Miracle-dz/UniFAR-FTUs huggingface dataset:
RODA: Reverse Operation Based Data Augmentation for Solving Math Word Problems

Automatically solving math word problems is a critical task in the field of natural language processing. Recent models have reached their performance bottleneck and require more high-quality data for training. We propose a novel data augmentation method that reverses the mathematical logic of math word problems to produce new high-quality math problems and introduce new knowledge points that can benefit learning the mathematical reasoning logic. We apply the augmented data on two SOTA math word problem solving models and compare our results with a strong data augmentation baseline. Experimental results show the effectiveness of our approach (we release our code and data at https://github.com/yiyunya/RODA).
Boosted Semantic Embedding based Discriminative Feature Generation for Texture Analysis

Learning discriminative features is crucial for various robotic applications such as object detection and classification. In this paper, we present a general framework for the analysis of the discriminative properties of haptic signals. Our focus is on two crucial components of a robotic perception system: discriminative feature extraction and metric-based feature transformation to enhance the separability of haptic signals in the projected space. We propose a set of hand-crafted haptic features (generated only from acceleration data), which enables discrimination of real-world textures. Since the Euclidean space does not reflect the underlying pattern in the data, we propose to learn an appropriate transformation function to project the feature onto the new space and apply different pattern recognition algorithms for texture classification and discrimination tasks. Unlike other existing methods, we use a triplet-based method for improved discrimination in the embedded space. We further demonstrate how to build a haptic vocabulary by selecting a compact set of the most distinct and representative signals in the embedded space. The experimental results show that the proposed features augmented with learned embedding improves the performance of semantic discrimination tasks such as classification and clustering and outperforms the related state-of-the-art.
Neurally Augmented ALISTA

It is well-established that many iterative sparse reconstruction algorithms can be unrolled to yield a learnable neural network for improved empirical performance. A prime example is learned ISTA (LISTA) where weights, step sizes and thresholds are learned from training data. Recently, Analytic LISTA (ALISTA) has been introduced, combining the strong empirical performance of a fully learned approach like LISTA, while retaining theoretical guarantees of classical compressed sensing algorithms and significantly reducing the number of parameters to learn. However, these parameters are trained to work in expectation, often leading to suboptimal reconstruction of individual targets. In this work we therefore introduce Neurally Augmented ALISTA, in which an LSTM network is used to compute step sizes and thresholds individually for each target vector during reconstruction. This adaptive approach is theoretically motivated by revisiting the recovery guarantees of ALISTA. We show that our approach further improves empirical performance in sparse reconstruction, in particular outperforming existing algorithms by an increasing margin as the compression ratio becomes more challenging.
On the Interaction Problem between a Compressible Viscous Fluid and a Nonlinear Thermoelastic Plate

In this paper we study the interaction problem between a nonlinear thermoelastic plate and a compressible viscous fluid with the adiabatic constant $\gamma>12/7$. The existence of a weak solution for this problem is obtained by constructing a time-continuous operator splitting scheme that decouples the fluid and the structure. The fluid sub-problem is given on a fixed reference domain in the arbitrary Lagrangian-Eulerian (ALE) formulation, and the continuity equation is damped on this domain as well. This allows the majority of the analysis to be performed on the fixed reference domain, while the convergence of the approximate pressure is obtained on the physical domain.
The dependence of the galaxy stellar-to-halo mass relation on galaxy morphology

