Miracle-dz/UniFAR-FTUs huggingface dataset:
Generic Neural Architecture Search via Regression

Most existing neural architecture search (NAS) algorithms are dedicated to and evaluated by the downstream tasks, e.g., image classification in computer vision. However, extensive experiments have shown that, prominent neural architectures, such as ResNet in computer vision and LSTM in natural language processing, are generally good at extracting patterns from the input data and perform well on different downstream tasks. In this paper, we attempt to answer two fundamental questions related to NAS. (1) Is it necessary to use the performance of specific downstream tasks to evaluate and search for good neural architectures? (2) Can we perform NAS effectively and efficiently while being agnostic to the downstream tasks? To answer these questions, we propose a novel and generic NAS framework, termed Generic NAS (GenNAS). GenNAS does not use task-specific labels but instead adopts regression on a set of manually designed synthetic signal bases for architecture evaluation. Such a self-supervised regression task can effectively evaluate the intrinsic power of an architecture to capture and transform the input signal patterns, and allow more sufficient usage of training samples. Extensive experiments across 13 CNN search spaces and one NLP space demonstrate the remarkable efficiency of GenNAS using regression, in terms of both evaluating the neural architectures (quantified by the ranking correlation Spearman's rho between the approximated performances and the downstream task performances) and the convergence speed for training (within a few seconds).
The Increase of the Airfoil Trailing Edge Noise and Unsteady Surface Pressure due to High Inflow Turbulence

Human factors, specifically visual impact and noise production, are the current main limitations for broader urban wind energy exploitation. Trailing edge noise, caused by the turbulent boundary layer interacting with the airfoil surface, is the primary source of noise of modern horizontal and vertical axis wind turbines. Low inflow turbulence levels do not affect the trailing edge noise. However, an inflow of high turbulence intensity has flow structures that can penetrate the boundary layer, increasing the velocity fluctuations inside the boundary layer and, consequently, the wall pressure fluctuations and trailing edge noise. This research investigates the effect of high free-stream turbulence, observed in the atmospheric boundary layer of urban zones, in the trailing edge noise generation. This was performed by measuring the increment of the turbulence inside of the boundary layer and surface pressure fluctuations near the trailing edge when an airfoil is submitted to high inflow turbulence. Experimental measurements were performed in the Aeroacoustic Wind Tunnel of the University of Twente on a NACA 0012 airfoil subjected to a turbulent inflow. The chord-based Reynolds number of the experiments ranged from 170 x 10^3 to 500 x 10^3. Results showed that high inflow turbulence significantly increases the velocity fluctuations and the integral length scale along the entire boundary layer, resulting in an increment of the surface pressure spectrum more than 6 dB and 10 dB in the entire frequency range for 10% and 20% of free-stream turbulence, respectively. The 10% free-stream turbulence increases the velocity fluctuations just in the low--frequency range, whereas the 20% inflow turbulence influences the velocity spectrum in the entire frequency range, increasing the size of the smallest structures of the turbulence. Amiet's theory is applied to predict the trailing edge far-field noise.
Linear regression under model uncertainty

We reexamine the classical linear regression model when the model is subject to two types of uncertainty: (i) some of covariates are either missing or completely inaccessible, and (ii) the variance of the measurement error is undetermined and changing according to a mechanism unknown to the statistician. By following the recent theory of sublinear expectation, we propose to characterize such mean and variance uncertainty in the response variable by two specific nonlinear random variables, which encompass an infinite family of probability distributions for the response variable in the sense of (linear) classical probability theory. The approach enables a family of estimators under various loss functions for the regression parameter and the parameters related to model uncertainty. The consistency of the estimators is established under mild conditions on the data generation process. Three applications are introduced to assess the quality of the approach including a forecasting model for the S\&P Index.
Composing Recurrent Spiking Neural Networks using Locally-Recurrent Motifs and Risk-Mitigating Architectural Optimization

In neural circuits, recurrent connectivity plays a crucial role in network function and stability. However, existing recurrent spiking neural networks (RSNNs) are often constructed by random connections without optimization. While RSNNs can produce rich dynamics that are critical for memory formation and learning, systemic architectural optimization of RSNNs is still an open challenge. We aim to enable systematic design of large RSNNs via a new scalable RSNN architecture and automated architectural optimization. We compose RSNNs based on a layer architecture called Sparsely-Connected Recurrent Motif Layer (SC-ML) that consists of multiple small recurrent motifs wired together by sparse lateral connections. The small size of the motifs and sparse inter-motif connectivity leads to an RSNN architecture scalable to large network sizes. We further propose a method called Hybrid Risk-Mitigating Architectural Search (HRMAS) to systematically optimize the topology of the proposed recurrent motifs and SC-ML layer architecture. HRMAS is an alternating two-step optimization process by which we mitigate the risk of network instability and performance degradation caused by architectural change by introducing a novel biologically-inspired"self-repairing"mechanism through intrinsic plasticity. The intrinsic plasticity is introduced to the second step of each HRMAS iteration and acts as unsupervised fast self-adaptation to structural and synaptic weight modifications introduced by the first step during the RSNN architectural"evolution". To the best of the authors' knowledge, this is the first work that performs systematic architectural optimization of RSNNs. Using one speech and three neuromorphic datasets, we demonstrate the significant performance improvement brought by the proposed automated architecture optimization over existing manually-designed RSNNs.
The k-Centre Selection Problem for Multidimensional Necklaces

