Miracle-dz/UniFAR-FTUs huggingface dataset:
SplitBrain: Hybrid Data and Model Parallel Deep Learning

The recent success of deep learning applications has coincided with those widely available powerful computational resources for training sophisticated machine learning models with huge datasets. Nonetheless, training large models such as convolutional neural networks using model parallelism (as opposed to data parallelism) is challenging because the complex nature of communication between model shards makes it difficult to partition the computation efficiently across multiple machines with an acceptable trade-off. This paper presents SplitBrain, a high performance distributed deep learning framework supporting hybrid data and model parallelism. Specifically, SplitBrain provides layer-specific partitioning that co-locates compute intensive convolutional layers while sharding memory demanding layers. A novel scalable group communication is proposed to further improve the training throughput with reduced communication overhead. The results show that SplitBrain can achieve nearly linear speedup while saving up to 67\% of memory consumption for data and model parallel VGG over CIFAR-10.
Biologically-plausible six-legged running : control and simulation

This thesis presents a controller which produces a stable, dynamic 1.4 meter per second run in a simulated twelve degree of freedom six-legged robot. The algorithm is relatively simple; it consists of only a few hand-tuned feedback loops and is defined by a total of 13 parameters. The control utilizes no vestibular-type inputs to actively control orientation. Evidence from perturbation, robustness, motion analysis, and parameter sensitivity tests indicate a high degree of stability in the simulated gait. The control approach generates a run with an aerial phase, utilizes force information to signal aerial phase leg retraction, has a forward running velocity determined by a single parameter, and couples stance and swing legs using angular momentum information. Both the hypotheses behind the control and the resulting gait are argued to be plausible models of biological locomotion. Thesis Supervisor: Hugh M. Herr Title: Instructor, MIT/Harvard Division of Health, Science, and Technology
Bounds on Area Involving Lattice Size

The lattice size of a lattice polygon $P$ is a combinatorial invariant of $P$ that was recently introduced in relation to the problem of bounding the total degree and the bi-degree of the defining equation of an algebraic curve. In this paper, we establish sharp lower bounds on the area of plane convex bodies $P\subset\mathbb{R}^2$ that involve the lattice size of $P$. In particular, we improve bounds given by Arnold, and Bárány and Pach. We also provide a classification of minimal lattice polygons $P\subset\mathbb{R}^2$ of fixed lattice size ${\operatorname{ls_\square}}(P)$.
An empirical user-study of text-based nonverbal annotation systems for human-human conversations

the substantial increase in the number of online human-human conversations and the usefulness of multimodal transcripts, there is a rising need for automated multimodal transcription systems to help us better understand the conversations. In this paper, we evaluated three methods to perform multimodal transcription. They were (1) Jefferson -- an existing manual system used widely by the linguistics community, (2) MONAH -- a system that aimed to make multimodal transcripts accessible and automated, (3) MONAH+ -- a system that builds on MONAH that visualizes machine attention. Based on 104 participants responses, we found that (1) all text-based methods significantly reduced the amount of information for the human users, (2) MONAH was found to be more usable than Jefferson, (3) Jefferson's relative strength was in chronemics (pace / delay) and paralinguistics (pitch / volume) annotations, whilst MONAH's relative strength was in kinesics (body language) annotations, (4) enlarging words' font-size based on machine attention was confusing human users as loudness. These results pose considerations for researchers designing a multimodal annotation system for the masses who would like a fully-automated or human-augmented conversational analysis system.
Normalizing flows for the real-time sign problem

We discuss the application of normalizing flows to bosonic lattice field theories with real-time sign problems. A normalizing flow, once it is found for such a lattice field theory, is guaranteed to solve its sign problem. We argue for the existence of normalizing flows for bosonic lattice field theories in the Schwinger-Keldish formalism in a few ways. We then discuss how this existence is a specific feature of bosonic theories: such arguments break down for fermionic systems, whether at finite density or in real-time.
K-core decomposition on super large graphs with limited resources

K-core decomposition is a commonly used metric to analyze graph structure or study the relative importance of nodes in complex graphs. Recent years have seen rapid growth in the scale of the graph, especially in industrial settings. For example, our industrial partner runs popular social applications with billions of users and is able to gather a rich set of user data. As a result, applying K-core decomposition on large graphs has attracted more and more attention from academics and the industry. A simple but effective method to deal with large graphs is to train them in the distributed settings, and some distributed K-core decomposition algorithms are also proposed. Despite their effectiveness, we experimentally and theoretically observe that these algorithms consume too many resources and become unstable on super-large-scale graphs, especially when the given resources are limited. In this paper, we deal with those super-large-scale graphs and propose a divide-and-conquer strategy on top of the distributed K-core decomposition algorithm. We evaluate our approach on three large graphs. The experimental results show that the consumption of resources can be significantly reduced, and the calculation on large-scale graphs becomes more stable than the existing methods. For example, the distributed K-core decomposition algorithm can scale to a large graph with 136 billion edges without losing correctness with our divide-and-conquer technique.
Variable selection, monotone likelihood ratio and group sparsity

