Miracle-dz/UniFAR-FTUs huggingface dataset:
Single-Pixel Image Reconstruction Based on Block Compressive Sensing and Deep Learning

Single-pixel imaging (SPI) is a novel imaging technique whose working principle is based on the compressive sensing (CS) theory. In SPI, data is obtained through a series of compressive measurements and the corresponding image is reconstructed. Typically, the reconstruction algorithm such as basis pursuit relies on the sparsity assumption in images. However, recent advances in deep learning have found its uses in reconstructing CS images. Despite showing a promising result in simulations, it is often unclear how such an algorithm can be implemented in an actual SPI setup. In this paper, we demonstrate the use of deep learning on the reconstruction of SPI images in conjunction with block compressive sensing (BCS). We also proposed a novel reconstruction model based on convolutional neural networks that outperforms other competitive CS reconstruction algorithms. Besides, by incorporating BCS in our deep learning model, we were able to reconstruct images of any size above a certain smallest image size. In addition, we show that our model is capable of reconstructing images obtained from an SPI setup while being priorly trained on natural images, which can be vastly different from the SPI images. This opens up opportunity for the feasibility of pretrained deep learning models for CS reconstructions of images from various domain areas.
Higher-dimensional counterexamples to Hamiltonicity

For d at least 2, we show that all graphs of d-polytopes have Hamiltonian line graph if and only if d is not 3: We exhibit a graph of a 3-polytope on 252 vertices whose line graph is not even traceable. Adapting a construction by Grunbaum and Motzkin, for large n we also construct simple 3-polytopes on 3n vertices in whose line graph any simple path is shorter than 10n^a, for some constant a<1. Moreover, we give four elementary counterexamples of plausible extensions to simplicial complexes of four famous results in Hamiltonian graph theory, namely: (1) There is a 2-complex without (tight, loose, or weak) Hamiltonian paths, where every vertex is in more than n/2 facets. (2) There is a 2-complex without (tight, loose, or weak) Hamiltonian paths that is maximally non-weakly-Hamiltonian. (3) There is a 2-complex without (tight, loose, or weak) Hamiltonian paths that is self-complementary. (4) There is a 2-strongly connected 2-complex whose square complex has no (tight, loose, or weak) Hamiltonian cycles.
Consistency test of the fine-structure constant from the whole ionization history

In cosmology, the fine-structure constant can affect the whole ionization history. However, the previous works confine themselves to the recombination epoch and give various strong constraints on the fine-structure constant. In this paper, we also take the reionization epoch into consideration and do a consistency test of the fine-structure constant from the whole ionization history. From the data combination of Planck 2018, BAO data, SNIa samples, SFR density from UV and IR measurements, and the Q HII constraints, we find the constraint on the fine-structure constant during the recombination epoch is α rec/α EM = 1.001494+0.002041 -0.002063 and its counterpart during the reionization epoch is α rei/α EM = 0.854034+0.031678 0.027209 at 68% C.L. They are not consistent with each other by 4. 64σ. A conservative explanation for such a discrepancy is that there are some issues in the data we used. We prefer a calibration of some important parameters involved in reconstructing the reionization history.
Cosmology as a Crossed Product

We suggest a new conceptual frame where inﬂationary Cosmology is quantum mechanically described using the Hilbert space representation of the crossed product of the type III factor associated with the algebra of local operators on the de Sitter static patch and the automorphism of translations in the emergent conformal time. In this formal framework scalar curvature ﬂuctuations are determined by the gravitational potential induced by the quantum variance of the generator of the conformal time automorphisms. Choosing as representative, in the Hilbert space of the crossed product, the eﬀective vacuum state for each mode and conformal time, we evaluate the quantum variance and the induced scalar curvature ﬂuctuations. This leads to a model independent characterization of the inﬂationary parameters (1 − n s ) ∼ 0 . 0318 and ǫ ∼ 0 . 0027 in good agreement with Planck experimental results. In this formal frame inﬂation extends the algebra of observables in a primordial dS expanding Universe. In this extension time evolution becomes an inner automorphism and the conjugated energy ﬂuctuations manifest as the observable scalar curvature ﬂuctuations.
Supervised Attribute Information Removal and Reconstruction for Image Manipulation

