Miracle-dz/UniFAR-FTUs huggingface dataset:
SPARE: A Single-Pass Neural Model for Relational Databases

While there has been extensive work on deep neural networks for images and text, deep learning for relational databases (RDBs) is still a rather unexplored field. One direction that recently gained traction is to apply Graph Neural Networks (GNNs) to RBDs. However, training GNNs on large relational databases (i.e., data stored in multiple database tables) is rather inefficient due to multiple rounds of training and potentially large and inefficient representations. Hence, in this paper we propose SPARE (Single-Pass Relational models), a new class of neural models that can be trained efficiently on RDBs while providing similar accuracies as GNNs. For enabling efficient training, different from GNNs, SPARE makes use of the fact that data in RDBs has a regular structure, which allows one to train these models in a single pass while exploiting symmetries at the same time. Our extensive empirical evaluation demonstrates that SPARE can significantly speedup both training and inference while offering competitive predictive performance over numerous baselines.
Efficient, Dynamic Locomotion through Step Placement with Straight Legs and Rolling Contacts

For humans, fast, efficient walking over flat ground represents the vast majority of locomotion that an individual experiences on a daily basis, and for an effective, real-world humanoid robot the same will likely be the case. In this work, we propose a locomotion controller for efficient walking over near-flat ground using a relatively simple, model-based controller that utilizes a novel combination of several interesting design features including an ALIP-based step adjustment strategy, stance leg length control as an alternative to center of mass height control, and rolling contact for heel-to-toe motion of the stance foot. We then present the results of this controller on our robot Nadia, both in simulation and on hardware. These results include validation of this controller's ability to perform fast, reliable forward walking at 0.75 m/s along with backwards walking, side-stepping, turning in place, and push recovery. We also present an efficiency comparison between the proposed control strategy and our baseline walking controller over three steady-state walking speeds. Lastly, we demonstrate some of the benefits of utilizing rolling contact in the stance foot, specifically the reduction of necessary positive and negative work throughout the stride.
Cache-Aided Communications in MISO Networks with Dynamic User Behavior

Coded caching (CC) can substantially enhance network performance by leveraging memory as an additional communication resource. However, the use of CC is challenging in various practical applications due to dynamic user behavior. The existing solutions, based on shared caching, cannot directly handle all scenarios where users freely enter and depart the network at any time as they are constrained by specific conditions on network parameters. This paper proposes a universally applicable shared-caching scheme for dynamic setups without any restriction on network parameters. The closed-form expressions for the achievable degrees of freedom (DoF) are computed for the resulting generalized scheme, and are shown to achieve the existing optimal bounds of the shared-cache model. Furthermore, a successive-interference-cancellation-free extension based on a fast iterative optimized beamformer design is devised to optimize the use of excess spatial dimensions freed by cache-aided interference cancellation. Extensive numerical experiments are carried out to assess the performance of the proposed scheme. In particular, the results demonstrate that while a dynamic setup may achieve a DoF substantially lower than the optimal DoF of shared caching, our proposed scheme significantly improves the performance at the finite signal-to-noise ratio compared to unicasting, which only benefits from the local caching gain.
DeepFracture: A Generative Approach for Predicting Brittle Fractures

In the realm of brittle fracture animation, generating realistic destruction animations with physics simulation techniques can be computationally expensive. Although methods using Voronoi diagrams or pre-fractured patterns work for real-time applications, they often lack realism in portraying brittle fractures. This paper introduces a novel learning-based approach for seamlessly merging realistic brittle fracture animations with rigid-body simulations. Our method utilizes BEM brittle fracture simulations to create fractured patterns and collision conditions for a given shape, which serve as training data for the learning process. To effectively integrate collision conditions and fractured shapes into a deep learning framework, we introduce the concept of latent impulse representation and geometrically-segmented signed distance function (GS-SDF). The latent impulse representation serves as input, capturing information about impact forces on the shape's surface. Simultaneously, a GS-SDF is used as the output representation of the fractured shape. To address the challenge of optimizing multiple fractured pattern targets with a single latent code, we propose an eight-dimensional latent space based on a normal distribution code within our latent impulse representation design. This adaptation effectively transforms our neural network into a generative one. Our experimental results demonstrate that our approach can generate significantly more detailed brittle fractures compared to existing techniques, all while maintaining commendable computational efficiency during run-time.
Improving Question Generation with Multi-level Content Planning

