Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent Hypernetworks are Surprisingly Strong in Meta-RL

Deep reinforcement learning (RL) is notoriously impractical to deploy due to sample inefficiency. Meta-RL directly addresses this sample inefficiency by learning to perform few-shot learning when a distribution of related tasks is available for meta-training. While many specialized meta-RL methods have been proposed, recent work suggests that end-to-end learning in conjunction with an off-the-shelf sequential model, such as a recurrent network, is a surprisingly strong baseline. However, such claims have been controversial due to limited supporting evidence, particularly in the face of prior work establishing precisely the opposite. In this paper, we conduct an empirical investigation. While we likewise find that a recurrent network can achieve strong performance, we demonstrate that the use of hypernetworks is crucial to maximizing their potential. Surprisingly, when combined with hypernetworks, the recurrent baselines that are far simpler than existing specialized methods actually achieve the strongest performance of all methods evaluated. We provide code at https://github.com/jacooba/hyper.
Provable advantages of kernel-based quantum learners and quantum preprocessing based on Grover's algorithm

There is an ongoing effort to find quantum speedups for learning problems. Recently, [Y. Liu et al., Nat. Phys. $\textbf{17}$, 1013--1017 (2021)] have proven an exponential speedup for quantum support vector machines by leveraging the speedup of Shor's algorithm. We expand upon this result and identify a speedup utilizing Grover's algorithm in the kernel of a support vector machine. To show the practicality of the kernel structure we apply it to a problem related to pattern matching, providing a practical yet provable advantage. Moreover, we show that combining quantum computation in a preprocessing step with classical methods for classification further improves classifier performance.
QuadricsNet: Learning Concise Representation for Geometric Primitives in Point Clouds

This paper presents a novel framework to learn a concise geometric primitive representation for 3D point clouds. Different from representing each type of primitive individually, we focus on the challenging problem of how to achieve a concise and uniform representation robustly. We employ quadrics to represent diverse primitives with only 10 parameters and propose the first end-to-end learning-based framework, namely QuadricsNet, to parse quadrics in point clouds. The relationships between quadrics mathematical formulation and geometric attributes, including the type, scale and pose, are insightfully integrated for effective supervision of QuaidricsNet. Besides, a novel pattern-comprehensive dataset with quadrics segments and objects is collected for training and evaluation. Experiments demonstrate the effectiveness of our concise representation and the robustness of QuadricsNet. Our code is available at \url{https://github.com/MichaelWu99-lab/QuadricsNet}
When Automated Assessment Meets Automated Content Generation: Examining Text Quality in the Era of GPTs

The use of machine learning (ML) models to assess and score textual data has become increasingly pervasive in an array of contexts including natural language processing, information retrieval, search and recommendation, and credibility assessment of online content. A significant disruption at the intersection of ML and text are text-generating large-language models such as generative pre-trained transformers (GPTs). We empirically assess the differences in how ML-based scoring models trained on human content assess the quality of content generated by humans versus GPTs. To do so, we propose an analysis framework that encompasses essay scoring ML-models, human and ML-generated essays, and a statistical model that parsimoniously considers the impact of type of respondent, prompt genre, and the ML model used for assessment model. A rich testbed is utilized that encompasses 18,460 human-generated and GPT-based essays. Results of our benchmark analysis reveal that transformer pretrained language models (PLMs) more accurately score human essay quality as compared to CNN/RNN and feature-based ML methods. Interestingly, we find that the transformer PLMs tend to score GPT-generated text 10-15\% higher on average, relative to human-authored documents. Conversely, traditional deep learning and feature-based ML models score human text considerably higher. Further analysis reveals that although the transformer PLMs are exclusively fine-tuned on human text, they more prominently attend to certain tokens appearing only in GPT-generated text, possibly due to familiarity/overlap in pre-training. Our framework and results have implications for text classification settings where automated scoring of text is likely to be disrupted by generative AI.
Three-dimensional Tracking of a Large Number of High Dynamic Objects from Multiple Views using Current Statistical Model

Three-dimensional tracking of multiple objects from multiple views has a wide range of applications, especially in the study of bio-cluster behavior which requires precise trajectories of research objects. However, there are significant temporal-spatial association uncertainties when the objects are similar to each other, frequently maneuver, and cluster in large numbers. Aiming at such a multi-view multi-object 3D tracking scenario, a current statistical model based Kalman particle filter (CSKPF) method is proposed following the Bayesian tracking-while-reconstruction framework. The CSKPF algorithm predicts the objects' states and estimates the objects' state covariance by the current statistical model to importance particle sampling efficiency, and suppresses the measurement noise by the Kalman filter. The simulation experiments prove that the CSKPF method can improve the tracking integrity, continuity, and precision compared with the existing constant velocity based particle filter (CVPF) method. The real experiment on fruitfly clusters also confirms the effectiveness of the CSKPF method.
Noise-in, Bias-out: Balanced and Real-time MoCap Solving