The goal of attribute manipulation is to control specified attribute(s) in given images. Prior work approaches this problem by learning disentangled representations for each attribute that enables it to manipulate the encoded source attributes to the target attributes. However, encoded attributes are often correlated with relevant image content. Thus, the source attribute information can often be hidden in the disentangled features, leading to unwanted image editing effects. In this paper, we propose an Attribute Information Removal and Reconstruction (AIRR) network that prevents such information hiding by learning how to remove the attribute information entirely, creating attribute excluded features, and then learns to directly inject the desired attributes in a reconstructed image. We evaluate our approach on four diverse datasets with a variety of attributes including DeepFashion Synthesis, DeepFashion Fine-grained Attribute, CelebA and CelebA-HQ, where our model improves attribute manipulation accuracy and top-k retrieval rate by 10% on average over prior work. A user study also reports that AIRR manipulated images are preferred over prior work in up to 76% of cases.
Factorized and Controllable Neural Re-Rendering of Outdoor Scene for Photo Extrapolation

Expanding an existing tourist photo from a partially captured scene to a full scene is one of the desired experiences for photography applications. Although photo extrapolation has been well studied, it is much more challenging to extrapolate a photo (i.e., selfie) from a narrow field of view to a wider one while maintaining a similar visual style. In this paper, we propose a factorized neural re-rendering model to produce photorealistic novel views from cluttered outdoor Internet photo collections, which enables the applications including controllable scene re-rendering, photo extrapolation and even extrapolated 3D photo generation. Specifically, we first develop a novel factorized re-rendering pipeline to handle the ambiguity in the decomposition of geometry, appearance and illumination. We also propose a composited training strategy to tackle the unexpected occlusion in Internet images. Moreover, to enhance photo-realism when extrapolating tourist photographs, we propose a novel realism augmentation process to complement appearance details, which automatically propagates the texture details from a narrow captured photo to the extrapolated neural rendered image. The experiments and photo editing examples on outdoor scenes demonstrate the superior performance of our proposed method in both photo-realism and downstream applications. Code and the supplementary material are available on the project webpage: https://zju3dv.github. io/neural_outdoor_rerender/.
Detection of 2460 double-lined spectroscopic binary candidates in the LAMOST-MRS using projected rotational velocities and a binary spectral model

We present a new method for the detection of double-lined spectroscopic binaries (SB2s) using Vsin i values from spectral fits. The method is tested on synthetic and real spectra from the Large Sky Area Multi-Object fiber Spectroscopic Telescope Medium Resolution Survey. It can reliably detect SB2 candidates for double-lined binaries with $V \sin {i}_1+V \sin {i}_2\lt 300\, {\rm km}\, {\rm s}^{-1}$ if the radial velocity separation is large enough. Using this method, we detected 2460 SB2 candidates, 1410 of which are new discoveries. We confirmed the correlation between the radial velocity separation estimated by the binary model and Vsin i0 estimated by the single-star model using the selected sample. Additionally, our method found one new SB2 candidate in open cluster M11.
Self-Play PSRO: Toward Optimal Populations in Two-Player Zero-Sum Games

In competitive two-agent environments, deep reinforcement learning (RL) methods based on the \emph{Double Oracle (DO)} algorithm, such as \emph{Policy Space Response Oracles (PSRO)} and \emph{Anytime PSRO (APSRO)}, iteratively add RL best response policies to a population. Eventually, an optimal mixture of these population policies will approximate a Nash equilibrium. However, these methods might need to add all deterministic policies before converging. In this work, we introduce \emph{Self-Play PSRO (SP-PSRO)}, a method that adds an approximately optimal stochastic policy to the population in each iteration. Instead of adding only deterministic best responses to the opponent's least exploitable population mixture, SP-PSRO also learns an approximately optimal stochastic policy and adds it to the population as well. As a result, SP-PSRO empirically tends to converge much faster than APSRO and in many games converges in just a few iterations.
Fibrational Linguistics (FibLang): Language Acquisition