This paper introduces the natural generalisation of necklaces to the multidimensional setting – multidimensional necklaces. One-dimensional necklaces are known as cyclic words, two-dimensional necklaces correspond to toroidal codes, and necklaces of dimension three can represent periodic motives in crystals. Our central results are two approximation algorithms for the k-Centre selection problem, where the task is to find k uniformly spaced objects within a set of necklaces. We show that it is NP-hard to verify a solution to this problem even in the one dimensional setting. This strong negative result is complimented with two polynomialtime approximation algorithms. In one dimension we provide a 1 + f(k,N) approximation algorithm where f(k,N) = logq (kN) N−logq (kN) − log 2 q(kN) 2N(N−logq (kN)) . For two dimensions we give a 1 + g(k,N) approximation algorithm where g(k,N) = logq (kN) N−logq (kN) − log 2 q(k) 2N(N−logq (kN)) . In both cases N is the size of the necklaces and q the size of the alphabet. Alongside our results for these new problems, we also provide the first polynomial time algorithms for counting, generating, ranking and unranking multidimensional necklaces.
An Empirical Evaluation of End-to-End Polyphonic Optical Music Recognition

Previous work has shown that neural architectures are able to perform optical music recognition (OMR) on monophonic and homophonic music with high accuracy. However, piano and orchestral scores frequently exhibit polyphonic passages, which add a second dimension to the task. Monophonic and homophonic music can be described as homorhythmic, or having a single musical rhythm. Polyphonic music, on the other hand, can be seen as having multiple rhythmic sequences, or voices, concurrently. We first introduce a workflow for creating large-scale polyphonic datasets suitable for end-to-end recognition from sheet music publicly available on the MuseScore forum. We then propose two novel formulations for end-to-end polyphonic OMR -- one treating the problem as a type of multi-task binary classification, and the other treating it as multi-sequence detection. Building upon the encoder-decoder architecture and an image encoder proposed in past work on end-to-end OMR, we propose two novel decoder models -- FlagDecoder and RNNDecoder -- that correspond to the two formulations. Finally, we compare the empirical performance of these end-to-end approaches to polyphonic OMR and observe a new state-of-the-art performance with our multi-sequence detection decoder, RNNDecoder.
CellProfiler Analyst 3.0: accessible data exploration and machine learning for image analysis

Abstract Summary Image-based experiments can yield many thousands of individual measurements describing each object of interest, such as cells in microscopy screens. CellProfiler Analyst is a free, open-source software package designed for the exploration of quantitative image-derived data and the training of machine learning classifiers with an intuitive user interface. We have now released CellProfiler Analyst 3.0, which in addition to enhanced performance adds support for neural network classifiers, identifying rare object subsets, and direct transfer of objects of interest from visualization tools into the Classifier tool for use as training data. This release also increases interoperability with the recently released CellProfiler 4, making it easier for users to detect and measure particular classes of objects in their analyses. Availability CellProfiler Analyst binaries for Windows and MacOS are freely available for download at https://cellprofileranalyst.org/. Source code is implemented in Python 3 and is available at https://github.com/CellProfiler/CellProfiler-Analyst/. A sample dataset is available at https://cellprofileranalyst.org/examples, based on images freely available from the Broad Bioimage Benchmark Collection.
Lifted contact dynamics for efficient direct optimal control of rigid body systems with contacts

We propose a novel and efficient lifting approach for the direct optimal control of rigid-body systems with contacts to improve the convergence properties of Newtontype methods. To relax the high nonlinearity, we consider all variables, including the state, acceleration, contact forces, and control input torques, as optimization variables and the inverse dynamics and acceleration-level contact constraints as equality constraints. We eliminate the update of the acceleration, contact forces, and their dual variables from the linear equation to be solved in each Newton-type iteration in an efficient manner. As a result, the computational cost per Newton-type iteration is almost identical to that of the conventional non-lifted Newtontype iteration that embeds contact dynamics in the state equation. We conducted numerical experiments on the wholebody optimal control of various quadrupedal gaits subject to the friction cone constraints considered in interior-point methods and demonstrated that the proposed method can significantly increase the convergence speed to more than twice that of the conventional non-lifted approach.
Hyperparameter-free and Explainable Whole Graph Embedding

