Miracle-dz/UniFAR-FTUs huggingface dataset:
Pick a Fight or Bite your Tongue: Investigation of Gender Differences in Idiomatic Language Usage

A large body of research on gender-linked language has established foundations regarding cross-gender differences in lexical, emotional, and topical preferences, along with their sociological underpinnings. We compile a novel, large and diverse corpus of spontaneous linguistic productions annotated with speakers’ gender, and perform a first large-scale empirical study of distinctions in the usage of figurative language between male and female authors. Our analyses suggest that (1) idiomatic choices reflect gender-specific lexical and semantic preferences in general language, (2) men’s and women’s idiomatic usages express higher emotion than their literal language, with detectable, albeit more subtle, differences between male and female authors along the dimension of dominance compared to similar distinctions in their literal utterances, and (3) contextual analysis of idiomatic expressions reveals considerable differences, reflecting subtle divergences in usage environments, shaped by cross-gender communication styles and semantic biases.
83% ImageNet Accuracy in One Hour

EfficientNets are a family of state-of-the-art image classification models based on efficiently scaled convolutional neural networks. Currently, EfficientNets can take on the order of days to train; for example, training an EfficientNet-B0 model takes 23 hours on a Cloud TPU v2-8 node. In this paper, we explore techniques to scale up the training of EfficientNets on TPU-v3 Pods with 2048 cores, motivated by speedups that can be achieved when training at such scales. We discuss optimizations required to scale training to a batch size of 65536 on 1024 TPU-v3 cores, such as selecting large batch optimizers and learning rate schedules as well as utilizing distributed evaluation and batch normalization techniques. Additionally, we present timing and performance benchmarks for EfficientNet models trained on the ImageNet dataset in order to analyze the behavior of EfficientNets at scale. With our optimizations, we are able to train EfficientNet on ImageNet to an accuracy of 83% in 1 hour and 4 minutes.
Methods for Pruning Deep Neural Networks

This paper presents a survey of methods for pruning deep neural networks. It begins by categorising over 150 studies based on the underlying approach used and then focuses on three categories: methods that use magnitude based pruning, methods that utilise clustering to identify redundancy, and methods that use sensitivity analysis to assess the effect of pruning. Some of the key influencing studies within these categories are presented to highlight the underlying approaches and results achieved. Most studies present results which are distributed in the literature as new architectures, algorithms and data sets have developed with time, making comparison across different studied difficult. The paper therefore provides a resource for the community that can be used to quickly compare the results from many different methods on a variety of data sets, and a range of architectures, including AlexNet, ResNet, DenseNet and VGG. The resource is illustrated by comparing the results published for pruning AlexNet and ResNet50 on ImageNet and ResNet56 and VGG16 on the CIFAR10 data to reveal which pruning methods work well in terms of retaining accuracy whilst achieving good compression rates. The paper concludes by identifying some research gaps and promising directions for future research.
Subcritical dynamos in rapidly rotating planar convection

We study dynamo action using numerical simulations of planar Boussinesq convection at rapid rotation (low Ekman numbers, Ek), focusing on subcritical dynamo action in which the dynamo is sustained for Rayleigh numbers, Ra, below the critical Rayleigh number for the onset of nonmagnetic convection, Ra$_c$. These solutions are found by first investigating the supercritical regime, in which the dynamo is able to generate a large-scale magnetic field that significantly influences the convective motions, with an associated Elsasser number of order Ek$^{1/3}$. Subcritical solutions are then found by tracking this solution branch into the subcritical regime, taking a supercritical solution and then gradually lowering the corresponding Rayleigh number. We show that decreasing the Ekman number leads to an extension of the subcritical range of Ra/Ra$_c$, down to an optimal value of Ek$=10^{-5}$. For magnetic Prandtl numbers of order unity, subcriticality is then hampered by the emergence of a large-scale mode at lower Ekman numbers when the dynamo driven by the smaller scale convection generates relatively stronger large-scale magnetic field. The inability of the large-scale mode to sustain dynamo action leads to an intermittent behaviour that appears to inhibit subcriticality. The subcritical solutions are also sensitive to the value of the magnetic Reynolds number (or equivalently, the magnetic Prandtl number, Pm), as values of the magnetic Reynolds number greater than 70 are required to produce dynamo action, but large values lead to fluctuations that are able to push the system too far from the subcritical branch and towards the trivial conducting state.
A Unifying Theory of Transition-based and Sequence Labeling Parsing