We investigate the dependence of the local galaxy stellar-to-halo mass relation (SHMR) on galaxy morphology. We use data from the Sloan Digital Sky Survey (SDSS) data release 7 with morphological classifications from Galaxy Zoo, and compare with the EAGLE cosmological simulation. At fixed halo mass in the mass range $10^{11.7}\! -\! 10^{12.9}\, {\rm M_{\odot }}$, the median stellar masses of SDSS disc galaxies are up to a factor of 1.4 higher than the median masses of their elliptical counterparts. However, when we switch from the stellar masses from Kauffmann et al. to those calculated by Chang et al. or Brinchmann et al., the median SHMR from discs and ellipticals coincides in this mass range. For halo masses larger than $10^{13}\, {\rm M_{\odot }}$, discs are less massive than ellipticals in same-mass haloes, regardless of whose stellar mass estimates we use. However, we find that for these high halo masses the results for discs may be affected by central/satellite misclassifications. The EAGLE simulation predicts that discs are up to a factor of 1.5 more massive than elliptical galaxies residing in same-mass haloes less massive than $10^{13}\, {\rm M_{\odot }}$, in agreement with the Kauffmann et al. data. Haloes with masses between 1011.5 and $10^{12}\, {\rm M_{\odot }}$, which host disc galaxies, were assembled earlier than those hosting ellipticals. This suggests that the discs are more massive because they had more time for gas accretion and star formation. In $10^{12}\! -\! 10^{12.5}\, {\rm M_{\odot }}$ haloes, the central black holes in elliptical galaxies grew faster and became more massive than their counterparts in disc galaxies. This suggests that in this halo mass range the ellipticals are less massive because active galactic nucleus feedback ejected more of the halo’s gas reservoir, reducing star formation, and suppressing the (re)growth of stellar discs.
Improving Target-side Lexical Transfer in Multilingual Neural Machine Translation

To improve the performance of Neural Machine Translation (NMT) for low-resource languages (LRL), one effective strategy is to leverage parallel data from a related high-resource language (HRL). However, multilingual data has been found more beneficial for NMT models that translate from the LRL to a target language than the ones that translate into the LRLs. In this paper, we aim to improve the effectiveness of multilingual transfer for NMT models that translate into the LRL, by designing a better decoder word embedding. Extending upon a general-purpose multilingual encoding method Soft Decoupled Encoding (Wang et al., 2019), we propose DecSDE, an efficient character n-gram based embedding specifically designed for the NMT decoder. Our experiments show that DecSDE leads to consistent gains of up to 1.8 BLEU on translation from English to four different languages.
Rank Position Forecasting in Car Racing

Rank position forecasting in car racing is a challenging problem when using a Deep Learning-based model over time-series data. It is featured with highly complex global dependency among the racing cars, with uncertainty resulted from existing and external factors; and it is also a problem with data scarcity. Existing methods, including statistical models, machine learning regression models, and several state-of-the-art deep forecasting models all perform not well on this problem. By an elaborate analysis of pit stop events, we find it critical to decompose the cause-and-effect relationship and model the rank position and pit stop events separately. In choosing a sub-model from different neural network models, we find the model with weak assumptions on the global dependency structure performs the best. Based on these observations, we propose RankNet, a combination of the encoder-decoder network and a separate Multilayer Perception network that is capable of delivering probabilistic forecasting to model the pit stop events and rank position in car racing. Further with the help of feature optimizations, RankNet demonstrates a significant performance improvement, where MAE improves 19% in two laps forecasting task and 7% in the stint forecasting task over the best baseline and is also more stable when adapting to unseen new data. Details of the model optimizations and performance profiling are presented. It is promising to provide useful interactions of neural networks in forecasting racing cars and shine a light on solutions to similar challenging issues in general forecasting problems.
Policy Learning Using Weak Supervision

Most existing policy learning solutions require the learning agents to receive high-quality supervision signals, e.g., rewards in reinforcement learning (RL) or high-quality expert's demonstrations in behavioral cloning (BC). These quality supervisions are either infeasible or prohibitively expensive to obtain in practice. We aim for a unified framework that leverages the weak supervisions to perform policy learning efficiently. To handle this problem, we treat the "weak supervisions" as imperfect information coming from a peer agent, and evaluate the learning agent's policy based on a "correlated agreement" with the peer agent's policy (instead of simple agreements). Our way of leveraging peer agent's information offers us a family of solutions that learn effectively from weak supervisions with theoretical guarantees. Extensive evaluations on tasks including RL with noisy reward, BC with weak demonstrations and standard policy co-training (RL + BC) show that the proposed approach leads to substantial improvements, especially when the complexity or the noise of the learning environments grows.
Aluminium-enriched metal-poor stars buried in the inner Galaxy

