Miracle-dz/UniFAR-FTUs huggingface dataset:
Hopcroft’s Problem, Log-Star Shaving, 2D Fractional Cascading, and Decision Trees

We revisit Hopcroft’s problem and related fundamental problems about geometric range searching. Given n points and n lines in the plane, we show how to count the number of point-line incidence pairs or the number of point-above-line pairs in O(n4/3) time, which matches the conjectured lower bound and improves the best previous time bound of \(n^{4/3}2^{O(\log ^*n)} \) obtained almost 30 years ago by Matoušek. We describe two interesting and different ways to achieve the result: the first is randomized and uses a new 2D version of fractional cascading for arrangements of lines; the second is deterministic and uses decision trees in a manner inspired by the sorting technique of Fredman (1976). The second approach extends to any constant dimension. Many consequences follow from these new ideas: for example, we obtain an O(n4/3)-time algorithm for line segment intersection counting in the plane, O(n4/3)-time randomized algorithms for distance selection in the plane and bichromatic closest pair and Euclidean minimum spanning tree in three or four dimensions, and a randomized data structure for halfplane range counting in the plane with O(n4/3) preprocessing time and space and O(n1/3) query time.
Charge-injection Device Imaging of Sirius with Contrast Ratios Greater than 1:26 Million

The intrinsic nature of many astronomical objects, such as binary and multiple systems, exoplanets, circumstellar and debris disks, and quasar host galaxies, introduces challenging requirements for observational instrumentation and techniques. In each case, we encounter situations where the light from bright sources hampers our ability to detect surrounding fainter targets. To explore all features of such astronomical scenes, we must perform observations at the maximum possible contrast ratios. Charge-injection devices (CIDs) are capable of potentially exceeding contrast ratios of log10(CR)>9 (i.e., 1 part in 1 billion) due to their unique readout architectures and inherent anti-blooming abilities. An on-sky testing of a commercially available CID, SpectraCAM XDR (SXDR), demonstrated raw contrast ratios from sub-optimal ground-based astronomical observations that imposed practical limits on the maximum achievable contrast ratios using CIDs. Here, we demonstrate the extreme contrast ratio imaging capabilities of the SXDR using observations of Sirius with the 1.0 m Jacobus Kapteyn Telescope, La Palma, Spain. Based on wavelet-based analysis and precise photometric and astrometric calibrations, we report a direct contrast ratio of Δm r = 18.54, log10(CR)=7.41±0.08 , or 1 part in 26 million. This is an order of magnitude higher compared to the previous CID results.
Deep Noise Suppression Maximizing Non-Differentiable PESQ Mediated by a Non-Intrusive PESQNet

Speech enhancement employing deep neural networks (DNNs) for denoising is called deep noise suppression (DNS). The DNS trained with mean squared error (MSE) losses cannot guarantee good perceptual quality. Perceptual evaluation of speech quality (PESQ) is a widely used metric for evaluating speech quality. However, the original PESQ algorithm is non-differentiable, therefore, cannot directly be used as optimization criterion for gradient-based learning. In this work, we propose an end-to-end non-intrusive <monospace><bold>PESQNet</bold></monospace> DNN to estimate the PESQ scores of the enhanced speech signal. Thus, by providing a reference-free perceptual loss, it serves as a mediator towards the DNS training, allowing to maximize the PESQ score of the enhanced speech signal. We illustrate the potential of our proposed <monospace><bold>PESQNet</bold></monospace>-mediated training on a strong baseline DNS. As further novelty, we propose to train the DNS and the <monospace><bold>PESQNet</bold></monospace> alternatingly to keep the <monospace><bold>PESQNet</bold></monospace> up-to-date and perform well specifically for the DNS under training. Detailed analysis shows that the <monospace><bold>PESQNet</bold></monospace> mediation further increases the DNS performance by about 0.1 PESQ points on synthetic test data and by 0.03 DNSMOS points on real test data, compared to training with the MSE-based loss. Our proposed method outperforms the Interspeech 2021 DNS Challenge baseline by 0.2 PESQ points on synthetic test data and 0.1 DNSMOS points on real test data. Furthermore, it improves on the same DNS trained with an approximated differentiable PESQ loss by about 0.4 PESQ points on synthetic test data and 0.2 DNSMOS points on real test data.
Compressed spectral screening for large-scale differential correlation analysis with application in selecting Glioblastoma gene modules

