Miracle-dz/UniFAR-FTUs huggingface dataset:
DAQE: Enhancing the Quality of Compressed Images by Finding the Secret of Defocus

—Image defocus is inherent in the physics of image formation caused by the optical aberration of lenses, providing plentiful information on image quality. Unfortunately, the existing quality enhancement approaches for compressed images neglect the inherent characteristic of defocus, resulting in inferior performance. This paper ﬁnds that in compressed images, the signiﬁcantly defocused regions are with better compression quality and two regions with different defocus values possess diverse texture patterns. These ﬁndings motivate our defocus-aware quality enhancement ( DAQE ) approach. Speciﬁcally, we propose a novel dynamic region-based deep learning architecture of the DAQE approach, which considers the region-wise defocus difference of compressed images in two aspects. (1) The DAQE approach employs fewer computational resources to enhance the quality of signiﬁcantly defocused regions, while more resources on enhancing the quality of other regions; (2) The DAQE approach learns to separately enhance diverse texture patterns for the regions with different defocus values, such that texture-wise one-on-one enhancement can be achieved. Extensive experiments validate the superiority of our DAQE approach in terms of quality enhancement and resource-saving, compared with other state-of-the-art approaches.
Prediction-aware and Reinforcement Learning based Altruistic Cooperative Driving

Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles (HVs) is challenging, as HVs continuously update their policies in response to AVs. In order to navigate safely in the presence of complex AV-HV social interactions, the AVs must learn to predict these changes. Humans are capable of navigating such challenging social interaction settings because of their intrinsic knowledge about other agents behaviors and use that to forecast what might happen in the future. Inspired by humans, we provide our AVs the capability of anticipating future states and leveraging prediction in a cooperative reinforcement learning (RL) decision-making framework, to improve safety and robustness. In this paper, we propose an integration of two essential and earlier-presented components of AVs: social navigation and prediction. We formulate the AV decision-making process as a RL problem and seek to obtain optimal policies that produce socially beneficial results utilizing a prediction-aware planning and social-aware optimization RL framework. We also propose a Hybrid Predictive Network (HPN) that anticipates future observations. The HPN is used in a multi-step prediction chain to compute a window of predicted future observations to be used by the value function network (VFN). Finally, a safe VFN is trained to optimize a social utility using a sequence of previous and predicted observations, and a safety prioritizer is used to leverage the interpretable kinematic predictions to mask the unsafe actions, constraining the RL policy. We compare our prediction-aware AV to state-of-the-art solutions and demonstrate performance improvements in terms of efficiency and safety in multiple simulated scenarios.
Depositing boron on Cu(111): Borophene or boride?

Large-area single-crystal surface structures were successfully prepared on Cu(111) substrate with boron deposition, which is critical for prospective applications. However, the proposed borophene structures do not match the scanning tunneling microscopy (STM) results very well, while the proposed copper boride is at odds with the traditional knowledge that ordered copper-rich borides normally do not exist due to small difference in electronegativity and large difference in atomic size. To clarify the controversy and elucidate the formation mechanism of the unexpected copper boride, we conducted systematic STM, X-ray photoelectron spectroscopy and angle-resolved photoemission spectroscopy investigations, confirming the synthesis of two-dimensional copper boride rather than borophene on Cu(111) after boron deposition under ultrahigh vacuum. First-principles calculations with defective surface models further indicate that boron atoms tend to react with Cu atoms near terrace edges or defects, which in turn shapes the intermediate structures of copper boride and leads to the formation of stable Cu-B monolayer via large-scale surface reconstruction eventually.
Self-consistent description of multipole strength in exotic nuclei: Method

We use the canonical Hartree-Fock-Bogoliubov basis to implement a self-consistent quasiparticle-random-phase approximation (QRPA) with arbitrary Skyrme energy density functionals and density-dependent pairing functionals. The point of the approach is to accurately describe multipole strength functions in spherical even-even nuclei, including weakly bound drip-line systems. We describe the method and carefully test its accuracy, particularly in handling spurious modes. To illustrate our approach, we calculate isoscalar and isovector monopole, dipole, and quadrupole strength functions in several Sn isotopes, both in the stable region and at the drip lines. We also investigate the consequences of neglecting the spin-orbit or Coulomb residual interactions in the QRPA.
Physical Aspects of Axonemal Beating and Swimming

