conducted with 643 users of WeRun, the Chinese leading mobile fitness app. Subsequent analyses revealed that both upward and downward social comparison mediated the positive relationship between fitness app use and body satisfaction. Moreover, the users’ social network size and gender played a moderating role in the social comparison processes. Fitness app use was positively related to downward comparison for male users; social network size moderated the relationship between fitness app use and upward comparison for female users. The utility of fitness apps in mitigating body dissatisfaction is also discussed.
Association of Reproductive Life Span and Age at Menopause With the Risk of Aneurysmal Subarachnoid Hemorrhage

Background and Objectives Subarachnoid hemorrhage from cerebral aneurysm remains a devastating disease with high mortality and morbidity. Cerebral aneurysm and its rupture are more prevalent in postmenopausal women and have been postulated to be hormonally influenced. The goal of this study was to investigate the associations of female-specific factors, including reproductive life span, age at menarche, and age at menopause, with the incidence of aneurysmal subarachnoid hemorrhage (aSAH) in women. Methods Participants in the Nurses' Health Study were followed up from 1980 or the time of reaching menopause until 2018. Only women with natural menopause or surgical menopause due to bilateral oophorectomy were included. Reproductive life span was defined by subtracting the age at menarche from the age at menopause. Multivariable-stratified proportional hazards models were used to study reproductive life span, age at menarche, and age at menopause with the incidence of aSAH. Multivariable models were adjusted for age, race, smoking, hysterectomy, hypertension, hyperlipidemia, body mass index, hormone therapy use, oral contraceptive use, and parity. Results A total of 97,398 postmenopausal women with reproductive life span data were included; 138 participants developed aSAH, which was confirmed on medical record review by a physician. A shorter reproductive life span (≤35 years) was associated with a 2-fold higher incidence of aSAH after multivariable adjustment (hazard ratio [HR] 2.0 [95% CI 1.4–2.8]). Early age at menopause (age <45 years) was similarly associated with a higher risk of aSAH (HR 2.1 [95% CI 1.4–3.1]), but age at menarche was not. Use of oral contraceptives and postmenopausal hormone therapy was not associated with the incidence of aSAH. Discussion An earlier age at menopause and a shorter reproductive life span duration (≤35 years) were associated with a higher risk of incident aSAH in women. No associations were noted for age at menarche, parity, oral contraceptive use, or postmenopausal therapy use.
Psychopathy and Protective Behavioral Strategies: PBS Mediates the Relationships between Primary and Secondary Psychopathy and Alcohol Consumption and Problems

Objective: The most common conceptualization of psychopathy is a two-factor model of primary psychopathy and secondary psychopathy. Primary psychopathy consists of interpersonally abusive behavior, and callousness. Secondary psychopathy includes impulsive and risky decision making. Past research has found that psychopathy is related to negative outcomes, including increased alcohol consumption and problems, and is inversely related to harm reduction behaviors. Protective behavioral strategies (PBS), behaviors designed to reduce alcohol consumption and associated problems, may mediate the relationship between psychopathy and alcohol pathology. The current study examined the relationship between psychopathy, each subtype of PBS use (serious harm reduction (SHR), manner of drinking (MD) and stopping/limiting drinking (SLD)), alcohol consumption, and alcohol problems. Method: Participants were n = 967 (61.22% female) college students. Participants completed measures assessing psychopathy, PBS use, alcohol consumption, and alcohol problems. A path analysis was conducted to examine the relationships between psychopathy, subtypes of PBS, and alcohol consumption and problems. Results: Results indicated an inverse relationship between primary psychopathy and both SHR PBS and MD PBS, while secondary psychopathy was inversely associated with all three PBS subtypes. SHR PBS was inversely associated with alcohol problems while MD PBS was inversely associated with alcohol consumption. Conclusion: These findings suggest specific PBS subtypes mediate the relationship between primary and secondary psychopathy and alcohol consumption and problems. It may be clinically pertinent to consider targeting PBS use, such as with motivational interviewing, when working with patients exhibiting psychopathy traits. Future research should consider these findings when examining alcohol use.
Smaller species but larger stages: Warming effects on inter‐ and intraspecific community size structure