In the pivotal variable selection problem, we derive the exact non-asymptotic minimax selector over the class of all $s$-sparse vectors, which is also the Bayes selector with respect to the uniform prior. While this optimal selector is, in general, not realizable in polynomial time, we show that its tractable counterpart (the scan selector) attains the minimax expected Hamming risk to within factor 2, and is also exact minimax with respect to the probability of wrong recovery. As a consequence, we establish explicit lower bounds under the monotone likelihood ratio property and we obtain a tight characterization of the minimax risk in terms of the best separable selector risk. We apply these general results to derive necessary and sufficient conditions of exact and almost full recovery in the location model with light tail distributions and in the problem of group variable selection under Gaussian noise.
The influence of individual stars on the long-term dynamics of comets C/2014 UN271 and C/2017 K2

In June 2021 the discovery of an unusual comet C/2014 UN271 Bernardinelli-Bernstein has been announced. Its cometary activity beyond Uranus orbit also has refreshed interest in similar objects, including C/2017K2 PanSTARRS. Another peculiarity of these objects is the long interval of positional data, taken at large heliocentric distances. These two comets are suitable candidates for a detailed investigation of their long-term motion outside the planetary zone. Using the selected orbital solutions, we aim at estimating the orbital parameters of their orbits at the previous perihelion passage. This might allow us to discriminate between dynamically old and new comets. To follow the dynamical evolution of long-period comets far outside the planetary zone, it is necessary to take into account both the perturbation caused by the overall Galactic gravitational potential and the actions of individual stars appearing in the solar neighborhood. To this aim, we applied the recently published methods based on stellar perturbers ephemerides. For C/2014 UN271 we obtained a precise orbital solution that can be propagated to the past and to the future. For C/2017 K2 we have to limit ourselves to study only the past motion since some signs of nongravitational effects can be found in recent positional observations. Therefore, we use a specially selected orbital solution suitable for past motion studies. Using these starting orbits, we propagated both comets to their previous perihelia. We also investigated the future motion of C/2014 UN271. Orbital evolution of these two comets appears to be sensitive to perturbations from several stars that closely approach the Sun. Unfortunately, some of these stars have 6D data with uncertainties too large to obtain definitive results for the studied comets; however, it appears that both comets were probably outside the planetary zone in the previous perihelion.
Learning Human Activity Patterns Using Clustered Point Processes With Active and Inactive States

Modeling event patterns is a central task in a wide range of disciplines. In applications such as studying human activity patterns, events often arrive clustered with sporadic and long periods of inactivity. Such heterogeneity in event patterns poses challenges for existing point process models. In this article, we propose a new class of clustered point processes that alternate between active and inactive states. The proposed model is flexible, highly interpretable, and can provide useful insights into event patterns. A composite likelihood approach and a composite EM estimation procedure are developed for efficient and numerically stable parameter estimation. We study both the computational and statistical properties of the estimator including convergence, consistency, and asymptotic normality. The proposed method is applied to Donald Trump’s Twitter data to investigate if and how his behaviors evolved before, during, and after the presidential campaign. Additionally, we analyze large-scale social media data from Sina Weibo and identify interesting groups of users with distinct behaviors.
Contrastive Learning of Semantic and Visual Representations for Text Tracking

Semantic representation is of great benefit to the video text tracking(VTT) task that requires simultaneously classifying, detecting, and tracking texts in the video. Most existing approaches tackle this task by appearance similarity in continuous frames, while ignoring the abundant semantic features. In this paper, we explore to robustly track video text with contrastive learning of semantic and visual representations. Correspondingly, we present an end-to-end video text tracker with Semantic and Visual Representations(SVRep), which detects and tracks texts by exploiting the visual and semantic relationships between different texts in a video sequence. Besides, with a light-weight architecture, SVRep achieves state-of-the-art performance while maintaining competitive inference speed. Specifically, with a backbone of ResNet-18, SVRep achieves an ${\rm ID_{F1}}$ of $\textbf{65.9\%}$, running at $\textbf{16.7}$ FPS, on the ICDAR2015(video) dataset with $\textbf{8.6\%}$ improvement than the previous state-of-the-art methods.
A Novel Generator with Auxiliary Branch for Improving GAN Performance