Real-time optical Motion Capture (MoCap) systems have not benefited from the advances in modern data-driven modeling. In this work we apply machine learning to solve noisy unstructured marker estimates in real-time and deliver robust marker-based MoCap even when using sparse affordable sensors. To achieve this we focus on a number of challenges related to model training, namely the sourcing of training data and their long-tailed distribution. Leveraging representation learning we design a technique for imbalanced regression that requires no additional data or labels and improves the performance of our model in rare and challenging poses. By relying on a unified representation, we show that training such a model is not bound to high-end MoCap training data acquisition, and exploit the advances in marker-less MoCap to acquire the necessary data. Finally, we take a step towards richer and affordable MoCap by adapting a body model-based inverse kinematics solution to account for measurement and inference uncertainty, further improving performance and robustness. Project page: moverseai.github. io/noise-tail.
QUANTITATIVE MANOMETRIC DETERMINATION OF CALCITE AND DOLOMITE IN SOILS AND LIMESTONES

The determination is based on the difference in rates of solution of calcite and dolomite. The CO2 evolved from reaction of a mixture of calcite and dolomite in excess HCl is measured at frequent intervals until the reaction is completed. The logarithm of the amount of CO2 equivalent to unreacted carbonate is plotted against time. Following the rapid and complete solution of calcite, the curve for dolomite assumes a much smaller slope and is linear for a period of time. Extrapolation of this linear portion of the curve to zero time gives the amount of CO2 equivalent to the dolomite initially present. The CO2 from calcite is obtained by difference from the total CO2. The amounts of calcite and dolomite found in a number of prepared mixtures by this intercept method were in good agreement with the amounts known to be present. Several samples of limestone and calcareous soil were analysed by the proposed method and the results were compared with X-ray diffraction and chemical data.
More attention to comprehensive assessment and individualized therapy of chronic obstructive pulmonary disease

Chronic obstructive pulmonary disease (COPD) is a largely preventable and treatable disease characterized by airflow limitation. The airflow limitation is usually progressive and associated with a chronic inflammatory response of the airways and lungs to noxious particles or gases [1]. Chronic obstructive pulmonary disease is heterogeneous and assumes different phenotypes. COPD patients with different phenotypes have different clinical features and characteristics, giving rise to difference in prognosis and responses to treatment. Along with the development of precision medicine, individualized therapy or specific treatment has received increasing attention and wide application. To focus on and implement individualized treatment is the core of the diagnosis and management of chronic obstructive pulmonary disease. Systematic assessment of the disease features and severity is the basis for individualized treatment.
Longitudinal Petrochemical Variation in the Mackenzie Dyke Swarm, Northwestern Canadian Shield

Previous magnetic fabric studies of the giant, radiating, 1.27 Ga Mackenzie dyke swarm concluded that flow patterns within the dykes support the concept of a mantle plume that is centred beneath the swarm focus and supplies magma to overlying flood basalts and developing radial dykes. To examine petrochemical implications of the model, compositional variation within the basalt sequence is compared with that of the dykes along a ‘stream line’ of the swarm between 400 km (just beneath the lavas) and 2100 km from its focus and in a parallel segment farther east. Evolution of tholeiitic magmas of the main sampled stream is recorded in the upward change of composition in the lava sequence from mg-numbers of 70 to 35. Underlying (feeder) dykes have a comparable range, but outward along the swarm the range of compositions narrows progressively towards its more evolved end, and at 2100 km, dyke compositions match those in upper levels of the lava sequence. REE and other trace element abundances show a similar contraction in range, and a shift towards more evolved compositions, both upward in the lava sequence and outward along the swarm. Apart from complications owing to crustal contamination, fundamental attributes of the magma (e.g. Zr/Y) change little with stratigraphic level or distance from the focus. The more easterly stream differs in its high proportion of alkalic compositions, suggesting the existence of distinctive subswarms. Normative mineral variation plots are consistent with fractionation in high- (main stream) to low-level (eastern stream) crustal magma chambers. Mackenzie magmatism is compatible with the plume model. Domal uplift related to plume activity initiated central graben collapse and outward-extending radial fractures, thus providing access to plume-derived magmas and loci for magma chamber and dyke swarm development. Multiple magma chambers, forming around the apex, each fed relatively independent subswarms of dykes. Uplift, accompanying fractionation, provided increasing magmatic head by which fractionating magmas could be dispatched to successively greater distances. This, and crystal settling in transport, accounts for the increasingly evolved nature of dykes with distance from the source.
Photooxidant dynamics in the Mediterranean basin in summer: Results from European research projects

