Miracle-dz/UniFAR-FTUs huggingface dataset:
The appeal to emotions in the discourse of populist political actors from Spain, Italy, France and the United Kingdom on Twitter

Western democracies live a populist impulse. In this context, the consolidation of digital technologies plays a key role due to their possibilities of efficiently spreading messages. This research examines the role that emotions play in the communication strategy on Twitter of populist actors from four European countries: Spain, Italy, France and the United Kingdom. The sample comprises the messages shared by four European political parties: Podemos, 5 Star Movement, National Front and UKIP and their respective leaders during the analysis period. In total, 9,128 messages are analyzed. The main findings show that fear, uncertainty or resentment are the emotions most frequently used by populist parties and leaders. Likewise, negative emotions have been observed in those messages whose primary actor is the political, economic and media classes, the European Union or immigration, while positive emotions, such as joy or pride, are associated with the figure of the leader and the party.
‘Tell me about the time you remember the best’: the effect of a remember best prompt on adults’ reports of a repeated emotionally stressful event

ABSTRACT In two experiments we investigated the effect of a ‘remember best’ prompt on recall of one instance of a repeated event. Adult female participants (N = 98 Experiment 1; N = 100 Experiment 2) engaged in imagined experiences of abusive relationship encounters on either one occasion (single group) or four occasions (repeat-last and repeat-best groups). Participants then completed a memory assessment for the target instance. In the single and repeat-last groups, participants recalled a pre-determined target instance (i.e. last instance for repeat-last group), while the repeat-best group self-selected which instance to discuss (i.e. the time they ‘remember best’). In Experiment 1, repeat-best participants selected an instance based on when it occurred, while in Experiment 2, participants identified and uniquely labelled the instance remembered best. Experiment 1 results revealed that the ‘remember best’ prompt was not effective in enhancing accurate retrieval of one instance, while the ‘remember best’ prompt in Experiment 2 did appear to enhance retrieval when compared to the control groups. These findings suggest that accurate retrieval of one instance of a repeated event might depend on how memory is cued. The forensic implications of these findings for testimonies of repeated abuse (e.g. domestic abuse) are discussed.
Experience: ARISTOTLE: wAke-up ReceIver-based, STar tOpology baTteryLEss sensor network

A truly ubiquitous, planet-wide Internet of Things requires ultra-low-power, long-lasting sensor nodes at its core so that it can be practically utilized in real-world scenarios without prohibitively high maintenance efforts. Recent advances in energy harvesting and low-power electronics have provided a solid foundation for the design of such sensor nodes. However, the issue of reliable two-way communication among such devices is still an active research undertaking due to the high energy footprint of traditional wireless transceivers. Although approaches such as radio duty cycling have proved beneficial for reducing the overall energy consumption of wireless sensor nodes, they come with trade-offs such as increased communication latency and complex protocols. To address these limitations, we propose ARISTOTLE, an ultra-low-power, wake-up receiver-based sensor node design employing a star network topology. We have deployed ARISTOTLE in two different venues for carrying out the task of weather data collection. In addition to reporting the results of the two deployments, we also evaluate several performance aspects of our proposed solution. ARISTOTLE has a mean power consumption of 236.67 uW while it is in sleep mode and monitoring the radio channel for incoming wake-up signals. Utilizing various sizes of supercapacitors, ARISTOTLE was able to reach system availabilities between 47.83% and 97.36% during our real-world deployments.
SpectraLux: Towards Exploiting the Full Spectrum with Passive VLC

In recent years, the number of wireless applications has increased significantly, resulting in the radio bands becoming expensive and prone to interference. There is a new research area aiming at mitigating these issues by creating communication links using ambient light. This area, called passive-VLC, not only exploits the visible light frequencies, but does so with low-power transmitters. All the previous work in passive-VLC, however, forget about individual wavelength bands of light, and do not exploit its wide spectrum, reducing the potential channel capacity. In this paper, we propose a novel method to transmit and decode data, using liquid crystal cells that modulate and consider the full spectrum, and put it to the test by prototyping a multi-symbol communication link. The main contribution of our work is to show that passive-VLC can move from spectrum-agnostic to spectrum-aware modulation. We explore this new domain by making use of a novel type of receiver (i.e., a spectrometer) and uncovering the advantages and caveats of this spectrum-aware approach.
Modeling Long-range Dependencies and Epipolar Geometry for Multi-view Stereo