The generator in the generative adversarial network (GAN) learns image generation in a coarse-to-fine manner in which earlier layers learn the overall structure of the image and the latter ones refine the details. To propagate the coarse information well, recent works usually build their generators by stacking up multiple residual blocks. Although the residual block can produce a high-quality image as well as be trained stably, it often impedes the information flow in the network. To alleviate this problem, this brief introduces a novel generator architecture that produces the image by combining features obtained through two different branches: the main and auxiliary branches. The goal of the main branch is to produce the image by passing through the multiple residual blocks, whereas the auxiliary branch is to convey the coarse information in the earlier layer to the later one. To combine the features in the main and auxiliary branches successfully, we also propose a gated feature fusion module (GFFM) that controls the information flow in those branches. To prove the superiority of the proposed method, this brief provides extensive experiments using various standard datasets including CIFAR-10, CIFAR-100, LSUN, CelebA-HQ, AFHQ, and tiny-ImageNet. Furthermore, we conducted various ablation studies to demonstrate the generalization ability of the proposed method. Quantitative evaluations prove that the proposed method exhibits impressive GAN performance in terms of Inception score (IS) and Frechet inception distance (FID). For instance, the proposed method boosts the FID and IS scores on the tiny-ImageNet dataset from 35.13 to 25.00 and 20.23 to 25.57, respectively.
Efficient Single-image Super-resolution Using Dual path Connections with Multiple scale Learning

Deep convolutional neural networks have been demonstrated to be effective for single-image super-resolution in recent years. On the one hand, residual connections and dense connections have been used widely to ease forward information and backward gradient flows to boost performance. However, current methods use residual connections and dense connections separately in most network layers in a sub-optimal way. On the other hand, although various networks and methods have been designed to improve computation efficiency, save parameters, or utilize training data of multiple scale factors for each other to boost performance, they either do super-resolution in high-resolution space to have a high computation cost or cannot share parameters between models of different scale factors to save parameters and inference time. To tackle these challenges, we propose an efficient single-image super-resolution network using dual path connections with multiple scale learning (EMSRDPN). By introducing dual path connections inspired by Dual path Networks into EMSRDPN, it uses residual connections and dense connections in an integrated way in most network layers. Dual path connections have the benefits of both reusing common features of residual connections and exploring new features of dense connections to learn a good representation for single-image super-resolution. To utilize the feature correlation of multiple scale factors, EMSRDPN shares all network units in low-resolution space between different scale factors to learn shared features and only uses a separate reconstruction unit for each scale factor, which can utilize training data of multiple scale factors to help each other to boost performance, meanwhile, which can save parameters and support shared inference for multiple scale factors to improve efficiency. Experiments show EMSRDPN achieves better performance and comparable or even better parameter and inference efficiency over state-of-the-art methods. Code will be available at https://github.com/yangbincheng/EMSRDPN.
Homogeneous Formulation of Convex Quadratic Programs for Infeasibility Detection

Convex Quadratic Programs (QPs) have come to play a central role in the computation of control action for constrained dynamical systems. In this paper, we present a novel Homogeneous QP (HQP) formulation which is obtained by embedding the original QP in a larger space. The key properties of the HQP are: (i) is always feasible, (ii) an optimal solution to QP can be readily obtained from a solution to HQP, and (iii) infeasibility of QP corresponds to a particular solution of HQP. An immediate consequence is that all the existing algorithms for QP are now also capable of robustly detecting infeasibility. In particular, we present an Infeasible Interior Point Method (IIPM) for the HQP and show polynomial iteration complexity when applied to HQP. A key distinction with prior IPM approaches is that we do not need to solve second-order cone programs. Numerical experiments on the formulation are provided using existing codes.
A General Traffic Shaping Protocol in E-Commerce

To approach different business objectives, online traffic shaping algorithms aim at improving exposures of a target set of items, such as boosting the growth of new commodities. Generally, these algorithms assume that the utility of each user-item pair can be accessed via a well-trained conversion rate prediction model. However, for real E-Commerce platforms, there are unavoidable factors preventing us from learning such an accurate model. In order to break the heavy dependence on accurate inputs of the utility, we propose a general online traffic shaping protocol for online E-Commerce applications. In our framework, we approximate the function mapping the bonus scores, which generally are the only method to influence the ranking result in the traffic shaping problem, to the numbers of exposures and purchases. Concretely, we approximate the above function by a class of the piece-wise linear function constructed on the convex hull of the explored data points. Moreover, we reformulate the online traffic shaping problem as linear programming where these piece-wise linear functions are embedded into both the objective and constraints. Our algorithm can straightforwardly optimize the linear programming in the prime space, and its solution can be simply applied by a stochastic strategy to fulfill the optimized objective and the constraints in expectation. Finally, the online A/B test shows our proposed algorithm steadily outperforms the previous industrial level traffic shaping algorithm.
AntiCopyPaster: Extracting Code Duplicates As Soon As They Are Introduced in the IDE