Graphs can be used to describe complex systems. Recently, whole graph embedding (graph representation learning) can compress a graph into a compact lower-dimension vector while preserving intrinsic properties, earning much attention. However, most graph embedding methods have problems such as tedious parameter tuning or poor explanation. This paper presents a simple and hyperparameter-free whole graph embedding method based on the DHC (Degree, H-index, and Coreness) theorem and Shannon Entropy (E), abbreviated as DHC-E. The DHC-E can provide a trade-off between simplicity and quality for supervised classification learning tasks involving molecular, social, and brain networks. Moreover, it performs well in lower-dimensional graph visualization. Overall, the DHC-E is simple, hyperparameter-free, and explainable for whole graph embedding with promising potential for exploring graph classification and lower-dimensional graph visualization.
The impact of traceability on software maintenance and evolution: A mapping study

Software traceability plays a critical role in software maintenance and evolution. We conducted a systematic mapping study with six research questions to understand the benefits, costs, and challenges of using traceability in maintenance and evolution. We systematically selected, analyzed, and synthesized 63 studies published between January 2000 and May 2020, and the results show that traceability supports 11 maintenance and evolution activities, among which change management is the most frequently supported activity; strong empirical evidence from industry is needed to validate the impact of traceability on maintenance and evolution; easing the process of change management is the main benefit of deploying traceability practices; establishing and maintaining traceability links is the main cost of deploying traceability practices; 13 approaches and 32 tools that support traceability in maintenance and evolution were identified; improving the quality of traceability links, the performance of using traceability approaches, and tools are the main traceability challenges in maintenance and evolution. The findings of this study provide a comprehensive understanding of deploying traceability practices in software maintenance and evolution phase and can be used by researchers for future directions and practitioners for making informed decisions while using traceability in maintenance and evolution.
Scalable Community Detection via Parallel Correlation Clustering

Graph clustering and community detection are central problems in modern data mining. The increasing need for analyzing billion-scale data calls for faster and more scalable algorithms for these problems. There are certain trade-offs between the quality and speed of such clustering algorithms. In this paper, we design scalable algorithms that achieve high quality when evaluated based on ground truth. We develop a generalized sequential and shared-memory parallel framework based on the LAMBDACC objective (introduced by Veldt et al.), which encompasses modularity and correlation clustering. Our framework consists of highly-optimized implementations that scale to large data sets of billions of edges and that obtain high-quality clusters compared to ground-truth data, on both unweighted and weighted graphs. Our empirical evaluation shows that this framework improves the state-of-the-art trade-offs between speed and quality of scalable community detection. For example, on a 30-core machine with two-way hyper-threading, our implementations achieve orders of magnitude speedups over other correlation clustering baselines, and up to 28.44× speedups over our own sequential baselines while maintaining or improving quality.
A Systematic Assessment on Android Third-Party Library Detection Tools

Third-party libraries (TPLs) have become a significant part of the Android ecosystem. Developers can employ various TPLs to facilitate their app development. Unfortunately, the popularity of TPLs also brings new security issues. For example, TPLs may carry malicious or vulnerable code, which can infect popular apps to pose threats to mobile users. Furthermore, TPL detection is essential for downstream tasks, such as vulnerabilities and malware detection. Thus, various tools have been developed to identify TPLs. However, no existing work has studied these TPL detection tools in detail, and different tools focus on different applications and techniques with performance differences. A comprehensive understanding of these tools will help us make better use of them. To this end, we conduct a comprehensive empirical study to fill the gap by evaluating and comparing all publicly available TPL detection tools based on six criteria: accuracy of TPL construction, effectiveness, efficiency, accuracy of version identification, resiliency to code obfuscation, and ease of use. Besides, we enhance these open-source tools by fixing their limitations, to improve their detection ability. Finally, we build an extensible framework that integrates all existing available TPL detection tools, providing an online service for the research community. We release the evaluation dataset and enhanced tools. According to our study, we also present the essential findings and discuss promising implications to the community; e.g., 1) Most existing TPL detection techniques more or less depend on package structure to construct in-app TPL candidates. However, using package structure as the module decoupling feature is error-prone. We hence suggest future researchers using the class dependency to substitute package structure. 2) Extracted features include richer semantic information (e.g., class dependencies) can achieve better resiliency to code obfuscation. 3) Existing tools usually have a low recall; that is because previous tools ignore some features of Android apps and TPLs, such as the compilation mechanism, the new format of TPLs, TPL dependency. Most existing tools cannot effectively find partial import TPLs, obfuscated TPLs, which directly limit their capability. 4) Existing tools are complementary to each other; we can build a better tool via combining the advantages of each tool. We believe our work provides a clear picture of existing TPL detection techniques and also gives a road-map for future research.
1.71 Tb/s Single-Channel and 56.51 Tb/s DWDM Transmission Over 96.5 km Field-Deployed SSMF