Differential co-expression analysis has been widely applied by scientists in understanding the biological mechanisms of diseases. However, the unknown differential patterns are often complicated; thus, models based on simplified parametric assumptions can be ineffective in identifying the differences. Meanwhile, the gene expression data involved in such analysis are in extremely high dimensions by nature, whose correlation matrices may not even be computable. Such a large scale seriously limits the application of most well-studied statistical methods. This paper introduces a simple yet powerful approach to the differential correlation analysis problem called compressed spectral screening. By leveraging spectral structures and random sampling techniques, our approach could achieve a highly accurate screening of features with complicated differential patterns while maintaining the scalability to analyze correlation matrices of $10^4$--$10^5$ variables within a few minutes on a standard personal computer. We have applied this screening approach in comparing a TCGA data set about Glioblastoma with normal subjects. Our analysis successfully identifies multiple functional modules of genes that exhibit different co-expression patterns. The findings reveal new insights about Glioblastoma's evolving mechanism. The validity of our approach is also justified by a theoretical analysis, showing that the compressed spectral analysis can achieve variable screening consistency.
Asynchronous Collaborative Localization by Integrating Spatiotemporal Graph Learning with Model-Based Estimation

Collaborative localization is an essential capability for a team of robots such as connected vehicles to collaboratively estimate object locations from multiple perspectives with reliant cooperation. To enable collaborative localization, four key challenges must be addressed, including modeling complex relationships between observed objects, fusing observations from an arbitrary number of collaborating robots, quantifying localization uncertainty, and addressing latency of robot communications. In this paper, we introduce a novel approach that integrates uncertainty-aware spatiotemporal graph learning and model-based state estimation for a team of robots to collaboratively localize objects. Specifically, we introduce a new uncertainty-aware graph learning model that learns spatiotemporal graphs to represent historical motions of the objects observed by each robot over time and provides uncertainties in object localization. Moreover, we propose a novel method for integrated learning and model-based state estimation, which fuses asynchronous observations obtained from an arbitrary number of robots for collaborative localization. We evaluate our approach in two collaborative object localization scenarios in simulations and on real robots. Experimental results show that our approach outperforms previous methods and achieves state-of-the-art performance on asynchronous collaborative localization.
Level-Crossing ADCs and Their Applications in Biomedical Readout Systems

This thesis describes the theory, design and implementation of levelcrossing ADCs and their system integration. The goal of this work is to explore methods at both the circuit level and the system level to improve the energy efficiency of LC-ADCs and integrate LC-ADCs in biomedical readout systems. Two single-bit capacitive LC-ADCs and an ECG recording front-end with a multi-bit current-mode LC-ADC are presented to demonstrate the effectiveness of these techniques.
Carbon Dioxide, Fusion, and Stellarators

The importance of stopping the increase in atmospheric carbon dioxide is more broadly appreciated than is the need for rational planning in order to achieve desired results. The November 5, 2021 statement by the U.S. Secretary of Energy on constructing a system for the direct removal of CO2 from the air may have broader implications. Even if CO2 emissions are stopped by 2050, such a system is required to restore the CO2 concentration to its present level in less than three centuries. Perhaps this statement will increase the recognition of the importance of reason relative to emotion in obtaining a solution to the CO2 problem. When reason dominates, the importance of fusion and the rapid development stellarators becomes manifest.
Parametric Design within an Atomic Design Process (ADP) applied to Spacecraft Design

This thesis describes research investigating the development of a model for the initial design of complex systems, with application to spacecraft design. The design model is called an atomic design process (ADP) and contains four fundamental stages (specifications, configurations, trade studies and drivers) that constitute the minimum steps of an iterative process that helps designers find a feasible solution. Representative design models from the aerospace industry are reviewed and are compared with the proposed model. The design model’s relevance, adaptability and scalability features are evaluated through a focused design task exercise with two undergraduate teams and a long-term design exercise performed by a spacecraft payload team. The implementation of the design model is explained in the context in which the model has been researched. This context includes the organization (a student-run research laboratory at the University of Michigan), its culture (academically oriented), members that have used the design model and the description of the information technology elements meant to provide support while using the model. This support includes a custom-built information management system that consolidates relevant information that is currently being used in the organization. The information is divided in three domains: personnel development history, technical knowledge base and laboratory operations. The focused study with teams making use of the design model to complete an engineering design exercise consists of the conceptual design of an autonomous system, including a carrier and a deployable lander that form the payload of a rocket with an altitude range of over 1000 meters. Detailed results from each of the stages of the design process while implementing the model are presented, and an increase in awareness of good design practices in the teams while using the model are explained. A long-term investigation using the design model consisting of the successful characterization of an imaging system for a spacecraft is presented. The spacecraft is designed to take digital color images from low Earth orbit. The dominant drivers from each stage of the design process are indicated as they were identified, with the accompanying hardware development leading to the final configuration that comprises the flight spacecraft.
A Generic Transformation for Optimal Node Repair in MDS Array Codes Over F2