In this work we show how FibLang, a category-theoretic framework concerned with the interplay between language and meaning, can be used to describe vocabulary acquisition, that is the process with which a speaker acquires new vocabulary (through experience or interaction). We model two different kinds of vocabulary acquisition, which we call 'by example' and 'by paraphrasis'. The former captures the idea of acquiring the meaning of a word by being shown a witness representing that word, as in 'understanding what a cat is, by looking at a cat'. The latter captures the idea of acquiring meaning by listening to some other speaker rephrasing the word with others already known to the learner. We provide a category-theoretic model for vocabulary acquisition by paraphrasis based on the construction of free promonads. We draw parallels between our work and Wittgenstein's dynamical approach to language, commonly known as 'language games'.
The SOFIA FEEDBACK Legacy Survey Dynamics and Mass Ejection in the Bipolar H ii Region RCW 36

We present [C ii] 158 μm and [O i] 63 μm observations of the bipolar H ii region RCW 36 in the Vela C molecular cloud, obtained within the SOFIA legacy project FEEDBACK, which is complemented with APEX 12/13CO (3–2) and Chandra X-ray (0.5–7 keV) data. This shows that the molecular ring, forming the waist of the bipolar nebula, expands with a velocity of 1–1.9 km s−1. We also observe an increased line width in the ring, indicating that turbulence is driven by energy injection from the stellar feedback. The bipolar cavity hosts blueshifted expanding [C ii] shells at 5.2 ± 0.5 ± 0.5 km s−1 (statistical and systematic uncertainty), which indicates that expansion out of the dense gas happens nonuniformly and that the observed bipolar phase might be relatively short (∼0.2 Myr). The X-ray observations show diffuse emission that traces a hot plasma, created by stellar winds, in and around RCW 36. At least 50% of the stellar wind energy is missing in RCW 36. This is likely due to leakage that is clearing even larger cavities around the bipolar RCW 36 region. Lastly, the cavities host high-velocity wings in [C ii], which indicates relatively high mass ejection rates (∼5 × 10−4 M ⊙ yr−1). This could be driven by stellar winds and/or radiation but remains difficult to constrain. This local mass ejection, which can remove all mass within 1 pc of RCW 36 in 1–2 Myr, and the large-scale clearing of ambient gas in the Vela C cloud indicate that stellar feedback plays a significant role in suppressing the star formation efficiency.
A Coupling Approach to Analyzing Games with Dynamic Environments

The theory of learning in games has extensively studied situations where agents respond dynamically to each other by optimizing a fixed utility function. However, in real situations, the strategic environment varies as a result of past agent choices. Unfortunately, the analysis techniques that enabled a rich characterization of the emergent behavior in static environment games fail to cope with dynamic environment games. To address this, we develop a general framework using probabilistic couplings to extend the analysis of static environment games to dynamic ones. Using this approach, we obtain sufficient conditions under which traditional characterizations of Nash equilibria with best response dynamics and stochastic stability with log-linear learning can be extended to dynamic environment games. As a case study, we pose a model of cyber threat intelligence sharing between firms and a simple dynamic game-theoretic model of social precautions in an epidemic, both of which feature dynamic environments. For both examples, we obtain conditions under which the emergent behavior is characterized in the dynamic game by performing the traditional analysis on a reference static environment game.
Efficient Spike Encoding Algorithms for Neuromorphic Speech Recognition

