Miracle-dz/UniFAR-FTUs huggingface dataset:
Minimax Regret for Cascading Bandits

Cascading bandits is a natural and popular model that frames the task of learning to rank from Bernoulli click feedback in a bandit setting. For the case of unstructured rewards, we prove matching upper and lower bounds for the problem-independent (i.e., gap-free) regret, both of which strictly improve the best known. A key observation is that the hard instances of this problem are those with small mean rewards, i.e., the small click-through rates that are most relevant in practice. Based on this, and the fact that small mean implies small variance for Bernoullis, our key technical result shows that variance-aware confidence sets derived from the Bernstein and Chernoff bounds lead to optimal algorithms (up to log terms), whereas Hoeffding-based algorithms suffer order-wise suboptimal regret. This sharply contrasts with the standard (non-cascading) bandit setting, where the variance-aware algorithms only improve constants. In light of this and as an additional contribution, we propose a variance-aware algorithm for the structured case of linear rewards and show its regret strictly improves the state-of-the-art.
Stability properties for a class of inverse problems

Abstract We establish Lipschitz stability properties for a class of inverse problems. In that class, the associated direct problem is formulated by an integral operator A m \mathcal{A}_{m} depending nonlinearly on a parameter 𝑚 and operating on a function 𝑢. In the inversion step, both 𝑢 and 𝑚 are unknown, but we are only interested in recovering 𝑚. We discuss examples of such inverse problems for the elasticity equation with applications to seismology and for the inverse scattering problem in electromagnetic theory. Assuming a few injectivity and regularity properties for A m \mathcal{A}_{m} , we prove that the inverse problem with a finite number of data points is solvable and that the solution is Lipschitz stable in the data. We show a reconstruction example illustrating the use of neural networks.
Efficient Fully Distributed Federated Learning with Adaptive Local Links

Nowadays, data-driven, machine and deep learning approaches have provided unprecedented performance in various complex tasks, including image classification and object detection, and in a variety of application areas, like autonomous vehicles, medical imaging and wireless communications. Traditionally, such approaches have been deployed, along with the involved datasets, on standalone devices. Recently, a shift has been observed towards the so-called Edge Machine Learning, in which centralized architectures are adopted that allow multiple devices with local computational and storage resources to collaborate with the assistance of a centralized server. The well-known federated learning approach is able to utilize such architectures by allowing the exchange of only parameters with the server, while keeping the datasets private to each contributing device. In this work, we propose a fully distributed, diffusion-based learning algorithm that does not require a central server and propose an adaptive combination rule for the cooperation of the devices. By adopting a classification task on the MNIST dataset, the efficacy of the proposed algorithm over corresponding counterparts is demonstrated via the reduction of the number of collaboration rounds required to achieve an acceptable accuracy level in non- IID dataset scenarios.
An Empirical Study of Memorization in NLP

A recent study by Feldman (2020) proposed a long-tail theory to explain the memorization behavior of deep learning models. However, memorization has not been empirically verified in the context of NLP, a gap addressed by this work. In this paper, we use three different NLP tasks to check if the long-tail theory holds. Our experiments demonstrate that top-ranked memorized training instances are likely atypical, and removing the top-memorized training instances leads to a more serious drop in test accuracy compared with removing training instances randomly. Furthermore, we develop an attribution method to better understand why a training instance is memorized. We empirically show that our memorization attribution method is faithful, and share our interesting finding that the top-memorized parts of a training instance tend to be features negatively correlated with the class label.
Integrating Vectorized Lexical Constraints for Neural Machine Translation

