Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-weight extrapolation on function spaces and applications

This paper is devoted to studying the two-weight extrapolation theory of Rubio de Francia. We start to establish endpoint extrapolation results including A1, Ap and A∞ extrapolation in the context of Banach function spaces and modular spaces. Furthermore, we present extrapolation for commutators in weighted Banach function spaces. Beyond that, we give several applications which can be easily obtained using extrapolation. First, we get local decay estimates for various operators and commutators. Second, Coifman-Fefferman inequalities are established and can be used to show some known sharp A1 inequalities. Third, MuckenhouptWheeden and Sawyer conjectures are also presented for many operators, which go beyond Calderón-Zygmund operators. Finally, we obtain two-weight inequalities for Littlewood-Paley operators and Fourier integral operators on weighted Banach function spaces.
Dynamic Industry Uncertainty Networks and the Business Cycle

We argue that uncertainty network structures extracted from option prices contain valuable information for business cycles. Classifying U.S. industries according to their contribution to system-related uncertainty across business cycles, we uncover an uncertainty hub role for the communications, industrials and information technology sectors, while shocks to materials, real estate and utilities do not create strong linkages in the network. Moreover, we find that this ex-ante network of uncertainty is a useful predictor of business cycles, especially when it is based on uncertainty hubs. The industry uncertainty network behaves counter-cyclically in that a tighter network tends to associate with future business cycle contractions.
ESTIMATES FOR THE MASSLESS RADIAL DIRAC-COULOMB EQUATION

It is now well understood that the charge needs to satisfy the condition |ν| ≤ 1 in order for the operator Dν to be self-adjoint in 3d: to be more precise, Dν happens to be essentially self-adjoint in the range |ν| ≤ √ 3 2 , and admits a distinguished self-adjoint extension in the range √ 3 2 < |ν| ≤ 1 (see [9] and references therein.). From the point of view of dispersive analysis, system (1.1) is quite delicate, as indeed the Coulomb potential is critical with respect to the scaling of the massless Dirac-Coulomb operator and, as it is well known, scaling critical perturbations can be very difficult to handle, as they typically rule out perturbation arguments.
Spectral signature of atmospheric winds in high-resolution transit observations

The study of exoplanet atmospheres showed large diversity compared to the planets in our solar system. Especially Jupiter type exoplanets orbiting their host star in close orbits, the socalled hot and ultra-hot Jupiters, have been studied in detail due to their enhanced atmospheric signature. Due to their tidally locked status, the temperature difference between the dayand nightside triggers atmospheric windswhich can lead to various fingerprints in the observations. Spatially resolved absorption lines during transit such as sodium (Na) could be a good tracer for such winds. Different works resolved the Naabsorption lines on different exoplanets which show different line widths. Assuming that this could be attributed to such zonal jet streams, this work models the effect of such winds on synthetic absorption lines. For this, transiting Jupiter type planets with rotational velocities similar to hot and ultra-hot Jupiter are considered. The investigation shows that high wind velocities could reproduce the broadening of Na-line profiles inferred in different high-resolution transit observations. There is a tendency that the broadening values decrease for planets with lower equilibrium temperature. This could be explained by atmospheric drag induced by the ionization of alkali lines which slow down the zonal jet streams, favoring their existence on hot Jupiter rather than ultra-hot Jupiter.
A Hitchhiker’s Guide to Structural Similarity

The Structural Similarity (SSIM) Index is a very widely used image/video quality model that continues to play an important role in the perceptual evaluation of compression algorithms, encoding recipes and numerous other image/video processing algorithms. Several public implementations of the SSIM and Multiscale-SSIM (MS-SSIM) algorithms have been developed, which differ in efficiency and performance. This “bendable ruler” makes the process of quality assessment of encoding algorithms unreliable. To address this situation, we studied and compared the functions and performances of popular and widely used implementations of SSIM, and we also considered a variety of design choices. Based on our studies and experiments, we have arrived at a collection of recommendations on how to use SSIM most effectively, including ways to reduce its computational burden.
Non-parametric Memory for Spatio-Temporal Segmentation of Construction Zones for Self-Driving

In this paper, we introduce a non-parametric memory representation for spatio-temporal segmentation that captures the local space and time around an autonomous vehicle (AV). Our representation has three important properties: (i) it remembers what it has seen in the past, (ii) it reinforces and (iii) forgets its past beliefs based on new evidence. Reinforcing is important as the first time we see an element we might be uncertain, e.g, if the element is heavily occluded or at range. Forgetting is desirable, as otherwise false positives will make the self driving vehicle behave erratically. Our process is informed by 3D reasoning, as occlusion is key to distinguishing between the desire to forget and to remember. We show how our method can be used as an online component to complement static world representations such as HD maps by detecting and remembering changes that should be superimposed on top of this static view due to such events.
Diversified Patch-based Style Transfer with Shifted Style Normalization

