Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Role of Channel Capacity in Learning Gaussian Mixture Models

This paper studies the sample complexity of learning the $k$ unknown centers of a balanced Gaussian mixture model (GMM) in $\mathbb{R}^d$ with spherical covariance matrix $\sigma^2\mathbf{I}$. In particular, we are interested in the following question: what is the maximal noise level $\sigma^2$, for which the sample complexity is essentially the same as when estimating the centers from labeled measurements? To that end, we restrict attention to a Bayesian formulation of the problem, where the centers are uniformly distributed on the sphere $\sqrt{d}\mathcal{S}^{d-1}$. Our main results characterize the exact noise threshold $\sigma^2$ below which the GMM learning problem, in the large system limit $d,k\to\infty$, is as easy as learning from labeled observations, and above which it is substantially harder. The threshold occurs at $\frac{\log k}{d} = \frac12\log\left( 1+\frac{1}{\sigma^2} \right)$, which is the capacity of the additive white Gaussian noise (AWGN) channel. Thinking of the set of $k$ centers as a code, this noise threshold can be interpreted as the largest noise level for which the error probability of the code over the AWGN channel is small. Previous works on the GMM learning problem have identified the minimum distance between the centers as a key parameter in determining the statistical difficulty of learning the corresponding GMM. While our results are only proved for GMMs whose centers are uniformly distributed over the sphere, they hint that perhaps it is the decoding error probability associated with the center constellation as a channel code that determines the statistical difficulty of learning the corresponding GMM, rather than just the minimum distance.
Better Together? An Evaluation of AI-Supported Code Translation

Generative machine learning models have recently been applied to source code, for use cases including translating code between programming languages, creating documentation from code, and auto-completing methods. Yet, state-of-the-art models often produce code that is erroneous or incomplete. In a controlled study with 32 software engineers, we examined whether such imperfect outputs are helpful in the context of Java-to-Python code translation. When aided by the outputs of a code translation model, participants produced code with fewer errors than when working alone. We also examined how the quality and quantity of AI translations affected the work process and quality of outcomes, and observed that providing multiple translations had a larger impact on the translation process than varying the quality of provided translations. Our results tell a complex, nuanced story about the benefits of generative code models and the challenges software engineers face when working with their outputs. Our work motivates the need for intelligent user interfaces that help software engineers effectively work with generative code models in order to understand and evaluate their outputs and achieve superior outcomes to working alone.
Extended Unconstrained Features Model for Exploring Deep Neural Collapse

The modern strategy for training deep neural networks for classification tasks includes optimizing the network's weights even after the training error vanishes to further push the training loss toward zero. Recently, a phenomenon termed"neural collapse"(NC) has been empirically observed in this training procedure. Specifically, it has been shown that the learned features (the output of the penultimate layer) of within-class samples converge to their mean, and the means of different classes exhibit a certain tight frame structure, which is also aligned with the last layer's weights. Recent papers have shown that minimizers with this structure emerge when optimizing a simplified"unconstrained features model"(UFM) with a regularized cross-entropy loss. In this paper, we further analyze and extend the UFM. First, we study the UFM for the regularized MSE loss, and show that the minimizers' features can have a more delicate structure than in the cross-entropy case. This affects also the structure of the weights. Then, we extend the UFM by adding another layer of weights as well as ReLU nonlinearity to the model and generalize our previous results. Finally, we empirically demonstrate the usefulness of our nonlinear extended UFM in modeling the NC phenomenon that occurs with practical networks.
Dependence of Pulsation Mode of Cepheids on Metallicity

The Cepheid variables in the Small Magellanic Cloud (SMC), the Large Magellanic Cloud (LMC), the Milky Way, M33, and M31 are used to examine the dependence of pulsation mode on metallicity, which was previously found in red supergiants. The initial samples of Cepheids are collected from the Cepheid catalogs identified from the Optical Gravitational Lensing Experiment, PS1, DIRECT, Wide-field Infrared Survey Explorer, and Zwicky Transient Facility surveys. The contaminants are removed with the help of the Gaia/EDR3 astrometric information for extra galaxies or by comparing the geometric distance and the distance from the P–L relation for the Milky Way. The division of fundamental (FU) and first-overtone (1O) modes is refined according to the gap between the two modes in the P–L diagram of the objects in each galaxy. The ratio of FU/(FU+1O) is found to be 0.59, 0.60, 0.69, 0.83, and 0.85 for the SMC, the LMC, the Milky Way, M33, and M31, respectively, in order of metallicity, which confirms that the pulsation mode depends on metallicity in the way that the ratio of FU/(FU+1O) increases with metallicity. This dependence is not changed if the incompleteness of the samples is taken into account.
Almost-optimal sublinear-time edit distance in the low distance regime

