Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimization Fabrics

This paper presents a theory of optimization fabrics, second-order differential equations that encode nominal behaviors on a space and can be used to define the behavior of a smooth optimizer. Optimization fabrics can encode commonalities among optimization problems that reflect the structure of the space itself, enabling smooth optimization processes to intelligently navigate each problem even when optimizing simple naive potential functions. Importantly, optimization over a fabric is inherently asymptotically stable. The majority of this paper is dedicated to the development of a tool set for the design and use of a broad class of fabrics called geometric fabrics. Geometric fabrics encode behavior as general nonlinear geometries which are covariant second-order differential equations with a special homogeneity property that ensures their behavior is independent of the system's speed through the medium. A class of Finsler Lagrangian energies can be used to both define how these nonlinear geometries combine with one another and how they react when potential functions force them from their nominal paths. Furthermore, these geometric fabrics are closed under the standard operations of pullback and combination on a transform tree. For behavior representation, this class of geometric fabrics constitutes a broad class of spectral semi-sprays (specs), also known as Riemannian Motion Policies (RMPs) in the context of robotic motion generation, that captures both the intuitive separation between acceleration policy and priority metric critical for modular design and are inherently stable. Therefore, geometric fabrics are safe and easier to use by less experienced behavioral designers. Application of this theory to policy representation and generalization in learning are discussed as well.
Approach for Document Detection by Contours and Contrasts

This paper considers arbitrary document detection performed on a mobile device. The classical contour-based approach often fails in cases featuring occlusion, complex background, or blur. The region-based approach, which relies on the contrast between object and background, does not have application limitations, however, its known implementations are highly resource-consuming. We propose a modification of the contour-based method, in which the competing contour location hypotheses are ranked according to the contrast between the areas inside and outside the border. In the experiments, such modification allows for the decrease of alternatives ordering errors by 40% and the decrease of the overall detection errors by 10%. The proposed method provides unmatched state-of-the-art performance on the open MIDV-500 dataset, and it demonstrates results comparable with state-of-the-art performance on the SmartDoc dataset.
nPrint: A Standard Data Representation for Network Traffic Analysis

Conventional detection and classification ("fingerprinting") problems involving network traffic commonly rely on either rule-based expert systems or machine learning models that are trained with manually engineered features derived from network traffic. Automated approaches in this area are typically tailored for specific problems. This paper presents nPrint, a standard, packet-based representation of network traffic that can be used as an input to train a variety of machine learning models without extensive feature engineering. We demonstrate that nPrint offers a suitable traffic representation for machine learning algorithms across three common network traffic classification problems: device fingerprinting, operating system fingerprinting, and application identification. We show that models trained with nPrint are at least as accurate as widely used tools, but in contrast do not rely on brittle, manually updated rules and features. Finally, we release nPrint as a publicly available software tool to encourage further use, testing, and extensions to the existing network traffic representation.
A quasar microlensing event towards J1249+3449?

We show that the optical flare event discovered by Graham et al. (2020) towards the active galactic nucleus J1249+3449 is fully consistent with being a quasar microlensing event due to a $\simeq 0.1 M_{\odot}$ star, although other explanations, such as that, mainly supported by Graham et al. (2020), of being the electromagnetic counterpart associated to a binary black hole merger, cannot be completely excluded at present.
Emergence of a Sharp Quantum Collective Mode in a One-Dimensional Fermi Polaron

The Fermi-polaron problem of a mobile impurity interacting with fermionic medium emerges in various contexts, ranging from the foundations of Landau's Fermi-liquid theory to electron-exciton interaction in semiconductors, to unusual properties of high-temperature superconductors. While classically the medium provides only a dissipative environment to the impurity, quantum picture of polaronic dressing is more intricate and arises from the interplay of few- and many-body aspects of the problem. The conventional expectation for the dynamics of Fermi polarons is that it is dissipative in character, and any excess energy is rapidly emitted away from the impurity as particle-hole excitations. Here we report a strikingly different type of polaron dynamics in a one-dimensional system of the impurity interacting repulsively with the fermions. When the total momentum of the system equals the Fermi momentum, there emerges a sharp collective mode corresponding to long-lived oscillations of the polaronic cloud surrounding the impurity. This mode can be observed experimentally with ultracold atoms using Ramsey interferometry and radio-frequency spectroscopy.
ReLU nets adapt to intrinsic dimensionality beyond the target domain

