Miracle-dz/UniFAR-FTUs huggingface dataset:
PHANGS–JWST First Results: Mapping the 3.3 μm Polycyclic Aromatic Hydrocarbon Vibrational Band in Nearby Galaxies with NIRCam Medium Bands

We present maps of the 3.3 μm polycyclic aromatic hydrocarbon (PAH) emission feature in NGC 628, NGC 1365, and NGC 7496 as observed with the Near-Infrared Camera imager on JWST from the PHANGS–JWST Cycle 1 Treasury project. We create maps that isolate the 3.3 μm PAH feature in the F335M filter (F335MPAH) using combinations of the F300M and F360M filters for removal of starlight continuum. This continuum removal is complicated by contamination of the F360M by PAH emission and variations in the stellar spectral energy distribution slopes between 3.0 and 3.6 μm. We modify the empirical prescription from Lai et al. to remove the starlight continuum in our highly resolved galaxies, which have a range of starlight- and PAH-dominated lines of sight. Analyzing radially binned profiles of the F335MPAH emission, we find that between 5% and 65% of the F335M intensity comes from the 3.3 μm feature within the inner 0.5 r 25 of our targets. This percentage systematically varies from galaxy to galaxy and shows radial trends within the galaxies related to each galaxy’s distribution of stellar mass, interstellar medium, and star formation. The 3.3 μm emission is well correlated with the 11.3 μm PAH feature traced with the MIRI F1130W filter, as is expected, since both features arise from C–H vibrational modes. The average F335MPAH/F1130W ratio agrees with the predictions of recent models by Draine et al. for PAHs with size and charge distributions shifted toward larger grains with normal or higher ionization.
Transformer Based Geocoding

In this paper, we formulate the problem of predicting a geolocation from free text as a sequence-to-sequence problem. Using this formulation, we obtain a geocoding model by training a T5 encoder-decoder transformer model using free text as an input and geolocation as an output. The geocoding model was trained on geo-tagged wikidump data with adaptive cell partitioning for the geolocation representation. All of the code including Rest-based application, dataset and model checkpoints used in this work are publicly available.
Tunable intracellular transport on converging microtubule morphologies

A common type of cytoskeletal morphology involves multiple converging microbutubules with their minus ends collected and stabilized by a microtubule organizing center (MTOC) in the interior of the cell. This arrangement enables the ballistic transport of cargo bound to microtubules, both dynein mediated transport towards the MTOC and kinesin mediated transport away from it, interspersed with diﬀusion for unbound cargo-motor complexes. Spatial and temporal positioning of the MTOC allows for bidirectional transport towards and away from speciﬁc organelles and locations within the cell and also the sequestering and subsequent dispersal of dynein transported cargo. The general principles governing dynamics, eﬃciency and tunability of such transport in the MTOC vicinity is not fully understood. To address this, we develop a one-dimensional model that includes advective transport towards an attractor (such as the MTOC), and diﬀusive transport that allows particles to reach absorbing boundaries (such as cellular membranes). We calculated the mean ﬁrst passage time (MFPT) for cargo to reach the boundaries as a measure of the eﬀectiveness of sequestering (large MFPT) and diﬀusive dispersal (low MFPT). We show that the MFPT experiences a dramatic growth in magnitude, transitioning from a low to high MFPT regime (dispersal to sequestering) over a window of cargo attachment/detachment rates that is close to in vivo values. Furthermore, we ﬁnd that increasing either the attachment or detachment rate, while ﬁxing the other, can result in optimal dispersal when the attractor is placed asymmetrically. Finally, we also describe a regime of rare events where the MFPT scales exponentially with advective velocity towards the attractor and the escape location becomes exponentially sensitive to the attractor positioning. Taken together, our results suggest that structures such as the MTOC allow for the sensitive control of the spatial and temporal features of transport and corresponding function under physiological conditions.
Continual Treatment Effect Estimation: Challenges and Opportunities

A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods mainly focus on source-specific and stationary observational data. In particular, such learning strategies assume that all observational data are already available during the training phase and from only one source. Along with the fast-growing segments of industrial applications, this assumption is unsubstantial in practice. Taking Alipay as an example, which is one of the world’s largest mobile payment platforms and offers financial services to billion-scale users, a tremendous amount of data containing much privacy-related information is produced daily and collected from different sources. In the following, we further elaborate this problem by two points. The first one is based on the characteristics of observational data, which are incrementally available from non-stationary data distributions. For instance, the electronic financial records for one marketing campaign are growing every day and they may be collected from different cities or even other countries. This characteristic implies that one cannot have access to all observational data at one time point and from one single source. The second reason is based on the realistic consideration of accessibility. For example, when new observational data are available, one may want to refine the previously trained model using both the new data and original data. However, it is likely that the original training data are no longer accessible due to a variety of reasons, e.g., legacy data may be unrecorded, proprietary, the sensitivity of financial data, too large to store, or subject to privacy constraint of personal information (Zhang et al. 2020). This practical concern of accessibility is ubiquitous in various academic and industrial applications. That’s what it boiled down to: in the era of big data, we face new challenges in causal inference with observational data. We
Snake paths in king and knight graphs

