Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Methods for Automated Algorithm Configuration

Algorithm configuration (AC) is concerned with the automated search of the most suitable parameter configuration of a parametrized algorithm. There is currently a wide variety of AC problem variants and methods proposed in the literature. Existing reviews do not take into account all derivatives of the AC problem, nor do they offer a complete classification scheme. To this end, we introduce taxonomies to describe the AC problem and features of configuration methods, respectively. We review existing AC literature within the lens of our taxonomies, outline relevant design choices of configuration approaches, contrast methods and problem variants against each other, and describe the state of AC in industry. Finally, our review provides researchers and practitioners with a look at future research directions in the field of AC.
Variance-Optimal Augmentation Logging for Counterfactual Evaluation in Contextual Bandits

Methods for offline A/B testing and counterfactual learning are seeing rapid adoption in search and recommender systems, since they allow efficient reuse of existing log data. However, there are fundamental limits to using existing log data alone, since the counterfactual estimators that are commonly used in these methods can have large bias and large variance when the logging policy is very different from the target policy being evaluated. To overcome this limitation, we explore the question of how to design data-gathering policies that most effectively augment an existing dataset of bandit feedback with additional observations for both learning and evaluation. To this effect, this paper introduces Minimum Variance Augmentation Logging (MVAL), a method for constructing logging policies that minimize the variance of the downstream evaluation or learning problem. We explore multiple approaches to computing MVAL policies efficiently, and find that they can be substantially more effective in decreasing the variance of an estimator than näıve approaches.
Towards Understanding CNAME based First-Party Cookie Tracking in the Field

Third-party web tracking is a common, and broadly used technique on the Web. Almost every step of users’ is tracked, analyzed, and later used in diﬀerent use cases (e.g., online advertisement). Diﬀerent defense mechanisms have emerged to counter these practices (e.g., the recent step of browser vendors to ban all third-party cookies). However, all of these countermeasures only target third-party trackers, and ignore the ﬁrst party because the narrative is that such monitoring is mostly used to improve the utilized service (e.g., analytical services). In this paper, we present a large-scale measurement study that analyzes tracking performed by the ﬁrst party but utilized by a third party to circumvent standard tracking preventing techniques (i.e., the ﬁrst party performs the tracking in the name of the third party). We visit the top 15,000 websites to analyze ﬁrst-party cookies used to track users and a technique called “DNS CNAME cloaking”, which can be used by a third party to place ﬁrst-party cookies. Using this data, we show that 76% sites in our dataset eﬀectively utilize such tracking techniques, and in a long-running analysis we show that the usage of such cookies increased by more than 50% over 2021. Furthermore, we shed light on the ecosystem utilizing ﬁrst-party trackers, and ﬁnd that the established trackers already use such tracking, presumably to avoid tracking blocking.
Training a Bidirectional GAN-based One-Class Classifier for Network Intrusion Detection

The network intrusion detection task is challenging because of the imbalanced and unlabeled nature of the dataset it operates on. Existing generative adversarial networks (GANs), are primarily used for creating synthetic samples from reals. They also have been proved successful in anomaly detection tasks. In our proposed method, we construct the trained encoder-discriminator as a one-class classifier based on Bidirectional GAN (Bi-GAN) for detecting anomalous traffic from normal traffic other than calculating expensive and complex anomaly scores or thresholds. Our experimental result illustrates that our proposed method is highly effective to be used in network intrusion detection tasks and outperforms other similar generative methods on the NSL-KDD dataset.
Data-Driven Stochastic Optimal Control using Linear Transfer Operators

We provide a data-driven framework for optimal control of a continuous-time stochastic dynamical system. The proposed framework relies on the linear operator theory involving linear Perron-Frobenius (P-F) and Koopman operators. Our first results involving the P-F operator provide a convex formulation to the optimal control problem in the dual space of densities. This convex formulation of the stochastic optimal control problem leads to an infinite-dimensional convex program. The finite-dimensional approximation of the convex program is obtained using a data-driven approximation of the P-F operator. Our second results demonstrate the use of the Koopman operator, which is dual to the P-F operator, for the stochastic optimal control design. We show that the Hamilton Jacobi Bellman (HJB) equation can be expressed using the Koopman operator. We provide an iterative procedure along the lines of a popular policy iteration algorithm based on the data-driven approximation of the Koopman operator for solving the HJB equation. The two formulations, namely the convex formulation involving P-F operator and Koopman based formulation using HJB equation, can be viewed as dual to each other where the duality follows due to the dual nature of P-F and Koopman operators. Finally, we present several numerical examples to demonstrate the efficacy of the developed framework.
PromptSource: An Integrated Development Environment and Repository for Natural Language Prompts