Lexically constrained neural machine translation (NMT), which controls the generation of NMT models with pre-specified constraints, is important in many practical scenarios. Due to the representation gap between discrete constraints and continuous vectors in NMT models, most existing works choose to construct synthetic data or modify the decoding algorithm to impose lexical constraints, treating the NMT model as a black box. In this work, we propose to open this black box by directly integrating the constraints into NMT models. Specifically, we vectorize source and target constraints into continuous keys and values, which can be utilized by the attention modules of NMT models. The proposed integration method is based on the assumption that the correspondence between keys and values in attention modules is naturally suitable for modeling constraint pairs. Experimental results show that our method consistently outperforms several representative baselines on four language pairs, demonstrating the superiority of integrating vectorized lexical constraints.
Polarization from Aligned Dust Grains in the β Pic Debris Disk

We present 870 μm Atacama Large Millimeter/submillimeter Array polarization observations of thermal dust emission from the iconic, edge-on debris disk β Pic. While the spatially resolved map does not exhibit detectable polarized dust emission, we detect polarization at the ∼3σ level when averaging the emission across the entire disk. The corresponding polarization fraction is P frac = 0.51% ± 0.19%. The polarization position angle χ is aligned with the minor axis of the disk, as expected from models of dust grains aligned via radiative alignment torques (RAT) with respect to a toroidal magnetic field (B-RAT) or with respect to the anisotropy in the radiation field (k-RAT). When averaging the polarized emission across the outer versus inner thirds of the disk, we find that the polarization arises primarily from the SW third. We perform synthetic observations assuming grain alignment via both k-RAT and B-RAT. Both models produce polarization fractions close to our observed value when the emission is averaged across the entire disk. When we average the models in the inner versus outer thirds of the disk, we find that k-RAT is the likely mechanism producing the polarized emission in β Pic. A comparison of timescales relevant to grain alignment also yields the same conclusion. For dust grains with realistic aspect ratios (i.e., s > 1.1), our models imply low grain-alignment efficiencies.
Which programming languages do hackers use? A survey at the German Chaos Computer Club

There are numerous articles about the programming languages most commonly used by hackers. Among them, however, there are hardly any scientific studies. One reason might be that hackers mainly operate anonymously and are difficult to reach. This paper aims to shed light on this interesting and relevant research question. In order to find answers, we conducted a survey among the members of the German Chaos Computer Club. As one of the world's largest organisations for information security and hacking, the club provides a good basis for our study. We examine the question of which programming languages are used by hackers as well as the importance of the programming language for their work. The paper offers first insights into the topic and can provide a starting point for further research.
Time dependent variations in X-ray Computed Tomography data during repeated scanning

12 High resolution laboratory X-ray Computed Tomography (CT) systems are capable of providing three 13 dimensional (3D) images at spatial resolutions ranging from hundreds of microns to better than 100nm. 14 Increasingly such images are being used to quantify features (porosity, defects, geometry, phase fractions, 15 etc) by segmenting the images into different regions on the basis of greyscale values. As a non-destructive 16 technique X-ray CT is particularly well suited to time-lapse (3D+time) studies, however, temporal variability 17 in both the source and or detector can alter the grey scale values of the reconstructed volumes. In this 18 paper we examine the extent of the greyscale variation as a function of time. Our results show that 19 detector performance is not constant over time and that decreasing source accelerating voltage and 20 decreasing exposure times introduce greater variability. We examine the implications of temporal 21 variations in contrast on the quantitative analysis of 3D images. Our work demonstrates the dangers of 22 using global greyscale automatic thresholding and shows that a careful choice of thresholding technique, 23 coupled with pre-experiment conditioning of detectors can minimise variability in quantities obtained at 24 different times. 25
Pattern recovery by SLOPE

SLOPE is a popular method for dimensionality reduction in the high-dimensional regression. Indeed some regression coefficient estimates of SLOPE can be null (sparsity) or can be equal in absolute value (clustering). Consequently, SLOPE may eliminate irrelevant predictors and may identify groups of predictors having the same influence on the vector of responses. The notion of SLOPE pattern allows to derive theoretical properties on sparsity and clustering by SLOPE. Specifically, the SLOPE pattern of a vector provides: the sign of its components (positive, negative or null), the clusters (indices of components equal in absolute value) and clusters ranking. In this article we give a necessary and sufficient condition for SLOPE pattern recovery of an unknown vector of regression coefficients.
Scalable Deep Reinforcement Learning Algorithms for Mean Field Games