For high-rate linear systematic maximum distance separable (MDS) codes, most early constructions could initially optimally repair all the systematic nodes but not all the parity nodes. Fortunately, this issue was first solved by Li <italic>et al.</italic> in (IEEE Trans. Inform. Theory, 64(9), 6257-6267, 2018), where a transformation that can convert any nonbinary MDS array code into another one with desired properties was proposed. However, the transformation does not work for binary MDS array codes. In this paper, we address this issue by proposing another generic transformation that can convert any <inline-formula> <tex-math notation="LaTeX">$[n, k]$ </tex-math></inline-formula> binary MDS array code into a new one, which endows any <inline-formula> <tex-math notation="LaTeX">$r=n-k\ge 2$ </tex-math></inline-formula> chosen nodes with optimal repair bandwidth and optimal rebuilding access properties, and at the same time, preserves the normalized repair bandwidth/rebuilding access for the remaining <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> nodes under some conditions. As two immediate applications, we show that 1) by applying the transformation multiple times, any binary MDS array code can be converted into one with optimal rebuilding access for all nodes, 2) any binary MDS array code with optimal repair bandwidth or optimal rebuilding access for the systematic nodes can be converted into one with the corresponding optimality property for all nodes.
Thermodynamic model of twisted bilayer graphene: Configuration entropy matters

Twisted bilayer materials have attracted tremendous attention due to their unique and novel properties. Here, we derive a thermodynamic model for twisted bilayer graphene (tBLG) within the framework of the classical statistical mechanics, based on which, the configuration entropy reflecting the number of micro-status in moire unit-cells, is directly derived from the Helmholtz free energy with a clear physical interpretation. More importantly, we show the configuration entropy of tBLG relative to the AB-stacked bilayer graphene is proportional to the logarithmic function of the ratio of moire period (am) and the atomic lattice constant (a) as StBLG − SAB = 24kB ln ( am a ), which we found dominates the Helmholtz free energy of tBLG and can well explain experimental observations in superlubric contacts. Our work provides a theoretical foundation for studying moire effect of incommensurate contact interfaces and could facilitate twisting based applications such as superlubricity.
Factorial Lower Bounds for (Almost) Random Order Streams

In this paper we introduce and study the STREAMINGCYCLES problem, a random order streaming version of the Boolean Hidden Hypermatching problem that has been instrumental in streaming lower bounds over the past decade. In this problem the edges of a graph G, comprising n/$\ell$ disjoint length-$\ell$ cycles on n vertices, are partitioned randomly among n players. Every edge is annotated with an independent uniformly random bit, and the players’ task is to output, for some cycle in G, the sum (modulo 2) of the bits on its edges, after one round of sequential communication.Our main result is an $\ell^{\Omega(\ell)}$ lower bound on the communication complexity of STREAMINGCYCLES, which is tight up to constant factors in the exponent. Applications of our lower bound for STREAMINGCYCLES include an essentially tight lower bound for component collection in (almost) random order graph streams, making progress towards a conjecture of Peng and Sohler [SODA’18] and the first exponential space lower bounds for random walk generation.
ScaleHLS: A New Scalable High-Level Synthesis Framework on Multi-Level Intermediate Representation

High-level synthesis (HLS) has been widely adopted as it significantly improves the hardware design productivity and enables efficient design space exploration (DSE). Existing HLS tools are built using compiler infrastructures largely based on a single-level abstraction, such as LLVM. How-ever, as HLS designs typically come with intrinsic structural or functional hierarchies, different HLS optimization problems are often better solved with different levels of abstractions. This paper proposes ScaleHLS 1, a new scalable and customizable HLS framework, on top of a multi-level compiler infrastructure called MLIR. ScaleHLS represents HLS designs at multiple representation levels and provides an HLS-dedicated analysis and transform library to solve the optimization problems at the suitable levels. Using this library, we provide a DSE engine to generate optimized HLS designs automatically. In addition, we develop an HLS C front-end and a C/C++ emission back-end to translate HLS designs into/from MLIR for enabling an end-to-end compilation flow. Experimental results show that, comparing to the baseline designs without manual directives insertion and code-rewriting, that are only optimized by Xilinx Vivado HLS, ScaleHLS improves the performances with amazing quality-of-results – up to 768.1× better on computation kernel level programs and up to 3825.0× better on neural network models.
Approach to the cellular automaton interpretation with deep learning

