Miracle-dz/UniFAR-FTUs huggingface dataset:
Complex Neural Spatial Filter: Enhancing Multi-Channel Target Speech Separation in Complex Domain

To date, mainstream target speech separation (TSS) approaches are formulated to estimate the complex ratio mask (cRM) of target speech in time-frequency domain under supervised deep learning framework. However, the existing methods are designed in the way that the real and imaginary parts of the cRM are separately modeled using real-valued training data pairs. The research motivation of this study is to design a deep model that fully exploits the temporal-spectral-spatial information of multi-channel signals for estimating cRM directly and efficiently in complex domain. As a result, a novel TSS network is designed consisting of two modules, a complex neural spatial filter (cNSF) and an MVDR. Essentially, cNSF is a cRM estimation model and an MVDR module is cascaded to the cNSF module to reduce the nonlinear speech distortions introduced by neural network. Specifically, to fit the cRM target, all input features of cNSF are reformulated into complex-valued representations. Then, to achieve good hierarchical feature abstraction, a complex deep neural network (cDNN) is delicately designed with U-Net structure. Experiments conducted on simulated multi-channel speech data demonstrate the proposed cNSF outperforms the baseline NSF by 12.1% scale-invariant signal-to-distortion ratio and 33.1% word error rate.
Constructing long-short stock portfolio with a new listwise learn-to-rank algorithm

Factor strategies have gained growing popularity in industry with the fast development of machine learning. Usually, multi-factors are fed to an algorithm for some cross-sectional return predictions, which are then further used to construct a long-short portfolio. Instead of predicting the value of the stock return, emerging studies predict a ranked stock list using the mature learn-to-rank technology. In this study, we propose a new listwise learn-to-rank loss function which aims to emphasize both the top and the bottom of a rank list. Our loss function, motivated by the long-short strategy, is endogenously shift-invariant and can be viewed as a direct generalization of ListMLE. Under different transformation functions, our loss can lead to consistency with binary classification loss or permutation level 0-1 loss. A probabilistic explanation for our model is also given as a generalized Plackett-Luce model. Based on a dataset of 68 factors in the China A-share market from 2006 to 2019, our empirical study has demonstrated the strength of our method which achieves an out-of-sample annual return of 38% with Sharpe ratio 2.
Machine Learning based Lie Detector applied to a Collected and Annotated Dataset

—Lie detection is considered a concern for everyone in their day to day life given its impact on human interactions. Hence, people are normally not only pay attention to what their interlocutors are saying but also try to inspect their visual appearances, including faces, to ﬁnd any signs that indicate whether the person is telling the truth or not. Unfortunately to date, the automatic lie detection, which may help us to understand this lying characteristics are still fairly limited. Mainly due to lack of a lie dataset and corresponding evaluations. In this work, we have collected a dataset that contains annotated images and 3D information of different participants faces during a card game that incentivise the lying. Using our collected dataset, we evaluated several types of machine learning based lie detector through generalize, personal and cross lie lie experiments. In these experiments, we showed the superiority of deep learning based model in recognizing the lie with best accuracy of 57% for generalized task and 63% when dealing with a single participant. Finally, we also highlight the limitation of the deep learning based lie detector when dealing with different types of lie tasks.
M3DETR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers

We present a novel architecture for 3D object detection, M3DETR, which combines different point cloud representations (raw, voxels, bird-eye view) with different feature scales based on multi-scale feature pyramids. M3DETR is the first approach that unifies multiple point cloud representations, feature scales, as well as models mutual relationships between point clouds simultaneously using transformers. We perform extensive ablation experiments that highlight the benefits of fusing representation and scale, and modeling the relationships. Our method achieves state-of-the-art performance on the KITTI 3D object detection dataset and Waymo Open Dataset. Results show that M3DETR improves the baseline significantly by 1.48% mAP for all classes on Waymo Open Dataset. In particular, our approach ranks 1st on the well-known KITTI 3D Detection Benchmark for both car and cyclist classes, and ranks 1st on Waymo Open Dataset with single frame point cloud input. Our code is available at: https://github.com/rayguan97/M3DETR.
RULF: Rust Library Fuzzing via API Dependency Graph Traversal