Stars with higher levels of aluminium and nitrogen enrichment are often key pieces in the chemical makeup of multiple populations in almost all globular clusters (GCs). There is also compelling observational evidence that some Galactic components could be partially built from dissipated GCs. The identification of such stars among metal-poor field stars may therefore provide insight into the composite nature of the Milky Way (MW) bulge and inner stellar halo, and could also reveal other chemical peculiarities. Here, based on APOGEE spectra, we report the discovery of 29 mildly metal-poor ([Fe/H] ≲ −0.7) stars with stellar atmospheres strongly enriched in aluminium (Al-rich stars: [Al/Fe] ≳ +0.5), well above the typical Galactic levels, located within the solar radius toward the bulge region, which lies in highly eccentric orbits (e ≳ 0.6). We find many similarities for almost all of the chemical species measured in this work with the chemical patterns of GCs, and therefore we propose that they have likely been dynamically ejected into the bulge and inner halo from GCs formed in situ and/or GCs formed in different progenitors of known merger events experienced by the MW, such as the Gaia-Sausage-Enceladus and/or Sequoia.
Barrier methods for minimal submanifolds in the Gibbons-Hawking ansatz.

We describe a barrier argument for compact minimal submanifolds in the multi-Eguchi-Hanson and in the multi-Taub-NUT spaces, which are hyperkaehler 4-manifolds given by the Gibbons-Hawking ansatz. This approach is used to obtain results towards a classification of compact minimal submanifolds in this setting. We also prove a converse of Tsai and Wang's result that relates the strong stability condition to the convexity of the distance function.
“LazImpa”: Lazy and Impatient neural agents learn to communicate efficiently

Previous work has shown that artificial neural agents naturally develop surprisingly non-efficient codes. This is illustrated by the fact that in a referential game involving a speaker and a listener neural networks optimizing accurate transmission over a discrete channel, the emergent messages fail to achieve an optimal length. Furthermore, frequent messages tend to be longer than infrequent ones, a pattern contrary to the Zipf Law of Abbreviation (ZLA) observed in all natural languages. Here, we show that near-optimal and ZLA-compatible messages can emerge, but only if both the speaker and the listener are modified. We hence introduce a new communication system, “LazImpa”, where the speaker is made increasingly lazy, i.e., avoids long messages, and the listener impatient, i.e., seeks to guess the intended content as soon as possible.
Entity Embedding as Game Representation

Procedural content generation via machine learning (PCGML) has shown success at producing new video game content with machine learning. However, the majority of the work has focused on the production of static game content, including game levels and visual elements. There has been much less work on dynamic game content, such as game mechanics. One reason for this is the lack of a consistent representation for dynamic game content, which is key for a number of statistical machine learning approaches. We present an autoencoder for deriving what we call "entity embeddings", a consistent way to represent different dynamic entities across multiple games in the same representation. In this paper we introduce the learned representation, along with some evidence towards its quality and future utility.
Optimal Neural Program Synthesis from Multimodal Specifications

Multimodal program synthesis, which leverages different types of user input to synthesize a desired program, is an attractive way to scale program synthesis to challenging settings; however, it requires integrating noisy signals from the user (like natural language) with hard constraints on the program's behavior. This paper proposes an optimal neural synthesis approach where the goal is to find a program that satisfies user-provided constraints while also maximizing the program's score with respect to a neural model. Specifically, we focus on multimodal synthesis tasks in which the user intent is expressed using combination of natural language (NL) and input-output examples. At the core of our method is a top-down recurrent neural model that places distributions over abstract syntax trees conditioned on the NL input. This model not only allows for efficient search over the space of syntactically valid programs, but it allows us to leverage automated program analysis techniques for pruning the search space based on infeasibility of partial programs with respect to the user's constraints. The experimental results on a multimodal synthesis dataset (StructuredRegex) show that our method substantially outperforms prior state-of-the-art techniques in terms of accuracy %, finds model-optimal programs more frequently, and explores fewer states during search.
Measuring the Complexity of Domains Used to Evaluate AI Systems

