Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Generalization of Visual Imitation Learning with Position-Invariant Regularization

—How the visual imitation learning models can generalize to novel unseen visual observations is a highly challenging problem. Such a generalization ability is very crucial for their real-world applications. Since this generalization problem has many different aspects, we focus on one case called spatial generalization , which refers to generalization to unseen setup of object (entity) locations in a task, such as a novel setup of object locations in the robotic manipulation problem. In this case, previous works observe that the visual imitation learning models will overfit to the absolute information (e.g., coordinates) rather than the relational information between objects, which is more important for decision making. As a result, the models will perform poorly in novel object location setups. Nevertheless, so far, it remains unclear how we can solve this problem effectively. Our insight into this problem is to explicitly remove the absolute information from the features learned by imitation learning models so that the models can use robust, relational information to make decisions. To this end, we propose a novel, position-invariant regularizer called POINT for generalization. The proposed regularizer will penalize the imitation learning model when its features contain absolute, positional information of objects. Various experiments demonstrate the effectiveness of our method.
Interaction Tasks for Explainable Recommender Systems

In the modern web experience, users interact with various types of recommender systems. In this literature study, we investigate the role of interaction in explainable recommender systems using 27 relevant papers from recommender systems, human-computer interaction, and visualization fields. We structure interaction approaches into 1) the task, 2) the interaction intent, 3) the interaction technique, and 4) the interaction effect on explainable recommender systems. We present a preliminary interaction taxonomy for designers and developers to improve the interaction design of explainable recommender systems. Findings based on exploiting the descriptive power of the taxonomy emphasize the importance of interaction in creating effective and user-friendly explainable recommender systems.
High-Performance Row Pattern Recognition Using Joins

The SQL standard introduced MATCH_RECOGNIZE in 2016 for row pattern recognition. Since then, MATCH_RECOGNIZE has been supported by several leading relation systems, they implemented this function using Non-Deterministic Finite Automaton (NFA). While NFA is suitable for pattern recognition in streaming scenarios, the current uses of NFA by the relational systems for historical data analysis scenarios overlook important optimization opportunities. We propose a new approach to use Join to speed up row pattern recognition in historical analysis scenarios for relational systems. Implemented as a logical plan rewrite rule, the new approach first filters the input relation to MATCH_RECOGNIZE using Joins constructed based on a subset of symbols taken from the PATTERN expression, then run the NFA-based MATCH_RECOGNIZE on the filtered rows, reducing the net cost. The rule also includes a specialized cardinality model for the Joins and a cost model for the NFA-based MATCH_RECOGNIZE operator for choosing an appropriate symbol set. The rewrite rule is applicable when the query pattern's definition is self-contained and either the input table has no duplicates or there is a window condition. Applying the rewrite rule to a query benchmark with 1,800 queries spanning over 6 patterns and 3 pattern definitions, we observed median speedups of 5.4X on Trino (v373 with ORC files on Hive), 57.5X on SQL Server (2019) using column store and 41.6X on row store.
Exploiting graph neural networks to perform finite-difference time-domain based optical simulations

Having an artificial neural network that solves Maxwell’s equations in a general setting is an intellectual challenge and a great utility. Recently, there have been multiple successful attempts to use artificial neural networks to predict electromagnetic fields, given a specific source and interacting material distribution. However, many of these attempts are limited in domain size and restricted to object shapes similar to the learned ones. Here, we overcome these restrictions by using graph neural networks (GNNs) that adapt the propagation scheme of the finite-difference time-domain (FDTD) method to solve Maxwell’s equations for a distinct time step. GNNs yield a significant advantage, i.e., size invariance, over conventional neural network architectures, such as convolutional or linear neural networks. Once trained, a GNN can work on graphs of arbitrary size and connectivity. This allows us to train them on the propagation procedure of electromagnetic fields on small domain sizes and, finally, expand the domain to an arbitrary scale. Moreover, GNNs can adapt to any material shape and work not only on structured grids, such as FDTD, but also on arbitrary meshes. This work may be seen as the first benchmark for field predictions with graph networks and could be expanded to more complex mesh-based optical simulations, e.g., those based on finite elements.
Bayesian Machine Learning and variational inference for on-site sensor calibration in Smart Bay Santa Teresa seawater monitoring

