Miracle-dz/UniFAR-FTUs huggingface dataset:
Better Zero-Shot Reasoning with Role-Play Prompting

Modern large language models (LLMs) exhibit a remarkable capacity for role-playing, enabling them to embody not only human characters but also non-human entities. This versatility allows them to simulate complex human-like interactions and behaviors within various contexts, as well as to emulate specific objects or systems. While these capabilities have enhanced user engagement and introduced novel modes of interaction, the influence of role-playing on LLMs' reasoning abilities remains underexplored. In this study, we introduce a strategically designed role-play prompting methodology and assess its performance under the zero-shot setting across twelve diverse reasoning benchmarks. Our empirical results illustrate that role-play prompting consistently surpasses the standard zero-shot approach across most datasets. Notably, in experiments conducted using ChatGPT, accuracy on AQuA rises from 53.5% to 63.8%, and on Last Letter from 23.8% to 84.2%. Upon further comparison with the Zero-Shot-CoT technique, which prompts the model to"think step by step", our study demonstrates that role-play prompting acts as a more effective trigger for the CoT process. This highlights its potential to augment the reasoning capabilities of LLMs. We release our code at https://github.com/NKU-HLT/Role-Play-Prompting.
Paring correlations within the micro-macroscopic approach for the level density

Level density $\rho(E,N,Z)$ is calculated for the two-component close- and open-shell nuclei with a given energy $E$, and neutron $N$ and proton $Z$ numbers, taking into account pairing effects within the microscopic-macroscopic approach (MMA). These analytical calculations have been carried out by using the semiclassical statistical mean-field approximations beyond the saddle-point method of the Fermi gas model in a low excitation-energies range. The level density $\rho$, obtained as function of the system entropy $S$, depends essentially on the condensation energy $E_{\rm cond}$ through the excitation energy $U$ in super-fluid nuclei. The simplest super-fluid approach, based on the BCS theory, accounts for a smooth temperature dependence of the pairing gap $\Delta$ due to particle number fluctuations. Taking into account the pairing effects in magic or semi-magic nuclei, excited below neutron resonances, one finds a notable pairing phase transition.Pairing correlations sometimes improve significantly the comparison with experimental data.
On Clark and Schaefer’s Contribution Model and its applicability to Human-Computer Collaboration

I discuss the suitability of Clark and Schaefer’s Contribution model of Grounding, as a design model for computer collaboration. This model is very appealing and, at a coarse level very influential and important for viewing dialogue as a collaborative process. While the main ideas are directly applicable and important considerations both for designers of collaborative systems and for those systems, themselves to attend to, the specific details are not particularly well suited to direct implementation.
Partial identification for discrete data with nonignorable missing outcomes

Nonignorable missing outcomes are common in real world datasets and often require strong parametric assumptions to achieve identification. These assumptions can be implausible or untestable, and so we may forgo them in favour of partially identified models that narrow the set of a priori possible values to an identification region. Here we propose a new nonparametric Bayes method that allows for the incorporation of multiple clinically relevant restrictions of the parameter space simultaneously. We focus on two common restrictions, instrumental variables and the direction of missing data bias, and investigate how these restrictions narrow the identification region for parameters of interest. Additionally, we propose a rejection sampling algorithm that allows us to quantify the evidence for these assumptions in the data. We compare our method to a standard Heckman selection model in both simulation studies and in an applied problem examining the effectiveness of cash-transfers for people experiencing homelessness.
Valley-polarized Josephson Junctions as gate-tunable $0$-$\pi$ qubit platforms

Recently, gate-defined Josephson junctions based on magic-angle twisted bilayer graphene (MATBG) have been fabricated. In such a junction, local electrostatic gating can create two superconducting regions connected by an interaction-driven valley-polarized state as the weak link. Due to the spontaneous time-reversal and inversion symmetry breaking of the valley-polarized state, novel phenomena such as the Josephson diode effect have been observed without applying external fields. Importantly, when the so-called nonreciprocity efficiency (which measures the sign and strength of the Josephson effect) changes sign, the energy-phase relation of the junction is approximate $F(\phi) \approx \cos(2\phi)$ where $F$ is the free energy and $\phi$ is the phase difference of the two superconductors. In this work, we show that such a MATBG-based Josephson junction, when shunted by a capacitor, can be used to realize the long-sought-after $0$-$\pi$ qubits which are protected from local perturbation-induced decoherence. Interestingly, by changing the junction parameters, transmon-like qubits with large anharmonicity can also be realized. In short, by utilizing the novel interaction-driven valley-polarized state in MATBG, a single gate-defined Josephson junction can be used to replace complicated superconducting circuits for realizing qubits that are protected from local perturbations.
Exploring the abyss in Kleene's computability theory