This article proposes a network, referred to as Multi-View Stereo TRansformer (MVSTR) for depth estimation from multi-view images. By modeling long-range dependencies and epipolar geometry, the proposed MVSTR is capable of extracting dense features with global context and 3D consistency, which are crucial for reliable matching in multi-view stereo (MVS). Specifically, to tackle the problem of the limited receptive field of existing CNN-based MVS methods, a global-context Transformer module is designed to establish intra-view long-range dependencies so that global contextual features of each view are obtained. In addition, to further enable features of each view to be 3D consistent, a 3D-consistency Transformer module with an epipolar feature sampler is built, where epipolar geometry is modeled to effectively facilitate cross-view interaction. Experimental results show that the proposed MVSTR achieves the best overall performance on the DTU dataset and demonstrates strong generalization on the Tanks & Temples benchmark dataset.
The Contribution of Self-Efficacy and Achievement Motivation on The SPIRIT Characters Building for The Students

This paper aimed to show how much Self-efficacy and Achievement Motivation contribute to the formation of SPIRIT character for the students in the post-covid-19 pandemic era when the education process is still ongoing with a combined method of online and offline learning. This research used quantitative methods and literature studies. The results of this research showed that self-efficacy and achievement motivation had not significance contribution to the SPIRIT characters building for the students. The other variables that are not discussed even have a much larger contribution. So it is interested to investigate this result. Even so, it does not mean that students do not need to pay attention to self-efficacy and achievement motivation, because even though they are small, they still need to be owned, so that the SPIRIT characters can be further developed in the post-covid-19 pandemic era.
Change of Phoria and Subjective Symptoms after Watching 2D and 3D Image

Purpose: The changes of phoria and subjective asthenopia before and after viewing were compared based on 2D image and two ways of 3D images, and presented for references of 3D image watching and production. Methods: Change in phoria was measured before and after watching 2D image, 3D-FPR and 3D-SG images for 30 minutes with a target of 41 university students at 20-30 years old (male 26, female 15). Paired t-test and Pearson correlation between changed phoria and subjective symptoms which were measured using questionnaires were evaluated by before and after watching each images. Results: Right after watching 2D image, exophoria was increased by 0.5 , in distance and near, but it was not a significant level. Right after watching 3D image, exophoria was increased by 1.0~1.5 , and 1.5~2.0 , in distance and near, respectively when compared with before watching. In the significant level, exophoria tended to increase. Changes in near was increased more by 0.5 , compared with those in distance. Changes based on way of 3D-FPR and 3D-SG image were less than 0.5 , and there was almost no difference. In terms of visual subjective symptoms, eye strain was increased in 3D image compared with that in 2D image. In addition, there was no difference depending on way of image. In terms of Pearson correlation between phoria change and eye strain, as exophoria was increased, eye strain was increased. Conclusions: Watching 3D image increased eye strain compared with watching 2D image, and accordingly exophoria tended to increase.
Exponential roughness layer and analytical model for turbulent boundary layer flow over rectangular-prism roughness elements

We conduct a series of large-eddy simulations (LES) to examine the mean flow behaviour within the roughness layer of turbulent boundary layer flow over rough surfaces. We consider several configurations consisting of arrays of rectangular-prism roughness elements with various spacings, aspect ratios and height distributions. The results provide clear evidence for exponential behaviour of the mean flow with respect to the wall normal distance. Such behaviour has been proposed before (see, e.g., Cionco, 1966 Tech. Rep. DTIC document), and is represented as $U(z)/U_{h}=\exp [a(z/h-1)]$ , where $U(z)$ is the spatially/temporally averaged fluid velocity, $z$ is the wall normal distance, $h$ represents the height of the roughness elements and $U_{h}$ is the velocity at $z=h$ . The attenuation factor $a$ depends on the density of the roughness element distribution and details of the roughness distribution on the wall. Once established, the generic velocity profile shape is used to formulate a fully analytical model for the effective drag exerted by turbulent flow on a surface covered with arrays of rectangular-prism roughness elements. The approach is based on the von Karman–Pohlhausen integral method, in which a shape function is assumed for the mean velocity profile and its parameters are determined based on momentum conservation and other fundamental constraints. In order to determine the attenuation parameter $a$ , wake interactions among surface roughness elements are accounted for by using the concept of flow sheltering. The model transitions smoothly between ‘ $k$ ’ and ‘ $d$ ’ type roughness conditions depending on the surface coverage density and the detailed geometry of roughness elements. Comparisons between model predictions and experimental/numerical data from the existing literature as well as LES data from this study are presented. It is shown that the analytical model provides good predictions of mean velocity and drag forces for the cases considered, thus raising the hope that analytical roughness modelling based on surface geometry is possible, at least for cases when the location of flow separation over surface elements can be easily predicted, as in the case of wall-attached rectangular-prism roughness elements.
Social Expectations for Charitable Giving in China