Bayesian Machine Learning and variational inference for on-site sensor calibration in Smart Bay Santa Teresa seawater monitoring / Ma, Q. ; Durin, G. ; Pennecchi, F. ; Lombardi, C. ; Petrioli, C. (2022). (Intervento presentato al convegno Mathmet international conference tenutosi a Arts et Métiers ParisTech – Campus de Paris, Parigi, Francia nel 2-4 Novembre 2022). Original Bayesian Machine Learning and variational inference for on-site sensor calibration in Smart Bay Santa Teresa seawater monitoring
ADRAGGAN: ADversarial training for RAtionale Generation: a GAN for moral dilemmas

r/AmITheAsshole: if a user has an argument or problem with morally ambiguous actors, they post their dilemma on this Reddit forum with one question in mind: "Am I the asshole? " Commenters flock to the forum, reading the morally ambiguous situation, and provide a verdict and rationale on who was at fault here. The best comments that provide the most agreed upon reasoning get voted by other users into the acclaimed top comment position. Here, we explore the use of a Generative Adversarial Network (GAN) architecture to train transformer architectures to generate top comments for Reddit Am I the Asshole (AITA) posts. To determine the optimal architecture for the generator, we trained four baseline models (T5 with and without repetition penalty, BART large, and BART base), eventually deciding on BART base. Subsequently, we explored various designs for our discriminator, including Convolutional Neural Network (CNN) / Transformer architectures, differing inputs, and a novel “Sentiment Teacher Forcing” (STF) method. Upon performing quantitative evaluations using BLEU, ROUGE, and Word Mover’s Distance (WMD), we found that our GAN approach leads to modest improvements in each of these metrics. More crucially for our task, we also performed a human evaluation study and found that our novel approach leads to more coherent, diverse, and sensible outputs which more accurately resemble the style of Reddit comments.
Investigating BERT through Fine-Tuned Regularization and Layer-Level Adaptations for Multi-Task Performance.

This project evaluates the minBERT model under different optimizations to compare and contrast how it performs in multi-task scenarios. Our motivation is to understand how low-level basic model adaptations can help improve performance when working towards multi-task efficiency. Specifically, we aim to investigate how regularization techniques like Bregman Proximal Point Optimization and smoothness induced regularization compare to layer adaptation methods, where each layer between BERT is adapted to account for generalization across tasks. Here, we will highlight how BERT performs when its current parameters are fine-tuned without any new additions, and when layer adaptations are done to learn task specific parameters within a reasonable budget using SMART ( SM oothness-inducing A dversarial R egularization and B R egman p R oximal poin T op T imization) [1] and adapter modules [2] as frameworks for executing fine-tuned regularization and layer-level adaptations, respectively.
TESS Delivers Five New Hot Giant Planets Orbiting Bright Stars from the Full Frame Images

We present the discovery and characterization of five hot and warm Jupiters — TOI-628 b (TIC 281408474; HD 288842), TOI-640 b (TIC 147977348), TOI-1333 b (TIC 395171208, BD+47 3521A), TOI-1478 b (TIC 409794137), and TOI-1601 b (TIC 139375960) — based on data from NASA’s Transiting Exoplanet Survey Satellite (TESS). The five planets were identified from the full frame images and were confirmed through a series of photometric and spectroscopic follow-up observations by the TESS Follow-up Observing Program (TFOP) Working Group. The planets are all Jovian size (RP = 1.01-1.77 RJ) and have masses that range from 0.85 to 6.33 MJ. The host stars of these systems have F and G spectral types (5595≤ Teff ≤ 6460 K) and are all relatively bright (9.5 < V < 10.8, 8.2 < K < 9.3) making them well-suited for future detailed characterization efforts. Three of the systems in our sample (TOI-640 b, TOI-1333 b, and TOI-1601 b) orbit subgiant host stars EVERYBODY GETS A GIANT PLANET! 3 (log g <4.1). TOI-640 b is one of only three known hot Jupiters to have a highly inflated radius (RP > 1.7RJ, possibly a result of its host star’s evolution) and resides on an orbit with a period longer than 5 days. TOI628 b is the most massive hot Jupiter discovered to date by TESS with a measured mass of 6.31+0.28 −0.30 MJ and a statistically significant, non-zero orbital eccentricity of e = 0.074+0.021 −0.022. This planet would not have had enough time to circularize through tidal forces from our analysis, suggesting that it might be remnant eccentricity from its migration. The longest period planet in this sample, TOI-1478 b (P = 10.18 days), is a warm Jupiter in a circular orbit around a near-Solar analogue. NASA’s TESS mission is continuing to increase the sample of well-characterized hot and warm Jupiters, complementing its primary mission goals.
Comparative Analysis of SimCSE for minBERT Optimization with Multiple Downstream Tasks

The anisotropy problem limits the expressiveness of sentence embeddings learned from pretrained large models, and contrastive learning with the SimCSE framework is a simple and effective method to alleviate this problem. In this paper, I introduce the SimCSE framework as a pretraining method for minBERT optimization, and the optimized model achieves an average score of 0.763 in multiple downstream tasks. In comparison with in-domain and cross-domain pretraining with the Masked Language Modeling task, the advantage of the supervised SimCSE approach is further revealed. The result of the ablation study indicates that cross-domain pretraining improves the general performance of the model, and the anisotropy problem of minBERT is shown to be alleviated with SimCSE in the case study.
Application of vibration signal detection in mine hoist fault monitoring system

