Miracle-dz/UniFAR-FTUs huggingface dataset:
Directional Analytic Discrete Cosine Frames

Block frames called directional analytic discrete cosine frames (DADCFs) are proposed for sparse image representation. In contrast to conventional overlapped frames, the proposed DADCFs require a reduced amount of 1) computational complexity, 2) memory usage, and 3) global memory access. These characteristics are strongly required for current high-resolution image processing. Specifically, we propose two DADCFs based on discrete cosine transform (DCT) and discrete sine transform (DST). The first DADCF is constructed from parallel separable transforms of DCT and DST, where the DST is permuted by row. The second DADCF is also designed based on DCT and DST, while the DST is customized to have no DC leakage property which is a desirable property for image processing. Both DADCFs have rich directional selectivity with slightly different characteristics each other and they can be implemented as non-overlapping block-based transforms, especially they form Parseval block frames with low transform redundancy. We perform experiments to evaluate our DADCFs and compare them with conventional directional block transforms in image recovery.
Ergodicity and hydrodynamic projections in quantum spin lattices at all frequencies and wavelengths

Obtaining rigorous and general results about the quantum dynamics of extended many-body systems is a difficult task. Given the panoply of phenomena that can be observed and are being investigated, it is crucial to delineate the boundaries of what is possible. In quantum lattice models, the Lieb-Robinson (LR) bound tells us that the spatial extent of operators grows at most linearly in time. But what happens within this light-cone? We obtain a universal form of ergodicity showing that operators get “thinner” almost everywhere within the light-cone. This includes what we believe is the first general result about decay of correlation functions within the LR light-cone, and is applicable to any locally interacting system in space-time translation invariant spatially mixing states. This weak notion of ergodicity is sufficient to prove a universal Boltzmann-Gibbs principle in all such systems: the projection of observables onto hydrodynamic modes at long times in correlation functions. In particular, we give an accurate formal definition of the complete space of hydrodynamic modes. This accounts for all types of dynamics, integrable or not. A surprising outcome, using Hilbert spaces of observables, is the realisation that all rigorous results, including hydrodynamic projections, are generalisable to arbitrary frequencies and wavelengths. This extends the concept of dynamical symmetries studied recently, and opens the door to the use of hydrodynamics to address oscillating behaviours at long times. We illustrate this by explaining the oscillatory algebraic decay of free fermion correlation functions using oscillatory hydrodynamic projections.
Algorithmic Probability of Large Datasets and the Simplicity Bubble Problem in Machine Learning

When mining large datasets in order to predict new data, limitations of the principles behind statistical machine learning pose a serious challenge not only to the Big Data deluge, but also to the traditional assumptions that data generating processes are biased toward low algorithmic complexity. Even when one assumes an underlying algorithmic-informational bias toward simplicity in ﬁnite dataset generators, we show that fully automated, with or without access to pseudo-random generators, computable learning algorithms, in particular those of statistical nature used in current approaches to machine learning (including deep learning), can always be deceived, naturally or artiﬁcially, by suﬃciently large datasets. In particular, we demonstrate that, for every ﬁnite learning algorithm, there is a suﬃciently large dataset size above which the algorithmic probability of an unpredictable deceiver is an upper bound (up to a multiplicative constant that only depends on the learning algorithm) for the algorithmic probability of any other larger dataset. In other words, very large and complex datasets are as likely to deceive learning algorithms into a “simplicity bubble” as any other particular dataset. These deceiving datasets guarantee that any prediction will diverge from the high-algorithmic-complexity globally optimal solution while converging toward the low-algorithmic-complexity locally optimal solution. We discuss the framework and empirical conditions for circumventing this de-ceptive phenomenon, moving away from statistical machine learning towards a stronger type of machine learning based on, or motivated by, the intrinsic power of algorithmic information theory and computability theory.
Trions in twisted bilayer graphene

Frank Schindler, Oskar Vafek, 3 and B. Andrei Bernevig 5, 6 Princeton Center for Theoretical Science, Princeton University, Princeton, NJ 08544, USA National High Magnetic Field Laboratory, Tallahassee, Florida, 32310, USA Department of Physics, Florida State University, Tallahassee, Florida 32306, USA Department of Physics, Princeton University, Princeton, NJ 08544, USA Donostia International Physics Center, P. Manuel de Lardizabal 4, 20018 Donostia-San Sebastian, Spain IKERBASQUE, Basque Foundation for Science, Bilbao, Spain
Effects of generator and static-load nonlinearities on electromechanical oscillations