We study the approximation of two-layer compositions $f(x) = g(\phi(x))$ via deep ReLU networks, where $\phi$ is a nonlinear, geometrically intuitive, and dimensionality reducing feature map. We focus on two complementary choices for $\phi$ that are intuitive and frequently appearing in the statistical literature. The resulting approximation rates are near optimal and show adaptivity to intrinsic notions of complexity, which significantly extend a series of recent works on approximating targets over low-dimensional manifolds. Specifically, we show that ReLU nets can express functions, which are invariant to the input up to an orthogonal projection onto a low-dimensional manifold, with the same efficiency as if the target domain would be the manifold itself. This implies approximation via ReLU nets is faithful to an intrinsic dimensionality governed by the target $f$ itself, rather than the dimensionality of the approximation domain. As an application of our approximation bounds, we study empirical risk minimization over a space of sparsely constrained ReLU nets under the assumption that the conditional expectation satisfies one of the proposed models. We show near-optimal estimation guarantees in regression and classifications problems, for which, to the best of our knowledge, no efficient estimator has been developed so far.
The Athena Class of Risk-Limiting Ballot Polling Audits

The main risk-limiting ballot polling audit in use today, BRAVO, is designed for use when single ballots are drawn at random and a decision regarding whether to stop the audit or draw another ballot is taken after each ballot draw ({\em ballot-by-ballot (B2)} audits). On the other hand, real ballot polling audits draw many ballots in a single round before determining whether to stop ({\em round-by-round (R2)} audits). We show that BRAVO results in significant inefficiency when directly applied to real R2 audits. We present the ATHENA class of R2 risk-limiting stopping rules, and prove that each rule is at least as efficient as the corresponding BRAVO stopping rule applied at the end of the round. We have software libraries implementing most of our results in both python and MATLAB. We show that ATHENA halves the number of ballots required, for all state margins in the 2016 US Presidential election and a first round with 90% stopping probability, when compared to BRAVO (stopping rule applied at the end of the round). We present simulation results supporting the 90% stopping probability claims and our claims for the risk accrued in the first round. Further, ATHENA reduces the number of ballots by more than a quarter for low margins, when compared to the BRAVO stopping rule applied on ballots in selection order. Thus ATHENA is more efficient even without information on selection order. These results are significant because current approaches to real ballot polling election audits use the B2 BRAVO rules, requiring about twice as much work on the part of election officials. Many states performing risk-limiting audits for the first time plan to use ballot polling audits in November 2020 for the US Presidential election and could substantially benefit from improvements.
MGPU-TSM: A Multi-GPU System with Truly Shared Memory

The sizes of GPU applications are rapidly growing. They are exhausting the compute and memory resources of a single GPU, and are demanding the move to multiple GPUs. However, the performance of these applications scales sub-linearly with GPU count because of the overhead of data movement across multiple GPUs. Moreover, a lack of hardware support for coherency exacerbates the problem because a programmer must either replicate the data across GPUs or fetch the remote data using high-overhead off-chip links. To address these problems, we propose a multi-GPU system with truly shared memory (MGPU-TSM), where the main memory is physically shared across all the GPUs. We eliminate remote accesses and avoid data replication using an MGPU-TSM system, which simplifies the memory hierarchy. Our preliminary analysis shows that MGPU-TSM with 4 GPUs performs, on average, 3.9x? better than the current best performing multi-GPU configuration for standard application benchmarks.
Deep Learning Based Defect Detection for Solder Joints on Industrial X-Ray Circuit Board Images

Quality control is of vital importance during electronics production. As the methods of producing electronic circuits improve, there is an increasing chance of solder defects during assembling the printed circuit board (PCB). Many technologies have been incorporated for inspecting failed soldering, such as X-ray imaging, optical imaging, and thermal imaging. With some advanced algorithms, the new technologies are expected to control the production quality based on the digital images. However, current algorithms sometimes are not accurate enough to meet the quality control. Specialists are needed to do a follow-up checking. For automated X-ray inspection, joint of interest on the X-ray image is located by region of interest (ROI) and inspected by some algorithms. Some incorrect ROIs deteriorate the inspection algorithm. The high dimension of X-ray images and the varying sizes of image dimensions also challenge the inspection algorithms. On the other hand, recent advances on deep learning shed light on image-based tasks and are competitive to human levels. In this paper, deep learning is incorporated in X-ray imaging based quality control during PCB quality inspection. Two artificial intelligence (AI) based models are proposed and compared for joint defect detection. The noised ROI problem and the varying sizes of imaging dimension problem are addressed. The efficacy of the proposed methods are verified through experimenting on a real-world 3D X-ray dataset. By incorporating the proposed methods, specialist inspection workload is largely saved.
A Neural-Symbolic Framework for Mental Simulation