Most of the Mediterranean Sea is surrounded by mountains 1500 m or higher. Their east and south facing slopes favor the early formation of upslope winds, reinforcing the sea breezes. These slopes also act as orographic chimneys which link the surface winds directly with their return flows aloft, creating recirculations. To characterize the dynamics of pollutants in the Mediterranean basin and to compose a mosaic of the atmospheric circulations involved, the European Commission (EC) supported the following projects: (1) meso-meteorological cycles of air pollution in the Iberian Peninsula (MECAPIP), 1988–1991, intended to document the atmospheric circulations over the Iberian Peninsula; (2) regional cycles of air pollution in the west central Mediterranean area (RECAPMA), 1990–1991, which extended the characterization from the Atlantic coast of Portugal to Italy; and (3) south European cycles of air pollution (SECAP), 1992–1995, for the whole of the basin. The level of interpretation of the data and the elaboration and validation of working hypotheses across the basin have followed, in turn, with the corresponding lags in space and time. The purpose of this paper is to present a summary (to 1995) of the documented, as well as the postulated, processes involved. The MECAPIP and RECAPMA projects have shown that stacked layer systems form along the Spanish Mediterranean coasts, 2–3 km deep and more than 300 km wide, with the most recent layers at the top and the older ones near the sea. These act as a reservoir for aged pollutants to reenter land the next day, and tracer experiments have shown that turnover times are from 2 to 3 days. During the night, part of this system drifts along the coast. Under strong insolation these circulations become “large natural photochemical reactors,” where most of the NOx emissions and other precursors are transformed into oxidants, acidic compounds, aerosols, and O3 (exceeding some EC directives for several months). Finally, the preliminary analysis of the data obtained in the SECAP project supports the hypothesis that pollutants emitted in the Mediterranean basin could be transported toward the Intertropical Convergence Zone, located along northern Africa in summer, and pumped directly into the upper troposphere. If this is verified, the Mediterranean basin could be one place where all the links from the local to the global scales could be identified and documented.
Semantic Map Learning of Traffic Light to Lane Assignment based on Motion Data

Understanding which traffic light controls which lane is crucial to navigate intersections safely. Autonomous vehicles commonly rely on High Definition (HD) maps that contain information about the assignment of traffic lights to lanes. The manual provisioning of this information is tedious, expensive, and not scalable. To remedy these issues, our novel approach derives the assignments from traffic light states and the corresponding motion patterns of vehicle traffic. This works in an automated way and independently of the geometric arrangement. We show the effectiveness of basic statistical approaches for this task by implementing and evaluating a pattern-based contribution method. In addition, our novel rejection method includes accompanying safety considerations by leveraging statistical hypothesis testing. Finally, we propose a dataset transformation to re-purpose available motion prediction datasets for semantic map learning. Our publicly available API for the Lyft Level 5 dataset enables researchers to develop and evaluate their own approaches55https://github.com/map-learning/tl2la.
Prediction of fluorescence quantum yields using the extended thawed Gaussian approximation.

Spontaneous emission and internal conversion rates are calculated within harmonic approximations and compared to the results obtained within the semi-classical extended thawed Gaussian approximation (ETGA). This is the first application of the ETGA in the calculation of internal conversion and emission rates for real molecular systems, namely, formaldehyde, fluorobenzene, azulene, and a dicyano-squaraine dye. The viability of the models as black-box tools for prediction of spontaneous emission and internal conversion rates is assessed. All calculations were done using a consistent protocol in order to investigate how different methods perform without previous experimental knowledge using density functional theory (DFT) and time-dependent DFT (TD-DFT) with B3LYP, PBE0, ωB97XD, and CAM-B3LYP functionals. Contrasting the results with experimental data shows that there are further improvements required before theoretical predictions of emission and internal conversion rates can be used as reliable indicators for the photo-luminescence properties of molecules. We find that the ETGA performs rather similar to the vertical harmonical model. Including anharmonicities in the calculation of internal conversion rates has a moderate effect on the quantitative results in the studied systems. The emission rates are fairly stable with respect to computational parameters, but the internal conversion rate reveals itself to be highly dependent on the choice of the spectral line shape function, particularly the width of the Lorentzian function, associated with homogeneous broadening.
Generative Filtering for Recursive Bayesian Inference with Streaming Data