Spiking Neural Networks are known to be very effective for neuromorphic processor implementations, achieving orders of magnitude improvements in energy efficiency and computational latency over traditional deep learning approaches. Comparable algorithmic performance was recently made possible as well with the adaptation of supervised training algorithms to the context of spiking neural networks. However, information including audio, video, and other sensor-derived data are typically encoded as real-valued signals that are not well-suited to spiking neural networks, preventing the network from leveraging spike timing information. Efficient encoding from real-valued signals to spikes is therefore critical and significantly impacts the performance of the overall system. To efficiently encode signals into spikes, both the preservation of information relevant to the task at hand as well as the density of the encoded spikes must be considered. In this paper, we study four spike encoding methods in the context of a speaker independent digit classification system: Send on Delta, Time to First Spike, Leaky Integrate and Fire Neuron and Bens Spiker Algorithm. We first show that all encoding methods yield higher classification accuracy using significantly fewer spikes when encoding a bio-inspired cochleagram as opposed to a traditional short-time Fourier transform. We then show that two Send On Delta variants result in classification results comparable with a state of the art deep convolutional neural network baseline, while simultaneously reducing the encoded bit rate. Finally, we show that several encoding methods result in improved performance over the conventional deep learning baseline in certain cases, further demonstrating the power of spike encoding algorithms in the encoding of real-valued signals and that neuromorphic implementation has the potential to outperform state of the art techniques.
QML for Argoverse 2 Motion Forecasting Challenge

To safely navigate in various complex traffic scenarios, autonomous driving systems are generally equipped with a motion forecasting module to provide vital information for the downstream planning module. For the real-world onboard applications, both accuracy and latency of a motion forecasting model are essential. In this report, we present an effective and efficient solution, which ranks the 3rd place in the Argoverse 2 Motion Forecasting Challenge 2022.
A rapidly mixing Markov chain from any gapped quantum many-body system

We consider the computational task of sampling a bit string x from a distribution π(x)=|⟨x|ψ⟩|2, where ψ is the unique ground state of a local Hamiltonian H. Our main result describes a direct link between the inverse spectral gap of H and the mixing time of an associated continuous-time Markov Chain with steady state π. The Markov Chain can be implemented efficiently whenever ratios of ground state amplitudes ⟨y|ψ⟩/⟨x|ψ⟩ are efficiently computable, the spectral gap of H is at least inverse polynomial in the system size, and the starting state of the chain satisfies a mild technical condition that can be efficiently checked. This extends a previously known relationship between sign-problem free Hamiltonians and Markov chains. The tool which enables this generalization is the so-called fixed-node Hamiltonian construction, previously used in Quantum Monte Carlo simulations to address the fermionic sign problem. We implement the proposed sampling algorithm numerically and use it to sample from the ground state of Haldane-Shastry Hamiltonian with up to 56 qubits. We observe empirically that our Markov chain based on the fixed-node Hamiltonian mixes more rapidly than the standard Metropolis-Hastings Markov chain.
Collaborative Machine Learning-Driven Internet of Medical Things - A Systematic Literature Review

The growing adoption of IoT devices for healthcare has enabled researchers to build intelligence using all the data produced by these devices. Monitoring and diagnosing health have been the two most common scenarios where such devices have proven beneficial. Achieving high prediction accuracy was a top priority initially, but the focus has slowly shifted to efficiency and higher throughput, and processing the data from these devices in a distributed manner has proven to help achieve both. Since the field of machine learning is vast with numerous state-of-the-art algorithms in play, it has been a challenge to identify the algorithms that perform best in different scenarios. In this literature review, we explored the distributed machine learning algorithms tested by the authors of the selected studies and identified the ones that achieved the best prediction accuracy in each healthcare scenario. While no algorithm performed consistently, Random Forest performed the best in a few studies. This could serve as a good starting point for future studies on collaborative machine learning on IoMT data.
Every Preference Changes Differently: Neural Multi-Interest Preference Model with Temporal Dynamics for Recommendation