We define a mapping from transition-based parsing algorithms that read sentences from left to right to sequence labeling encodings of syntactic trees. This not only establishes a theoretical relation between transition-based parsing and sequence-labeling parsing, but also provides a method to obtain new encodings for fast and simple sequence labeling parsing from the many existing transition-based parsers for different formalisms. Applying it to dependency parsing, we implement sequence labeling versions of four algorithms, showing that they are learnable and obtain comparable performance to existing encodings.
Reasoning Over History: Context Aware Visual Dialog

While neural models have been shown to exhibit strong performance on single-turn visual question answering (VQA) tasks, extending VQA to a multi-turn, conversational setting remains a challenge. One way to address this challenge is to augment existing strong neural VQA models with the mechanisms that allow them to retain information from previous dialog turns. One strong VQA model is the MAC network, which decomposes a task into a series of attention-based reasoning steps. However, since the MAC network is designed for single-turn question answering, it is not capable of referring to past dialog turns. More specifically, it struggles with tasks that require reasoning over the dialog history, particularly coreference resolution. We extend the MAC network architecture with Context-aware Attention and Memory (CAM), which attends over control states in past dialog turns to determine the necessary reasoning operations for the current question. MAC nets with CAM achieve up to 98.25% accuracy on the CLEVR-Dialog dataset, beating the existing state-of-the-art by 30% (absolute). Our error analysis indicates that with CAM, the model’s performance particularly improved on questions that required coreference resolution.
Visualisation of law and legal Process: An opportunity missed

Visual representation of the law and legal process can aid in recall and discussion of complicated legal concepts, yet is a skill rarely taught in law schools. This work investigates the use of flowcharts and similar process-oriented diagrams in contemporary legal literature through a literature review and concept-based content analysis. Information visualisations (infovis) identified in the literature are classified into 11 described archetypal diagram types, and the results describe their usage quantitatively by type, year, publication venue and legal domain. We found that the use of infovis in legal literature is extremely rare, identifying not more than 10 articles in each calendar year. We also identified that the concept flow diagram is most commonly used, and that Unified Modelling Language (UML) is the most frequently applied representational approach. This work posits a number of serious questions for legal educators and practicing lawyers regarding how infovis in legal education and practice may improve access to justice, legal education and lay comprehension of complex legal frameworks and processes. It concludes by asking how we can expect communities to understand and adhere to laws that have become so complex and verbose as to be incomprehensible even to many of those who are learned in the law?
A combined analysis of the H0 late time direct measurements and the impact on the Dark Energy sector

We combine 23 Hubble constant measurements based on Cepheids-SN Ia, TRGB-SN Ia, Miras-SN Ia, Masers, Tully Fisher, Surface Brightness Fluctuations, SN II, Time-delay Lensing, Standard Sirens and $\gamma$-ray Attenuation, obtaining our best {\it optimistic} $H_0$ estimate, that is $H_0=72.94\pm0.75$ km/s/Mpc at 68\% CL. This is in $5.9\sigma$ tension with the $\Lambda$CDM model, therefore we evaluate its impact on the extended Dark Energy cosmological models that can alleviate the tension. We find more than $4.9\sigma$ evidence for a phantom Dark Energy equation of state in the $w$CDM scenario, the cosmological constant ruled out at more than $3\sigma$ in a $w_0w_a$CDM model and more than $5.7\sigma$ evidence for a coupling between Dark Matter and Dark Energy in the IDE scenario. Finally, we check the robustness of our results, and we quote two additional combinations of the Hubble constant. The {\it ultra-conservative} estimate, $H_0=72.7\pm 1.1$ km/s/Mpc at 68\% CL, is obtained removing the Cepheids-SN Ia and the Time-Delay Lensing based measurements, and confirms the evidence for new physics.
Deep Diacritization: Efficient Hierarchical Recurrence for Improved Arabic Diacritization