We revisit the task of computing the edit distance in sublinear time. In the (k,K)-gap edit distance problem we are given oracle access to two strings of length n and the task is to distinguish whether their edit distance is at most k or at least K. It has been established by Goldenberg, Krauthgamer and Saha (FOCS ’19), with improvements by Kociumaka and Saha (FOCS ’20), that the (k,k2)-gap problem can be solved in time O(n/k + poly(k)). One of the most natural questions in this line of research is whether the (k,k2)-gap is best-possible for the running time O(n/k + poly(k)). In this work we answer this question by significantly improving the gap. Specifically, we show that in time O(n/k + poly(k)) we can even solve the (k,k1+o(1))-gap problem. This is the first algorithm that breaks the (k,k2)-gap in this running time. Our algorithm is almost optimal in the following sense: In the low distance regime (k ≤ n0.19) our running time becomes O(n/k), which matches a known n/k1+o(1) lower bound for the (k,k1+o(1))-gap problem up to lower order factors. Our result also reveals a surprising similarity of Hamming distance and edit distance in the low distance regime: For both, the (k,k1+o(1))-gap problem has time complexity n/k1± o(1) for small k. In contrast to previous work, which employed a subsampled variant of the Landau-Vishkin algorithm, we instead build upon the algorithm of Andoni, Krauthgamer and Onak (FOCS ’10) which approximates the edit distance in almost-linear time O(n1+ε) within a polylogarithmic factor. We first simplify their approach and then show how to to effectively prune their computation tree in order to obtain a sublinear-time algorithm in the given time bound. Towards that, we use a variety of structural insights on the (local and global) patterns that can emerge during this process and design appropriate property testers to effectively detect these patterns.
Physical Factors Governing the Shape of the Miram Curve Knee in Thermionic Emission

In a current density versus temperature ( ${J}$ – ${T}{)}$ (Miram) curve in thermionic electron emission, experimental measurements often yield a smooth transition between the exponential region and the saturated emission regions, which is sometimes referred to as the “roll-off” or “Miram curve knee. ” The shape of the Miram curve knee is an important figure of merit for thermionic vacuum cathodes. Specifically, cathodes with a sharp Miram curve knee at low temperature with a flat saturated emission current are typically preferred. Our previous work on modeling nonuniform thermionic emission revealed that the space charge effect and the patch field effect are key pieces of physics, which set the Miram curve shape. This work provides a more complete understanding of the physical factors connecting these physical effects and their relative impact on the shape of the knee. For our analyses, we use a periodic, equal-width striped (“zebra crossing”) work function distribution as a model system and illustrate how the space charge and patch field effects restrict the emission current density near the Miram curve knee. Our results indicate that there are three main physical parameters that significantly impact the shape of the Miram curve: 1) the normalized work function difference; 2) the patch-diode size ratio; and 3) the patch-diode field ratio. These parameters relate the patch size, work function values, anode–cathode voltage, and anode–cathode gap distance to the shape of the Miram curve, providing new understanding and a guide to the design of thermionic cathodes used as electron sources in vacuum electronic devices.
A deep dive into the consistently toxic 1% of Twitter

Misbehavior in online social networks (OSN) is an ever-growing phenomenon. The research to date tends to focus on the deployment of machine learning to identify and classify types of misbehavior such as bullying, aggression, and racism to name a few. The main goal of identification is to curb natural and mechanical misconduct and make OSNs a safer place for social discourse. Going beyond past works, we perform a longitudinal study of a large selection of Twitter profiles, which enables us to characterize profiles in terms of how consistently they post highly toxic content. Our data spans 14 years of tweets from 122K Twitter profiles and more than 293M tweets. From this data, we selected the most extreme profiles in terms of consistency of toxic content and examined their tweet texts, and the domains, hashtags, and URLs they shared. We found that these selected profiles keep to a narrow theme with lower diversity in hashtags, URLs, and domains, they are thematically similar to each other (in a coordinated manner, if not through intent), and have a high likelihood of bot-like behavior (likely to have progenitors with intentions to influence). Our work contributes a substantial and longitudinal online misbehavior dataset to the research community and establishes the consistency of a profile's toxic behavior as a useful factor when exploring misbehavior as potential accessories to influence operations on OSNs.
Typography-MNIST (TMNIST): an MNIST-Style Image Dataset to Categorize Glyphs and Font-Styles