User embeddings (vectorized representations of a user) are essential in recommendation systems. Numerous approaches have been proposed to construct a representation for the user in order to find similar items for retrieval tasks, and they have been proven effective in industrial recommendation systems as well. Recently people have discovered the power of using multiple embeddings to represent a user, with the hope that each embedding represents the user’s interest in a certain topic. With multi-interest representation, it’s important to model the user’s preference over the different topics and how the preference change with time. However, existing approaches either fail to estimate the user’s affinity to each interest or unreasonably assume every interest of every user fades with an equal rate with time, thus hurting the recall of candidate retrieval. In this paper, we propose the Multi-Interest Preference (MIP) model, an approach that not only produces multi-interest for users by using the user’s sequential engagement more effectively but also automatically learns a set of weights to represent the preference over each embedding so that the candidates can be retrieved from each interest proportionally. Extensive experiments have been done on various industrial-scale datasets to demonstrate the effectiveness of our approach. 1
Experimental emulation for OTFS waveform RF-impairments

Orthogonal time-frequency space (OTFS) waveform exceeds the challenges that face orthogonal frequency division multiplex- ing (OFDM) in the high-mobility environment with high time-frequency dispersive channels. Since practical pulse shaping design and RF-impairments effects have a direct impact on waveform behavior, this paper investigates experimental implementation for practical pulse shaping design and RF-impairments that affect OTFS waveform performance and compares them to OFDM waveform as a benchmark. Firstly, the doubly-dispersive channel effect is analyzed, then an experimental framework is established for investigating the RF-impairments include non-linearity, Carrier frequency offset, I/Q-imbalances, DC-offset, and phase noise are considered. The experiments were conducted in a real indoor wireless environment using software-deﬁned radio (SDR)based on the Keysight EXG X-Serie devices. The experimental results validate the accuracy of the theoretical results.
Using Fuzzy Matching of Queries to optimize Database workloads

Directed Acyclic Graphs (DAGs) are commonly used in Databases and Big Data computational engines like Apache Spark for representing the execution plan of queries. We refer to such graphs as Query Directed Acyclic Graphs (QDAGs). This paper uses similarity hashing to arrive at a fingerprint such that the fingerprint embodies the compute requirements of the query for QDAGs. The fingerprint, thus obtained, can be used to predict the runtime behaviour of a query based on queries executed in the past having similar QDAGs. We discuss two approaches to arrive at a fingerprint, their pros and cons and how aspects of both approaches can be combined to improve the predictions. Using a hybrid approach, we demonstrate that we are able to predict runtime behaviour of a QDAG with more than 80% accuracy.
Relighting4D: Neural Relightable Human from Videos

Human relighting is a highly desirable yet challenging task. Existing works either require expensive one-light-at-a-time (OLAT) captured data using light stage or cannot freely change the viewpoints of the rendered body. In this work, we propose a principled framework, Relighting4D , that enables free-viewpoints relighting from only human videos under unknown illuminations. Our key insight is that the space-time varying geometry and reflectance of the human body can be decomposed as a set of neural fields of normal, occlusion, diffuse, and specular maps. These neural fields are further integrated into reflectance-aware physically based rendering, where each vertex in the neural field absorbs and reflects the light from the environment. The whole framework can be learned from videos in a self-supervised manner, with physically informed priors designed for regularization. Extensive experiments on both real and synthetic datasets demonstrate that our framework is capable of relighting dynamic human actors with free-viewpoints. Codes are available at https://github.com/FrozenBurning/Relighting4D .
Analysis of Catastrophic Forgetting for Random Orthogonal Transformation Tasks in the Overparameterized Regime

Overparameterization is known to permit strong generalization performance in neural networks. In this work, we provide an initial theoretical analysis of its effect on catastrophic forgetting in a continual learning setup. We show experimentally that in permuted MNIST image classification tasks, the generalization performance of multilayer perceptrons trained by vanilla stochastic gradient descent can be improved by overparameterization, and the extent of the performance increase achieved by overparameterization is comparable to that of state-of-the-art continual learning algorithms. We provide a theoretical explanation of this effect by studying a qualitatively similar two-task linear regression problem, where each task is related by a random orthogonal transformation. We show that when a model is trained on the two tasks in sequence without any additional regularization, the risk gain on the first task is small if the model is sufficiently overparameterized.