We propose a novel architecture for labelling character sequences that achieves state-of-the-art results on the Tashkeela Arabic diacritization benchmark. The core is a two-level recurrence hierarchy that operates on the word and character levels separately—enabling faster training and inference than comparable traditional models. A cross-level attention module further connects the two and opens the door for network interpretability. The task module is a softmax classifier that enumerates valid combinations of diacritics. This architecture can be extended with a recurrent decoder that optionally accepts priors from partially diacritized text, which improves results. We employ extra tricks such as sentence dropout and majority voting to further boost the final result. Our best model achieves a WER of 5.34%, outperforming the previous state-of-the-art with a 30.56% relative error reduction.
Global and local statistics in turbulent emulsions

Abstract Turbulent emulsions are complex physical systems characterized by a strong and dynamical coupling between small-scale droplets and large-scale rheology. By using a specifically designed Taylor–Couette shear flow system, we are able to characterize the statistical properties of a turbulent emulsion made of oil droplets dispersed in an ethanol–water continuous solution, at an oil volume fraction up to 40 %. We find that the dependence of the droplet size on the Reynolds number of the flow at a volume fraction of 1 % can be well described by the Hinze criterion. The distribution of droplet sizes is found to follow a log-normal distribution, hinting at a fragmentation process as the possible mechanism dominating droplet formation. Additionally, the effective viscosity of the turbulent emulsion increases with the volume fraction of the dispersed oil phase, and decreases when the shear strength is increased. We find that the dependence of the effective viscosity on the shear rate can be described by the Herschel–Bulkley model, with a flow index monotonically decreasing with increasing oil volume fraction. This finding indicates that the degree of shear thinning systematically increases with the volume fraction of the dispersed phase. The current findings have important implications for bridging the knowledge on turbulence and low-Reynolds-number emulsion flows to turbulent emulsion flows.
Oil Yield of Sunflower Cultivated With Different Water Depths and Nitrogen Doses

Sunflower is adapted to different soil and climate conditions, but its water and nitrogen requirements are not well defined. This study was carried out in Apodi, Rio Grande do Norte, Brazil, to evaluate the oil yield of sunflower in response to irrigation depth and nitrogen dose. A randomized block experimental design was used with a factorial scheme with four replications. The factors tested were irrigation depths corresponding to 58, 80, 100 and 120% of crop evapotranspiration (water-use efficiency variable), and nitrogen doses corresponding to 40, 100, 200 and 370% of the standard dose. Data were submitted to analysis of variance by the F-test, followed by the Tukey test of means and regression analysis. The increase in the water depth until 100% ETc and in the nitrogen dose until 260 kg ha-1 promoted increase in the values of all variables, but at higher nitrogen doses the variables decreased. The response surfaces showed stronger response to nitrogen dose for the water depths around 100% ETc and for the lower nitrogen doses, and greater water use efficiency for the production of oil for the water depths around 100% ETc, independently of nitrogen dose.
Context-Aware Cross-Attention for Non-Autoregressive Translation

Non-autoregressive translation (NAT) significantly accelerates the inference process by predicting the entire target sequence. However, due to the lack of target dependency modelling in the decoder, the conditional generation process heavily depends on the cross-attention. In this paper, we reveal a localness perception problem in NAT cross-attention, for which it is difficult to adequately capture source context. To alleviate this problem, we propose to enhance signals of neighbour source tokens into conventional cross-attention. Experimental results on several representative datasets show that our approach can consistently improve translation quality over strong NAT baselines. Extensive analyses demonstrate that the enhanced cross-attention achieves better exploitation of source contexts by leveraging both local and global information.
Visual Companion for Booklovers