We present Typography-MNIST (TMNIST), a dataset comprising of 565,292 MNIST-style grayscale images representing 1,812 unique glyphs in varied styles of 1,355 Google-fonts. The glyph-list contains common characters from over 150 of the modern and historical language scripts with symbol sets, and each font-style represents varying subsets of the total unique glyphs. The dataset has been developed as part of the CognitiveType project which aims to develop eye-tracking tools for real-time mapping of type to cognition and to create computational tools that allow for the easy design of typefaces with cognitive properties such as readability. The dataset and scripts to generate MNIST-style images for glyphs in different font styles are freely available at https://github.com/aiskunks/CognitiveType.
Winding movement and condition monitoring of power transformers in service

Power transformers are the most expensive pieces of equipment in a power substation. It is very important to prevent failures and detect problems in a transformer as early as possible. This thesis reviews the diagnostic and monitoring tests and equipment available to assess the condition of transformers and to provide an early warning of potential failure. One problem that is particularly difficult to detect is winding looseness, which can cause movement of the windings and may cause transformer failure. If the looseness or movement can be detected over time, action can be taken to prevent such failures. Frequency Response Analysis (FRA) techniques have the potential to detect winding problems, but the method also contains some uncertainty. In this thesis, Experiments on off-line and online FRA techniques were performed both in the laboratory and on transformers in the field throughout North America. New data assessment methods were developed that will help interpret future on-site transformer condition assessment tests. The key test circuit parameters and their effects on frequency response measurements were studied, covering issues such as the effects that external measurement leads and high voltage bushings on the test results, measuring shunt impedance, requirements for recording equipment, the measurement frequency ranges to identify winding problems, the measurement frequency range related to the amount of winding movement, criteria to be used for winding movement ranking, etc. A computer simulation was conducted on a simplified network transformer model. The principal findings from the simulation are similar to the experimental work. Based on the experimental investigation of the FRA method and the transformer model simulation, a new FRA technique was developed that is feasible for on-line applications or for monitoring on-site. To overcome the shortcomings of the existing FRA method, such as frequency limitations and on-line application, the High-Frequency Internal Frequency Response Analysis method (HIFRA) was developed. It uses an injected wide-band frequency signal through the transformer bushing, in combination with an internal FRA measurement. This new technique is very promising for future application for on-line FRA measurements. TABLE OF CONTENTS ABSTRACT ii TABLE OF CONTENTS . < iii LIST OF TABLES v LIST OF FIGURES vi GLOSSARY OF ABBREVIATIONS xi ACKNOWLEDGEMENT xii CHAPTER I OVERVIEW 1 1.
DeepTx: Deep Learning Beamforming With Channel Prediction

Machine learning algorithms have recently been considered for many tasks in the field of wireless communications. Previously, we have proposed the use of a deep fully convolutional neural network (CNN) for receiver processing and shown it to provide considerable performance gains. In this study, we focus on machine learning algorithms for the transmitter. In particular, we consider beamforming and propose a CNN which, for a given uplink channel estimate as input, outputs downlink channel information to be used for beamforming. The CNN is trained in a supervised manner considering both uplink and downlink transmissions with a loss function that is based on UE receiver performance. The main task of the neural network is to predict the channel evolution between uplink and downlink slots, but it can also learn to handle inefficiencies and errors in the whole chain, including the actual beamforming phase. The provided numerical experiments demonstrate the improved beamforming performance.
Travelling waves in a PDE-ODE coupled system with nonlinear diffusion

We analyze travelling wave (TW) solutions for nonlinear systems consisting of an ODE coupled to a degenerate PDE with a diffusion coefficient that vanishes as the solution tends to zero and blows up as it approaches its maximum value. Stable TW solutions for such systems have previously been observed numerically as well as in biological experiments on the growth of cellulolytic biofilms. In this work, we provide an analytical justification for these observations and prove existence and stability results for TW solutions of such models. Using the TW ansatz and a first integral, the system is reduced to an autonomous dynamical system with two unknowns. Analysing the system in the corresponding phase–plane, the existence of a unique TW is shown, which possesses a sharp front and a diffusive tail, and is moving with a constant speed. The linear stability of the TW in two space dimensions is proven under suitable assumptions on the initial data. Finally, numerical simulations are presented that affirm the theoretical predictions on the existence, stability and parametric dependence of the TWs.
Learning to Detect People on the Fly: A Bio-inspired Event-based Visual System for Drones

—We demonstrate for the ﬁrst time that a biologically-plausible spiking neural network (SNN) equipped with Spike-Timing-Dependent Plasticity (STDP) learning can continuously learn to detect walking people on the ﬂy using retina-inspired, event-based camera data. Our pipeline works as follows. First, a short sequence of event data ( < 2 minutes), capturing a walking human from a ﬂying drone, is shown to a convolutional SNN-STDP system which also receives teacher spiking signals from a convolutional readout (forming a semi-supervised system). Then, STDP adaptation is stopped and the learned system is assessed on testing sequences. We conduct several experiments to study the effect of key mechanisms in our system and we compare our precision-recall performance to conventionally-trained CNNs working with either RGB or event-based camera frames.
Iterative Bilinear Temporal-Spectral Fusion for Unsupervised Time-Series Representation Learning