Abstract Global warming can alter size distributions of animal communities, but the contribution of size shifts within versus between species to such changes remains unknown. In particular, it is unclear if expected body size shrinkage in response to warming, observed at the interspecific level, can be used to infer similar size shifts within species. In this study, we compare warming effects on interspecific (relative species abundance) versus intraspecific (relative stage abundance) size structure of competing consumers by analyzing stage‐structured bioenergetic food web models consisting of one or two consumer species and two resources, parameterized for pelagic plankton. Varying composition and temperature and body size dependencies in these models, we predicted interspecific versus intraspecific size structure across temperature. We found that warming shifted community size structure toward dominance of smaller species, in line with empirical evidence summarized in our review of 136 literature studies. However, this result emerged only given a size–temperature interaction favoring small over large individuals in warm environments. In contrast, the same mechanism caused an intraspecific shift toward dominance of larger (adult) stages, reconciling disparate observations of size responses within and across zooplankton species in the literature. As the empirical evidence for warming‐driven stage shifts is scarce and equivocal, we call for more experimental studies on intraspecific size changes with warming. Understanding the global warming impacts on animal communities requires that we consider and quantify the relative importance of mechanisms concurrently shaping size distributions within and among species.
ITTR: Unpaired Image-to-Image Translation with Transformers

Unpaired image-to-image translation is to translate an image from a source domain to a target domain without paired training data. By utilizing CNN in extracting local semantics, various techniques have been developed to improve the translation performance. However, CNN-based generators lack the ability to capture long-range dependency to well exploit global semantics. Recently, Vision Transformers have been widely investigated for recognition tasks. Though appealing, it is inappropriate to simply transfer a recognition-based vision transformer to image-to-image translation due to the generation difficulty and the computation limitation. In this paper, we propose an effective and efficient architecture for unpaired Image-to-Image Translation with Transformers (ITTR). It has two main designs: 1) hybrid perception block (HPB) for token mixing from different receptive fields to utilize global semantics; 2) dual pruned self-attention (DPSA) to sharply reduce the computational complexity. Our ITTR outperforms the state-of-the-arts for unpaired image-to-image translation on six benchmark datasets.
Analytical insight into dark matter subhalo boost factors for Sommerfeld-enhanced s- and p-wave γ-ray signals

As searches for thermal and self-annihilating dark matter (DM) intensify, it becomes crucial to include as many relevant physical processes and ingredients as possible to refine signal predictions, in particular those which directly relate to the intimate properties of DM. We investigate the combined impact of DM subhalos and of the (velocity-dependent) Sommerfeld enhancement of the annihilation cross section. Both features are expected to play an important role in searches for thermal DM particle candidates with masses around or beyond TeV, or in scenarios with a light dark sector. We provide a detailed analytical description of the phenomena at play, and show how they scale with the subhalo masses and the main Sommerfeld parameters. We derive approximate analytical expressions that can be used to estimate the overall boost factors resulting from these combined effects, from which the intricate phenomenology can be better understood. DM subhalos lead to an increase of the Sommerfeld effect by several orders of magnitude (for both s- and p-wave annihilation processes), especially on resonances, which makes them critical to get sensible gamma-ray signal predictions for typical targets of different masses (from dwarf galaxies to galaxy clusters).
FlowFormer: A Transformer Architecture for Optical Flow

We introduce optical Flow transFormer, dubbed as FlowFormer, a transformer-based neural network architecture for learning optical flow. FlowFormer tokenizes the 4D cost volume built from an image pair, encodes the cost tokens into a cost memory with alternate-group transformer (AGT) layers in a novel latent space, and decodes the cost memory via a recurrent transformer decoder with dynamic positional cost queries. On the Sintel benchmark, FlowFormer achieves 1.159 and 2.088 average end-point-error (AEPE) on the clean and final pass, a 16.5% and 15.5% error reduction from the best published result (1.388 and 2.47). Besides, FlowFormer also achieves strong generalization performance. Without being trained on Sintel, FlowFormer achieves 1.01 AEPE on the clean pass of Sintel training set, outperforming the best published result (1.29) by 21.7%.
Classification of gamma-ray targets for velocity-dependent and subhalo-boosted dark-matter annihilation