Due to the harsh working environment and various types of faults in mine hoists, the implementation of conventional detection is relatively difficult. In order to improve the maintenance and fault detection efficiency of mine hoist, a portable vibration detection system is designed and applied in this paper, which can judge the type and characteristics of mechanical faults according to the different responses of vibration signals. According to the test conditions of the vibration system, the overall structure and functional composition of the hardware system are established. The key functional modules are designed, including signal conditioning module, main control module, vibration sensor and power management module. The main reasons that affect and produce the hoist fault and mechanical vibration are analyzed. The wavelet packet analysis method is proposed to realize the decomposition of low frequency and high frequency signals to improve the signal resolution of the whole frequency band in the frequency domain. The vibration test system has the function of real-time data transmission and control. The realization methods include WiFi and reserved RS485 communication interface, which can match the communication rate with the data sampling rate. The amplifying circuit of the electric signal is designed to effectively ensure that the output voltage of the charge amplifier is within the input range of the amplified ADC value. In order to verify the working effect of the system, the most common gearbox and bearing failures are taken as examples. The response signals of vibration test and diagnosis can be effectively obtained, and the fault types and characteristics can be accurately judged. Through confusion matrix analysis, the reliability of the system detection index is effectively verified. The research results show that the data of the vibration detection system is stable and reliable, and can achieve good troubleshooting effect. It also can be known that different fault types can excite different vibration responses, especially at resonance frequencies. According to the verification, the data of the vibration detection system is stable and reliable, and can achieve good troubleshooting effect.
The Elephant in the Room: Variable Dependency in GNN-based SAT Solving

Boolean satisfiability problem (SAT) is fundamental to many applications. Existing works have used graph neural networks (GNNs) for (approximate) SAT solving. Typical GNNbased end-to-end SAT solvers predict SAT solutions concurrently. We show that for a group of symmetric SAT problems, the concurrent prediction is guaranteed to produce a wrong answer because it neglects the dependency among Boolean variables in SAT problems. We propose AsymSAT, a GNNbased architecture which integrates recurrent neural networks to generate dependent predictions for variable assignments. The experiment results show that dependent variable prediction extends the solving capability of the GNN-based method as it improves the number of solved SAT instances on large test sets. CCS Concepts: • Hardware → Theorem proving and SAT solving.
Using Knowledge Graph Embeddings from Biomedical Language Models to Infer Drug Repurposing Candidates for Rare Diseases

Approaches for finding new drugs are often costly and ineffective. On the other hand, available drugs that are currently being used to treat certain diseases could potentially be repurposed for others. Recent studies have attempted to represent known drug-disease interactions as networks in order to predict unknown edges that might correspond to novel drug-disease interactions (Kim et al., 2022). In particular, one method to do this is to create an embedding representation of a knowledge graph containing known drug-disease interactions and perform link prediction to predict new edges in the graph. Recently, several embedding methods have leveraged the use of language models to take advantage of both the structure of knowledge graph and its textual components when creating representations (Yao et al., 2019) At the same time, open source NLP tools have enabled the creation of large networks of biomedical relationships (Percha and Altman, 2018). In this project, we modify existing state of the art (SOTA) language model embedding methods to biomedical applications and frame the drug repurposing problem as a knowledge graph completion task. We introduce three new findings: (1) a novel dataset of known drug-disease treatments derived from the Global Network of Biomedical Relationships (GNBR) to test network based prediction tasks for drug repurposing, and (2) a knowledge graph embedding method that leverages language models and the existing biomedical corpora (Percha and Altman, 2018), achieving improved performance over SOTA baselines, and (3) a faster variant of BioBERT Lee et al. (2020) called biobert-tiny, pretrained on six million PubMed abstracts and publicly available on Hugging Face 1 . The code for this project is open source 2 .
calable self attraction and loading calculations for unstructured ocean tide odels

Self attraction and earth-loading effects are important for accurately modeling global tides. A common approach of handling this forcing is to expand mass anomalies into spherical harmonics, which are scaled by load Love numbers to account for elastic earth deformation. We investigate two different approaches to perform these calculations for ocean models that employ unstructured meshes and distributed memory parallelization. The first approach leverages a highly efficient spherical harmonics library, but requires all-toone and one-to-all communications and interpolation operations between the unstructured and a structured mesh. This approach is compared to a parallel algorithm that computes the spherical harmonic transformations directly on the unstructured mesh with an all-reduce communication. Our results show that although the unstructured mesh calculations are more expensive, the scalability of the unstructured mesh approach allows for more efficient spherical harmonics transforms for high-resolution meshes and large processor counts. This methodology enables the efficient inclusion of tidal dynamics large-scale Earth system model simulations.
The consistency and stability of school effect in Colombian high school education