Kleene’s computability theory based on the S1–S9 computation schemes constitutes a model for computing with objects of any finite type and extends Turing’s ‘machine model’ which formalises computing with real numbers. A fundamental distinction in Kleene’s framework is between normal and non-normal functionals where the former compute the associated Kleene quantifier ∃ n and the latter do not. Historically, the focus was on normal functionals, but recently new non-normal functionals have been studied based on well-known theorems, the weakest among which seems to be the uncountability of the reals. These new non-normal functionals are fundamentally different from historical examples like Tait’s fan functional: the latter is computable from ∃ 2 , while the former are computable in ∃ 3 but not in weaker oracles. Of course, there is a great divide or abyss separating ∃ 2 and ∃ 3 and we identify slight variations of our new non-normal functionals that are again computable in ∃ 2 , i.e. fall on different sides of this abyss. Our examples are based on mainstream mathematical notions, like quasi-continuity, Baire classes, bounded variation, and semi-continuity from real analysis.
An Interpretable Machine Learning Model with Deep Learning-based Imaging Biomarkers for Diagnosis of Alzheimer's Disease

Machine learning methods have shown large potential for the automatic early diagnosis of Alzheimer's Disease (AD). However, some machine learning methods based on imaging data have poor interpretability because it is usually unclear how they make their decisions. Explainable Boosting Machines (EBMs) are interpretable machine learning models based on the statistical framework of generalized additive modeling, but have so far only been used for tabular data. Therefore, we propose a framework that combines the strength of EBM with high-dimensional imaging data using deep learning-based feature extraction. The proposed framework is interpretable because it provides the importance of each feature. We validated the proposed framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, achieving accuracy of 0.883 and area-under-the-curve (AUC) of 0.970 on AD and control classification. Furthermore, we validated the proposed framework on an external testing set, achieving accuracy of 0.778 and AUC of 0.887 on AD and subjective cognitive decline (SCD) classification. The proposed framework significantly outperformed an EBM model using volume biomarkers instead of deep learning-based features, as well as an end-to-end convolutional neural network (CNN) with optimized architecture.
PoFEL: Energy-efficient Consensus for Blockchain-based Hierarchical Federated Learning

Facilitated by mobile edge computing, client-edge-cloud hierarchical federated learning (HFL) enables communication-efficient model training in a widespread area but also incurs additional security and privacy challenges from intermediate model aggregations and remains the single point of failure issue. To tackle these challenges, we propose a blockchain-based HFL (BHFL) system that operates a permissioned blockchain among edge servers for model aggregation without the need for a centralized cloud server. The employment of blockchain, however, introduces additional overhead. To enable a compact and efficient workflow, we design a novel lightweight consensus algorithm, named Proof of Federated Edge Learning (PoFEL), to recycle the energy consumed for local model training. Specifically, the leader node is selected by evaluating the intermediate FEL models from all edge servers instead of other energy-wasting but meaningless calculations. This design thus improves the system efficiency compared with traditional BHFL frameworks. To prevent model plagiarism and bribery voting during the consensus process, we propose Hash-based Commitment and Digital Signature (HCDS) and Bayesian Truth Serum-based Voting (BTSV) schemes. Finally, we devise an incentive mechanism to motivate continuous contributions from clients to the learning task. Experimental results demonstrate that our proposed BHFL system with the corresponding consensus protocol and incentive mechanism achieves effectiveness, low computational cost, and fairness.
Py-Tetrad and RPy-Tetrad: A New Python Interface with R Support for Tetrad Causal Search

We give novel Python and R interfaces for the (Java) Tetrad project for causal modeling, search, and estimation. The Tetrad project is a mainstay in the literature, having been under consistent development for over 30 years. Some of its algorithms are now classics, like PC and FCI; others are recent developments. It is increasingly the case, however, that researchers need to access the underlying Java code from Python or R. Existing methods for doing this are inadequate. We provide new, up-to-date methods using the JPype Python-Java interface and the Reticulate Python-R interface, directly solving these issues. With the addition of some simple tools and the provision of working examples for both Python and R, using JPype and Reticulate to interface Python and R with Tetrad is straightforward and intuitive.
Brain-Inspired Computational Intelligence via Predictive Coding

Artificial intelligence (AI) is rapidly becoming one of the key technologies of this century. The majority of results in AI thus far have been achieved using deep neural networks trained with the error backpropagation learning algorithm. However, the ubiquitous adoption of this approach has highlighted some important limitations such as substantial computational cost, difficulty in quantifying uncertainty, lack of robustness, unreliability, and biological implausibility. It is possible that addressing these limitations may require schemes that are inspired and guided by neuroscience theories. One such theory, called predictive coding (PC), has shown promising performance in machine intelligence tasks, exhibiting exciting properties that make it potentially valuable for the machine learning community: PC can model information processing in different brain areas, can be used in cognitive control and robotics, and has a solid mathematical grounding in variational inference, offering a powerful inversion scheme for a specific class of continuous-state generative models. With the hope of foregrounding research in this direction, we survey the literature that has contributed to this perspective, highlighting the many ways that PC might play a role in the future of machine learning and computational intelligence at large.
General hydrodynamic features of elastoviscoplastic fluid flows through randomised porous media