Robustness is a key concern for Rust library development because Rust promises no risks of undefined behaviors if developers use safe APIs only. Fuzzing is a practical approach for examining the robustness of programs. However, existing fuzzing tools are not directly applicable to library APIs due to the absence of fuzz targets. It mainly relies on human efforts to design fuzz targets case by case which is labor-intensive. To address this problem, this paper proposes a novel automated fuzz target generation approach for fuzzing Rust libraries via API dependency graph traversal. We identify several essential requirements for library fuzzing, including validity and effectiveness of fuzz targets, high API coverage, and efficiency. To meet these requirements, we first employ breadth-first search with pruning to find API sequences under a length threshold, then we backward search longer sequences for uncovered APIs, and finally we optimize the sequence set as a set covering problem. We implement our fuzz target generator and conduct fuzzing experiments with AFL++ on several real-world popular Rust projects. Our tool finally generates 7 to 118 fuzz targets for each library with API coverage up to 0.92. We exercise each target with a threshold of 24 hours and find 30 previously-unknown bugs from seven libraries.
Spherical Formulation of Geometric Motion Segmentation Constraints in Fisheye Cameras

We introduce a visual motion segmentation method employing spherical geometry for fisheye cameras and automated driving. Three commonly used geometric constraints in pin-hole imagery (the positive height, positive depth and epipolar constraints) are reformulated to spherical coordinates, making them invariant to specific camera configurations as long as the camera calibration is known. A fourth constraint, known as the anti-parallel constraint, is added to resolve motion-parallax ambiguity, to support the detection of moving objects undergoing parallel or near-parallel motion with respect to the host vehicle. A final constraint constraint is described, known as the spherical three-view constraint, is described though not employed in our proposed algorithm. Results are presented and analyzed that demonstrate that the proposal is an effective motion segmentation approach for direct employment on fisheye imagery.
Identifying Offensive Expressions of Opinion in Context

Classic information extraction techniques consist in building questions and answers about the facts. Indeed, it is still a challenge to subjective information extraction systems to identify opinions and feelings in context. In sentiment-based NLP tasks, there are few resources to information extraction, above all offensive or hateful opinions in context. To fill this important gap, this short paper provides a new cross-lingual and contextual offensive lexicon, which consists of explicit and implicit offensive and swearing expressions of opinion, which were annotated in two different classes: context dependent and context-independent offensive. In addition, we provide markers to identify hate speech. Annotation approach was evaluated at the expression-level and achieves high human inter-annotator agreement. The provided offensive lexicon is available in Portuguese and English languages.
On the elegance of Ramanujan's series for $\pi$

Re presenting the traditional proof of Srinivasa Ramanujan’s own favorite series for the reciprocal of π : 1 π = √ 8 9801 +∞ ∑ n=0 (4n)! (n! )4 1103 + 26390n 3964n , as well as several other examples of Ramanujan’s infinite series. As a matter of fact, the derivation of such formulae has involved specialized knowledge of identities of classical functions and modular functions. The Archimedes’ constant π appears in many formulae [2] in various areas of mathematics and physics, such as : James Gregory (1671) +∞ ∑ n=0 (−1)n 2n+ 1 = π 4 , (0.1) Leonhard Euler (1734) +∞ ∑ n=0 1 n = π 6 , (0.2) Carl Friedrich Gauss (1809) ∫ +∞ −∞ e−x 2 dx = √ π , (0.3) Stephen Hawking (1974) T = 1 8πkB ~c GM . (0.4) The irrationality of π was first proven by Jean-Henri Lambert in 1761. Finally in 1882, Ferdinand von Lindemann established its transcendence, thus laying to rest the problem of « squaring the circle ». 1. Aesthetics in mathematics ? In 2014, researchers in neurobiology [14] from the University College London (in United Kingdom) used functional MRI to image the brain activity of 15 mathematicians (aged from 22 to 32 years, postgraduate or postdoctoral level, all recruited from colleges in London) when they viewed mathematical formulae. Each subject was given 60 mathematical formulae including (0.1), (0.2) or (0.3) that correspond successively to arctan(1), ζ(2) and Γ ( 1 2 ) to study at leisure and rate as ugly [−1], neutral [0] or beautiful [+1]. Note the absence of the nonsimple continued fraction : William Brouncker (1655) 4 π = 1 + 1 2 + 3 2 + 5 2 + 7 2 + 9 2 + . (1.1) in their list. Results of the study showed that the one most consistently rated as « ugly » was Equation (14) : 1 π = √ 8 9801 +∞ ∑ n=0 (4n)! (n! ) 1103 + 26390n 3964n , (1.2) an infinite series due to Ramanujan with an average rating of −0, 7333 ! Truly, beauty is in the eye of the beholder. Since the starting point of (1.2) lays upon the new foundations of elliptic integrals instilled by the works of both Niels Henrik Abel and Carl Gustav J. Jacobi [9] in the 19 century, we might remember the premonitory words of Felix Klein : « When I was a student, Abelian functions were, as an effect of the Jacobian tradition, considered the uncontested summit of mathematics, and each of us was ambitious to make progress in this field. And now ? The younger generation hardly knows Abelian functions. » Date: April 27, 2021.
Optimal Dynamic Regret in Exp-Concave Online Learning