Gamma-ray observations have long been used to constrain the properties of dark matter (DM), with a strong focus on weakly interacting massive particles annihilating through velocity-independent processes. However, in the absence of clear-cut observational evidence for the simplest candidates, the interest of the community in more complex DM scenarios involving a velocity-dependent cross-section has been growing steadily over the past few years. We present the first systematic study of velocity-dependent DM annihilation (in particular p-wave annihilation and Sommerfeld enhancement) in a variety of astrophysical objects, not only including the well-studied Milky Way dwarf satellite galaxies, but nearby dwarf irregular galaxies and local galaxy clusters as well. Particular attention is given to the interplay between velocity dependence and DM halo substructure. Uncertainties related to halo mass, phase-space and substructure modelling are also discussed in this velocity-dependent context. We show that, for s-wave annihilation, extremely large subhalo boost factors are to be expected, up to 1011 in clusters and up to 106–107 in dwarf galaxies where subhalos are usually assumed not to play an important role. Boost factors for p-wave annihilation are smaller but can still reach 103 in clusters. The angular extension of the DM signal is also significantly impacted, with e.g. the cluster typical emission radius increasing by a factor of order 10 in the s-wave case. We also compute the signal contrast of the objects in our sample with respect to annihilation happening in the Milky Way halo. Overall, we find that the hierarchy between the brightest considered targets depends on the specific details of the assumed particle-physics model.
Image Retrieval from Contextual Descriptions

The ability to integrate context, including perceptual and temporal cues, plays a pivotal role in grounding the meaning of a linguistic utterance. In order to measure to what extent current vision-and-language models master this ability, we devise a new multimodal challenge, Image Retrieval from Contextual Descriptions (ImageCoDe). In particular, models are tasked with retrieving the correct image from a set of 10 minimally contrastive candidates based on a contextual description.As such, each description contains only the details that help distinguish between images.Because of this, descriptions tend to be complex in terms of syntax and discourse and require drawing pragmatic inferences. Images are sourced from both static pictures and video frames.We benchmark several state-of-the-art models, including both cross-encoders such as ViLBERT and bi-encoders such as CLIP, on ImageCoDe.Our results reveal that these models dramatically lag behind human performance: the best variant achieves an accuracy of 20.9 on video frames and 59.4 on static pictures, compared with 90.8 in humans.Furthermore, we experiment with new model variants that are better equipped to incorporate visual and temporal context into their representations, which achieve modest gains. Our hope is that ImageCoDE will foster progress in grounded language understanding by encouraging models to focus on fine-grained visual differences.
Does Audio Deepfake Detection Generalize?

Current text-to-speech algorithms produce realistic fakes of human voices, making deepfake detection a much-needed area of research. While researchers have presented various techniques for detecting audio spoofs, it is often unclear exactly why these architectures are successful: Preprocessing steps, hyperparameter settings, and the degree of fine-tuning are not consistent across related work. Which factors contribute to success, and which are accidental? In this work, we address this problem: We systematize audio spoofing detection by re-implementing and uniformly evaluating architectures from related work. We identify overarching features for successful audio deepfake detection, such as using cqtspec or logspec features instead of melspec features, which improves performance by 37% EER on average, all other factors constant. Additionally, we evaluate generalization capabilities: We collect and publish a new dataset consisting of 37.9 hours of found audio recordings of celebrities and politicians, of which 17.2 hours are deepfakes. We find that related work performs poorly on such real-world data (performance degradation of up to one thousand percent). This may suggest that the community has tailored its solutions too closely to the prevailing ASVSpoof benchmark and that deepfakes are much harder to detect outside the lab than previously thought.
Psychosocial stress and cancer risk: a narrative review