Linear analysis methods, such as eigenanalysis and Prony analysis, are becoming standard tools in studying power system electromechanical oscillations. Because the actual system is nonlinear, large-amplitude oscillatory swings will not necessarily have frequency and damping characteristics indicated by the eigensolution. Understanding the differences can be critical in comparing eigenanalysis and simulated results, and in applying Prony analysis to the nonlinear swings. This paper investigates the effects of excitation limiting, magnetic saturation, the swing equation, and static-load model properties on the linearity of a power swing. Simulation, eigenanalysis, and Prony analysis results are compared on a four-machine system operating under several conditions. It is found that significant differences occur between the eigenanalysis and simulation results when heavy excitation limiting occurs during the swing. Generator magnetic saturation tends to cause the simulated results to have more effective damping than the eigensolution. Other nonlinearities cause little or no differences. Prony analysis tends to match the simulated swing, except if excitation limiting occurs.
Toward a New Science of Common Sense

Common sense has always been of interest in AI, but has rarely taken center stage. Despite its mention in one of John McCarthy's earliest papers and years of work by dedicated researchers, arguably no AI system with a serious amount of general common sense has ever emerged. Why is that? What's missing? Examples of AI systems' failures of common sense abound, and they point to AI's frequent focus on expertise as the cause. Those attempting to break the brittleness barrier, even in the context of modern deep learning, have tended to invest their energy in large numbers of small bits of commonsense knowledge. But all the commonsense knowledge fragments in the world don't add up to a system that actually demonstrates common sense in a human-like way. We advocate examining common sense from a broader perspective than in the past. Common sense is more complex than it has been taken to be and is worthy of its own scientific exploration.
A Tale of Three Dust Populations: Variable R V and Extreme Polarization along Sight Lines toward ζ Ophiuchi

Dust permeates the interstellar medium, reddening and polarizing background starlight, but dust properties vary with local environment. In order to characterize dust in a highly irradiated diffuse cloud, we measure the reddening and optical polarization toward 27 stars surrounding the midlatitude b = +24° O9.2IV star ζ Ophiuchi, using new optical spectroscopy and polarimetry. We incrementally deredden and depolarize with distance, allowing us to distinguish dust components along these sight lines. The data indicate three distinct dust populations: a foreground component characteristic of average Milky Way dust (R V ≈ 3.1, d ≲ 180 pc), a highly polarizing mid-distance component in the vicinity of ζ Oph (R V ≈ 2.4, 200 pc < d < 300 pc), and a nonpolarizing distant component (R V ≈ 3.6, 600 pc < d < 2000 pc). Prominent 8 μm infrared striations spanning the field of view likely have high polycyclic aromatic hydrocarbon content and are illuminated by ζ Oph. Foreground-subtracted polarizations roughly align with these striations, which, we argue, lie immediately behind ζ Oph and constitute the highly polarizing mid-distance dust. This component polarizes very efficiently (P V > 9.1E(B − V)), implying a high degree of grain alignment and suggesting that the bulk of the polarization occurs in a small fraction of the volume. The large R V in the distant component reveals that dust above the Galactic plane (z > 250 pc) may contain a greater fraction of large grains than the Milky Way average.
Supercritical percolation on finite transitive graphs I: Uniqueness of the giant component

Let $(G_n)_{n \geq 1} = ((V_n,E_n))_{n \geq 1}$ be a sequence of finite, connected, vertex-transitive graphs with volume tending to infinity. We say that a sequence of parameters $(p_n)_{n \geq 1}$ in $[0,1]$ is supercritical with respect to Bernoulli bond percolation $\mathbb P_p^G$ if there exists $\varepsilon>0$ and $N<\infty$ such that \[ \mathbb P_{(1-\varepsilon)p_n}^{G_n} \left( \text{the largest cluster contains at least $\varepsilon |V_n|$ vertices}\right) \geq \varepsilon \] for every $n\geq N$ with $p_n<1$. We prove that if $(G_n)_{n \geq 1}$ is sparse, meaning that the degrees are sublinear in the number of vertices, then the supercritical giant cluster is unique with high probability in the sense that if $(p_n)_{n \geq 1}$ is supercritical then \[ \lim_{n\to\infty}\mathbb P_{p_n}^{G_n} \left( \text{the second largest cluster contains at least $c|V_n|$ vertices} \right) = 0 \] for every $c>0$. This result is new even under the stronger hypothesis that $(G_n)_{n \geq 1}$ has uniformly bounded vertex degrees, in which case it verifies a conjecture of Benjamini (2001). Previous work of many authors had established the same theorem for complete graphs, tori, hypercubes, and bounded degree expander graphs, each using methods that are highly specific to the examples they treated. We also give a complete solution to the problem of supercritical uniqueness for dense vertex-transitive graphs, establishing a simple necessary and sufficient isoperimetric condition for uniqueness to hold.
Dimension‐independent Markov chain Monte Carlo on the sphere