A numerical study of yield-stress fluids flowing in porous media is presented. The porous media is randomly constructed by non-overlapping mono-dispersed circular obstacles. Two class of rheological models are investigated: elastoviscoplastic fluids (i.e. Saramito model) and viscoplastic fluids (i.e. Bingham model). A wide range of practical Weissenberg and Bingham numbers is studied at three different levels of porosities of the media. The emphasis is on revealing some physical transport mechanisms of yield-stress fluids in porous media when the elastic behaviour of this kind of fluids is incorporated. Thus, computations of elastoviscoplastic fluids are performed and are compared with the viscoplastic fluid flow properties. At a constant Weissenberg number, the pressure drop increases both with the Bingham number and the solid volume fraction of obstacles. However, the effect of elasticity is less trivial. At low Bingham numbers, the pressure drop of an elastoviscoplastic fluid increases compared to a viscoplastic fluid, while at high Bingham numbers we observe drag reduction by elasticity. At the yield limit (i.e. infinitely large Bingham numbers), elasticity of the fluid systematically promotes yielding: elastic stresses help the fluid to overcome the yield stress resistance at smaller pressure gradients. We observe that elastic effects increase with both Weissenberg and Bingham numbers. In both cases, elastic effects finally make the elastoviscoplastic flow unsteady, which consequently can result in chaos and turbulence. Keywords: Yield-stress fluids; Viscoplastic fluids; Elastoviscoplastic fluids; Porous media
Degrees of Separation: A Flexible Type System for Data Race Prevention

Data races are a notorious problem in parallel programming. There has been great research interest in type systems that statically prevent data races. Despite the progress in the safety and usability of these systems, lots of existing approaches enforce strict anti-aliasing principles to prevent data races. The adoption of them is often intrusive, in the sense that it invalidates common programming patterns and requires paradigm shifts. We propose Capture Separation Calculus (System CSC), a calculus based on Capture Calculus (System CC<:box), that achieves static data race freedom while being non-intrusive. It allows aliasing in general to permit common programming patterns, but tracks aliasing and controls them when that is necessary to prevent data races. We study the formal properties of System CSC by establishing its type safety and data race freedom. Notably, we establish the data race freedom property by proving the confluence of its reduction semantics. To validate the usability of the calculus, we implement it as an extension to the Scala 3 compiler, and use it to type-check the examples in the paper.
Semantic Similarity Loss for Neural Source Code Summarization

This paper presents an improved loss function for neural source code summarization. Code summarization is the task of writing natural language descriptions of source code. Neural code summarization refers to automated techniques for generating these descriptions using neural networks. Almost all current approaches involve neural networks as either standalone models or as part of a pretrained large language models e.g., GPT, Codex, LLaMA. Yet almost all also use a categorical cross-entropy (CCE) loss function for network optimization. Two problems with CCE are that 1) it computes loss over each word prediction one-at-a-time, rather than evaluating a whole sentence, and 2) it requires a perfect prediction, leaving no room for partial credit for synonyms. We propose and evaluate a loss function to alleviate this problem. In essence, we propose to use a semantic similarity metric to calculate loss over the whole output sentence prediction per training batch, rather than just loss for each word. We also propose to combine our loss with traditional CCE for each word, which streamlines the training process compared to baselines. We evaluate our approach over several baselines and report an improvement in the vast majority of conditions.
Playing with Words: Comparing the Vocabulary and Lexical Richness of ChatGPT and Humans

The introduction of Artificial Intelligence (AI) generative language models such as GPT (Generative Pre-trained Transformer) and tools such as ChatGPT has triggered a revolution that can transform how text is generated. This has many implications, for example, as AI-generated text becomes a significant fraction of the text, would this have an effect on the language capabilities of readers and also on the training of newer AI tools? Would it affect the evolution of languages? Focusing on one specific aspect of the language: words; will the use of tools such as ChatGPT increase or reduce the vocabulary used or the lexical richness? This has implications for words, as those not included in AI-generated content will tend to be less and less popular and may eventually be lost. In this work, we perform an initial comparison of the vocabulary and lexical richness of ChatGPT and humans when performing the same tasks. In more detail, two datasets containing the answers to different types of questions answered by ChatGPT and humans, and a third dataset in which ChatGPT paraphrases sentences and questions are used. The analysis shows that ChatGPT tends to use fewer distinct words and lower lexical richness than humans. These results are very preliminary and additional datasets and ChatGPT configurations have to be evaluated to extract more general conclusions. Therefore, further research is needed to understand how the use of ChatGPT and more broadly generative AI tools will affect the vocabulary and lexical richness in different types of text and languages.
DiffV2S: Diffusion-based Video-to-Speech Synthesis with Vision-guided Speaker Embedding