We discuss a two-dimensional model for the dynamics of axonemal deformations driven by internally generated forces of molecular motors. Our model consists of an elastic ﬁlament pair connected by active elements. We derive the dynamic equations for this system in presence of internal forces. In the limit of small deformations, a perturbative approach allows us to calculate ﬁlament shapes and the tension proﬁle. We demonstrate that periodic ﬁlament motion can be generated via a self-organization of elastic ﬁlaments and molecular motors. Oscillatory motion and the propagation of bending waves can occur for an initially non-moving state via an instability termed Hopf bifurcation. Close to this instability, the behavior of the system is shown to be independent of microscopic details of the axoneme and the force-generating mechanism. The oscillation frequency however does depend on properties of the molecular motors. We calculate the oscillation frequency at the bifurcation point and show that a large frequency range is accessible by varying the axonemal length between 1 and 50 µ m. We calculate the velocity of swimming of a ﬂagellum and discuss the eﬀects of boundary conditions and externally applied forces on the axonemal oscillations.
Exploring Discrete Diffusion Models for Image Captioning

The image captioning task is typically realized by an auto-regressive method that decodes the text tokens one by one. We present a diffusion-based captioning model, dubbed the name DDCap, to allow more decoding flexibility. Unlike image generation, where the output is continuous and redundant with a fixed length, texts in image captions are categorical and short with varied lengths. Therefore, naively applying the discrete diffusion model to text decoding does not work well, as shown in our experiments. To address the performance gap, we propose several key techniques including best-first inference, concentrated attention mask, text length prediction, and image-free training. On COCO without additional caption pre-training, it achieves a CIDEr score of 117.8, which is +5.0 higher than the auto-regressive baseline with the same architecture in the controlled setting. It also performs +26.8 higher CIDEr score than the auto-regressive baseline (230.3 v.s.203.5) on a caption infilling task. With 4M vision-language pre-training images and the base-sized model, we reach a CIDEr score of 125.1 on COCO, which is competitive to the best well-developed auto-regressive frameworks. The code is available at https://github.com/buxiangzhiren/DDCap.
Aging with GRACE: Lifelong Model Editing with Discrete Key-Value Adaptors

Deployed language models decay over time due to shifting inputs, changing user needs, or emergent world-knowledge gaps. When such problems are identified, we want to make targeted edits while avoiding expensive retraining. However, current model editors, which modify such behaviors of pre-trained models, degrade model performance quickly across multiple, sequential edits. We propose GRACE, a lifelong model editing method, which implements spot-fixes on streaming errors of a deployed model, ensuring minimal impact on unrelated inputs. GRACE writes new mappings into a pre-trained model's latent space, creating a discrete, local codebook of edits without altering model weights. This is the first method enabling thousands of sequential edits using only streaming errors. Our experiments on T5, BERT, and GPT models show GRACE's state-of-the-art performance in making and retaining edits, while generalizing to unseen inputs. Our code is available at https://www.github. com/thartvigsen/grace}.
Quantifying Human Bias and Knowledge to guide ML models during Training

This paper discusses a crowdsourcing based method that we designed to quantify the importance of different attributes of a dataset in determining the outcome of a classification problem. This heuristic, provided by humans acts as the initial weight seed for machine learning models and guides the model towards a better optimal during the gradient descent process. Often times when dealing with data, it is not uncommon to deal with skewed datasets, that over represent items of certain classes, while underrepresenting the rest. Skewed datasets may lead to unforeseen issues with models such as learning a biased function or overfitting. Traditional data augmentation techniques in supervised learning include oversampling and training with synthetic data. We introduce an experimental approach to dealing with such unbalanced datasets by including humans in the training process. We ask humans to rank the importance of features of the dataset, and through rank aggregation, determine the initial weight bias for the model. We show that collective human bias can allow ML models to learn insights about the true population instead of the biased sample. In this paper, we use two rank aggregator methods Kemeny Young and the Markov Chain aggregator to quantify human opinion on importance of features. This work mainly tests the effectiveness of human knowledge on binary classification (Popular vs Not-popular) problems on two ML models: Deep Neural Networks and Support Vector Machines. This approach considers humans as weak learners and relies on aggregation to offset individual biases and domain unfamiliarity.
Recent Advances in Neural-symbolic Systems: A Survey

—In recent years, neural systems have displayed highly effective learning ability and superior perception intelligence, but have been found to lack cognitive ability with effective reasoning. In the contrast, symbolic systems have exceptional cognitive intelligence, but their learning capabilities are poor compared to neural systems. Considering the advantages and disadvantages of both methodologies, an ideal solution is to combine neural systems and symbolic systems, a approach that produces neural-symbolic systems with powerful perception and cognition. In this paper, we survey recent advances in neural-symbolic systems from four perspectives: challenges, methods, applications, and future directions. This paper aims to advance this emerging area of research by providing researchers with a holistic and comprehensive overview of the ﬁeld that highlights the state-of-the-art and identiﬁes promising future research directions.
On the Pointwise Behavior of Recursive Partitioning and Its Implications for Heterogeneous Causal Effect Estimation