We consider Bayesian analysis on high‐dimensional spheres with angular central Gaussian priors. These priors model antipodally symmetric directional data, are easily defined in Hilbert spaces and occur, for instance, in Bayesian density estimation and binary level set inversion. In this paper we derive efficient Markov chain Monte Carlo methods for approximate sampling of posteriors with respect to these priors. Our approaches rely on lifting the sampling problem to the ambient Hilbert space and exploit existing dimension‐independent samplers in linear spaces. By a push‐forward Markov kernel construction we then obtain Markov chains on the sphere which inherit reversibility and spectral gap properties from samplers in linear spaces. Moreover, our proposed algorithms show dimension‐independent efficiency in numerical experiments.
Dual Path Structural Contrastive Embeddings for Learning Novel Objects

Learning novel classes from a very few labeled samples has attracted increasing attention in machine learning areas. Recent research on either meta-learning based or transfer-learning based paradigm demonstrates that gaining information on a good feature space can be an effective solution to achieve favorable performance on few-shot tasks. In this paper, we propose a simple but effective paradigm that decouples the tasks of learning feature representations and classifiers and only learns the feature embedding architecture from base classes via the typical transfer-learning training strategy. To maintain both the generalization ability across base and novel classes and discrimination ability within each class, we propose a dual path feature learning scheme that effectively combines structural similarity with contrastive feature construction. In this way, both inner-class alignment and inter-class uniformity can be well balanced, and result in improved performance. Experiments on three popular benchmarks show that when incorporated with a simple prototype based classifier, our method can still achieve promising results for both standard and generalized few-shot problems in either an inductive or transductive inference setting.
Model Selection in Batch Policy Optimization

We study the problem of model selection in batch policy optimization: given a fixed, partial-feedback dataset and $M$ model classes, learn a policy with performance that is competitive with the policy derived from the best model class. We formalize the problem in the contextual bandit setting with linear model classes by identifying three sources of error that any model selection algorithm should optimally trade-off in order to be competitive: (1) approximation error, (2) statistical complexity, and (3) coverage. The first two sources are common in model selection for supervised learning, where optimally trading-off these properties is well-studied. In contrast, the third source is unique to batch policy optimization and is due to dataset shift inherent to the setting. We first show that no batch policy optimization algorithm can achieve a guarantee addressing all three simultaneously, revealing a stark contrast between difficulties in batch policy optimization and the positive results available in supervised learning. Despite this negative result, we show that relaxing any one of the three error sources enables the design of algorithms achieving near-oracle inequalities for the remaining two. We conclude with experiments demonstrating the efficacy of these algorithms.
Emitter Spacing and Geometry of Wetted Soil Volume

A method for determining width and depth of the wetted soil volume under an emitter is described. The proposed method is tested using computed and experimental results from a plane flow model. A procedure for computing optimal emitter spacing based on the geometry of the wetted soil volume and on the cost of the lateral is demonstrated using computed results from a cylindrical flow model.
Classical and Quantum Evolution in a Simple Coherent Neutrino Problem

The extraordinary neutrino ﬂux produced in extreme astrophysical environments like the early universe, core-collapse supernovae and neutron star mergers may produce coherent quantum neutrino oscillations on macroscopic length scales. The Hamiltonian describing this evolution can be mapped into quantum spin models with all-to-all couplings arising from neutrino-neutrino forward scattering. To date many studies of these oscillations have been performed in a mean-ﬁeld limit where the neutrinos time evolve in a product state. In this paper we examine a simple two-beam model evolving from an initial product state and compare the mean-ﬁeld and many-body evolution. The symmetries in this model allow us to solve the real-time evolution for the quantum many-body system for hundreds or thousands of spins, far beyond what would be possible in a more general case with an exponential number (2 N ) of quantum states. We compare mean-ﬁeld and many-body solutions for diﬀerent initial product states and ratios of one- and two-body couplings, and ﬁnd that in all cases in the limit of inﬁnite spins the mean-ﬁeld (product state) and many-body solutions coincide for simple observables. This agreement can be understood as a consequence of the fact that the typical initial condition represents a very local but dense distribution about a mean energy in the spectrum of the Hamiltonian. We explore quantum information measures like entanglement entropy and purity of the many-body solutions, ﬁnding intriguing relationships between the quantum information measures and the dynamical behavior of simple physical observables.
Physics Constrained Flow Neural Network for Short-Timescale Predictions in Data Communications Networks

Machine learning is gaining growing momentum in various recent models for the dynamic analysis of information flows in data communications networks. These preliminary models often rely on off-the-shelf learning models to predict from historical statistics while disregarding the physics governing the generating behaviors of these flows. This paper instead introduces Flow Neural Network (FlowNN) to improve the feature representation with learned physical bias. This is implemented by an induction layer, working upon the embedding layer, to impose the physics connected data correlations, and a self-supervised learning strategy with stop-gradient to make the learned physics universal. For the short-timescale network prediction tasks, FlowNN achieves 17% - 71% of loss decrease than the state-of-the-art baselines on both synthetic and real-world networking datasets, which shows the strength of this new approach.
Distilling the Knowledge of Romanian BERTs Using Multiple Teachers