Gram-based and patch-based approaches are two important research lines of image style transfer. Recent diversified Gram-based methods have been able to produce multiple and diverse reasonable solutions for the same content and style inputs. However, as another popular research interest, the diversity of patch-based methods remains challenging due to the stereotyped style swapping process based on nearest patch matching. To resolve this dilemma, in this paper, we dive into the core style swapping process of patch-based style transfer and explore possible ways to diversify it. What stands out is an operation called shifted style normalization (SSN), the most effective and efficient way to empower existing patch-based methods to generate diverse results for arbitrary styles. The key insight is to use an important intuition that neural patches with higher activation values could contribute more to diversity. Theoretical analyses and extensive experiments are conducted to demonstrate the effectiveness of our method, and compared with other possible options and state-of-the-art algorithms, it shows remarkable superiority in both diversity and efficiency.
Mind the Gap when Conditioning Amortised Inference in Sequential Latent-Variable Models

Amortised inference enables scalable learning of sequential latent-variable models (LVMs) with the evidence lower bound (ELBO). In this setting, variational posteriors are often only partially conditioned. While the true posteriors depend, e.g., on the entire sequence of observations, approximate posteriors are only informed by past observations. This mimics the Bayesian filter -- a mixture of smoothing posteriors. Yet, we show that the ELBO objective forces partially-conditioned amortised posteriors to approximate products of smoothing posteriors instead. Consequently, the learned generative model is compromised. We demonstrate these theoretical findings in three scenarios: traffic flow, handwritten digits, and aerial vehicle dynamics. Using fully-conditioned approximate posteriors, performance improves in terms of generative modelling and multi-step prediction.
The Lop-sided Spiral Galaxy NGC 247: Clues to a Possible Interaction with NGC 253

Observations that span a broad range of wavelengths are used to examine asymmetries in the disk of the nearby late-type spiral galaxy NGC 247. The northern spiral arm is over-luminous at all wavelengths when compared with other parts of the galaxy at similar galactocentric radii, while the density of very luminous red stars in the void that is immediately south of this arm matches that in other parts of the disk at the same galactocentric radius. Two bubbles with spatial extents of many kiloparsecs are identified in the disk, and many of the young stars in the southern disk of NGC 247 are located in the walls of these structures. Dynamical age estimates of these bubbles coincide with the last large-scale star formation event in the nucleus, suggesting that there was large-scale star formation throughout the disk of NGC 247 a few hundred megayears in the past. Morphological similarities are seen with the classical lop-sided galaxy NGC 4027, and it is concluded that NGC 247 is a significantly lop-sided spiral galaxy. The void in the northern disk is then the area between the main body of the disk and the northern arm viewed in projection. The implications of a lop-sided morphology for NGC 247 in the context of interactions with its nearby starburst galaxy companion NGC 253 are discussed.
Learning by Watching: Physical Imitation of Manipulation Skills from Human Videos

Learning from visual data opens the potential to accrue a large range of manipulation behaviors by leveraging human demonstrations without specifying each of them mathe-matically, but rather through natural task specification. In this paper, we present Learning by Watching (LbW), an algorithmic framework for policy learning through imitation from a single video specifying the task. The key insights of our method are two-fold. First, since the human arms may not have the same morphology as robot arms, our framework learns unsupervised human to robot translation to overcome the morphology mis-match issue. Second, to capture the details in salient regions that are crucial for learning state representations, our model performs unsupervised keypoint detection on the translated robot videos. The detected keypoints form a structured representation that contains semantically meaningful information and can be used directly for computing reward and policy learning. We evaluate the effectiveness of our LbW framework on five robot manipulation tasks, including reaching, pushing, sliding, coffee making, and drawer closing. Extensive experimental evaluations demonstrate that our method performs favorably against the state-of-the-art approaches. More results and analysis are available at pair.toronto. edu/lbw-kp/.
Tiny Transducer: A Highly-Efficient Speech Recognition Model on Edge Devices

This paper proposes an extremely lightweight phone-based transducer model with a tiny decoding graph on edge devices. First, a phone synchronous decoding (PSD) algorithm based on blank label skipping is first used to speed up the transducer decoding process. Then, to decrease the deletion errors introduced by the high blank score, a blank label deweighting approach is proposed. To reduce parameters and computation, deep feedforward sequential memory network (DFSMN) layers are used in the transducer encoder, and a CNN-based stateless predictor is adopted. SVD technology compresses the model further. WFST-based decoding graph takes the context-independent (CI) phone posteriors as input and allows us to flexibly bias user-specific information. Finally, with only 0.9M parameters after SVD, our system could give a relative 9.1% - 20.5% improvement compared with a bigger conventional hybrid system on edge devices.
Uplink Beam Management for Millimeter Wave Cellular MIMO Systems with Hybrid Beamforming