This paper addresses the problem of generating questions from a given context and an answer, specifically focusing on questions that require multi-hop reasoning across an extended context. Previous studies have suggested that key phrase selection is essential for question generation (QG), yet it is still challenging to connect such disjointed phrases into meaningful questions, particularly for long context. To mitigate this issue, we propose MultiFactor, a novel QG framework based on multi-level content planning. Specifically, MultiFactor includes two components: FA-model, which simultaneously selects key phrases and generates full answers, and Q-model which takes the generated full answer as an additional input to generate questions. Here, full answer generation is introduced to connect the short answer with the selected key phrases, thus forming an answer-aware summary to facilitate QG. Both FA-model and Q-model are formalized as simple-yet-effective Phrase-Enhanced Transformers, our joint model for phrase selection and text generation. Experimental results show that our method outperforms strong baselines on two popular QG datasets. Our code is available at https://github.com/zeaver/MultiFactor.
Identification of Abnormality in Maize Plants From UAV Images Using Deep Learning Approaches

Early identification of abnormalities in plants is an important task for ensuring proper growth and achieving high yields from crops. Precision agriculture can significantly benefit from modern computer vision tools to make farming strategies addressing these issues efficient and effective. As farming lands are typically quite large, farmers have to manually check vast areas to determine the status of the plants and apply proper treatments. In this work, we consider the problem of automatically identifying abnormal regions in maize plants from images captured by a UAV. Using deep learning techniques, we have developed a methodology which can detect different levels of abnormality (i.e., low, medium, high or no abnormality) in maize plants independently of their growth stage. The primary goal is to identify anomalies at the earliest possible stage in order to maximize the effectiveness of potential treatments. At the same time, the proposed system can provide valuable information to human annotators for ground truth data collection by helping them to focus their attention on a much smaller set of images only. We have experimented with two different but complimentary approaches, the first considering abnormality detection as a classification problem and the second considering it as a regression problem. Both approaches can be generalized to different types of abnormalities and do not make any assumption about the abnormality occurring at an early plant growth stage which might be easier to detect due to the plants being smaller and easier to separate. As a case study, we have considered a publicly available data set which exhibits mostly Nitrogen deficiency in maize plants of various growth stages. We are reporting promising preliminary results with an 88.89\% detection accuracy of low abnormality and 100\% detection accuracy of no abnormality.
Combining Policy Gradient and Safety-Based Control for Autonomous Driving

With the advancement of data-driven techniques, addressing continuous con-trol challenges has become more efficient. However, the reliance of these methods on historical data introduces the potential for unexpected decisions in novel scenarios. To enhance performance in autonomous driving and collision avoidance, we propose a symbiotic fusion of policy gradient with safety-based control. In this study, we em-ploy the Deep Deterministic Policy Gradient (DDPG) algorithm to enable autono-mous driving in the absence of surrounding vehicles. By training the vehicle's driving policy within a stable and familiar environment, a robust and efficient learning pro-cess is achieved. Subsequently, an artificial potential field approach is utilized to formulate a collision avoidance algorithm, accounting for the presence of surround-ing vehicles. Furthermore, meticulous consideration is given to path tracking meth-ods. The amalgamation of these approaches demonstrates substantial performance across diverse scenarios, underscoring its potential for advancing autonomous driving while upholding safety standards.
Understanding Addition in Transformers

Understanding the inner workings of machine learning models like Transformers is vital for their safe and ethical use. This paper presents an in-depth analysis of a one-layer Transformer model trained for integer addition. We reveal that the model divides the task into parallel, digit-specific streams and employs distinct algorithms for different digit positions. Our study also finds that the model starts calculations late but executes them rapidly. A rare use case with high loss is identified and explained. Overall, the model's algorithm is explained in detail. These findings are validated through rigorous testing and mathematical modeling, contributing to the broader works in Mechanistic Interpretability, AI safety, and alignment. Our approach opens the door for analyzing more complex tasks and multi-layer Transformer models.
Investigating Emotions, Social Roles, Well-being and Mobile Usage Behaviors During COVID-19 Home Confinement

Pandemics and epidemics have been an essential part of human history. It has powerfully shaped human civilization, affecting politics, economics, medicine, and everyday human lives. Home confinement, one of the social isolation restrictions, has proven to be an effective way to prevent a pandemic and significantly reduce the spread of the virus. However, these measures have also led to negative psychological effects, which can be more severe than the physical discomfort caused by the pandemic itself. To address this issue, it is crucial to understand how being confined at home affects people's psychological states and their use of digital devices. To this end, we conducted an in-situ study with 32 participants living in states affected by COVID-19 lockdowns for three weeks. We examined human emotions, social roles, well-being, and mobile usage behaviors and explored the predictability of affective states using digital traces. Extensive experimental results show that COVID-19 home confinement is associated with decreased valence and increased work time and that human well-being could be accurately inferred from mobile usage behaviors. We also present a set of interesting findings on how different factors of mobile usage affect human emotions. Overall, the findings of this research have great potential to inform the development of interventions and remote programs to support individuals in similar situations, such as those who are ill or undergoing post-operative home rehabilitation and home work.
Vision-Based Mobile App GUI Testing: A Survey

