Miracle-dz/UniFAR-FTUs huggingface dataset:
Two-block vs. Multi-block ADMM: An empirical evaluation of convergence

Alternating Direction Method of Multipliers (ADMM) has become a widely used optimization method for convex problems, particularly in the context of data mining in which large optimization problems are often encountered. ADMM has several desirable properties, including the ability to decompose large problems into smaller tractable sub-problems and ease of parallelization, that are essential in these scenarios. The most common form of ADMM is the two-block, in which two sets of primal variables are updated alternatingly. Recent years have seen advances in multi-block ADMM, which update more than two blocks of primal variables sequentially. In this paper, we study the empirical question: {\em Is two-block ADMM always comparable with sequential multi-block ADMM solving an equivalent problem? } In the context of optimization problems arising in multi-task learning, through a comprehensive set of experiments we surprisingly show that multi-block ADMM consistently outperformed two-block ADMM on optimization performance, and as a consequence on prediction performance, across all datasets and for the entire range of dual step sizes. Our results have an important practical implication: rather than simply using the popular two-block ADMM, one may considerably benefit from experimenting with multi-block ADMM applied to an equivalent problem.
Hardware/Software Co-Exploration of Neural Architectures

We propose a novel hardware and software co-exploration framework for efficient neural architecture search (NAS). Different from existing hardware-aware NAS which assumes a fixed hardware design and explores the NAS space only, our framework simultaneously explores both the architecture search space and the hardware design space to identify the best neural architecture and hardware pairs that maximize both test accuracy and hardware efficiency. Such a practice greatly opens up the design freedom and pushes forward the Pareto frontier between hardware efficiency and test accuracy for better design tradeoffs. The framework iteratively performs a two-level (fast and slow) exploration. Without lengthy training, the fast exploration can effectively fine-tune hyperparameters and prune inferior architectures in terms of hardware specifications, which significantly accelerates the NAS process. Then, the slow exploration trains candidates on a validation set and updates a controller using the reinforcement learning to maximize the expected accuracy together with the hardware efficiency. In this article, we demonstrate that the co-exploration framework can effectively expand the search space to incorporate models with high accuracy, and we theoretically show that the proposed two-level optimization can efficiently prune inferior solutions to better explore the search space. The experimental results on ImageNet show that the co-exploration NAS can find solutions with the same accuracy, 35.24% higher throughput, 54.05% higher energy efficiency, compared with the hardware-aware NAS.
Learning to Reason with Relational Video Representation for Question Answering

How does machine learn to reason about the content of a video in answering a question? A Video QA system must simultaneously understand language, represent visual content over space-time, and iteratively transform these representations in response to lingual content in the query, and finally arriving at a sensible answer. While recent advances in textual and visual question answering have come up with sophisticated visual representation and neural reasoning mechanisms, major challenges in Video QA remain on dynamic grounding of concepts, relations and actions to support the reasoning process. We present a new end-to-end layered architecture for Video QA, which is composed of a question-guided video representation layer and a generic reasoning layer to produce answer. The video is represented using a hierarchical model that encodes visual information about objects, actions and relations in space-time given the textual cues from the question. The encoded representation is then passed to a reasoning module, which in this paper, is implemented as a MAC net. The system is evaluated on the SVQA (synthetic) and TGIF-QA datasets (real), demonstrating state-of-the-art results, with a large margin in the case of multi-step reasoning.
Avocado: Photometric Classification of Astronomical Transients with Gaussian Process Augmentation

Upcoming astronomical surveys such as the Large Synoptic Survey Telescope (LSST) will rely on photometric classification to identify the majority of the transients and variables that they discover. We present a set of techniques for photometric classification that can be applied even when the training set of spectroscopically confirmed objects is heavily biased toward bright, low-redshift objects. Using Gaussian process regression to model arbitrary light curves in all bands simultaneously, we “augment” the training set by generating new versions of the original light curves covering a range of redshifts and observing conditions. We train a boosted decision tree classifier on features extracted from the augmented light curves, and we show how such a classifier can be designed to produce classifications that are independent of the redshift distributions of objects in the training sample. Our classification algorithm was the best-performing among the 1094 models considered in the blinded phase of the Photometric LSST Astronomical Time-Series Classification Challenge, scoring 0.468 on the organizers’ logarithmic-loss metric with flat weights for all object classes in the training set, and achieving an AUC of 0.957 for classification of SNe Ia. Our results suggest that spectroscopic campaigns used for training photometric classifiers should focus on typing large numbers of well-observed, intermediate-redshift transients, instead of attempting to type a sample of transients that is directly representative of the full data set being classified. All of the algorithms described in this paper are implemented in the avocado software package (https://www.github.com/kboone/avocado).
Temperature dependence of magnetic resonance in ferrimagnetic GdFeCo alloys