A snake path in a graph $G$ is a path in $G$ which is also an induced subgraph of $G$. For all $n$, we find the greatest length of a snake path in the $n \times n$ king graph and we give a complete description of the paths which attain this greatest length. The even and odd cases behave very differently. We also estimate the greatest length of a snake path or cycle in the $m \times n$ knight graph, for all $m$ and $n$.
A Theory of Human-Like Few-Shot Learning

We aim to bridge the gap between our common-sense few-sample human learning and large-data machine learning. We derive a theory of human-like few-shot learning from von-Neuman-Landauer's principle. modelling human learning is difficult as how people learn varies from one to another. Under commonly accepted definitions, we prove that all human or animal few-shot learning, and major models including Free Energy Principle and Bayesian Program Learning that model such learning, approximate our theory, under Church-Turing thesis. We find that deep generative model like variational autoencoder (VAE) can be used to approximate our theory and perform significantly better than baseline models including deep neural networks, for image recognition, low resource language processing, and character recognition.
Explaining Imitation Learning through Frames

As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable artificial intelligence methods, we proposed a model-agnostic explaining framework for IL models called R2RISE. R2RISE aims to explain the overall policy performance with respect to the frames in demonstrations. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames' importance equality, the effectiveness of the importance map, and connections between importance maps from different IL models. The result shows that R2RISE successfully distinguishes important frames from the demonstrations.
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark

The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research. To address these issues, we propose a Multi-Relational Graph-Based Twitter Account Detection Benchmark (MGTAB), the first standardized graph-based benchmark for account detection. To our knowledge, MGTAB was built based on the largest original data in the field, with over 1.55 million users and 130 million tweets. MGTAB contains 10,199 expert-annotated users and 7 types of relationships, ensuring high-quality annotation and diversified relations. In MGTAB, we extracted the 20 user property features with the greatest information gain and user tweet features as the user features. In addition, we performed a thorough evaluation of MGTAB and other public datasets. Our experiments found that graph-based approaches are generally more effective than feature-based approaches and perform better when introducing multiple relations. By analyzing experiment results, we identify effective approaches for account detection and provide potential future research directions in this field. Our benchmark and standardized evaluation procedures are freely available at: https://github.com/GraphDetec/MGTAB.
Magnetic-field-induced nonequilibrium structures in a ferrofluid emulsion

Using optical microscopy, we studied magnetic-field-induced structures in a confined ferrofluid emulsion where the magnetic field is applied quickly as a step function. Columnar, bent-wall-like, and labyrinthine structures in three dimensions are observed, corresponding to disks, ``worms,'' and branchlike patterns in cross-sectional area normal to the magnetic-field direction. These two-dimensional structures are characterized by both the ratio of worms to total aggregates and the average complexity 〈C〉 of the aggregates in a given image. ``Phase'' diagrams are obtained to characterize disk (columnar) to worm (bent-wall) structural transitions as a function of the thickness of the cell used to confine the sample along the field direction, the particle volume fraction, and the rate of the magnetic-field application. The distribution of aggregate complexity for a given image is characterized by the skewness and quality factor to describe the symmetry and width of complexity distribution. The results show that increasing either cell thickness L, particle volume fraction \ensuremath{\Phi}, or magnetic field ramping rate R increases the average complexity of the formed patterns as $〈C〉=1.8{\ensuremath{\Phi}}^{3.11}L+0.141{\mathrm{log}}_{10}(R)+0.83,$ as well as the symmetry and the range of the complexity. This relation can be understood qualitatively. For fast ramping rate R or increasing \ensuremath{\Phi} (decreasing the interparticle distance and thus increasing the particle interaction), the strong magnetic interaction between particles does not allow particles enough time to explore the lowest-energy state (columnar structures) before being locked into local energy minima (labyrinthine structures). The L dependence of the complexity supports molecular dynamics simulation results: Chains form first and then aggregate to form complex structures; longer chains have a larger range of attraction.
GRB minimum variability timescale with Insight-HXMT and Swift. Implications for progenitor models, dissipation physics, and GRB classifications