Decision tree learning is increasingly being used for pointwise inference. Important applications include causal heterogenous treatment effects and dynamic policy decisions, as well as conditional quantile regression and design of experiments, where tree estimation and inference is conducted at specific values of the covariates. In this paper, we call into question the use of decision trees (trained by adaptive recursive partitioning) for such purposes by demonstrating that they can fail to achieve polynomial rates of convergence in uniform norm with non-vanishing probability, even with pruning. Instead, the convergence may be arbitrarily slow or, in some important special cases, such as honest regression trees, fail completely. We show that random forests can remedy the situation, turning poor performing trees into nearly optimal procedures, at the cost of losing interpretability and introducing two additional tuning parameters. The two hallmarks of random forests, subsampling and the random feature selection mechanism, are seen to each distinctively contribute to achieving nearly optimal performance for the model class considered.
Fitting covariance matrix models to simulations

Data analysis in cosmology requires reliable covariance matrices. Covariance matrices derived from numerical simulations often require a very large number of realizations to be accurate. When a theoretical model for the covariance matrix exists, the parameters of the model can often be fit with many fewer simulations. We write a likelihood-based method for performing such a fit. We demonstrate how a model covariance matrix can be tested by examining the appropriate χ 2 distributions from simulations. We show that if model covariance has amplitude freedom, the expectation value of second moment of χ 2 distribution with a wrong covariance matrix will always be larger than one using the true covariance matrix. By combining these steps together, we provide a way of producing reliable covariances without ever requiring running a large number of simulations. We demonstrate our method on two examples. First, we measure the two-point correlation function of halos from a large set of 10000 mock halo catalogs. We build a model covariance with 2 free parameters, which we fit using our procedure. The resulting best-fit model covariance obtained from just 100 simulation realizations proves to be as reliable as the numerical covariance matrix built from the full 10000 set. We also test our method on a setup where the covariance matrix is large by measuring the halo bispectrum for thousands of triangles for the same set of mocks. We build a block diagonal model covariance with 2 free parameters as an improvement over the diagonal Gaussian covariance. Our model covariance passes the χ 2 test only partially in this case, signaling that the model is insufficient even using free parameters, but significantly improves over the Gaussian one.
Massively Parallel Computation on Embedded Planar Graphs

Many of the classic graph problems cannot be solved in the Massively Parallel Computation setting (MPC) with strongly sublinear space per machine and $o(\log n)$ rounds, unless the 1-vs-2 cycles conjecture is false. This is true even on planar graphs. Such problems include, for example, counting connected components, bipartition, minimum spanning tree problem, (approximate) shortest paths, and (approximate) diameter/radius. In this paper, we show a way to get around this limitation. Specifically, we show that if we have a ``nice'' (for example, straight-line) embedding of the input graph, all the mentioned problems can be solved with $O(n^{2/3+\epsilon})$ space per machine in $O(1)$ rounds. In conjunction with existing algorithms for computing the Delaunay triangulation, our results imply an MPC algorithm for exact Euclidean minimum spanning thee (EMST) that uses $O(n^{2/3 + \epsilon})$ space per machine and finishes in $O(1)$ rounds. This is the first improvement over a straightforward use of the standard Bor\r{u}vka's algorithm with the Dauleanay triangulation algorithm of Goodrich [SODA 1997] which results in $\Theta(\log n)$ rounds. This also partially negatively answers a question of Andoni, Nikolov, Onak, and Yaroslavtsev [STOC 2014], asking for lower bounds for exact EMST. We extend our algorithms to work with embeddings consisting of curves that are not ``too squiggly"(as formalized by the total absolute curvature). We do this via a new lemma which we believe is of independent interest and could be used to parameterize other geometric problems by the total absolute curvature. We also state several open problems regarding massively parallel computation on planar graphs.
THE SIMULTANEOUS DETECTION OF 22- AND 321-GHZ H2O MASER EMISSION TOWARDS THE SYMBIOTIC MIRA R AQUARII

We report high spatial and spectral resolution measurements of masers towards R Aqr and H1−36, both of which are examples of the sub-class of symbiotic stars that contain a long-period Mira-type variable. Our observations have resulted in the first detection of 321-GHz H2O maser action towards a symbiotic Mira — R Aqr. Comparison with simultaneous 22-GHz H2O maser data suggests that the masers do not have the same properties as those in the circumstellar envelopes of field Miras. R Aqr's 22-/321-GHz peak flux density and luminosity ratios are low, as is the line width ratio. Continuum and spectral-line maps indicate that the 22-GHz maser and free–free emission are aligned. Three mechanisms can reproduce the data with varying degrees of success. All three lead naturally to normal levels of maser emission in SiO and 321-GHz H2O and anomalously weak OH and 22-GHz H2O masers. In the most convincing model, UV radiation and a fast wind from the companion remove the Mira's envelope of dusty, molecular gas, leaving a relatively small cavity of dense, neutral material within a large, ionized nebula. Excitation temperatures suggest that 321-GHz masers are normally excited close to the Mira whilst 22-GHz masers are more remote; in R Aqr, therefore, the 22-GHz masers do not form under optimum conditions. Instead, we see weak and narrow lines that form closer to the Mira, consistent with our high-resolution maps.
A method to measure the presampling MTF in digital radiography using Wiener deconvolution