PromptSource is a system for creating, sharing, and using natural language prompts. Prompts are functions that map an example from a dataset to a natural language input and target output. Using prompts to train and query language models is an emerging area in NLP that requires new tools that let users develop and refine these prompts collaboratively. PromptSource addresses the emergent challenges in this new setting with (1) a templating language for defining data-linked prompts, (2) an interface that lets users quickly iterate on prompt development by observing outputs of their prompts on many examples, and (3) a community-driven set of guidelines for contributing new prompts to a common pool. Over 2,000 prompts for roughly 170 datasets are already available in PromptSource. PromptSource is available at https://github.com/bigscience-workshop/promptsource.
VindiCo: Privacy Safeguard Against Adaptation Based Spyware in Human-in-the-Loop IoT

Personalized IoT adapts their behavior based on contextual information, such as user behavior and location. Unfortunately, the fact that personalized IoT adapts to user context opens a side-channel that leaks private information about the user. To that end, we start by studying the extent to which a malicious eavesdropper can monitor the actions taken by an IoT system and extract users' private information. In particular, we show two concrete instantiations (in the context of mobile phones and smart homes) of a new category of spyware which we refer to as Context-Aware Adaptation Based Spyware (SpyCon). Experimental evaluations show that the developed SpyCon can predict users' daily behavior with an accuracy of 90.3%. The rest of this paper is devoted to introducing VindiCo, a software mechanism designed to detect and mitigate possible SpyCon. Being new spyware with no known prior signature or behavior, traditional spyware detection that is based on code signature or app behavior is not adequate to detect SpyCon. Therefore, VindiCo proposes a novel information-based detection engine along with several mitigation techniques to restrain the ability of the detected SpyCon to extract private information. By having general detection and mitigation engines, VindiCo is agnostic to the inference algorithm used by SpyCon. Our results show that VindiCo reduces the ability of SpyCon to infer user context from 90.3% to the baseline accuracy (accuracy based on random guesses) with negligible execution overhead.
A deep complex network with multi-frame filtering for stereophonic acoustic echo cancellation

In hands-free communication system, the coupling between the loudspeaker and the microphone will generate echo signal, which can severely impair the quality of communication. Mean-while, various types of noise in the communication environment further destroy the speech quality and intelligibility. It is hard to extract the near-end signal from the microphone input signal within one step, especially in low signal-to-noise ratios. In this paper, we propose a multi-stage approach to address this issue. On the one hand, we decompose the echo cancellation into two stages, including linear echo cancellation module and residual echo suppression module. A multi-frame ﬁltering strategy is introduced to beneﬁt estimating linear echo by utilizing more inter-frame information. On the other hand, we decouple the complex spectral mapping into magnitude estimation and complex spectra reﬁne. Experimental results demonstrate that our proposed approach achieves stage-of-the-art performance over previous advanced algorithms under various conditions.
Cerebral oxygen extraction fraction MRI: Techniques and
 applications

The human brain constitutes 2% of the body's total mass but uses 20% of the oxygen. The rate of the brain's oxygen utilization can be derived from a knowledge of cerebral blood flow and the oxygen extraction fraction (OEF). Therefore, OEF is a key physiological parameter of the brain's function and metabolism. OEF has been suggested to be a useful biomarker in a number of brain diseases. With recent advances in MRI techniques, several MRI‐based methods have been developed to measure OEF in the human brain. These MRI OEF techniques are based on the T2 of blood, the blood signal phase, the magnetic susceptibility of blood‐containing voxels, the effect of deoxyhemoglobin on signal behavior in extravascular tissue, and the calibration of the BOLD signal using gas inhalation. Compared to 15O PET, which is considered the “gold standard” for OEF measurement, MRI‐based techniques are non‐invasive, radiation‐free, and are more widely available. This article provides a review of these emerging MRI‐based OEF techniques. We first briefly introduce the role of OEF in brain oxygen homeostasis. We then review the methodological aspects of different categories of MRI OEF techniques, including their signal mechanisms, acquisition methods, and data analyses. The strengths and limitations of the techniques are discussed. Finally, we review key applications of these techniques in physiological and pathological conditions.
Topology Optimization with Tetra-kai-decahedra and Spheroidal Masks

Funding information A novel meshing scheme, based on regular tetra-kaidecahedron, also referred to as truncated octahedron, cells is presented for use in spatial topology optimization. A tetrakai-decahedron mesh ensures face connectivity between elements thereby eliminating singular solutions from the solution space. Various other benefits of implementing the said mesh are also highlighted, and the corresponding finite element is introduced. Material mask overlay strategy orMMOS, a feature basedmethod for topology optimization is extended for use in 3-dimensions (MMOS-3D) via the aforementioned finite element and spheroidal negative masks. Formulation for density computation and sensitivity analysis for gradient based optimization is developed. Examples on traditional structural topology optimization problems are presented with detailed discussion on efficacy of the proposed approach.
Local Minimum Principle for an Optimal Control Problem with a Nonregular Mixed Constraint