We consider the problem of the Zinkevich (2003)-style dynamic regret minimization in online learning with exp-concave losses. We show that whenever improper learning is allowed, a Strongly Adaptive online learner achieves the dynamic regret of $\tilde O^*(n^{1/3}C_n^{2/3} \vee 1)$ where $C_n$ is the total variation (a.k.a. path length) of the an arbitrary sequence of comparators that may not be known to the learner ahead of time. Achieving this rate was highly nontrivial even for squared losses in 1D where the best known upper bound was $O(\sqrt{nC_n} \vee \log n)$ (Yuan and Lamperski, 2019). Our new proof techniques make elegant use of the intricate structures of the primal and dual variables imposed by the KKT conditions and could be of independent interest. Finally, we apply our results to the classical statistical problem of locally adaptive non-parametric regression (Mammen, 1991; Donoho and Johnstone, 1998) and obtain a stronger and more flexible algorithm that do not require any statistical assumptions or any hyperparameter tuning.
LGD-GCN: Local and Global Disentangled Graph Convolutional Networks

Disentangled Graph Convolutional Network (DisenGCN) is an encouraging framework to disentangle the latent factors arising in a real-world graph. However, it relies on disentangling information heavily from a local range (i.e., a node and its 1-hop neighbors), while the local information in many cases can be uneven and incomplete, hindering the interpretabiliy power and model performance of DisenGCN. In this paper\footnote{This paper is a lighter version of \href{https://jingweio.github. io/assets/pdf/tnnls22.pdf}{"Learning Disentangled Graph Convolutional Networks Locally and Globally"} where the results and analysis have been reworked substantially. Digital Object Identifier \url{https://doi.org/10.1109/TNNLS.2022. 3195336}. }, we introduce a novel Local and Global Disentangled Graph Convolutional Network (LGD-GCN) to capture both local and global information for graph disentanglement. LGD-GCN performs a statistical mixture modeling to derive a factor-aware latent continuous space, and then constructs different structures w.r. t. different factors from the revealed space. In this way, the global factor-specific information can be efficiently and selectively encoded via a message passing along these built structures, strengthening the intra-factor consistency. We also propose a novel diversity promoting regularizer employed with the latent space modeling, to encourage inter-factor diversity. Evaluations of the proposed LGD-GCN on the synthetic and real-world datasets show a better interpretability and improved performance in node classification over the existing competitive models. Code is available at \url{https://github.com/jingweio/LGD-GCN}.
Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets

Data is the engine of modern computer vision, which necessitates collecting large-scale datasets. This is expensive, and guaranteeing the quality of the labels is a major challenge. In this paper, we investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. While methods that exploit learnt models for labeling exist, a surprisingly prevalent approach is to query humans for a fixed number of labels per datum and aggregate them, which is expensive. Building on prior work on online joint probabilistic modeling of human annotations and machine-generated beliefs, we propose modifications and best practices aimed at minimizing human labeling effort. Specifically, we make use of advances in self-supervised learning, view annotation as a semi-supervised learning problem, identify and mitigate pitfalls and ablate several key design choices to propose effective guidelines for labeling. Our analysis is done in a more realistic simulation that involves querying human la-belers, which uncovers issues with evaluation using existing worker simulation methods. Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average, a 2.7x and 6.7x improvement over prior work and manual annotation, respectively.1
Erosion of Volatiles by Micrometeoroid Bombardment on Ceres and Comparison to the Moon and Mercury

Ceres, the largest reservoir of water in the main belt, was recently visited by the Dawn spacecraft, which revealed several areas bearing H2O-ice features. Independent telescopic observations showed a water exosphere of currently unknown origin. We explore the effects of meteoroid impacts on Ceres by considering the topography obtained from the Dawn mission using a widely used micrometeoroid model and ray-tracing techniques. Meteoroid populations with 0.01–2 mm diameters are considered. We analyze the short-term effects Ceres experiences during its current orbit, as well as long-term effects over the entire precession cycle. We find that the entire surface is subject to meteoroid bombardment, leaving no areas in permanent shadow with respect to meteoroid influx. The equatorial parts of Ceres produce 80% more ejecta than the polar regions due to the large impact velocity of long-period comets. Mass flux, energy flux, and ejecta production vary seasonally by a factor of 3–7 due to the inclined eccentric orbit. Compared to Mercury and the Moon, Ceres experiences significantly smaller effects of micrometeoroid bombardment, with a total mass flux of 4.5 ± 1.2 × 10−17 kg m−2 s−1. On average, Mercury is subjected to a 50× larger mass flux and generates 700× more ejecta than Ceres, while the lunar mass flux is 10× larger and the ejecta generation is 30× larger than on Ceres. For these reasons, we find that meteoroid impacts are an unlikely candidate for the production of a water exosphere or significant excavation of surface features. The surface turnover rate from the micrometeoroid populations considered is estimated to be 1.25 Myr on Ceres.
Estimating Future VaR from Value Samples and Applications to Future Initial Margin