We developed a plugin for IntelliJ IDEA called AntiCopyPaster, which tracks the pasting of code fragments inside the IDE and suggests the appropriate Extract Method refactoring to combat the propagation of duplicates. Unlike the existing approaches, our tool is integrated with the developer’s workflow, and pro-actively recommends refactorings. Since not all code fragments need to be extracted, we develop a classification model to make this decision. When a developer copies and pastes a code fragment, the plugin searches for duplicates in the currently opened file, waits for a short period of time to allow the developer to edit the code, and finally inferences the refactoring decision based on a number of features. Our experimental study on a large dataset of 18,942 code fragments mined from 13 Apache projects shows that AntiCopyPaster correctly recommends Extract Method refactorings with an F-score of 0.82. Furthermore, our survey of 59 developers reflects their satisfaction with the developed plugin’s operation. The plugin and its source code are publicly available on GitHub at https://github.com/JetBrains-Research/anti-copy-paster. The demonstration video can be found on YouTube: https://youtu.be/_wwHg-qFjJY.
Effects of Plasticity Functions on Neural Assemblies

We explore the effects of various plasticity functions on assemblies of neurons. To bridge the gap between experimental and computational theories we make use of a conceptual framework, the Assembly Calculus, which is a formal system for the description of brain function based on assemblies of neurons. The Assembly Calculus includes operations for projecting, associating, and merging assemblies of neurons. Our research is focused on simulating different plasticity functions with Assembly Calculus. Our main contribution is the modification and evaluation of the projection operation. We experiment with Oja’s and Spike Time-Dependent Plasticity (STDP) rules and test the effect of various hyper-parameters.
Sufficient Statistic Memory AMP

Approximate message passing (AMP) type algorithms have been widely used in the signal reconstruction of certain large random linear systems. A key feature of the AMP-type algorithms is that their dynamics can be correctly described by state evolution. While state evolution is a useful analytic tool, its convergence is not guaranteed. To solve the convergence problem of the state evolution of AMP-type algorithms in principle, this paper proposes a sufficient-statistic memory AMP (SS-MAMP) algorithm framework under the conditions of right-unitarily invariant sensing matrices, Lipschitz-continuous local processors and the sufficient-statistic constraint (i.e., the current message of each local processor is a sufficient statistic of the signal vector given the current and all preceding messages). We show that the covariance matrices of SS-MAMP are L-banded and convergent, which is an optimal framework (from the local MMSE/LMMSE perspective) for AMP-type algorithms given the Lipschitz-continuous local processors. Given an arbitrary MAMP, we can construct an SS-MAMP by damping, which not only ensures the convergence of the state evolution, but also preserves the orthogonality, i.e., its dynamics can be correctly described by state evolution. As a byproduct, we prove that the Bayes-optimal orthogonal/vector AMP (BO-OAMP/VAMP) is an SS-MAMP. As an example, we construct a sufficient-statistic Bayes-optimal MAMP (SS-BO-MAMP) whose state evolution converges to the minimum (i.e., Bayes-optimal) mean square error (MSE) predicted by replica methods when it has a unique fixed point. In addition, the MSE of SS-BO-MAMP is not worse than the original BO-MAMP. Finally, simulations are provided to support the theoretical results.
A method of using computer simulation to assess the functional performance of football boots

This thesis details the development of Finite Element Analysis (FEA) techniques to simulate assembly and functional performance of football boots within a virtual environment. With a highly competitive market and seasonal changes in boot design common, the current design process can require numerous iterations, each adding time and cost to the development cycle. Using a reliable model allows evaluation of novel design concepts without the necessity to manufacture physical prototypes, and thus has potential financial benefits as well as reducing development time. A modelling approach was developed to construct a three dimensional boot model using FEA techniques, simulating the assembly of representative boot constituent parts based on manufacturing patterns, geometries and materials. Comparison between the modelled and physical boots demonstrated good agreement. Assessment of physical boot manufacture enabled the validation of the simulated assembly techniques, with digital image correlation hardware and software used to provide experimental measurements of the surface deformation. Good agreement was reported, demonstrating the predictive capabilities of FEA. Extensive review of literature provided applicable loading conditions of the boot during game play, with bending and torsional stiffness identified as important parameters. Boundary conditions associated with the foot during these movements provided a platform from which mechanical tests were used and developed to quantify boot function. Modelling techniques were developed and applied to the assembled FEA boot model, simulating the loading conditions to verify the validity when compared with experimental measurements. Bending and torsional stiffness extracted from the model were compared with the physical equivalent, demonstrating good predictive capabilities. The model was able to represent bending stiffness of the physical equivalent within 5.6% of an accepted boot range up to 20°, with torsional stiffness represented within the accepted range between 10° inversion to 7.5° eversion, corresponding to a large proportion of match play. Two case studies proved the applicability of the FEA techniques to simulate assembly and determine mechanical functionality virtually through a combination of automated modelling methods and a bespoke framework, demonstrating how it could be implemented within the industrial design process.