We consider the simplest optimal control problem with one nonregular mixed constraint G(x, u) 6 0, i.e. when the gradient Gu(x, u) can vanish on the surface G = 0. Using the Dubovitskii–Milyutin theorem on the approximate separation of convex cones, we prove a first order necessary condition for a weak minimum in the form of the so-called “local minimum principle”, which is formulated in terms of functions of bounded variation, integrable functions, and LebesgueStieltjes measures, and does not use functionals from (L∞)∗ . Two illustrative examples are given. The work is based on the book by Milyutin [3].
Proton Decay in SUSY GUTs

I review proton decay in the SUSY SU(5) GUTs assuming the mini-split SUSY breaking model. In the mini-split SUSY breaking model, the squark and slepton masses are O(10^{(2-3)}) TeV while the gaugino masses are O(1) TeV. As the result, even the minimal SUSY SU(5) GUT is still viable in the model. We present the motivation of the mini-split SUSY model and discuss the future prospects of proton decay searches in the SUSY SU(5) GUTs.
Improving Lyrics Alignment Through Joint Pitch Detection

In recent years, the accuracy of automatic lyrics alignment methods has increased considerably. Yet, many current approaches employ frameworks designed for automatic speech recognition (ASR) and do not exploit properties specific to music. Pitch is one important musical attribute of singing voice but it is often ignored by current systems as the lyrics content is considered independent of the pitch. In practice, however, there is a temporal correlation between the two as note starts often correlate with phoneme starts. At the same time the pitch is usually annotated with high temporal accuracy in ground truth data while the timing of lyrics is often only available at the line (or word) level. In this paper, we propose a multi-task learning approach for lyrics alignment that incorporates pitch and thus can make use of a new source of highly accurate temporal information. Our results show that the accuracy of the alignment result is indeed improved by our approach. As an additional contribution, we show that integrating boundary detection in the forced-alignment algorithm reduces cross-line errors, which improves the accuracy even further.
Topological Classification in a Wasserstein Distance Based Vector Space

Classification of large and dense networks based on topology is very difficult due to the computational challenges of extracting meaningful topological features from real-world networks. In this paper we present a computationally tractable approach to topological classification of networks by using principled theory from persistent homology and optimal transport to define a novel vector representation for topological features. The proposed vector space is based on the Wasserstein distance between persistence barcodes. The 1-skeleton of the network graph is employed to obtain 1-dimensional persistence barcodes that represent connected components and cycles. These barcodes and the corresponding Wasserstein distance can be computed very efficiently. The effectiveness of the proposed vector space is demonstrated using support vector machines to classify simulated networks and measured functional brain networks. 1 ar X iv :2 20 2. 01 27 5v 1 [ cs . L G ] 2 F eb 2 02 2
PARCEL: Physics-based unsupervised contrastive representation learning for parallel MR imaging

—With the successful application of deep learning to magnetic resonance (MR) imaging, parallel imaging techniques based on neural networks have attracted wide attention. However, in the absence of high-quality, fully sampled datasets for training, the performance of these methods is limited. To address this issue, this paper proposes a Physics-bAsed unsupeRvised Contrastive rEpresentation Learning (PARCEL) method to speed up parallel MR imaging. Specifically, PARCEL has a parallel framework to contrastively learn two branches of model-based unrolling networks directly from augmented undersampled k-space data. A sophisticated co-training loss with three essential components has been designed to guide the two networks in capturing the inherent features and representations for MR images. And the final MR image is reconstructed with the trained contrastive networks. PARCEL was evaluated on in vivo datasets and compared to five state-of-the-art methods. The results show that PARCEL is able to learn useful representations for more accurate MR reconstructions without relying on fully sampled datasets. The code will be made available at https://github.com/ternencewu123/PARCEL.
Beamforming Feedback-based Model-Driven Angle of Departure Estimation Toward Legacy Support in WiFi Sensing: An Experimental Study

In this study, we experimentally validated the possibility of estimating the angle of departure (AoD) using multiple signal classification (MUSIC) with only WiFi control frames for beamforming feedback (BFF), defined in IEEE 802.11ac/ax. The examined BFF-based MUSIC is a model-driven algorithm that does not require a pre-obtained database. This is in contrast with most existing BFF-based sensing techniques, which are data-driven and require a pre-obtained database. Moreover, BFF-based MUSIC affords an alternative AoD estimation method without requiring access to the channel state information (CSI). Extensive experimental and numerical evaluations demonstrate that BFF-based MUSIC can successfully estimate the AoDs for multiple propagation paths. Moreover, the evaluations performed in this study reveal that BFF-based MUSIC, where BFF is a highly compressed version of CSI in IEEE 802.11ac/ax, achieves an error of AoD estimation that is comparable to that of CSI-based MUSIC.
An elementary proof of Ramanujan's identity for odd zeta values