Consistency and stability are considered scientific properties of school effect. These properties are important in determining if school effect is constant along subjects and are perdurable during the time. The purpose of this research is to estimate continuity and stability in Colombian high school education during seven years over the past decade for the four main subjects in Colombia: mathematics, language, natural and social science. With this purpose in mind, a retrospective ex post facto study was conducted. The analysis was done for 7, 626 schools and multilevel models of three levels and longitudinal multilevel models were used. As adjusted variables, socioeconomic and sociocultural levels and gender of students were used. The school variable was the tuition. The results indicated that the correlation was over 0.39 for all the subjects; the higher correlations were found among language, natural and social science and the school effect showed change along the time for every subject and by the tuition. The Colombian educational system displays consistency within subjects, but its school effect is not stable and is dependent on the socio-economic variable. The paper discusses the implications of these results for the Colombian educational system and the effect for quality and equity, and their relationship with the development of the country. © 2016 Published by Future Academy www.FutureAcademy. org.uk
Best practice guide for the assessment of EMF exposure from vehicle Wireless Power Transfer systems

Best practice guide for the assessment of EMF exposure from vehicle Wireless Power Transfer systems / Ankarson, Peter; Bottauscio, Oriano; Clarke, Bob; Freschi, Fabio; Guilizzoni, Roberta; Harmon, Stuart; Laporta, Erika; Pichon, Lionel; Bruna Romero, Jorge; Zilberti, Luca; Zucca, Mauro. Unico:(2021), pp. 1-64. Original Best practice guide for the assessment of EMF exposure from vehicle Wireless Power Transfer systems
Contextual Question Answering using variations of BiDAF and QANet

Our goal with this project is to better take into account the compositional properties of language, as measured by the performance on the task of closed-domain question answering in the Stanford Question Answering Dataset (SQuAD) 2.0. In this vein, we implemented and studied two high-performing question answering models, which may be able to achieve this goal: Bidirectional Attention Flow (BiDAF), and QANet. Firstly, we extended the BiDAF implementation by Chris Chute [1], by adding character embeddings to handle unknown words and by adding self-attention to capture dependencies within the input text. Secondly, we implemented QANet from scratch, in order to better capture dependencies within the input text. We experimented with variations of layers and attention heads. Thirdly, we evaluated how our best-performing models perform on a variety of question types and answer types. We assumed our baseline to be Chris Chute’s implementation of BiDAF, which has an F1 score of 62.45 and an Exact Match (EM) score of 59.17. Our best performing model, based on QANet, achieved an F1 score of 65.61 and an EM score of 62.31.
Losing to Win: Evaluating the Success of Various Loss Functions Within Multitask Settings

BERT, a pretrained transformer model, has shown promising results in multitask learning, where it can be fine-tuned on multiple NLP tasks in parallel. Importantly, multitask learning augments the efficacy, efficiency, and generalizability of the model’s contextualized embeddings, which motivates the further exploration of multitask fine-tuning. In this project, I implement and extend core components of the BERT model and its companion AdamW optimizer. These fine-tuning extensions are accomplished by investigating various loss functions, including a cumulative multitask loss, cosine similarity loss, multiple negatives ranking loss, and mean squared error loss. With said extensions, I further fine-tune the model’s contextualized embeddings to perform well on multiple sentence-level tasks, being sentiment analysis, paraphrase detection, and the analysis of semantic textual similarity. We find that the joint use of the cumulative multitask and mean squared error loss functions yield the best results on the three tasks, with a sentiment analysis accuracy of 0.514, and paraphrase detection accuracy of 0.789, and a semantic textual similarity coefficient of 0.589. These results illustrate the effectiveness of multitask fine-tuning, which can extend the range of tasks over which BERT can successfully operate.
Sampling-Based MPC Using a GPU-parallelizable Physics Simulator as Dynamic Model: an Open Source Implementation with IsaacGym

—We present a method for solving finite horizon optimal control problems using a generic physics simulator as the dynamical model. In particular, we present an open-source implementation of a model predictive path integral controller (MPPI), that uses the GPU-parallelizable IsaacGym simulator as the dynamical model to compute the forward dynamics of the system. This allows one to effortlessly solve complex contact-rich tasks such as for example, non-prehensile manipulation of a variety of objects, or picking with a mobile manipulator. Since there is no explicit dynamic modeling required from a user, the repository is easily extendable to different objects and robots, as we show in the experiments section. This makes this method a powerful and accessible tool to solve a large variety of contact-rich tasks. The code available at https://github.com/tud-airlab/mppi-isaac [R EPO WILL BE