Graphical User Interface (GUI) has become one of the most significant parts of mobile applications (apps). It is a direct bridge between mobile apps and end users, which directly affects the end user's experience. Neglecting GUI quality can undermine the value and effectiveness of the entire mobile app solution. Significant research efforts have been devoted to GUI testing, one effective method to ensure mobile app quality. By conducting rigorous GUI testing, developers can ensure that the visual and interactive elements of the mobile apps not only meet functional requirements but also provide a seamless and user-friendly experience. However, traditional solutions, relying on the source code or layout files, have met challenges in both effectiveness and efficiency due to the gap between what is obtained and what app GUI actually presents. Vision-based mobile app GUI testing approaches emerged with the development of computer vision technologies and have achieved promising progress. In this survey paper, we provide a comprehensive investigation of the state-of-the-art techniques on 226 papers, among which 78 are vision-based studies. This survey covers different topics of GUI testing, like GUI test generation, GUI test record&replay, GUI testing framework, etc. Specifically, the research emphasis of this survey is placed mostly on how vision-based techniques outperform traditional solutions and have gradually taken a vital place in the GUI testing field. Based on the investigation of existing studies, we outline the challenges and opportunities of (vision-based) mobile app GUI testing and propose promising research directions with the combination of emerging techniques.
Mean Estimation Under Heterogeneous Privacy Demands

Differential Privacy (DP) is a well-established framework to quantify privacy loss incurred by any algorithm. Traditional formulations impose a uniform privacy requirement for all users, which is often inconsistent with real-world scenarios in which users dictate their privacy preferences individually. This work considers the problem of mean estimation, where each user can impose their own distinct privacy level. The algorithm we propose is shown to be minimax optimal and has a near-linear run-time. Our results elicit an interesting saturation phenomenon that occurs. Namely, the privacy requirements of the most stringent users dictate the overall error rates. As a consequence, users with less but differing privacy requirements are all given more privacy than they require, in equal amounts. In other words, these privacy-indifferent users are given a nontrivial degree of privacy for free, without any sacrifice in the performance of the estimator.
Simulation of high-speed impact of surfactant-laden drops

We develop a computational model to simulate the immediate post-impact spreading behaviour of surfactant-laden drops that impact a flat and solid surface. The model is built on the InterFoam solver (OpenFOAM software), which uses the volume-of-fluid method to solve the Navier-Stokes equations. In order to incorporate surfactant in the bulk and on the interface, we make numerous modifications and extensions, such as coupling the volume-of-fluid method with a level-set method. Simulations demonstrate the accumulation of surfactant in the vicinity of the moving contact line, especially during the formation of the rim. Gradients of surfactant at the liquid-air interface lead to Marangoni forces that oppose the drop spreading, while high-velocity impacts reduce the overall surface tension and increase the magnitude of Marangoni forces. Both of these phenomena, which tend to reduce the maximum spreading, are highly dependent on the surfactant properties. Our computational methodology expands the potential for utilising Computational Fluid Dynamics to model complex interfacial flows that involve surfactants, leading to various opportunities in the future.
Describing the speed of sound peak of isospin-asymmetric cold strongly interacting matter using effective models

The non-monotonic behavior of the speed of sound for isospin imbalanced strongly interacting matter, found by recent lattice QCD simulations, can be reproduced within the Nambu--Jona-Lasinio model and Linear Sigma Model with quarks when the couplings become isospin chemical potential-dependent. The introduction of medium-dependent couplings can potentially affect the equivalence between the thermodynamic relations and their definitions from statistical mechanics. We describe the procedure to compensate for the introduction of medium-dependent couplings to preserve the correct thermodynamic identities. We find the isospin chemical potential dependence for the couplings from the isospin density LQCD data and, after finding the compensating function to correctly describe the pressure, we show that the description of the square of the speed of sound reported by LQCD is well reproduced when using the found medium-dependent couplings in both models.
Reliability of Numerical Solutions in Transient Chaos