We present a neural-symbolic framework for observing the environment and continuously learning visual semantics and intuitive physics to reproduce them in an interactive simulation. The framework consists of five parts, a neural-symbolic hybrid network based on capsules for inverse graphics, an episodic memory to store observations, an interaction network for intuitive physics, a meta-learning agent that continuously improves the framework and a querying language that acts as the framework's interface for simulation. By means of lifelong meta-learning, the capsule network is expanded and trained continuously, in order to better adapt to its environment with each iteration. This enables it to learn new semantics using a few-shot approach and with minimal input from an oracle over its lifetime. From what it learned through observation, the part for intuitive physics infers all the required physical properties of the objects in a scene, enabling predictions. Finally, a custom query language ties all parts together, which allows to perform various mental simulation tasks, such as navigation, sorting and simulation of a game environment, with which we illustrate the potential of our novel approach.
Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering

Healthcare question answering assistance aims to provide customer healthcare information, which widely appears in both Web and mobile Internet. The questions usually require the assistance to have proficient healthcare background knowledge as well as the reasoning ability on the knowledge. Recently a challenge involving complex healthcare reasoning, HeadQA dataset, has been proposed, which contains multiple-choice questions authorized for the public healthcare specialization exam. Unlike most other QA tasks that focus on linguistic understanding, HeadQA requires deeper reasoning involving not only knowledge extraction, but also complex reasoning with healthcare knowledge. These questions are the most challenging for current QA systems, and the current performance of the state-of-the-art method is slightly better than a random guess. In order to solve this challenging task, we present a Multi-step reasoning with Knowledge extraction framework (MurKe). The proposed framework first extracts the healthcare knowledge as supporting documents from the large corpus. In order to find the reasoning chain and choose the correct answer, MurKe iterates between selecting the supporting documents, reformulating the query representation using the supporting documents and getting entailment score for each choice using the entailment model. The reformulation module leverages selected documents for missing evidence, which maintains interpretability. Moreover, we are striving to make full use of off-the-shelf pre-trained models. With less trainable weight, the pre-trained model can easily adapt to healthcare tasks with limited training samples. From the experimental results and ablation study, our system is able to outperform several strong baselines on the HeadQA dataset.
Technical note: the measurement of soft faeces production is affected by the type of collar.

The present study aimed to compare two types of plastic collar, either a rigid and flat one or a flexible and conical one, for collecting sofl faeces. Measurements were performed in two successive series (separated by 5 days) on a group of 10 hybrid adult rabbits (mean live weight = 3590 g) fed ad libitum a commercial pelleted diet. Rabbits equipped with a flat collar showed a slightly higher (not significant) feed intake, and a higher hard faeces excretion (+ 10%). The quantity of soft faeces collected was 20% higher (P=0.05) in rabbits wearing a flat collar, compared to a conical one (resp. 28.2 vs 22.0 g dry matter/day). The use of a flat and rigid collar instead of a flexible one is recommended to obtain a more accurate total collection of soft faeces in adult rabbit.
PrismDB: Read-aware Log-structured Merge Trees for Heterogeneous Storage

In recent years, emerging hardware storage technologies have focused on divergent goals: better performance or lower cost-per-bit of storage. Correspondingly, data systems that employ these new technologies are optimized either to be fast (but expensive) or cheap (but slow). We take a different approach: by combining multiple tiers of fast and low-cost storage technologies within the same system, we can achieve a Pareto-efficient balance between performance and cost-per-bit. This paper presents the design and implementation of PrismDB, a novel log-structured merge tree based key-value store that exploits a full spectrum of heterogeneous storage technologies (from 3D XPoint to QLC NAND). We introduce the notion of "read-awareness" to log-structured merge trees, which allows hot objects to be pinned to faster storage, achieving better tiering and hot-cold separation of objects. Compared to the standard use of RocksDB on flash in datacenters today, PrismDB's average throughput on heterogeneous storage is 2.3$\times$ faster and its tail latency is more than an order of magnitude better, using hardware than is half the cost.
CrowDEA: Multi-view Idea Prioritization with Crowds

Given a set of ideas collected from crowds with regard to an open-ended question, how can we organize and prioritize them in order to determine the preferred ones based on preference comparisons by crowd evaluators? As there are diverse latent criteria for the value of an idea, multiple ideas can be considered as “the best”. In addition, evaluators can have different preference criteria, and their comparison results often disagree. In this paper, we propose an analysis method for obtaining a subset of ideas, which we call frontier ideas, that are the best in terms of at least one latent evaluation criterion. We propose an approach, called CrowDEA, which estimates the embeddings of the ideas in the multiple-criteria preference space, the best viewpoint for each idea, and preference criterion for each evaluator, to obtain a set of frontier ideas. Experimental results using real datasets containing numerous ideas or designs demonstrate that the proposed approach can effectively prioritize ideas from multiple viewpoints, thereby detecting frontier ideas. The embeddings of ideas learned by the proposed approach provide a visualization that facilitates observation of the frontier ideas. In addition, the proposed approach prioritizes ideas from a wider variety of viewpoints, whereas the baselines tend to use to the same viewpoints; it can also handle various viewpoints and prioritize ideas in situations where only a limited number of evaluators or labels are available.
Exact, Parallelizable Dynamic Time Warping Alignment with Linear Memory