An innumerable number of individual choices go into discovering a new book. There are unmistakably two groups of booklovers: those who like to search online, follow other people's latest readings, or simply react to a system's recommendations; and those who love to wander between library stacks, lose themselves behind bookstore shelves, or simply hide behind piles of (un)organized books. Depending on which group a person may fall into, there are two distinct and corresponding mediums that inform his or her choices: digital, that provides efficient retrieval of information online, and physical, a more tactile pursuit that leads to unexpected discoveries and promotes serendipity. How could we possibly bridge the gap between these seemingly disparate mediums into an integrated system that can amplify the benefits they both offer? In this paper, we present the BookVIS application, which uses book-related data and generates personalized visualizations to follow users in their quest for a new book. In this new redesigned version, the app brings associative visual connections to support intuitive exploration of easily retrieved digital information and its relationship with the physical book in hand. BookVIS keeps track of the user's reading preferences and generates a dataSelfie as an individual snapshot of a personal taste that grows over time. Usability testing has also been conducted and has demonstrated the app's ability to identify distinguishable patterns in readers' tastes that could be further used to communicate personal preferences in new "shelf-browsing" iterations. By efficiently supplementing the user's cognitive information needs while still supporting the spontaneity and enjoyment of the book browsing experience, BookVIS bridges the gap between real and online realms, and maximizes the engagement of personalized mobile visual clues.
FUV and NIR size of the HI selected low surface brightness galaxies

How low surface brightness galaxies (LSBGs) form stars and assemble stellar mass is one of the most important questions related to understanding the LSBG population. We select a sample of 381 HI bright LSBGs with both far ultraviolet (FUV) and near infrared (NIR) observations to investigate the star formation rate (SFR) and stellar mass scales, and the growth mode. We measure the FUV and NIR radii of our sample, which represent the star-forming and stellar mass distribution scales respectively. We also compare the FUV and H band radius-stellar mass relation with archival data, to identify the SFR and stellar mass structure difference between the LSBG population and other galaxies. Since galaxy HI mass has a tight correlation with the HI radius, we can also compare the HI and FUV radii to understand the distribution of HI gas and star formation activities. Our results show that most of the HI selected LSBGs have extended star formation structure. The stellar mass distribution of LSBGs may have a similar structure to disk galaxies at the same stellar mass bins, but the star-forming activity of LSBGs happens at a larger radius than the high surface density galaxies, which may help to identify the LSBG sample from the wide-field deep u band image survey. The HI is also distributed at larger radii, implying a steeper (or not) Kennicutt-Schmidt relation for LSBGs.
Stability breaking, concentration breaking and asymptotic analysis in two thermal insulation problems

In this paper, we study two thermal insulation problems introduced by Bucur-Buttazzo-Nitsch: the energy problem and the eigenvalue problem. For the energy problem, by assuming that the heat source function is radial, we obtain necessary and sufficient condition on the heat source function such that ball configurations are stationary and stable along smooth volume preserving flows. This implies the stability breaking result: let $m$ be the total amount of insulating material, then under certain conditions for the heat source, there exists $m_1>0$ such that when $m\ge m_1$, ball is a stable shape, and when $m<m_1$, ball is not stable. Also in the energy problem, we prove that given a nonradial domain $\Omega$, there exists a constant $m_2(\Omega)>0$ such that when $m> m_2(\Omega)$, the insulating material will concentrate on the whole boundary of $\Omega$, and otherwise when $m < m_2(\Omega)$. In the eigenvalue problem, let $\lambda_m(B_R)$ be the eigenvalue functional on the ball of radius $R$, and $m_0$ be the symmetry breaking number on a ball shown in \cite{BBN}. We prove that the limit of $m\lambda_m(B_R)$ as $m$ goes to $m_0$, converges to the number at which $m\lambda_m$ reaches exactly half of its range for $m \in (0,\infty)$. Stability of ball shape is also studied in this problem.
Accretion bursts in magnetized gas-dust protoplanetary disks