In dealing with nonlinear systems, it is common to use numerical solutions. Unlike the careful behavior towards the numerical results in chaotic regions, the validity of numerical results in regions of transient chaos might not always be taken into consideration. This article demonstrates that using numerical methods to solve systems undergoing transient chaos can be challenging and sometimes unreliable. To illustrate this issue, we use the Lorenz system [1] in the region of transient chaos as an example. We show how the result of the computation might completely change when using different mathematically equivalent expressions. This raises the question of which result should be relied on. To answer this question, we propose a method based on the Lyapunov exponent to determine the reliability of the numerical solution and apply it to the provided example. In fact, this method checks a necessary condition for the validity of the numerical solution. Then, by increasing the precision to the extent suggested by our method, we show that the result of our studied case passes this test. In the end, we briefly discuss the scope and limits of our method.
CONNECTIVITY CONSERVATION OF THE GREAT GREEN MACAW'S LANDSCAPE IN COSTA RICA AND NICARAGUA (1994-2012)

Important fragments of primary and secondary forest in southeastern Nicaragua and northern Costa Rica are being threatened by anthropogenic actions. Since 1994, a research and conservation project focused on the great green macaw (Ara ambiguus) has allowed us to implement integral conservation actions at the landscape level including the creation of an alliance of 22 organizations working together to establish and develop the San Juan-La Selva Biological Corridor and the Maquenque National Wildlife Refuge as its core area. Since 2000, this experience has been replicated in Nicaragua and the links between both countries have been strengthened, resulting in a grassroots transboundary campaign focused on promoting the awareness of the ecology and conservation of the great green macaw in the lowlands of the San Juan River. The most important results to date have been the understanding, acceptance and concern of the major stakeholders regarding the challenges faced by the great green macaw, a positive shift in land use change dynamics at the landscape level and the recuperation of the population of the great green macaw. highlighted that the nesting range of the macaws in Costa Rica had been reduced by 90 per cent since the early 20 th Century (Powell et al., 1999). Preliminary studies found that the great green macaw’s limited distribution and relatively large home range, combined with its dependence on a complex array of food resources, implied that the protection of its habitat and resources would benefit a multitude of other species that reside in these lowland wet forests. The potential of this species as an ‘umbrella species’ for the fauna and flora of the habitat where it thrives makes it a key species to study in order to set conservation priorities, particularly due to the fact that the study site was lacking an important protected area. The northern zone of the range had suffered the highest deforestation rate in Costa Rica in the 1980s and 90s, leaving less than 30 per cent of the original forest standing (Chassot et al., 2005). Nevertheless, several studies found that the forests in this region remained amongst the most diverse in
Aberrant Expression of TET2 Accounts for DNA Hypomethylation in Varicocele

Objective: Epigenetic modifications such as DNA methylation play a key role in male infertility etiology. This study aimed to explore the global DNA methylation status in testicular spermatogenic cells of varicocele-induced rats and consider their semen quality, with a focus on key epigenetic marks, namely 5-methylcytosine (5-mC) and 5-hydroxymethylcytosine (5-hmC), as well as the mRNA and proteins of ten-eleven translocation (TET) methylcytosine dioxygenases 1-3. Materials and Methods: In this experimental study, 24 mature male Wistar rats (8 in each group) were assigned amongst the control, sham, and varicocele groups. Sperm quality was assessed, and DNA methylation patterns of testicular spermatogenic cells were investigated using reverse transcription-polymerase chain reaction (RT-PCR), western blot, and immunofluorescence techniques. Results: Sperm parameters, chromatin and DNA integrity were significantly lower, and sperm lipid peroxidation significantly increased in varicocele-induced rats in comparison with control rats. During spermatogenesis in rat testis, 5-mC and 5-hmC epigenetic marks, and TET1-3 mRNA and proteins were expressed. In contrast to the 5-mC fluorescent signal which was presented in all testicular cells, the 5-hmC fluorescent signal was presented exclusively in spermatogonia and a few spermatids. In varicocele-induced rats, the 5-mC signal decreased in all cells within the tubules, whereas a strong signal of 5-hmC was detected in seminiferous tubules compared to the control group. As well, the levels of TET2 mRNA and protein expression were significantly upregulated in varicocele-induced rats in comparison with the control group. Also, our results showed that the varicocele-induced animals exhibited strong fluorescent signals of TET1-3 in testicular cells, whereas weak fluorescent signals were identified in the seminiferous tubules of the control animals. Conclusion: Consequently, we showed TET2 upregulation and the 5-hmC gain at testicular levels are associated with varicocele and sperm quality decline, and therefore they can be exploited as potential biomarkers of spermatogenesis.