Audio alignment is a fundamental preprocessing step in many MIR pipelines. For two audio clips with M and N frames, respectively, the most popular approach, dynamic time warping (DTW), has O(MN) requirements in both memory and computation, which is prohibitive for frame-level alignments at reasonable rates. To address this, a variety of memory efficient algorithms exist to approximate the optimal alignment under the DTW cost. To our knowledge, however, no exact algorithms exist that are guaranteed to break the quadratic memory barrier. In this work, we present a divide and conquer algorithm that computes the exact globally optimal DTW alignment using O(M+N) memory. Its runtime is still O(MN), trading off memory for a 2x increase in computation. However, the algorithm can be parallelized up to a factor of min(M, N) with the same memory constraints, so it can still run more efficiently than the textbook version with an adequate GPU. We use our algorithm to compute exact alignments on a collection of orchestral music, which we use as ground truth to benchmark the alignment accuracy of several popular approximate alignment schemes at scales that were not previously possible.
A Survey of Smart Contract Formal Specification and Verification

A smart contract is a computer program that allows users to automate their actions on the blockchain platform. Given the significance of smart contracts in supporting important activities across industry sectors including supply chain, finance, legal, and medical services, there is a strong demand for verification and validation techniques. Yet, the vast majority of smart contracts lack any kind of formal specification, which is essential for establishing their correctness. In this survey, we investigate formal models and specifications of smart contracts presented in the literature and present a systematic overview to understand the common trends. We also discuss the current approaches used in verifying such property specifications and identify gaps with the hope to recognize promising directions for future work.
Integrin α5/β1 Mediates Fibronectin-dependent Epithelial Cell Proliferation through Epidermal Growth Factor Receptor Activation

Human integrin α5 was transfected into the integrin α5/β1–negative intestinal epithelial cell line Caco-2 to study EGF receptor (EGFR) and integrin α5/β1 signaling interactions involved in epithelial cell proliferation. On uncoated or fibronectin-coated plastic, the integrin α5 and control (vector only) transfectants grew at similar rates. In the presence of the EGFR antagonistic mAb 225, the integrin α5 transfectants and controls were significantly growth inhibited on plastic. However, when cultured on fibronectin, the integrin α5 transfectants were not growth inhibited by mAb 225. The reversal of mAb 225–mediated growth inhibition on fibronectin for the integrin α5 transfectants correlated with activation of the EGFR, activation of MAPK, and expression of proliferating cell nuclear antigen. EGFR kinase activity was necessary for both MAPK activation and integrin α5/β1–mediated cell proliferation. Although EGFR activation occurred when either the integrin α5–transfected or control cells were cultured on fibronectin, coprecipitation of the EGFR with SHC could be demonstrated only in the integrin α5–transfected cells. These results suggest that integrin α5/β1 mediates fibronectin-induced epithelial cell proliferation through activation of the EGFR.
Hybrid Transformer/CTC Networks for Hardware Efficient Voice Triggering

We consider the design of two-pass voice trigger detection systems. We focus on the networks in the second pass that are used to re-score candidate segments obtained from the first-pass. Our baseline is an acoustic model(AM), with BiLSTM layers, trained by minimizing the CTC loss. We replace the BiLSTM layers with self-attention layers. Results on internal evaluation sets show that self-attention networks yield better accuracy while requiring fewer parameters. We add an auto-regressive decoder network on top of the self-attention layers and jointly minimize the CTC loss on the encoder and the cross-entropy loss on the decoder. This design yields further improvements over the baseline. We retrain all the models above in a multi-task learning(MTL) setting, where one branch of a shared network is trained as an AM, while the second branch classifies the whole sequence to be true-trigger or not. Results demonstrate that networks with self-attention layers yield $\sim$60% relative reduction in false reject rates for a given false-alarm rate, while requiring 10% fewer parameters. When trained in the MTL setup, self-attention networks yield further accuracy improvements. On-device measurements show that we observe 70% relative reduction in inference time. Additionally, the proposed network architectures are $\sim$5X faster to train.