We report an industry leading optical dense wavelength division multiplexing (DWDM) field trial with line rates per channel $\ge 1.66$ Tb/s using 130 GBaud dual-polarization probabilistic constellation shaping 256-ary quadrature amplitude modulation (DP-PCS256QAM) in a high capacity data center interconnect (DCI) scenario. This research trial was performed on 96.5 km of field-deployed standard single mode G.652 fiber infrastructure of Deutsche Telekom in Germany employing Erbium-doped fiber amplifier (EDFA)-only amplification. A total of 34 channels were transmitted with 150 GHz spacing for a total fiber capacity of 56.51 Tb/s and a spectral efficiency higher than 11bit/s/Hz. In the single-channel transmission scenario 1.71 Tb/s was achieved over the same link. In addition, we successfully demonstrate record net bitrates of 1.88 Tb/s in back-to-back (B2B) using 130 GBaud DP-PCS400QAM.
Summary Explorer: Visualizing the State of the Art in Text Summarization

This paper introduces Summary Explorer, a new tool to support the manual inspection of text summarization systems by compiling the outputs of 55 state-of-the-art single document summarization approaches on three benchmark datasets, and visually exploring them during a qualitative assessment. The underlying design of the tool considers three well-known summary quality criteria (coverage, faithfulness, and position bias), encapsulated in a guided assessment based on tailored visualizations. The tool complements existing approaches for locally debugging summarization models and improves upon them. The tool is available at https://tldr.webis. de/
2QAN: a quantum compiler for 2-local qubit hamiltonian simulation algorithms

Simulating quantum systems is one of the most important potential applications of quantum computers. The high-level circuit defining the simulation needs to be compiled into one that complies with hardware limitations such as qubit architecture (connectivity) and instruction (gate) set. General-purpose quantum compilers work at the gate level and have little knowledge of the mathematical properties of quantum applications, missing further optimization opportunities. Existing application-specific compilers only apply advanced optimizations in the scheduling procedure and are restricted to the CNOT or CZ gate set. In this work, we develop a compiler, named 2QAN, to optimize quantum circuits for 2-local qubit Hamiltonian simulation problems, a framework which includes the important quantum approximate optimization algorithm (QAOA). In particular, we exploit the flexibility of permuting different operators in the Hamiltonian (no matter whether they commute) and propose permutation-aware techniques for qubit routing, gate optimization and scheduling to minimize compilation overhead. 2QAN can target different architectures and different instruction sets. Compilation results on four applications (up to 50 qubits) and three quantum computers (namely, Google Sycamore, IBMQ Montreal and Rigetti Aspen) show that 2QAN outperforms state-of-the-art general-purpose compilers and application-specific compilers. Specifically, 2QAN can reduce the number of inserted SWAP gates by 11.5X, reduce overhead in hardware gate count by 68.5X, and reduce overhead in circuit depth by 21X. Experimental results on the Montreal device demonstrate that benchmarks compiled by 2QAN achieve the highest fidelity.
HTTP2vec: Embedding of HTTP Requests for Detection of Anomalous Traffic

Hypertext transfer protocol (HTTP) is one of the most widely used protocols on the Internet. As a consequence, most attacks (i.e., SQL injection, XSS) use HTTP as the transport mechanism. Therefore, it is crucial to develop an intelligent solution that would allow to effectively detect and filter out anomalies in HTTP traffic. Currently, most of the anomaly detection systems are either rule-based or trained using manually selected features. We propose utilizing modern unsupervised language representation model for embedding HTTP requests and then using it to classify anomalies in the traffic. The solution is motivated by methods used in Natural Language Processing (NLP) such as Doc2Vec which could potentially capture the true understanding of HTTP messages, and therefore improve the efficiency of Intrusion Detection System. In our work, we not only aim at generating a suitable embedding space, but also at the interpretability of the proposed model. We decided to use the current state-of-the-art RoBERTa, which, as far as we know, has never been used in a similar problem. To verify how the solution would work in real word conditions, we train the model using only legitimate traffic. We also try to explain the results based on clusters that occur in the vectorized requests space and a simple logistic regression classifier. We compared our approach with the similar, previously proposed methods. We evaluate the feasibility of our method on three different datasets: CSIC2010, CSE-CIC-IDS2018 and one that we prepared ourselves. The results we show are comparable to others or better, and most importantly - interpretable.