We provide a macroscopic theory and experimental results for magnetic resonances of antiferromagnetically-coupled ferrimagnets. Our theory, which interpolates the dynamics of antiferromagnets and ferromagnets smoothly, can describe ferrimagnetic resonances across the angular momentum compensation point. We also present experimental results for spin-torque induced ferrimagnetic resonance at several temperatures. The spectral analysis based on our theory reveals that the Gilbert damping parameter, which has been considered to be strongly temperature dependent, is insensitive to temperature. We envision that our work will facilitate further investigation of ferrimagnetic dynamics by providing a theoretical framework suitable for a broad range of temperatures.
Smoothed Analysis of Order Types

Consider an ordered point set $P = (p_1,\ldots,p_n)$, its order type (denoted by $\chi_P$) is a map which assigns to every triple of points a value in $\{+,-,0\}$ based on whether the points are collinear(0), oriented clockwise(-) or counter-clockwise(+). An abstract order type is a map $\chi : \left[\substack{n\\3}\right] \rightarrow \{+,-,0\}$ (where $\left[\substack{n\\3}\right]$ is the collection of all triples of a set of $n$ elements) that satisfies the following condition: for every set of five elements $S\subset [n]$ its induced order type $\chi_{|S}$ is realizable by a point set. To be precise, a point set $P$ realizes an order type $\chi$,if $\chi_P(p_i,p_j,p_k) = \chi(i,j,k)$, for all $i<j<k$. Planar point sets are among the most basic and natural geometric objects of study in Discrete and Computational Geometry. Properties of point sets are relevant in theory and practice alike. It is known, that deciding if an abstract order type is realizable is complete for the existential theory of the reals. Our results show that order type realizability is much easier for realistic instances than in the worst case. In particular, we can recognize instances in "expected \NP-time". This is one of the first $\exists\mathbb{R}$-complete problems analyzed under the lens of Smoothed Analysis.
Reliability Optimization for Complicated Systems with a Choice of Redundancy Strategies (TECHNICAL NOTE)

Redundancy allocation is one of the common techniques to increase the reliability of the bridge systems. Many studies on the general redundancy allocation problems assume that the redundancy strategy for each subsystem is predetermined and fixed. In general, active redundancy has received more attention in the past. However, in real world, a particular system design contains both active and cold-standby redundancies, and the choice of the redundancy strategy becomes an additional decision variable. So, the problem is to select redundancy level for each subsystem, component and the best redundancy strategy in order to maximize the system reliability under system-level constraints. This paper presents a new mathematical model for redundancy allocation problem (RAP) for the bridge systems when the redundancy strategy can be selected for individual subsystems. The problem is classified as an NP-hard problem. In this paper, a special version of genetic algorithm (GA) is applied, which has been modified for constrained integer nonlinear problems. Finally, computational results for a typical scenario are presented.
Dunhuang Grottoes Painting Dataset and Benchmark

This document introduces the background and the usage of the Dunhuang Grottoes Dataset and the benchmark. The documentation first starts with the background of the Dunhuang Grotto, which is widely recognised as an priceless heritage. Given that digital method is the modern trend for heritage protection and restoration. Follow the trend, we release the first public dataset for Dunhuang Grotto Painting restoration. The rest of the documentation details the painting data generation. To enable a data driven fashion, this dataset provided a large number of training and testing example which is sufficient for a deep learning approach. The detailed usage of the dataset as well as the benchmark is described.
Let's measure run time! Extending the IR replicability infrastructure to include performance aspects

Establishing a docker-based replicability infrastructure offers the community a great opportunity: measuring the run time of information retrieval systems. The time required to present query results to a user is paramount to the users satisfaction. Recent advances in neural IR re-ranking models put the issue of query latency at the forefront. They bring a complex trade-off between performance and effectiveness based on a myriad of factors: the choice of encoding model, network architecture, hardware acceleration and many others. The best performing models (currently using the BERT transformer model) run orders of magnitude more slowly than simpler architectures. We aim to broaden the focus of the neural IR community to include performance considerations -- to sustain the practical applicability of our innovations. In this position paper we supply our argument with a case study exploring the performance of different neural re-ranking models. Finally, we propose to extend the OSIRRC docker-based replicability infrastructure with two performance focused benchmark scenarios.
Faster provable sieving algorithms for the Shortest Vector Problem and the Closest Vector Problem on lattices in 𝓁p norm