Background It is unclear which psychological factors (stressors, emotional correlates, and psychophysiological markers) induce cancer risk. This currently limits the potential for prevention strategies. Purpose The aim of this review is to bring forth evidence of stress as a determinant of cancer risk from a public health perspective, written for a broad public of practitioners and scientists. Methods Based on a semisystematic literature search, the impact of different aspects/types of stress and the potential physiological and behavioral pathways are summarized, while highlighting further research, public health and clinical implications. Results Between 2007 and 2020, 65 case–control or cohort studies have been identified. Apart from overall cancer (N = 24), 12 cancer types have been associated with psychological stress with most for breast (N = 21), colorectal (N = 11) and lung/prostate/pancreas cancer (N = 8 each). Although the evidence regarding the mechanisms is still scarce, cancer development in relation to stress might be due to interacting and combined effects of different stress(or) types, but such interaction has not really been tested yet. The path from stress towards cancer incidence consists of a biological pathway with endocrinology and immunology as well as stress-induced behavioral pathways, including smoking, alcoholism, sleep disruption, an unhealthy diet, and low physical activity together with the related phenomenon of obesity. Conclusion Not only the stress but also the stress-induced lifestyle should be targeted for cancer prevention and treatment. Future research should include a more diverse spectrum of cancer types (not only hormonal related like breast cancer) and of stress measures while also considering behavioral covariates.
Incorporating Dynamic Semantics into Pre-Trained Language Model for Aspect-based Sentiment Analysis

Aspect-based sentiment analysis (ABSA) predicts sentiment polarity towards a specific aspect in the given sentence. While pre-trained language models such as BERT have achieved great success, incorporating dynamic semantic changes into ABSA remains challenging. To this end, in this paper, we propose to address this problem by Dynamic Re-weighting BERT (DR-BERT), a novel method designed to learn dynamic aspect-oriented semantics for ABSA. Specifically, we first take the Stack-BERT layers as a primary encoder to grasp the overall semantic of the sentence and then fine-tune it by incorporating a lightweight Dynamic Re-weighting Adapter (DRA). Note that the DRA can pay close attention to a small region of the sentences at each step and re-weigh the vitally important words for better aspect-aware sentiment understanding. Finally, experimental results on three benchmark datasets demonstrate the effectiveness and the rationality of our proposed model and provide good interpretable insights for future semantic modeling.
A cellular automaton decoder for a noise-bias tailored color code

Self-correcting quantum memories demonstrate robust properties that can be exploited to improve active quantum error-correction protocols. Here we propose a cellular automaton decoder for a variation of the color code where the bases of the physical qubits are locally rotated, which we call the XYZ color code. The local transformation means our decoder demonstrates key properties of a two-dimensional fractal code if the noise acting on the system is infinitely biased towards dephasing, namely, no string-like logical operators. As such, in the high-bias limit, our local decoder reproduces the behavior of a partially self-correcting memory. At low error rates, our simulations show that the memory time diverges polynomially with system size without intervention from a global decoder, up to some critical system size that grows as the error rate is lowered. Furthermore, although we find that we cannot reproduce partially self-correcting behavior at finite bias, our numerics demonstrate improved memory times at realistic noise biases. Our results therefore motivate the design of tailored cellular automaton decoders that help to reduce the bandwidth demands of global decoding for realistic noise models.
An alternative proof of Tataru’s dispersive estimates

Abstract The aim of this article is to give an alternative proof of Tataru’s dispersive estimates for wave equations posed on the hyperbolic space. Based on the formula for the wave kernel on ℍ n {\mathbb{H}^{n}} , we give the proof from the perspective of Bessel potentials, by exploiting various facts about Gamma functions, modified Bessel functions, and Bessel potentials. This leads to our proof being more self-contained than that in [D. Tataru, Strichartz estimates in the hyperbolic space and global existence for the semilinear wave equation, Trans. Amer. Math. Soc. 353 2001, 2, 795–807].