Mean Field Games (MFGs) have been introduced to efficiently approximate games with very large populations of strategic agents. Recently, the question of learning equilibria in MFGs has gained momentum, particularly using model-free reinforcement learning (RL) methods. One limiting factor to further scale up using RL is that existing algorithms to solve MFGs require the mixing of approximated quantities such as strategies or $q$-values. This is far from being trivial in the case of non-linear function approximation that enjoy good generalization properties, e.g. neural networks. We propose two methods to address this shortcoming. The first one learns a mixed strategy from distillation of historical data into a neural network and is applied to the Fictitious Play algorithm. The second one is an online mixing method based on regularization that does not require memorizing historical data or previous estimates. It is used to extend Online Mirror Descent. We demonstrate numerically that these methods efficiently enable the use of Deep RL algorithms to solve various MFGs. In addition, we show that these methods outperform SotA baselines from the literature.
M-SENA: An Integrated Platform for Multimodal Sentiment Analysis

M-SENA is an open-sourced platform for Multimodal Sentiment Analysis. It aims to facilitate advanced research by providing flexible toolkits, reliable benchmarks, and intuitive demonstrations. The platform features a fully modular video sentiment analysis framework consisting of data management, feature extraction, model training, and result analysis modules. In this paper, we first illustrate the overall architecture of the M-SENA platform and then introduce features of the core modules. Reliable baseline results of different modality features and MSA benchmarks are also reported. Moreover, we use model evaluation and analysis tools provided by M-SENA to present intermediate representation visualization, on-the-fly instance test, and generalization ability test results. The source code of the platform is publicly available at https://github.com/thuiar/M-SENA.
Finite-depth scaling of infinite quantum circuits for quantum critical points

The scaling of the entanglement entropy at a quantum critical point allows us to extract universal properties of the state, e.g., the central charge of a conformal field theory. With the rapid improvement of noisy intermediate-scale quantum (NISQ) devices, these quantum computers present themselves as a powerful tool to study critical many-body systems. We use finite-depth quantum circuits suitable for NISQ devices as a variational ansatz to represent ground states of critical, infinite systems. We find universal finite-depth scaling relations for these circuits and verify them numerically at two different critical points, i.e., the critical Ising model with an additional symmetry-preserving term and the critical XXZ model.
Correlations Between Sentiment Analysis of Movie Tweets

This thesis explores correlations between sentiment analysis ratings from movie tweets, film critics' ratings, and box office earnings. Two sentiment analysis tools, NLTK and Pattern, are used to collect and automatically extract the sentiments behind tweets about five movies released during summer 2013: Despicable Me 2, The BUng Ring, Man of Steel, Monsters University, and World War Z. The average ratings for each movie are compared to the scores for each movie on Metacritic and Rotten Tomatoes. Overall, for most of the movies the results from the sentiment analysis ratings and film critics were similar, although in some instances they did vary considerably. Additionally, Pattern corresponded better with the film critics' ratings than NLTK. Furthermore, through graphical and statistical analysis, correlations between these sets of ratings and the box office earnings are studied. The analysis did not result in significant correlations to make concrete conclusive predictions. • I have been extremely fortunate to have Prof. Nathan Sanders and Prof. Deepak Kumar as my advisors and faculty readers. Without their direction and guidance, this thesis would not have been possible. I am also grateful to my student readers, Lisa Bao and Kelsey Ryan, for all their helpful comments and suggestions, which helped me refine my arguments. Lastly, I would like to thank the other Linguistics seniors, Katherine Marcoux, Chaney Harter, Karuna Doraiswamy, and Raminta Holden, who have served as a sounding board for my ideas every week during Linguistics Study Hour and provided constant support and encouragement. 1
CoMeFa: Compute-in-Memory Blocks for FPGAs