The dissipation process of GRB prompt emission is still unknown. Study of temporal variability may provide a unique way to discriminate the imprint of the inner engine activity from geometry and propagation related effects. We define the minimum variability timescale (MVT) as the shortest duration of individual pulses that shape a light curve for a sample of GRBs and test correlations with peak luminosity, Lorentz factor, and jet opening angle. We compare these correlations with predictions from recent numerical simulations for a relativistic structured -- possibly wobbling -- jet and assess the value of MTV as probe of prompt-emission physics. We used the peak detection algorithm mepsa to identify the shortest pulse within a GRB time history and estimate its full width half maximum (FWHM). We applied this framework to two sets of GRBs: Swift (from 2005 to July 2022) and Insight-HXMT (from June 2017 to July 2021, including 221009A). We then selected 401 GRBs with measured z to test for correlations. On average short GRBs have significantly shorter MVT than long GRBs. The MVT distribution of short GRBs with extended emission such as 060614 and 211211A is compatible only with that of short GRBs. This provides a new clue on the progenitor's nature. The MVT for long GRBs anticorrelates with peak luminosity. We confirm the anticorrelation with the Lorentz factor and find a correlation with the jet opening angle as estimated from the afterglow, along with an inverse correlation with the number of pulses. The MVT can identify the emerging putative new class of long GRBs that are suggested to be produced by compact binary mergers. For otherwise typical long GRBs, the different correlations between MVT and peak luminosity, Lorentz factor, jet opening angle, and number of pulses can be explained within the context of structured, possibly wobbling, weakly magnetised relativistic jets. (summarised)
Surveillance Face Anti-Spoofing

Face Anti-spoofing (FAS) is essential to secure face recognition systems from various physical attacks. However, recent research generally focuses on short-distance applications (i.e., phone unlocking) while lacking consideration of long-distance scenes (i.e., surveillance security checks). In order to promote relevant research and fill this gap in the community, we collect a large-scale Su rveillance Hi gh-Fi delity Mask (SuHiFiMask) dataset captured under 40 surveillance scenes, which has 101 subjects from different age groups with $232~3\text{D}$ attacks (high-fidelity masks), $200~2\text{D}$ attacks (posters, portraits, and screens), and 2 adversarial attacks. In this scene, low image resolution and noise interference are new challenges faced in surveillance FAS. Together with the SuHiFiMask dataset, we propose a Contrastive Quality-Invariance Learning (CQIL) network to alleviate the performance degradation caused by image quality from three aspects: 1) An Image Quality Variable module (IQV) is introduced to recover image information associated with discrimination by combining the super-resolution network. 2) Using generated sample pairs to simulate quality variance distributions to help contrastive learning strategies obtain robust feature representation under quality variation. 3) A Separate Quality Network (SQN) is designed to learn discriminative features independent of image quality. Finally, a large number of experiments verify the quality of the SuHiFiMask dataset and the superiority of the proposed CQIL.
Gender Diversity in Ownership and Firm Innovativeness in Emerging Markets. The Mediating Roles of R&D Investments and External Capital

Despite recent evidence linking gender diversity in the firm with firm innovativeness, we know little about the underlying mechanisms. Building on and extending the Upper Echelon and entrepreneurship literature, we address two lingering questions: why and how does gender diversity in firm ownership affect firm innovativeness? We use survey data collected from 7,848 owner-managers of SMEs across 29 emerging markets to test our hypotheses. Our findings demonstrate that firms with higher gender diversity in ownership are more likely to invest in R&D and rely upon a breadth of external capital, with such differentials explaining sizeable proportions of the higher likelihood of overall firm innovativeness, product and process, as well as organizational and marketing innovations exhibited by their firms. Our findings are robust to corrections for alternative measurement of focal variables, sensitivity to outliers and subsamples, and endogenous self-selection concerns.
Analysis of Label-Flip Poisoning Attack on Machine Learning Based Malware Detector

With the increase in machine learning (ML) applications in different domains, incentives for deceiving these models have reached more than ever. As data is the core backbone of ML algorithms, attackers shifted their interest towards polluting the training data itself. Data credibility is at even higher risk with the rise of state-of-art research topics like open design principles, federated learning, and crowd-sourcing. Since the machine learning model depends on different stakeholders for obtaining data, there are no existing reliable automated mechanisms to verify the veracity of data from each source.Malware detection is arduous due to its malicious nature with the addition of metamorphic and polymorphic ability in the evolving samples. ML has proven to solve the zero-day malware detection problem, which is unresolved by traditional signature- based approaches. The poisoning of malware training data can allow the malware files to go undetected by the ML-based malware detectors, helping the attackers to fulfill their malicious goals. A feasibility analysis of the data poisoning threat in the malware detection domain is still lacking. Our work will focus on two major sections: training ML-based malware detectors and poisoning the training data using the label-poisoning approach. We will analyze the robustness of different machine learning models against data poisoning with varying volumes of poisoning data.
StarGraph: Knowledge Representation Learning based on Incomplete Two-hop Subgraph