The rapid rise of philanthropy in China has motivated extensive research on why people make charitable donations as a personal decision, but few studies have explored the social dimension of these decisions. We propose that the legacy of government welfare provision and the culture of trust have led Chinese citizens to form different expectations for others in philanthropic situations. Our survey results point to some interesting asymmetries: Generalized trust and institutional trust toward local governments inflate people’s expectation for philanthropic contributions from others, whereas particularized trust and trust toward the central government reduce it. Also, Chinese citizens expect government employees to make larger contributions, but they don’t expect charities with government backing to receive correspondingly larger donations. We conclude with some observations on how the unique pattern of social expectation may shape the future of Chinese philanthropy.
MOMA-LRG: Language-Refined Graphs for Multi-Object Multi-Actor Activity Parsing

Video-language models (VLMs), large models pre-trained on numerous but noisy video-text pairs from the internet, have revolutionized activity recognition through their remarkable generalization and open-vocabulary capabilities. While complex human activities are often hierarchical and compositional, most existing tasks for evaluating VLMs focus only on high-level video understanding, making it difficult to accurately assess and interpret the ability of VLMs to understand complex and fine-grained human activities. Inspired by the recently proposed MOMA framework, we define activity graphs as a single universal representation of human activities that encompasses video understanding at the activity, subactivity, and atomic action level. We redefine activity parsing as the overarching task of activity graph generation, requiring understanding human activities across all three levels. To facilitate the evaluation of models on activity parsing, we introduce MOMA-LRG (Multi-Object Multi-Actor Language-Refined Graphs), a large dataset of complex human activities with activity graph annotations that can be readily transformed into natural language sentences. Lastly, we present a model-agnostic and lightweight approach to adapting and evaluating VLMs by incorporating structured knowledge from activity graphs into VLMs, addressing the individual limitations of language and graphical models. We demonstrate strong performance on activity parsing and few-shot video classification, and our framework is intended to foster future research in the joint modeling of videos, graphs, and language.
A profile Godambe information of power transformations for ARCH time series

ABSTRACT Due to Godambe (1985), one can obtain the Godambe optimum estimating functions (EFs) each of which is optimum (in the sense of maximizing the Godambe information) within a linear class of EFs. Quasi-likelihood scores can be viewed as special cases of the Godambe optimum EFs (see, for instance, Hwang and Basawa, 2011). The paper concerns conditionally heteroscedastic time series with unknown likelihood. Power transformations are introduced in innovations to construct a class of Godambe optimum EFs. A “best” power transformation for Godambe innovation is then obtained via maximizing the “profile” Godambe information. To illustrate, the KOrea Stock Prices Index is analyzed for which absolute value transformation and square transformation are recommended according to the ARCH(1) and GARCH(1,1) models, respectively.
Functional and pharmacological analyses of visual habituation learning in larval zebrafish

Habituation allows animals to learn to ignore persistent but inconsequential stimuli. Despite being the most basic form of learning, a consensus model on the underlying mechanisms has yet to emerge. To probe relevant mechanisms we took advantage of a visual habituation paradigm in larval zebrafish, where larvae reduce their reactions to abrupt global dimming (a dark flash). We used Ca2+ imaging during repeated dark flashes and identified 12 functional classes of neurons that differ based on their rate of adaptation, stimulus response shape, and anatomical location. While most classes of neurons depressed their responses to repeated stimuli, we identified populations that did not adapt, or that potentiated their response. These neurons were distributed across brain areas, consistent with a distributed learning process. Using a small molecule-screening approach, we confirmed that habituation manifests from multiple distinct molecular mechanisms, and we have implicated molecular pathways in habituation, including: Melatonin, Estrogen and GABA signaling. However, by combining anatomical analyses and pharmacological manipulations with Ca2+ imaging, we failed to identify a simple relationship between pharmacology, altered activity patterns, and habituation behaviour. Collectively, our work indicates that habituation occurs via a complex and distributed plasticity processes that cannot be captured by a simple model. Therefore, untangling the mechanisms of habituation will likely require dedicated approaches aimed at sub-component mechanisms underlying this multidimensional learning process.
S3GC: Scalable Self-Supervised Graph Clustering

We study the problem of clustering graphs with additional side-information of node features. The problem is extensively studied, and several existing methods exploit Graph Neural Networks to learn node representations [29]. However, most of the existing methods focus on generic representations instead of their cluster-ability or do not scale to large scale graph datasets. In this work, we propose S 3 GC which uses contrastive learning along with Graph Neural Networks and node features to learn clusterable features. We empirically demonstrate that S 3 GC is able to learn the correct cluster structure even when graph information or node features are individually not informative enough to learn correct clusters. Finally, using extensive evaluation on a variety of benchmarks, we demonstrate that S 3 GC is able to signiﬁcantly outperform state-of-the-art methods in terms of clustering accuracy – with as much as 5% gain in NMI – while being scalable to graphs of size 100M.
Using DNA flow-stretching assay as a tool to validate the tagging of DNA-binding proteins for single-molecule experiments