Block RAMs (BRAMs) are the storage houses of FPGAs, providing extensive on-chip memory bandwidth to the compute units implemented using Logic Blocks (LBs) and Digital Signal Processing (DSP) slices. We propose modifying BRAMs to convert them to CoMeFa (Compute-In-Memory Blocks for FPGAs) RAMs. These RAMs provide highly-parallel compute-in-memory by combining computation and storage capabilities in one block. CoMeFa RAMs utilize the true dual port nature of FPGA BRAMs and contain multiple programmable single-bit bit-serial processing elements. CoMeFa RAMs can be used to compute in any precision, which is extremely important for evolving applications like Deep Learning. Adding CoMeFa RAMs to FPGAs significantly increases their compute density. We explore and propose two architectures of these RAMs: CoMeFa-D (optimized for delay) and CoMeFa-A (optimized for area). Compared to existing proposals, CoMeFa RAMs do not require changing the underlying SRAM technology like simultaneously activating multiple rows on the same port, and are practical to implement. CoMeFa RAMs are versatile blocks that find applications in numerous diverse parallel applications like Deep Learning, signal processing, databases, etc. By augmenting an Intel Arria-10-like FPGA with CoMeFa-D (CoMeFa-A) RAMs at the cost of 3.8% (1.2%) area, and with algorithmic improvements and efficient mapping, we observe a geomean speedup of 2.55x (1.85x), across several representative benchmarks. Replacing all or some BRAMs with CoMeFa RAMs in FPGAs can make them better accelerators of modern compute-intensive workloads.
Proportional Budget Allocations: A Systematization

We contribute to the programme of lifting proportionality axioms from the multi-winner voting setting to participatory budgeting. We deﬁne novel proportionality axioms for participatory budgeting and test them on known proportionality-driven rules such as Phragm´en and Rule X. We investi-gate logical implications among old and new axioms and provide a systematic overview of proportionality criteria in participatory budgeting.
Estimation strategies in the presence of non-coverage in the German Microcensus-Panel : An evaluation using SOEP data ∗

The German Microcensus is a rotating panel with units staying in the survey for four observations. Because of the very large sample size and the mandatory participation it appears to be a valuable data base for short duration analysis. However, the German Microcensus uses area sampling that does not consider participants who left the area. Consequently, there is no information on participants after they moved. We propose two different estimation strategies in the presence of missing data caused by residential movers. To assess their performance we use the German Socio-Economic Panel. The example we analyze is the estimation of labour force flows. The results indicate that both estimation strategies lead to estimates that are less biased.
Event-Based Dense Reconstruction Pipeline

Event cameras are a new type of sensors that are different from traditional cameras. Each pixel is triggered asynchronously by event. The trigger event is the change of the brightness irradiated on the pixel. If the increment or decrement of brightness is higher than a certain threshold, an event is output. Compared with traditional cameras, event cameras have the advantages of high dynamic range and no motion blur. Since events are caused by the apparent motion of intensity edges, the majority of 3D reconstructed maps consist only of scene edges, i.e., semi-dense maps, which is not enough for some applications. In this paper, we propose a pipeline to realize event-based dense reconstruction. First, deep learning is used to reconstruct intensity images from events. And then, structure from motion (SfM) is used to estimate camera intrinsic, extrinsic and sparse point cloud. Finally, multi-view stereo (MVS) is used to complete dense reconstruction.
Towards Reproducible Network Traffic Analysis