In this paper, we will consider the deep learning systems that can learn fundamental physics theory based on cellular automaton interpretation (CAI). First, assuming that we can map quantum states to cellular automaton (CA) and calculate the time-evolved CA for any initial CA by knowing the time-evolution law of the given system, we will show that there exists a convolutional neural network (CNN) architecture that can learn the time-evolution law of this system with only the calculated data set for a time-reversible CA. Mathematically, finding a CNN architecture that can learn CA rule is equivalent to showing that a time-evolution operator can be approximated as a finite composition of time-independent linear functions and ReLU type non-linear functions, as the possible associated generator of approximation may absorbs the information about the dynamics. Going one step further, we will discuss the correspondence between the quantum system and deep learning architecture and relate the concept of moduli space of Riemann surfaces to deep learning parameters when considering interactions. Finally, for the CA model in which the dimensional reduction in quantum gravity was first presented, we will discuss the CNN architecture that can find the non-trivial evolution law for holographic direction in a deductive way without the label. It is suggested that the limits to this effort can be improved through AdS/CFT correspondance.
BlueFog: Make Decentralized Algorithms Practical for Optimization and Deep Learning

Decentralized algorithm is a form of computation that achieves a global goal through local dynamics that relies on low-cost communication between directly-connected agents. On large-scale optimization tasks involving distributed datasets, decentralized algorithms have shown strong, sometimes superior, performance over distributed algorithms with a central node. Recently, developing decentralized algorithms for deep learning has attracted great attention. They are considered as low-communication-overhead alternatives to those using a parameter server or the Ring-Allreduce protocol. However, the lack of an easy-to-use and efficient software package has kept most decentralized algorithms merely on paper. To fill the gap, we introduce BlueFog, a python library for straightforward, high-performance implementations of diverse decentralized algorithms. Based on a unified abstraction of various communication operations, BlueFog offers intuitive interfaces to implement a spectrum of decentralized algorithms, from those using a static, undirected graph for synchronous operations to those using dynamic and directed graphs for asynchronous operations. BlueFog also adopts several system-level acceleration techniques to further optimize the performance on the deep learning tasks. On mainstream DNN training tasks, BlueFog reaches a much higher throughput and achieves an overall $1.2\times \sim 1.8\times$ speedup over Horovod, a state-of-the-art distributed deep learning package based on Ring-Allreduce. BlueFog is open source at https://github.com/Bluefog-Lib/bluefog.
Towards Debiasing Temporal Sentence Grounding in Video

The temporal sentence grounding in video (TSGV) task is to locate a temporal moment from an untrimmed video, to match a language query, i.e., a sentence. Without considering bias in moment annotations (e.g., start and end positions in a video), many models tend to capture statistical regularities of the moment annotations, and do not well learn cross-modal reasoning between video and language query. In this paper, we propose two debiasing strategies, data debiasing and model debiasing, to"force"a TSGV model to capture cross-modal interactions. Data debiasing performs data oversampling through video truncation to balance moment temporal distribution in train set. Model debiasing leverages video-only and query-only models to capture the distribution bias, and forces the model to learn cross-modal interactions. Using VSLNet as the base model, we evaluate impact of the two strategies on two datasets that contain out-of-distribution test instances. Results show that both strategies are effective in improving model generalization capability. Equipped with both debiasing strategies, VSLNet achieves best results on both datasets.
An Open Access Dataset of Tweets related to Exoskeletons and 100 Research Questions

: The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications in different fields. With the projected increase in the diverse uses of exoskeletons in the next few years in these application domains and beyond, it is crucial to study, interpret, and analyze user perspectives, opinions, and feedback related to exoskeletons, for which a comprehensive dataset of communications related to exoskeletons is necessary. The Internet of Everything style of today's living, characterized by people spending more time on the Internet than ever before, holds the potential for developing such a dataset by the mining of relevant web behavior data from social media communications, which have increased exponentially in the last few years. Twitter, one such social media platform, is highly popular amongst all age groups, who communicate on diverse topics via tweets while sharing their views, opinions, perspectives, and feedback towards the same. To address this research challenge, this paper makes multiple scientific contributions to this field. First, it presents a novel approach of mining tweets that is not bound by any restrictions on the number of days during which the tweets can be mined. Second, by using this approach, it presents an open-access dataset of approximately 20,000 tweets related to exoskeletons, that were posted over a period of 231 days. Finally, based on a review of 108 emerging works in this field, the paper discusses multiple interdisciplinary applications of this dataset and presents 100 research questions for researchers to study, analyze, and investigate.
Integration Of Fault Detection And Indentification Into A Fault Tolerant Automated Highway System

This work focuses on vehicle fault detection and identification. It also describes a vehicle health management approach based on analytic redundancy. It describes a point design of fault detection filters and parity equations that are developed for the vehicle longitudinal mode. A preliminary design of a range sensor fault monitoring system is outlined as an application of a new decentralized fault detection filter. This system combines dynamic state information already generated by the existing filter designs with inter-vehicle analytic redundancy