Due to the enhanced labeling capability of maleimide-based fluorescent probes, lysine-cysteine-lysine (KCK) tags are frequently added to proteins for visualization. In this study, we employed in vitro single-molecule DNA flow-stretching assay as a sensitive way to assess the impact of the KCK-tag on the property of DNA-binding proteins. Using Bacillus subtilis ParB as an example, we show that, although no noticeable changes were detected by in vivo fluorescence imaging and chromatin immunoprecipitation (ChIP) assays, the KCK-tag substantially altered ParB’s DNA compaction rates, its response to nucleotide binding and to the presence of the specific sequence (parS) on the DNA. While it is typically assumed that short peptide tags minimally perturb protein function, our results urge researchers to carefully validate the use of tags for protein labeling. Our comprehensive analysis can be expanded and used as a guide to assess the impacts of other tags on DNA-binding proteins in single-molecule assays. Motivation Single-molecule fluorescence microscopy has been extensively used in modern biology to define the molecular action of proteins. Appending short peptide tags is a common strategy to enhance fluorescence labeling. In this Resources article, we evaluate the impact of a commonly used tag, the lysine-cysteine-lysine (KCK) tag, on protein behavior in single-molecule DNA flow-stretching assay, which is a sensitive and versatile method to understand the action of DNA-binding proteins. Our motivation is to provide researchers with an experimental framework to validate the fluorescently labeled DNA-binding proteins in single-molecule methods.
Discovering and Overcoming Limitations of Noise-engineered Data-free Knowledge Distillation

Distillation in neural networks using only the samples randomly drawn from a Gaussian distribution is possibly the most straightforward solution one can think of for the complex problem of knowledge transfer from one network (teacher) to the other (student). If successfully done, it can eliminate the requirement of teacher’s training data for knowledge distillation and avoid often arising privacy concerns in sensitive applications such as healthcare. There have been some recent attempts at Gaussian noise-based data-free knowledge distillation, however, none of them offer a consistent or reliable solution. We identify the shift in the distribution of hidden layer activation as the key limiting factor, which occurs when Gaussian noise is fed to the teacher network instead of the accustomed training data. We propose a simple solution to mitigate this shift and show that for vision tasks, such as classification, it is possible to achieve a performance close to the teacher by just using the samples randomly drawn from a Gaussian distribution. We validate our approach on CIFAR10, CIFAR100, SVHN, and Food101 datasets. We further show that in situations of sparsely available original data for distillation, the proposed Gaussian noise-based knowledge distillation method can outperform the distillation using the available data with a large margin. Our work lays the foundation for further research in the direction of noise-engineered knowledge distillation using random samples.
Masked Generative Adversarial Networks are Data-Efficient Generation Learners

This paper shows that masked generative adversarial networks (MaskedGAN) are robust image generation learners with limited training data. The idea of MaskedGAN is simple: it randomly masks out certain image information for effective GAN training with limited data. We develop two masking strategies that work along orthogonal dimensions of training images, including a shifted spatial masking that masks the images in spatial dimensions with random shifts, and a balanced spectral masking that masks certain image spectral bands with self-adaptive probabilities. The two masking strategies complement each other which together encourage more challenging holistic learning from limited training data, ultimately suppressing trivial solutions and failures in GAN training. Albeit simple, extensive experiments show that MaskedGAN achieves superior performance consistently across different network architectures ( e.g. , CNNs including BigGAN and StyleGAN-v2 and Transformers including TransGAN and GANformer) and datasets ( e.g. , CIFAR-10, CIFAR-100, ImageNet, 100-shot, AFHQ, FFHQ and Cityscapes).
Resolving the data ambiguity for periodic crystals

The fundamental model of all solid crystalline materials is a periodic set of atomic centers considered up to rigid motion in Euclidean space. The major obstacle to materials discovery was highly ambiguous representations of periodic crystals that didn’t allow fast and reliable comparisons and led to numerous (near-) duplicates in many databases of experimental and simulated crystals. This paper exemplarily resolves the ambiguity by invariants, which are descriptors without false negatives. The new Pointwise Distance Distributions (PDD) is a numerical matrix with a near-linear time complexity and an exactly computable metric. The strongest theoretical result is generic completeness (absence of false positives) for all ﬁnite and periodic sets of points in any dimension. The strength of PDD is shown by 200B+ pairwise comparisons of all periodic structures in the world’s largest collection (Cambridge Structural Database) of existing materials over two days on a modest desktop.