Analysis techniques are critical for gaining insight into network traffic given both the higher proportion of encrypted traffic and increasing data rates. Unfortunately, the domain of network traffic analysis suffers from a lack of standardization, leading to incomparable results and barriers to reproducibility. Unlike other disciplines, no standard dataset format exists, forcing researchers and practitioners to create bespoke analysis pipelines for each individual task. Without standardization researchers cannot compare"apples-to-apples", preventing us from knowing with certainty if a new technique represents a methodological advancement or if it simply benefits from a different interpretation of a given dataset. In this work, we examine irreproducibility that arises from the lack of standardization in network traffic analysis. First, we study the literature, highlighting evidence of irreproducible research based on different interpretations of popular public datasets. Next, we investigate the underlying issues that have lead to the status quo and prevent reproducible research. Third, we outline the standardization requirements that any solution aiming to fix reproducibility issues must address. We then introduce pcapML, an open source system which increases reproducibility of network traffic analysis research by enabling metadata information to be directly encoded into raw traffic captures in a generic manner. Finally, we use the standardization pcapML provides to create the pcapML benchmarks, an open source leaderboard website and repository built to track the progress of network traffic analysis methods.
Block-Level Interference Exploitation Precoding without Symbol-by-Symbol Optimization

Symbol-level precoding (SLP) based on the concept of constructive interference (CI) is shown to be superior to traditional block-level precoding (BLP), however at the cost of a symbol-by-symbol optimization during the precoding design. In this paper, we propose a CI-based block-level precoding (CI-BLP) scheme for the downlink transmission of a multi-user multiple-input single-output (MU-MISO) communication system, where we design a constant precoding matrix to a block of symbol slots to exploit CI for each symbol slot simultaneously. A single optimization problem is formulated to maximize the minimum CI effect over the entire block, thus reducing the computational cost of traditional SLP as the optimization problem only needs to be solved once per block. By leveraging the Karush-Kuhn-Tucker (KKT) conditions and the dual problem formulation, the original optimization problem is finally shown to be equivalent to a quadratic programming (QP) over a simplex. Numerical results validate our derivations and exhibit superior performance for the proposed CI-BLP scheme over traditional BLP and SLP methods, thanks to the relaxed block-level power constraint.
Post-selection inference for e-value based confidence intervals

Suppose that one can construct a valid $(1-\delta)$-confidence interval (CI) for each of $K$ parameters of potential interest. If a data analyst uses an arbitrary data-dependent criterion to select some subset $S$ of parameters, then the aforementioned CIs for the selected parameters are no longer valid due to selection bias. We design a new method to adjust the intervals in order to control the false coverage rate (FCR). The main established method is the"BY procedure"by Benjamini and Yekutieli (JASA, 2005). The BY guarantees require certain restrictions on the selection criterion and on the dependence between the CIs. We propose a new simple method which, in contrast, is valid under any dependence structure between the original CIs, and any (unknown) selection criterion, but which only applies to a special, yet broad, class of CIs that we call e-CIs. To elaborate, our procedure simply reports $(1-\delta|S|/K)$-CIs for the selected parameters, and we prove that it controls the FCR at $\delta$ for confidence intervals that implicitly invert e-values; examples include those constructed via supermartingale methods, via universal inference, or via Chernoff-style bounds, among others. The e-BY procedure is admissible, and recovers the BY procedure as a special case via a particular calibrator. Our work also has implications for post-selection inference in sequential settings, since it applies at stopping times, to continuously-monitored confidence sequences, and under bandit sampling. We demonstrate the efficacy of our procedure using numerical simulations and real A/B testing data from Twitter.
A Framework for Controlling Multi-Robot Systems Using Bayesian Optimization and Linear Combination of Vectors

—We propose a general framework for creating parameterized control schemes for decentralized multi-robot systems. A variety of tasks can be seen in the decentralized multi-robot literature, each with many possible control schemes. For several of them, the agents choose control velocities using algorithms that extract information from the environment and combine that information in meaningful ways. From this basic formation, a framework is proposed that classiﬁes each robots’ measurement information as sets of relevant scalars and vectors and creates a linear combination of the measured vector sets. Along with an optimizable parameter set, the scalar measurements are used to generate the coefﬁcients for the linear combination. With this framework and Bayesian optimization, we can create effective control systems for several multi-robot tasks, including cohesion and segregation, pattern formation, and searching/foraging.