Conventional representation learning algorithms for knowledge graphs (KG) map each entity to a unique embedding vector, ignoring the rich information contained in the neighborhood. We propose a method named StarGraph, which gives a novel way to utilize the neighborhood information for large-scale knowledge graphs to obtain entity representations. An incomplete two-hop neighborhood subgraph for each target node is at first generated, then processed by a modified self-attention network to obtain the entity representation, which is used to replace the entity embedding in conventional methods. We achieved SOTA performance on ogbl-wikikg2 and got competitive results on fb15k-237. The experimental results proves that StarGraph is efficient in parameters, and the improvement made on ogbl-wikikg2 demonstrates its great effectiveness of representation learning on large-scale knowledge graphs. The code is now available at \url{https://github.com/hzli-ucas/StarGraph}.
Antecedents of and Reactions to Emotions in the United States and Japan

In this study, we examined the degree of cultural similarity and specificity in emotional experience by asking subjects in the United States and Japan to report their experiences and reactions concerning seven different emotions. The data used for this study were part of a larger cross-cultural study of emotion antecedents and reactions involving more than 2,000 subjects in 27 countries (Wallbott & Scherer, 1986). The American-Japanese comparison is a particularly interesting one, given theoretical and anecdotal evidence suggesting differences in emotion processes between the two cultures, especially concerning the "inscrutability" of the Japanese. Data were gathered specifically to test the component-process model of emotion. The data yielded a high degree of cultural agreement concerning many aspects of the antecedent/evaluation process, replicating the general findings from all 27 cultures, and suggesting a large degree of universality in emotional experience. There were cultural differences as well, which mostly centered on reactive/expressive aspects of the emotion. The findings are discussed in terms of a model of emotion that incorporates the possibility of innate emotion programs (to account for the large degree of cross-cultural similarity) setting the stage for cultural constants in emotion, but allows for cultural and individual learning processes to account for cultural differences in other aspects.
The ARPEGE/ALADIN limited area ensemble prediction system: the impact of global targeted singular vectors

In this paper the present state of the establishment of a LAMEPS (Limited Area Ensemble Prediction System) system for Central Europe is described. Sensitivity studies were performed in order to explore whether or not it is possible to optimize the existing ARPEGE global ensemble system for Central Europe by changing the optimization area and optimization time used for the global singular vector computations. With this purpose several optimization areas and times were defined and tested through case studies and longer test periods. Verification results show that the proper choice of the singular vector optimization domain and optimization time can increase the spread and on average improves the skill of the ensemble for the Central European area. This conclusion was found to be valid for the global forecasts and the limited area predictions (i.e. the simple downscaling of the global model) as well. The experiments also demonstrate that the dynamical downscaling in itself does not result in significantly better skill. Therefore the computation of mesoscale initial perturbations might be desirable for a more efficient short-range ensemble system. Along this line research started in the field of singular vectors computed inside the limited area model ALADIN.
Irrigated urban trees exhibit greater functional trait plasticity compared to natural stands

Urbanization creates novel ecosystems comprised of species assemblages and environments with no natural analogue. Moreover, irrigation can alter plant function compared to non-irrigated systems. However, the capacity of irrigation to alter functional trait patterns across multiple species is unknown but may be important for the dynamics of urban ecosystems. We evaluated the hypothesis that urban irrigation influences plasticity in functional traits by measuring carbon-gain and water-use traits of 30 tree species planted in Southern California, USA spanning a coastal-to-desert gradient. Tree species respond to irrigation through increasing the carbon-gain trait relationship of leaf nitrogen per specific leaf area compared to their native habitat. Moreover, most species shift to a water-use strategy of greater water loss through stomata when planted in irrigated desert-like environments compared to coastal environments, implying that irrigated species capitalize on increased water availability to cool their leaves in extreme heat and high evaporative demand conditions. Therefore, irrigated urban environments increase the plasticity of trait responses compared to native ecosystems, allowing for novel response to climatic variation. Our results indicate that trees grown in water-resource-rich urban ecosystems can alter their functional traits plasticity beyond those measured in native ecosystems, which can lead to plant trait dynamics with no natural analogue.