Hybrid analog and digital BeamForming (HBF) is one of the enabling transceiver technologies for millimeter Wave (mmWave) Multiple Input Multiple Output (MIMO) systems. This technology offers highly directional communication, which is able to confront the intrinsic characteristics of mmWave signal propagation. However, the small coherence time in mmWave systems, especially under mobility conditions, renders efficient Beam Management (BM) in standalone mmWave communication a very difficult task. In this paper, we consider HBF transceivers with planar antenna panels and design a multilevel beam codebook for the analog beamformer comprising flat top beams with variable widths. These beams exhibit an almost constant array gain for the whole desired angle width, thereby facilitating efficient hierarchical BM. Focusing on the uplink communication, we present a novel beam training algorithm with dynamic beam ordering, which is suitable for the stringent latency requirements of the latest mmWave standard discussions. Our simulation results showcase the latency performance improvement and received signal-to-noise ratio with different variations of the proposed scheme over the optimum beam training scheme based on exhaustive narrow beam search.
Intact-VAE: Estimating Treatment Effects under Unobserved Confounding

As an important problem of causal inference, we discuss the identification and estimation of treatment effects under unobserved confounding. Representing the confounder as a latent variable, we propose Intact-VAE, a new variant of variational autoencoder (VAE), motivated by the prognostic score that is sufficient for identifying treatment effects. We theoretically show that, under certain settings, treatment effects are identified by our model, and further, based on the identifiability of our model (i.e., determinacy of representation), our VAE is a consistent estimator with representation balanced for treatment groups. Experiments on (semi-)synthetic datasets show state-of-the-art performance under diverse settings.
Dissecting the Meme Magic: Understanding Indicators of Virality in Image Memes

Despite the increasingly important role played by image memes, we do not yet have a solid understanding of the elements that might make a meme go viral on social media. In this paper, we investigate what visual elements distinguish image memes that are highly viral on social media from those that do not get re-shared, across three dimensions: composition, subjects, and target audience. Drawing from research in art theory, psychology, marketing, and neuroscience, we develop a codebook to characterize image memes, and use it to annotate a set of 100 image memes collected from 4chan's Politically Incorrect Board (/pol/). On the one hand, we find that highly viral memes are more likely to use a close-up scale, contain characters, and include positive or negative emotions. On the other hand, image memes that do not present a clear subject the viewer can focus attention on, or that include long text are not likely to be re-shared by users. We train machine learning models to distinguish between image memes that are likely to go viral and those that are unlikely to be re-shared, obtaining an AUC of 0.866 on our dataset. We also show that the indicators of virality identified by our model can help characterize the most viral memes posted on mainstream online social networks too, as our classifiers are able to predict 19 out of the 20 most popular image memes posted on Twitter and Reddit between 2016 and 2018. Overall, our analysis sheds light on what indicators characterize viral and non-viral visual content online, and set the basis for developing better techniques to create or moderate content that is more likely to catch the viewer's attention.
On Data-Augmentation and Consistency-Based Semi-Supervised Learning

Recently proposed consistency-based Semi-Supervised Learning (SSL) methods such as the $\Pi$-model, temporal ensembling, the mean teacher, or the virtual adversarial training, have advanced the state of the art in several SSL tasks. These methods can typically reach performances that are comparable to their fully supervised counterparts while using only a fraction of labelled examples. Despite these methodological advances, the understanding of these methods is still relatively limited. In this text, we analyse (variations of) the $\Pi$-model in settings where analytically tractable results can be obtained. We establish links with Manifold Tangent Classifiers and demonstrate that the quality of the perturbations is key to obtaining reasonable SSL performances. Importantly, we propose a simple extension of the Hidden Manifold Model that naturally incorporates data-augmentation schemes and offers a framework for understanding and experimenting with SSL methods.
Deterministic Decremental SSSP and Approximate Min-Cost Flow in Almost-Linear Time

In the decremental single-source shortest paths problem, the goal is to maintain distances from a fixed source $s$ to every vertex $v$ in an m-edge graph undergoing edge deletions. In this paper, we conclude a long line of research on this problem by showing a near-optimal deterministic data structure that maintains (1 + E) -approximate distance estimates and runs in m1+o(1)total update time. Our result, in particular, removes the oblivious adversary assumption required by the previous breakthrough result by Henzinger et al. [FOCS'14], which leads to our second result: the first almost-linear time algorithm for (1 - E) -approximate min-cost flow in undirected graphs where capacities and costs can be taken over edges and vertices. Previously, algorithms for max flow with vertex capacities, or min-cost flow with any capacities required super-linear time. Our result essentially completes the picture for approximate flow in undirected graphs. The key technique of the first result is a novel framework that allows us to treat low-diameter graphs like expanders. This allows us to harness expander properties while bypassing shortcomings of expander decomposition, which almost all previous expander-based algorithms needed to deal with. For the second result, we break the notorious flow-decomposition barrier from the multiplicative-weight-update framework using randomization.
Big Data application in congestion detection and classification using Apache spark