Running large-scale pre-trained language models in computationally constrained environments remains a challenging problem yet to be addressed, while transfer learning from these models has become prevalent in Natural Language Processing tasks. Several solutions, including knowledge distillation, network quantization, or network pruning have been previously proposed; however, these approaches focus mostly on the English language, thus widening the gap when considering low-resource languages. In this work, we introduce three light and fast versions of distilled BERT models for the Romanian language: Distil-BERT-base-ro, Distil-RoBERT-base, and DistilMulti-BERT-base-ro. The first two models resulted from the individual distillation of knowledge from two base versions of Romanian BERTs available in literature, while the last one was obtained by distilling their ensemble. To our knowledge, this is the first attempt to create publicly available Romanian distilled BERT models, which were thoroughly evaluated on five tasks: part-of-speech tagging, named entity recognition, sentiment analysis, semantic textual similarity, and dialect identification. Our experimental results argue that the three distilled models offer performance comparable to their teachers, while being twice as fast on a GPU and ~35% smaller. In addition, we further test the similarity between the predictions of our students versus their teachers by measuring their label and probability loyalty, together with regression loyalty - a new metric introduced in this work.
Technology adoption: breaking down barriers using a virtual reality design support tool for hybrid concrete

The use of hybrid concrete technologies as a viable solution to traditional frame design has been inhibited by a general lack of information. The uptake of this technology has therefore been slow and parochial, as designers have tended to stay loyal to ‘traditional’, tried and tested technologies—their ‘comfort zone’. This research identifies the barriers associated with technology adoption at industry, organization and individual levels; and uses hybrid concrete as the core context for discussion. The role of an enabling design support tool (HyCon) is presented, which can allow designers to overcome these inhibitors by providing an immersive, interactive and information‐rich environment through which design solutions can be explored. This collaborative research project describes a ‘proof of concept’ design support tool to promote the use of hybrid concrete in structural frames. It encompasses knowledge creation, application and sharing functionality, to envision support and use of this new technology—thereby promoting organizational learning. Research findings note that, while design tools have a valuable role to play, it is also important to appreciate that the uptake of any new technology is a social phenomenon, and that subsequent adoption/uptake requires careful embedding and augmentation into company organizational systems in order to leverage advantage.
Statistics of motor vehicle ignition noise at VHF/UHF

Temporal sweeps of the amplitudes of electromagnetic vehicle ignition noise were made in Ottawa at six fixed frequencies in the range from 150 to 500 MHz using a 10 kHz bandwidth. Statistical analyses were performed of both the amplitude and temporal characteristics of the impulsive noise. The amplitudes approximately followed a lognormal distribution. The pulse spacings were uniformly distributed between about 5 and 15 ms and the pulse durations were about 150 /spl mu/s long.
ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation

Pre-trained language models have achieved state-of-the-art results in various Natural Language Processing (NLP) tasks. GPT-3 has shown that scaling up pre-trained language models can further exploit their enormous potential. A unified framework named ERNIE 3.0 was recently proposed for pre-training large-scale knowledge enhanced models and trained a model with 10 billion parameters. ERNIE 3.0 outperformed the state-of-the-art models on various NLP tasks. In order to explore the performance of scaling up ERNIE 3.0, we train a hundred-billion-parameter model called ERNIE 3.0 Titan with up to 260 billion parameters on the PaddlePaddle platform. Furthermore, we design a self-supervised adversarial loss and a controllable language modeling loss to make ERNIE 3.0 Titan generate credible and controllable texts. To reduce the computation overhead and carbon emission, we propose an online distillation framework for ERNIE 3.0 Titan, where the teacher model will teach students and train itself simultaneously. ERNIE 3.0 Titan is the largest Chinese dense pre-trained model so far. Empirical results show that the ERNIE 3.0 Titan outperforms the state-of-the-art models on 68 NLP datasets.
Droplets on Lubricant Infused Surfaces: The slow dynamics of skirt formation

A key question in the interaction of droplets with liquid-infused surfaces is what determines the apparent contact angle of droplets. Previous work has used measured values of the skirt geometry (e.g. the radius of curvature of the skirt) to determine this. Here, we consider theoretically the equilibrium of a droplet on a smooth (inverse opal) lubricant-coated surface, and argue that the small effect of gravity within the skirt and the size of the substrate are important for determining the final equilibrium. However, we also show that the evolution towards this ultimate equilibrium is extremely slow (on the order of days for typical experimental parameter values). We therefore suggest that previous experiments on inverse opal or nano-textured surfaces may have observed only slowly-evolving transients, rather than ‘true’ equilibria, potentially explaining why a wide range of skirt sizes have been reported.