There is currently a rapid increase in the number of challenge problem, benchmarking datasets and algorithmic optimization tests for evaluating AI systems. However, there does not currently exist an objective measure to determine the complexity between these newly created domains. This lack of cross-domain examination creates an obstacle to effectively research more general AI systems. We propose a theory for measuring the complexity between varied domains. This theory is then evaluated using approximations by a population of neural network based AI systems. The approximations are compared to other well known standards and show it meets intuitions of complexity. An application of this measure is then demonstrated to show its effectiveness as a tool in varied situations. The experimental results show this measure has promise as an effective tool for aiding in the evaluation of AI systems. We propose the future use of such a complexity metric for use in computing an AI system's intelligence.
Block Chain and Internet of Nano-Things for Optimizing Chemical Sensing in Smart Farming

The use of Internet of Things (IoT) with the Internet of Nano Things (IoNT) can further expand decision making systems (DMS) to improve reliability as it provides a new spectrum of more granular level data to make decisions. However, growing concerns such as data security, transparency and processing capability challenge their use in real-world applications. DMS integrated with Block Chain (BC) technology can contribute immensely to overcome such challenges. The use of IoNT and IoT along with BC for making DMS has not yet been investigated. This study proposes a BC-powered IoNT (BC-IoNT) system for sensing chemicals level in the context of farm management. This is a critical application for smart farming, which aims to improve sustainable farm practices through controlled delivery of chemicals. BC-IoNT system includes a novel machine learning model formed by using the Langmuir molecular binding model and the Bayesian theory, and is used as a smart contract for sensing the level of the chemicals. A credit model is used to quantify the traceability and credibility of farms to determine if they are compliant with the chemical standards. The accuracy of detecting the chemicals of the distributed BC-IoNT approach was >90% and the centralized approach was <80%. Also, the efficiency of sensing the level of chemicals depends on the sampling frequency and variability in chemical level among farms.
Stock2Vec: A Hybrid Deep Learning Framework for Stock Market Prediction with Representation Learning and Temporal Convolutional Network

We have proposed to develop a global hybrid deep learning framework to predict the daily prices in the stock market. With representation learning, we derived an embedding called Stock2Vec, which gives us insight for the relationship among different stocks, while the temporal convolutional layers are used for automatically capturing effective temporal patterns both within and across series. Evaluated on S&P 500, our hybrid framework integrates both advantages and achieves better performance on the stock price prediction task than several popular benchmarked models.
Critical behaviour at the onset of synchronization in a neuronal model

The presence of both critical behavior and oscillating patterns in brain dynamics is a very interesting issue. In this paper, we consider a model for a neuron population, where each neuron is modeled by an over-damped rotator. We find that in the space of external parameters, there exist some regions that system shows synchronization. Interestingly, just at the transition point, the avalanche statistics show a power-law behavior. Also, in the case of small systems, the (partially) synchronization and power-law behavior can happen at the same time.
Temperature Regulation and Torpidity in the Pygmy Mouse, Baiomys taylori

DURING our recent efforts to review estivation and relate it to hibernation (Hudson and Bartholomew, 1964) we became convinced of the paucity of data for definition of the normal spectrum of temperature regulation of homeotherms and heterotherms-an essential step prior to differentiation of hibernation, estivation, and heterothermy. While many species of mammals exhibit daily cycles of body temperatures, in some cases the amplitude of the cycle may vary widely enough to be designated as torpidity (Morrison and Ryser, 1959; Tucker, 1962). Some species of small mammals allow their body temperatures to drop below levels considered typical of non-hibernators yet cannot survive body temperatures as low as those routinely tolerated by the hibernators (Kayser, 1961; Tucker, 1962; MacMillen, 1964). In general, the greatest diurnal variation in body temperature appears to occur in the smallest mammals and is probably related to their high energy requirements. Baiomys taylori is a very small cricetid rodent weighing between 6 and 9 g whose origin is subtropical with a distribution extending only to the southern part of the western United States (Blair, 1941). The study of its temperature regulation
Cutoff Phenomenon for Cyclic Dynamics on Hypercube.