In this paper we give provable sieving algorithms for the Shortest Vector Problem (SVP) and the Closest Vector Problem (CVP) on lattices in $\ell_p$ norm for $1\leq p\leq\infty$. The running time we get is better than existing provable sieving algorithms, except the Discrete Gaussian based algorithm by Aggarwal et al.[2015], but this algorithm works only for the Euclidean norm. We build on the randomized sieving framework of Ajtai, Kumar and Sivakumar [2001,2002], where they used a sieving sub-routine that runs in time quadratic in the number of sampled vectors. We give a new sieving procedure that works for all $\ell_p$ norm and runs in time linear in the number of sampled vectors. The main idea is to divide the space (hypersphere) into regions (hypercubes) such that each vector can be mapped efficiently to a region. This is an extension of the sieving technique in Aggarwal and Mukhopadhyay[2018], where it has been used only for the $\ell_{\infty}$ norm. This improves the time complexity of the overall algorithm as compared to previous analyses of AKS algorithm in $\ell_p$ norm [Blomer and Naewe(2009),Arvind and Joglekar(2008)]. In the $\ell_2$ norm, using the birthday paradox we achieve a time complexity of $2^{2.49n+o(n)}$, as compared to the time complexity of $2^{2.571n+o(n)}$ of AKS (using birthday paradox) [Hanrot et al.(2011)]. We further modify our linear sieving technique and introduce a mixed sieving procedure. At first a point is mapped to a hypercube within a sphere (as done in linear sieve) and then within each hypercube we perform a quadratic sieve like AKS. This helps us achieve a time complexity of $2^{2.25n+o(n)}$ in $\ell_2$ norm, which is better than the time complexity of List Sieve Birthday ($2^{2.465n+o(n)}$) [Pujol and Stehle(2009),Hanrot et al.(2011)]. We also adopt our techniques for approximation algorithms of the above problems.
Efficient quasiparticle traps with low dissipation through gap engineering

Quasiparticles represent an intrinsic source of perturbation for superconducting qubits, leading to both dissipation of the qubit energy and dephasing. Recently, it has been shown that normal-metal traps may efficiently reduce the quasiparticle population and improve the qubit lifetime, provided the trap surpasses a certain characteristic size. Moreover, while the trap itself introduces new relaxation mechanisms, they are not expected to harm state-of-the-art transmon qubits under the condition that the traps are not placed too close to extremal positions where electric fields are high. Here, we study a different type of trap, realized through gap engineering. We find that gap-engineered traps relax the remaining constraints imposed on normal metal traps. Firstly, the characteristic trap size, above which the trap is efficient, is reduced with respect to normal metal traps, such that here, strong traps are possible in smaller devices. Secondly, the losses caused by the trap are now greatly reduced, providing more flexibility in trap placement. The latter point is of particular importance, since for efficient protection from quasiparticles, the traps ideally should be placed close to the active parts of the qubit device, where electric fields are typically high.
Conjectures about the structure of strong- and weak-coupling expansions of a few ground-state observables in the Lieb-Liniger and Yang-Gaudin models

In this paper, we apply experimental number theory to two integrable quantum models in one dimension, the Lieb-Liniger Bose gas and the Yang-Gaudin Fermi gas with contact interactions. We identify patterns in weak- and strong-coupling series expansions of the ground-state energy, local correlation functions and pressure. Based on the most accurate data available in the literature, we make a few conjectures about their mathematical structure and extrapolate to higher orders
Multivariate Variational Mode Decomposition

We present a generic extension of variational mode decomposition (VMD) algorithm to multivariate or multichannel data. The proposed method utilizes a model for multivariate modulated oscillations that is based on the presence of a joint or common frequency component among all channels of input data. We then formulate a variational optimization problem that aims to extract an ensemble of band-limited modes containing inherent multivariate modulated oscillations present in the data. The cost function to be minimized is the sum of bandwidths of all signal modes across all input data channels, which is a generic extension of the cost function used in standard VMD to multivariate data. Minimization of the resulting variational model is achieved through the alternating direction method of multipliers (ADMM) that yields an optimal set of multivariate modes in terms of narrow bandwidth and corresponding center frequencies. The proposed extension is elegant as it does not require any extra user-defined parameters for its operation i.e., it uses the same parameters as standard VMD. We demonstrate the effectiveness of the proposed method through results obtained from extensive simulations involving test (synthetic) and real world multivariate data sets. Specifically, we highlight the utility of the proposed method in two real world applications which include the separation of alpha rhythms in multivariate electroencephalogram (EEG) data and the decomposition of bivariate cardiotocographic signals that consist of fetal heart rate and maternal uterine contraction (FHR-UC) as its two channels.
Decaying turbulence and magnetic fields in galaxy clusters