Unsupervised/self-supervised time series representation learning is a challenging problem because of its complex dynamics and sparse annotations. Existing works mainly adopt the framework of contrastive learning with the time-based augmentation techniques to sample positives and negatives for contrastive training. Nevertheless, they mostly use segment-level augmentation derived from time slicing, which may bring about sampling bias and incorrect optimization with false negatives due to the loss of global context. Besides, they all pay no attention to incorporate the spectral information in feature representation. In this paper, we propose a uniﬁed framework, namely Bilinear Temporal-Spectral Fusion ( BTSF ). Speciﬁcally, we ﬁrstly utilize the instance-level augmentation with a simple dropout on the entire time series for maximally capturing long-term dependencies. We devise a novel iterative bilinear temporal-spectral fusion to explicitly encode the afﬁnities of abundant time-frequency pairs, and iteratively reﬁnes representations in a fusion-and-squeeze manner with Spectrum-to-Time (S2T) and Time-to-Spectrum (T2S) Aggregation modules. We ﬁrstly conducts downstream evaluations on three major tasks for time series including classiﬁcation, forecasting and anomaly detection. Experimental results shows that our BTSF consistently signiﬁcantly outperforms the state-of-the-art methods.
Cooperation of Cross-border E-commerce: A reputation and trust perspective

ABSTRACT As a business mode, cross-border e-commerce responds to the trend of globalization. It is an effective means for numerous enterprises to open up new businesses, expand new markets, and overcome trade barriers. More and more enterprises are beginning to explore cross-border e-commerce. Notwithstanding, there is no theoretical guidance for the selection of cross-border e-commerce partners. This leads to the inefficiency of cross-border cooperation. How scientifically and rationally facilitate the inter-firm cooperation is a topic worthy intensive study. Drawing upon cognitive behavior theory, relational governance theory, and transactional cost theory, this paper examines the mechanism of corporate reputation of cross-border e-commerce on inter-firm cooperation under different levels of information sharing. Based on the analysis of the questionnaire data of cross-border e-commerce enterprises, it finds that the corporate reputation of cross-border e-commerce has a significant impact on inter-firm trust and both corporate reputation and inter-firm trust can promote inter-firm cooperation; inter-firm trust partially mediates the relationship between corporate reputation and inter-firm cooperation; with the increase of the levels of information sharing, the positive impact of inter-firm trust on inter-firm cooperation weakens. The conclusions provide theoretical basis for the selection of cross-border e-commerce partners.
Intentionally Addressing Nested Systems of Power in Schooling through Teacher Solidarity Co-Design

Abstract Teacher solidarity co-design is a special case of participatory design research that emphasizes the unique power dynamics of partnering with teachers who are multiply positioned in schooling, educational policy and research, and society. Through contrastive case analysis of four instrumental cases, five principles that characterize teacher solidarity co-design emerged. Collectively, the cases traverse the professional life-course of teachers in a variety of contexts but foreground co-learning and relationality between teachers and researchers in their efforts to create transformational change in schools. Additionally, the analysis of the cases centers our own experiences and insights as former teachers who are currently educational researchers. The principles account for the complex and nested systems of power that teachers occupy within efforts that seek to transform schools into more equitable and just spaces.
Investigating the Processes of Teacher and Researcher Empowerment and Learning in Co-design Settings

Abstract Discussions about power have only recently begun to appear in the learning sciences literature. Most of this important work takes a critical perspective; the present work complements these efforts by examining power sharing as a catalyst for empowerment in teacher-researcher co-design. Even though teacher-researcher collaborations are discussed in the literature as contexts for empowerment, less is known about the processes that enable empowerment and their connection to learning. This case study examined co-design interactions to identify processes and conditions of empowerment in the context of designing a module to integrate Responsible Research and Innovation in elementary school science education. The co-design team consisted of seven in-service science teachers and one researcher. The main data corpus included ten face to face and online co-design meetings of over 13 hours of video, supplemented by co-design documentation, teacher interviews, and survey data. The analysis of the co-design interactions identified facilitating conditions for supporting power sharing during the co-design, which attended to socio-structural conditions to support the co-design activities and included the anticipation of real-world impact through classroom implementations. Findings suggest that teacher and researcher empowerment develop through power sharing which helps increase access to information and resources and the development of knowledge and skills, thus enabling teachers to make decisions on what and how to teach and researchers to provide just-in-time support.