The cutoff phenomena for Markovian dynamics have been observed and rigorously verified for a multitude of models, particularly for Glauber-type dynamics on spin systems. However, prior studies have barely considered irreversible chains. In this work, the cutoff phenomenon of certain cyclic dynamics are studied on the hypercube $\Sigma_{n} = Q^{V_{n}}$, where $Q = \{1, 2, 3\}$ and $V_{n} = \{1,. ,n\}$. The main feature of these dynamics is the fact that they are represented by an irreversible Markov chain. Based on the coupling modifications suggested in a previous study of the cutoff phenomenon for the Curie-Weiss-Potts model, a comprehensive proof is presented.
Predictive models for stage and risk classification in head and neck squamous cell carcinoma (HNSCC)

Machine learning techniques are increasingly used in the analysis of high throughput genome sequencing data to better understand the disease process and design of therapeutic modalities. In the current study, we have applied state of the art machine learning (ML) algorithms (Random Forest (RF), Support Vector Machine Radial Kernel (svmR), Adaptive Boost (AdaBoost), averaged Neural Network (avNNet), and Gradient Boosting Machine (GBM)) to stratify the HNSCC patients in early and late clinical stages (TNM) and to predict the risk using miRNAs expression profiles. A six miRNA signature was identified that can stratify patients in the early and late stages. The mean accuracy, sensitivity, specificity, and area under the curve (AUC) was found to be 0.84, 0.87, 0.78, and 0.82, respectively indicating the robust performance of the generated model. The prognostic signature of eight miRNAs was identified using LASSO (least absolute shrinkage and selection operator) penalized regression. These miRNAs were found to be significantly associated with overall survival of the patients. The pathway and functional enrichment analysis of the identified biomarkers revealed their involvement in important cancer pathways such as GP6 signalling, Wnt signalling, p53 signalling, granulocyte adhesion, and dipedesis. To the best of our knowledge, this is the first such study and we hope that these signature miRNAs will be useful for the risk stratification of patients and the design of therapeutic modalities.
The health-related quality of life of Syrian refugee women in their reproductive age

Background Health-Related Quality of Life (HRQoL) for refugee women in reproductive age is highly affected by physical, political, psychosocial and environmental conditions in countries of asylum. HRQoL is enormously affected by the satisfaction of this vulnerable group with the physical, psychological, emotional and social care services provided in this critical time. Therefore, this study aimed toassess the HRQoL among Syrian refugee women of reproductive age living outside camps in Jordan. Methods A cross-sectional correlational study was conducted with a convenience sample of 523 Syrian refugee women in the host communities in Jordan.Health-related quality of life (HRQOL) was measured using the short-form 36 (SF-36) questionnaire. Results Significant negative correlations were found between SF-36 individual subscales score and the length of marriage, the number of children, parity and family income. The strongest correlations were between pain scale and length of marriage (r = − .21), and between Energy/Fatigue and ‘number of children’ (r = − .21). Conversely, antenatal care was positively correlated with physical, role emotional, pain, and general health. Physical functioning and general health were predicted significantly with less years of marriage, younger age at marriage, less violence and by higher family income. Conclusion This study suggests low HRQoL scores for women of reproductive age across all domains. Several factors such as years of marriage, age at marriage, the number of children, violence, antenatal care and family income affected the women’s general health. The provision of appropriate and accessible reproductive and maternal healthcare services in antenatal visits is critical for ensuring the immediate and long-term health and wellbeing of refugee women and their families.