We explore the decay of turbulence and magnetic fields generated by fluctuation dynamo action in the context of galaxy clusters where such a decaying phase can occur in the aftermath of a major merger event. Using idealized numerical simulations that start from a kinetically dominated regime we focus on the decay of the steady state rms velocity and the magnetic field for a wide range of conditions that include varying the compressibility of the flow, the forcing wavenumber, and the magnetic Prandtl number. Irrespective of the compressibility of the flow, both the rms velocity and the rms magnetic field decay as a power law in time. In the subsonic case we find that the exponent of the power law is consistent with the −3/5 scaling reported in previous studies. However, in the transonic regime both the rms velocity and the magnetic field initially undergo rapid decay with an ≈t−1.1 scaling with time. This is followed by a phase of slow decay where the decay of the rms velocity exhibits an ≈−3/5 scaling in time, while the rms magnetic field scales as ≈−5/7. Furthermore, analysis of the Faraday rotation measure (RM) reveals that the Faraday RM also decays as a power law in time ≈t−5/7; steeper than the ∼t−2/5 scaling obtained in previous simulations of magnetic field decay in subsonic turbulence. Apart from galaxy clusters, our work can have potential implications in the study of magnetic fields in elliptical galaxies.
User Preference Prediction in Visual Data on Mobile Devices

In this paper we consider the user modeling given the photos and videos from the gallery on a mobile device. We propose the novel user preference prediction engine based on scene understanding, object detection and face recognition. At first, all faces in a gallery are clustered and all private photos and videos with faces from large clusters are processed on the embedded system in offline mode. Other photos are sent to the remote server to be analyzed by very deep models. The visual features of each photo are aggregated into a single user descriptor using the neural attention block. The proposed pipeline is implemented for the Android mobile platform. Experimental results with a subset of Amazon Home and Kitchen, Places2 and Open Images datasets demonstrate the possibility to process images very efficiently without accuracy degradation.
Characterizations of nested GVZ-groups by central series

Many properties of groups can be defined by the existence of a particular normal series. The classic examples being solvability, supersolvability and nilpotence. Among the nilpotent groups are the so-called nested GVZ-groups --- groups where the centers of the irreducible characters form a chain, and where every irreducible character vanishes off of its center. In this paper, we show that nested GVZ-groups can be characterized by the existence of a certain ascending central series, or by the existence of a certain descending central series.
Detecting parity effect in a superconducting device in the presence of parity switches

We show that a superconducting device can show a clear parity effect in the number of electrons even when there is, on average, a single nonequilibrium quasiparticle present, by monitoring in real time the charge state of a superconducting island connected to normal leads by tunnel junctions. The quasiparticles are created by Cooper pairs breaking on the island at a rate of a few kHz. We demonstrate that the pair breaking is caused by the backaction of the single-electron transistor used as a charge detector. With sufficiently low probing currents, our superconducting island is free of quasiparticles 97% of the time.
Modeling the relationship between regional activation and functional connectivity during wakefulness and sleep.

Global brain activity self-organizes into discrete patterns characterized by distinct behavioral observables and modes of information processing. The human thalamocortical system is a densely connected network where local neural activation reciprocally influences coordinated collective dynamics. We introduce a semi-empirical model to investigate the relationship between regional activation and long-range functional connectivity in the different brain states visited during the natural wake-sleep cycle. Our model combines functional magnetic resonance imaging (fMRI) data, in vivo estimates of structural connectivity, and anatomically-informed priors that constrain the independent variation of regional activation. As expected, priors based on functionally coherent networks resulted in the best fit between empirical and simulated brain activity. We show that sleep progressively divided the cortex into regions presenting opposite dynamical behavior: frontoparietal regions approached a bifurcation towards local oscillatory dynamics, while sensorimotor regions presented stable dynamics governed by noise. In agreement with human electrophysiological experiments, sleep onset induced subcortical deactivation and uncoupling, which was subsequently reversed for deeper stages. Finally, we introduced external forcing of variable intensity to simulate external perturbations, and identifiedthe key regionsespecially relevant for the recovery of wakefulness from deep sleep. Our model represents sleep as a state where long-range decoupling and regional deactivation coexist with the latent capacity for a rapid transition towards wakefulness. The mechanistic insights provided by our simulations allow the in silico parametric exploration of such transitions in terms of external perturbations, with potential applications for the control of physiological and pathological brain states.
The Eﬀect of Women’s Education on Polygyny: Schools, Matching and the Marriage Market in Cameroon

— Polygyny is prevalent in a number of African countries today, but it has been decreasing steadily in the last decades. Women’s education seems like a good candidate to explain this decline. We take advantage of the expansion of primary schooling in Cameroon after World War II to estimate the causal eﬀect of education on a variety of marriage market outcomes. We ﬁnd that the eﬀect of education on the probability for a woman to be in a polygynous union strongly diﬀers by type of school: while one additional year of private, Christian education decreases this probability by 30 percentage points, one additional year of public education increases it by 7 percentage points. We then estimate the parameters of a matching model of the marriage market and ﬁnd that the positive eﬀect of public education on polygyny is explained by assortative mating on education and the fact that more educated men are more likely to be polygynous.