With the era of big data, an explosive amount of information is now available. This enormous increase of Big Data in both academia and industry requires large-scale data processing systems. A large body of research is behind optimizing Spark's performance to make it state of the art, a fast and general data processing system. Many science and engineering fields have advanced with Big Data analytics, such as Biology, finance, and transportation. Intelligent transportation systems (ITS) gain popularity and direct benefit from the richness of information. The objective is to improve the safety and management of transportation networks by reducing congestion and incidents. The first step toward the goal is better understanding, modeling, and detecting congestion across a network efficiently and effectively. In this study, we introduce an efficient congestion detection model. The underlying network consists of 3017 segments in I-35, I-80, I-29, and I-380 freeways with an overall length of 1570 miles and averaged (0.4-0.6) miles per segment. The result of congestion detection shows the proposed method is 90% accurate while has reduced computation time by 99.88%.
Dynamic Ternary Content-Addressable Memory Is Indeed Promising: Design and Benchmarking Using Nanoelectromechanical Relays

Ternary content addressable memory (TCAM) has been a critical component in caches, routers, etc., in which density, speed, power efficiency, and reliability are the major design targets. There have been the conventional low-write-power but bulky SRAM-based TCAM design, and also denser but less reliable or higher-write-power TCAM designs using nonvolatile memory (NVM) devices. Meanwhile, some TCAM designs using dynamic memories have been also proposed. Although dynamic design TCAM is denser than CMOS SRAM TCAM and more reliable than NVM TCAM, the conventional row-by-row refresh operations land up with a bottleneck of interference with normal TCAM activities. Therefore, this paper proposes a custom low-power dynamic TCAM using nanoelectromechanical (NEM) relay devices utilizing one-shot refresh to solve the memory refresh problem. By harnessing the unique NEM relay characteristics with a proposed novel cell structure, the proposed TCAM occupies a small footprint of only 3 transistors (with two NEM relays integrated on the top through the back-end-of-line process), which significantly outperforms the density of 16-transistor SRAM-based TCAM. In addition, evaluations show that the proposed TCAM improves the write energy efficiency by 2.31x, 131x, and 13.5x over SRAM, RRAM, and FeFET TCAMs, respectively; The search energy-delay-product is improved by 12.7x, 1.30x, and 2.83x over SRAM, RRAM, and FeFET TCAMs, respectively.
Remarks on noncommutativity and scale anomaly in planar quantum mechanics

A study of a surprising connection between noncommutativity and the anomalous scale or dilatation symmetry is presented for a generalized quantum Hall system due to time dilatation transformations. On using the "Peierls substitution" scheme, it is shown that noncommutativity between spatial coordinates emerges naturally at a large magnetic field limit. Thereafter, we derive a path-integral action for the corresponding noncommutative quantum system. We also discuss the equivalence between the considered noncommutative system and the generalized Landau problem thus rendering an effective commmutative description. By exploiting the path-integral method due to Fujikawa, we derive an expression for the unintegrated scale or dilatation anomaly for the generalized Landau system, wherein the anomalies are identified with Jacobian factors arising from measure change under scale transformation and is subsequently renormalised. In fact, we derive exact expressions of anomalous Ward identities from which one may point out the existence of scale anomaly which is a purely quantum effect induced from the noncommutative structure between spatial coordinates.
Real-Time LSM-Trees for HTAP Workloads

Real-time analytics systems employ hybrid data layouts in which data are stored in different formats throughout their lifecycle. Recent data are stored in a row-oriented format to serve OLTP workloads and support high insert rates, while older data are transformed to a column-oriented format for OLAP access patterns. We observe that a Log-Structured Merge (LSM) Tree is a natural fit for a lifecycle-aware storage engine due to its high write throughput and level-oriented structure, in which records propagate from one level to the next over time. To build a lifecycle-aware storage engine using an LSM-Tree, we make a crucial modification to allow different data layouts in different levels, ranging from purely row-oriented to purely column-oriented, leading to a Real-Time LSM-Tree. We give a cost model and an algorithm to design a Real-Time LSM-Tree that is suitable for a given workload, followed by an experimental evaluation of LASER - a prototype implementation of our idea built on top of the RocksDB key-value store.