We developed a novel method for determining the presampling modulation transfer function (MTF) of digital radiography systems from slanted edge images based on Wiener deconvolution. The degraded supersampled edge spread function (ESF) was obtained from simulated slanted edge images with known MTF in the presence of poisson noise, and its corresponding ideal ESF without degration was constructed according to its central edge position. To meet the requirements of the absolute integrable condition of Fourier transformation, the origianl ESFs were mirrored to construct the symmetric pattern of ESFs. Then based on Wiener deconvolution technique, the supersampled line spread function (LSF) could be acquired from the symmetric pattern of degraded supersampled ESFs in the presence of ideal symmetric ESFs and system noise. The MTF is then the normalized magnitude of the Fourier transform of the LSF. The determined MTF showed a strong agreement with the theoritical true MTF when appropriated Wiener parameter was chosen. The effects of Wiener parameter value and the width of square-like wave peak in symmetric ESFs were illustrated and discussed. In conclusion, an accurate and simple method to measure the presampling MTF was established using Wiener deconvolution technique according to slanted edge images.
On the Identification of Nonstationary Factor Models and Their Application to Atmospheric Data Analysis

Abstract A numerical framework for data-based identification of nonstationary linear factor models is presented. The approach is based on the extension of the recently developed method for identification of persistent dynamical phases in multidimensional time series, permitting the identification of discontinuous temporal changes in underlying model parameters. The finite element method (FEM) discretization of the resulting variational functional is applied to reduce the dimensionality of the resulting problem and to construct the numerical iterative algorithm. The presented method results in the sparse sequential linear minimization problem with linear constrains. The performance of the framework is demonstrated for the following two application examples: (i) in the context of subgrid-scale parameterization for the Lorenz model with external forcing and (ii) in an analysis of climate impact factors acting on the blocking events in the upper troposphere. The importance of accounting for the nonstationarit.
From Novice To Expert: An Assessment To Measure Strategies Students Implement While Learning To Read Primary Scientific Literature

Primary Scientific Literature (PSL) has been used in undergraduate classrooms as a way to engage students with the research process and to increase science literacy. Most curricula lack any formal training for undergraduates to critically read PSL even though most undergraduate science courses require students to engage with PSL at some level. ABSTRACT Primary Scientific Literature (PSL) has been used in undergraduate classrooms as a way to engage students with the research process and to increase science literacy. Most curricula lack any formal training for undergraduates to critically read PSL even though most undergraduate science courses require students to engage with PSL at some level. In addition, there are limited studies exploring the process by which expertise in reading PSL develops in undergraduates. In this study, we adapted behaviors that expert and novice PSL readers exhibit into a quantitative assessment tool, the PSL Reading Strategies Assessment, to evaluate undergraduates’ development of reading strategies when learning to read PSL. Factor analysis and reliability measures were implemented to determine the structure of our assessment tool. Our results show the PSL Reading Strategies Assessment is sensitive enough to measure differences among student populations, suggesting that it can be used as a diagnostic tool to guide instructors and researchers as they change curricula, implement new teaching strategies, and strive to develop students’ science literacy. Moreover, our data show that developing expert-like reading strategies in students learning to read PSL is not easy. Simply reading a PDF does little to promote the development of reading strategies in students learning to read PSL.
Substructure of a MAP streamer

A study is undertaken of the substructure of an upper‐level potential‐vorticity streamer that approached the European Alps during the field phase of the Mesoscale Alpine Programme (MAP). The diagnosis is based upon a Lagrangian Forward Projection (LFP) technique which can provide a spatial refinement of the operational European Centre for Medium‐Range Weather Forecasts analysis fields. The reconstructed fields capture several notable fine‐scale features of the streamer's structure that include spiral arms of a vortex‐like feature and a richly structured western flank to the streamer comprising a deep primary fold surmounted by a significant secondary fold with an intermediate distinctive striation that extends back into the stratosphere.
Diffraction efficiency improvement in high spatial frequency holographic gratings stored in PVA/AA photopolymers: several ACPA concentrations

High spatial frequency in holographic gratings is difficult to obtain due to limitations of the recording material. In this paper, the results obtained after storing holographic transmission gratings with a spatial frequency of 2656 lines/mm in a material based on polyvinyl alcohol and acrylamide (PVA/AA) are presented. A chain transfer agent, 4, 4′-azobis (4-cyanopentanoic acid) (ACPA) was incorporated in the composition of the material to improve the response of the material at a high spatial frequency. Different concentrations of ACPA were used in order to find the optimal concentration giving maximum diffraction efficiency for high spatial frequencies.