The Riemann zeta function ζ(s) is one of the most important special functions of Mathematics. While the critical strip 0 < R (s) < 1 is undoubtedly the most important region in the complex plane on account of the unsolved problem regarding location of non-trivial zeros of ζ(s), namely, the Riemann Hypothesis, the right-half plane R (s) > 1 also has its own share of interesting unsolved problems to contribute to. It is quite well known that many number theoretic properties of odd zeta values are nowadays still unsolved mysteries, such as the rationality, transcendence and existence of closed-forms. Only in 1978 did Apéry [2] famously proved that ζ(3) is irrational. This was later reproved in a variety of ways by several authors, in particular Beukers [10] who devised a simple approach involving certain integrals over [0, 1]. In the early 2000s, an important work of Rivoal [21], and Ball and Rivoal [4] determined that infinitely many values of ζ at odd integers are irrational, and the work of Zudilin [27] proved that at least one among ζ(5), ζ(7), ζ(9) and ζ(11) is irrational. A very recent result due to Rivoal and Zudilin [22] states that at least two of the numbers ζ(5), ζ(7), . , ζ(69) are irrational. Moreover, for any pair of positive integers a and b, Haynes and Zudilin [17, Theorem 1] have shown that either there are infinitely many m ∈ N for which ζ(am+ b) is irrational, or the sequence {qm} ∞ m=1 of common denominators of the rational elements of the set {ζ(a+ b), . , ζ(am+ b)} grows super-exponentially, that is, q 1/m m → ∞ as m → ∞. Despite these advances, to this day no value of ζ(2n+ 1) with n > 2 is known to be irrational. A folklore conjecture states that the numbers π, ζ(3), ζ(5), ζ(7), . are algebraically independent over the rationals. This conjecture is predicted by the Grothendieck’s period conjecture for mixed Tate motives. But both conjectures are far out of reach and we do not even know the transcendence of a single odd zeta value. One should mention that Brown [11] has in the past few years outlined a simple geometric approach to understand the structures involved in Beukers’s proof of irrationality of ζ(3) and how this may generalize to other odd zeta values. Ramanujan made many beautiful and elegant discoveries in his short life of 32 years. One of the most remarkable formulas suggested by Ramanujan that has attracted the attention of several mathematicians over the years is the following intriguing identity involving the odd values of the Riemann zeta function [6, 1.2]:
Asymptotic Links between Signal Processing, Acoustic Metamaterials, and Biology

Biomimicry is a powerful science that takes advantage of nature's remarkable ability to devise innovative solutions to challenging problems. In this work, we use asymptotic methods to develop the mathematical foundations for the exchange of design inspiration and features between biological hearing systems, signal processing algorithms and acoustic metamaterials. Our starting point is a concise asymptotic analysis of high-contrast acoustic metamaterials. We are able to fine tune this graded structure to mimic the biomechanical properties of the cochlea, at the same scale. We then turn our attention to developing a biomimetic signal processing algorithm. We use the response of the cochlea-like metamaterial as an initial filtering layer and then add additional biomimetic processing stages, designed to mimic the human auditory system's ability to recognise the global properties of natural sounds. This demonstrates the three-way exchange of ideas that, thanks to our analysis, is possible between signal processing, metamaterials and biology.
Adversarial Rain Attack and Defensive Deraining for DNN Perception

Rain often poses inevitable threats to deep neural network (DNN) based perception systems, and a comprehensive investigation of the potential risks of the rain to DNNs is of great importance. However, it is rather difficult to collect or synthesize rainy images that can represent all rain situations that would possibly occur in the real world. To this end, in this paper, we start from a new perspective and propose to combine two totally different studies, i.e., rainy image synthesis and adversarial attack. We first present an adversarial rain attack, with which we could simulate various rain situations with the guidance of deployed DNNs and reveal the potential threat factors that can be brought by rain. In particular, we design a factor-aware rain generation that synthesizes rain streaks according to the camera exposure process and models the learnable rain factors for adversarial attack. With this generator, we perform the adversarial rain attack against the image classification and object detection. To defend the DNNs from the negative rain effect, we also present a defensive deraining strategy, for which we design an adversarial rain augmentation that uses mixed adversarial rain layers to enhance deraining models for downstream DNN perception. Our large-scale evaluation on various datasets demonstrates that our synthesized rainy images with realistic appearances not only exhibit strong adversarial capability against DNNs, but also boost the deraining models for defensive purposes, building the foundation for further rain-robust perception studies.