Aims. Accretion bursts triggered by the magnetorotational instability (MRI) in the innermost disk regions were studied for protoplanetary gas-dust disks that formed from prestellar cores of a various mass Mcore and mass-to-magnetic flux ratio λ. Methods. Numerical magnetohydrodynamics simulations in the thin-disk limit were employed to study the long-term (~1.0 Myr) evolution of protoplanetary disks with an adaptive turbulent α-parameter, which explicitly depends on the strength of the magnetic field and ionization fraction in the disk. The numerical models also feature the co-evolution of gas and dust, including the back-reaction of dust on gas and dust growth. Results. A dead zone with a low ionization fraction of x≲10−13 and temperature on the order of several hundred Kelvin forms in the inner disk soon after its formation, extending from several to several tens of astronomical units depending on the model. The dead zone features pronounced dust rings that are formed due to the concentration of grown dust particles in the local pressure maxima. Thermal ionization of alkaline metals in the dead zone trigger the MRI and associated accretion burst, which is characterized by a sharp rise, small-scale variability in the active phase, and fast decline once the inner MRI-active region is depleted of matter. The burst occurrence frequency is highest in the initial stages of disk formation and is driven by gravitational instability (GI), but it declines with diminishing disk mass-loading from the infalling envelope. There is a causal link between the initial burst activity and the strength of GI in the disk fueled by mass infall from the envelope. We find that the MRI-driven burst phenomenon occurs for λ = 2–10, but diminishes in models with Mcore ≲ M⊙, suggesting a lower limit on the stellar mass for which the MRI-triggered burst can occur. Conclusions. The MRI-triggered bursts occur for a wide range of mass-to-magnetic flux ratios and initial cloud core masses. The burst occurrence frequency is highest in the initial disk formation stage and reduces as the disk evolves from a gravitationally unstable to a viscous-dominated state. The MRI-triggered bursts are intrinsically connected with the dust rings in the inner disk regions, and both can be a manifestation of the same phenomenon, that is to say the formation of a dead zone.
A Closer Look at Linguistic Knowledge in Masked Language Models: The Case of Relative Clauses in American English

Transformer-based language models achieve high performance on various tasks, but we still lack understanding of the kind of linguistic knowledge they learn and rely on. We evaluate three models (BERT, RoBERTa, and ALBERT), testing their grammatical and semantic knowledge by sentence-level probing, diagnostic cases, and masked prediction tasks. We focus on relative clauses (in American English) as a complex phenomenon needing contextual information and antecedent identification to be resolved. Based on a naturalistic dataset, probing shows that all three models indeed capture linguistic knowledge about grammaticality, achieving high performance.Evaluation on diagnostic cases and masked prediction tasks considering fine-grained linguistic knowledge, however, shows pronounced model-specific weaknesses especially on semantic knowledge, strongly impacting models’ performance. Our results highlight the importance of (a)model comparison in evaluation task and (b) building up claims of model performance and the linguistic knowledge they capture beyond purely probing-based evaluations.
Opinion dynamics of online social network users: a micro-level analysis

ABSTRACT In this paper, we present an empirical study of the opinion dynamics of a large-scale sample of online social network users. We estimate users’ opinions as continuous scalars based on their subscriptions to information sources and analyze how friendship connections affect the dynamics of these estimations. Distinguishing between positive (toward friends’ opinions) and negative (away from friends’ opinions) opinion shifts, we find that the existence and magnitude of both types of shifts are positively related (largely through linear or inverted U-shaped form) to the distance in opinions between a user and their friends. The distance additionally moderates the balance between positive and negative movements: if the distance is within a certain moderate range, there is a relatively high chance of a positive shift.