In the streaming data setting, where data arrive continuously or in frequent batches and there is no pre-determined amount of total data, Bayesian models can employ recursive updates, incorporating each new batch of data into the model parameters' posterior distribution. Filtering methods are currently used to perform these updates efficiently, however, they suffer from eventual degradation as the number of unique values within the filtered samples decreases. We propose Generative Filtering, a method for efficiently performing recursive Bayesian updates in the streaming setting. Generative Filtering retains the speed of a filtering method while using parallel updates to avoid degenerate distributions after repeated applications. We derive rates of convergence for Generative Filtering and conditions for the use of sufficient statistics instead of fully storing all past data. We investigate the alleviation of filtering degradation through simulation and Ecological species count data.
Self-Regulation of Learning and Academic Delay of Gratification: Gender and Ethnic Differences Among College Students

Self-regulated learners engage in self-generated thoughts, actions, and feelings while pursuing academic goals. The most successful learners use appropriate learning strategies and maintain high levels of motivation. Few studies on the self-regulation of learning have examined individual differences such as gender and ethnicity among college students. The study examined gender and ethnic differences in the relationships between academic performance, self-regulation, motivation, and delay of gratification using correlational analyses. The study also investigated whether students from diverse gender and ethnic groups differed with regard to their use of self-regulation, motivation, delay of gratification, and academic performance using multivariate and univariate analyses of variance. Participants were 364 college students enrolled in introductory psychology courses in a public university. The results suggested the presence of gender and ethnic differences in motivation, use of cognitive strategies, and delay of gratification, and use of self-regulation of learning is ambiguous. The reported self-efficacy beliefs of all groups of students were moderately to highly correlated with grades. The results also suggested that, independent of gender or ethnicity, the student's evaluation of the importance and usefulness of the course task was related to achievement in the class. There were differences in the relationships between delay of gratification and the use of cognitive strategies, between ethnicity and gender groups. In examining the mean differences of each group, there was a significant difference between course grades, with Caucasian students obtaining higher grades. In addition, minority males also reported significantly lower self-confidence in their ability to perform academic tasks.
Effect of shape on mechanical properties and deformation behavior of Cu nanowires: An atomistic simulations study

We study the effect of nanowire shape on mechanical properties and deformation behaviour of Cu nanowires using atomistic simulations. Simulations were carried out on $[100]$ nanowires with different shapes such as triangular, square, pentagon, hexagon and circular.Results indicate yield strength is different for different shapes. In both cases, triangular nanowire exhibit the lowest yield strength, while circular nanowire is the strongest. Deformation in all the nanowires is dominated by partials slip and twinning. Due to twinning, different shapes expose different surfaces at the twinned region. All nanowires show ductile failure and square nanowire exhibits the highest failure strain, while it is lowest for triangular nanowire.
Effect of Completion Heterogeneity in a Horizontal Well With Multiple Fractures on the Long-Term Forecast in Shale-Gas Reservoirs

Shale gas reservoirs have become a significant source of gas supply in North America because of the advancement of drilling and stimulation techniques enabling commercial development. The most popular method for exploiting shale gas reservoirs today is the use of long horizontal wells completed with multiple-fracturing stages [multifractured horizontal wells (MFHW)]. The stimulation process may result in biwing fractures or a complex hydraulic-fracture network. However, there is no method to differentiate between these two scenarios with production data analysis alone, making accurate forecasting difficult. For simplicity, hydraulic fractures are often considered biwing when analyzing production data. A conceptual model that is often used for analyzing MFHWs is that of a homogeneous completion in which all fractures have the same length. However, fractures of equal length are rarely if ever observed (Ambrose et al. 2011). In this paper, production data from heterogeneous MFHWs (i.e., where all fracture lengths are not the same) is studied for reservoirs with extremely low permeability. First, the simplified forecasting method of Nobakht et al. (2012), developed for homogeneous completions, is extended to heterogeneous completions. For one specific case, the Arps' decline exponent is correlated to the heterogeneity of the completion. It is found that, as expected, Arps' decline exponent (used after the end of linear flow) increases with the heterogeneity of the completion. Finally, it is shown that ignoring the heterogeneity of the completion can have a material effect on the long-term forecast. We have assumed planar hydraulic-fracture geometries for our modelling in this work and discuss the implications of this when more-complex fracture geometries are created. This seems to be more common in shale gas reservoirs. We provide an example of low-complexity, planar fracture geometries created near an MFHW and observed on an image log at an offset well.