Recent research has demonstrated impressive results in video-to-speech synthesis which involves reconstructing speech solely from visual input. However, previous works have struggled to accurately synthesize speech due to a lack of sufficient guidance for the model to infer the correct content with the appropriate sound. To resolve the issue, they have adopted an extra speaker embedding as a speaking style guidance from a reference auditory information. Nevertheless, it is not always possible to obtain the audio information from the corresponding video input, especially during the inference time. In this paper, we present a novel vision-guided speaker embedding extractor using a self-supervised pretrained model and prompt tuning technique. In doing so, the rich speaker embedding information can be produced solely from input visual information, and the extra audio information is not necessary during the inference time. Using the extracted vision-guided speaker embedding representations, we further develop a diffusion-based video-to-speech synthesis model, so called DiffV2S, conditioned on those speaker embeddings and the visual representation extracted from the input video. The proposed DiffV2S not only maintains phoneme details contained in the input video frames, but also creates a highly intelligible mel-spectrogram in which the speaker identities of the multiple speakers are all preserved. Our experimental results show that DiffV2S achieves the state-of-the-art performance compared to the previous video-to-speech synthesis technique.
Building a “trap model” of glassy dynamics from a local structural predictor of rearrangements

Here we introduce a variation of the trap model of supercooled liquids based on softness, a particle-based variable identified by machine learning that quantifies the local structural environment and energy barrier for the particle to rearrange. As in the trap model, we assume that each particle's softness, and hence energy barrier, evolves independently. We show that our model makes qualitatively reasonable predictions of behaviors such as the dependence of fragility on density in a model supercooled liquid. We also show failures of the model, indicating in some cases signs that softness may be missing important information, and in other cases features that may only be explained by correlations neglected in the trap model.
Bat activity correlated with migratory insect bioflows in the Pyrenees

High altitude mountain passes in the Pyrenees are known to be important migratory hotspots for autumn migrating insects originating from large swathes of northern Europe. In the Pyrenees, prior research has focused on diurnal migratory insects. In this study, we investigate the nocturnal component of the migratory assemblage and ask if this transient food source is also used by bat species. Three seasons of insect trapping revealed 66 species of four different orders, 90% of which were Noctuid moths, including the destructive pest Helicoverpa armigera, otherwise known as the cotton bollworm. Acoustic bat detectors revealed that high activity of Nyctalus spp. and Tadarida teniotis bats were closely synchronized with the arrival of the migratory moths, suggesting this food source is important for both resident and migratory bats to build or maintain energy reserves. Bats of the Nyctalus spp. are likely migrating through the study site using fly-and-forage strategies or stopping over in the area, while resident T. teniotis may be exploiting the abundant food source to build fat stores for hibernation. This study shows that nocturnal migratory insects are abundant in the Pyrenees during autumn and interact during migration, not only with their co-migrant bats but also with resident bat species.
SPATIAL VARIABILITY OF CROP AND SOIL PROPERTIES IN A CROP-LIVESTOCK INTEGRATED SYSTEM

The knowledge of spatial variability soil properties is useful in the rational use of inputs, as in the site specific application of lime and fertilizer. The objective of this work was to map and evaluate the spatial variability of the corn and pasture, soil chemical and physical properties in crop-livestock integrated system. The study was conducted in an area of 6.9 ha of a Typic Paleudult in Sao Carlos, SP, Brazil. The summer crop corn was sowed together with the forage crop Brachiaria brizantha in the system of crop-livestock rotation. A regular hexagon sampling grid design with 6 sub-samples was adopted for each hectare. The values of soil P, K, Ca, Mg, and CEC, basis saturation, clay and sand were analyzed by traditional soil testing in georreferenced samples collected at 0–0.2 m depth. Soil electrical conductivity (EC) was measured with a contact sensor. The site was evaluated at the end of the corn season (April) and for the forage (October) by imageries from the Landsat 5 using remote sensing techniques and a geographic information system. Normalized difference vegetation index (NDVI) was used to interpret imageries. Spatial continuity of crop and soil properties was modeled using semivariograms. Maps contours of crop and forage were obtained by kriging, and maps of soil properties by using inverse distance weighting interpolation. Results from this study showed that the NDVI was associated with ECa and soil parameters indicating crop and pasture variations on crop-livestock integrated system. Sampling density adopted was insufficient for an adequate characterization of the spatial variability of soil parameters as pH, O.M. , P, K, V%, CEC, clay and sand. Estimated VRT maps compared to estimated uniformly applied lime and P and K fertilizer recommendation indicate that VRT could be more adequate to lime and potash recommendation, and and would have little effect on P fertilization.