Predicting future values at risk (fVaR) is an important problem in finance. They arise in the modelling of future initial margin requirements for counterparty credit risk and future market risk VaR. One is also interested in derived quantities such as: i) Dynamic Initial Margin (DIM) and Margin Value Adjustment (MVA) in the counterparty risk context; and ii) risk weighted assets (RWA) and Capital Value Adjustment (KVA) for market risk. This paper describes several methods that can be used to predict fVaRs. We begin with the Nested MC-empirical quantile method as benchmark, but it is too computationally intensive for routine use. We review several known methods and discuss their novel applications to the problem at hand. <br><br>The techniques considered include computing percentiles from distributions (Normal and Johnson) that were matched to parametric moments or percentile estimates, quantile regressions methods, and others with more specific assumptions or requirements. <br><br>We also consider how limited inner simulations can be used to improve the performance of these techniques. The paper also provides illustrations, results, and visualizations of intermediate and final results for the various approaches and methods.
Attention on Global-Local Embedding Spaces in Recommender Systems

—In this study, we present a novel clustering-based collaborative ﬁltering (CF) method for recommender systems. Clustering-based CF methods can effectively deal with data sparsity and scalability problems. However, most of them are applied to a single embedding space, which might not characterize complex user-item interactions well. We argue that user-item interactions should be observed from multiple views and characterized in an adaptive way. To address this issue, we leveraged the relation between global space and local clusters to construct multiple embedding spaces by learning variant training datasets and loss functions. An attention model was then built to provide a dynamic blended representation according to the relative importance of the embedding spaces for each user-item pair, forming a ﬂexible measure to characterize variant user-item interactions. Substantial experiments were performed and evaluated on four popular benchmark datasets. The results show that the proposed method is effective and competitive compared to several CF methods where only one embedding space is considered.
Goodwillie Calculus and Geometric Stacks

We show Goodwillie’s calculus of functors and n-geometric Dstacks share similar features by starting to focus on the convergence of Taylor towers for homotopy functors and the fact that RF (A) ∼= holimRF (A≤n) for geometric stacks, where {A≤n} provides a Postnikov tower of some given A ∈ sk-Alg. From there we show we have parallel results, such as similar homotopy fibers of connecting maps in towers, as well as polynomial approximations, pointwise approximations and reconstruction theorems for towers. ∗rg.mathematics@gmail.com
Language ID Prediction from Speech Using Self-Attentive Pooling

This memo describes NTR-TSU submission for SIGTYP 2021 Shared Task on predicting language IDs from speech. Spoken Language Identification (LID) is an important step in a multilingual Automated Speech Recognition (ASR) system pipeline. For many low-resource and endangered languages, only single-speaker recordings may be available, demanding a need for domain and speaker-invariant language ID systems. In this memo, we show that a convolutional neural network with a Self-Attentive Pooling layer shows promising results for the language identification task.
Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium

In game-theoretic learning, several agents are simultaneously following their individual interests, so the environment is non-stationary from each player's perspective. In this context, the performance of a learning algorithm is often measured by its regret. However, no-regret algorithms are not created equal in terms of game-theoretic guarantees: depending on how they are tuned, some of them may drive the system to an equilibrium, while others could produce cyclic, chaotic, or otherwise divergent trajectories. To account for this, we propose a range of no-regret policies based on optimistic mirror descent, with the following desirable properties: i) they do not require any prior tuning or knowledge of the game; ii) they all achieve O(\sqrt{T}) regret against arbitrary, adversarial opponents; and iii) they converge to the best response against convergent opponents. Also, if employed by all players, then iv) they guarantee O(1) social regret; while v) the induced sequence of play converges to Nash equilibrium with O(1) individual regret in all variationally stable games (a class of games that includes all monotone and convex-concave zero-sum games).
Balancing Accuracy and Latency in Multipath Neural Networks

The growing capacity of neural networks has strongly contributed to their success at complex machine learning tasks and the computational demand of such large models has, in turn, stimulated a significant improvement in the hardware necessary to accelerate their computations. However, models with high latency aren't suitable for limited-resource environments such as hand-held and IoT devices. Hence, many deep learning techniques aim to address this problem by developing models with reasonable accuracy without violating the limited-resource constraint. In this work, we use a one-shot neural architecture search model to implicitly evaluate the performance of an intractable number of multipath neural networks. Combining this architecture search with a pruning technique and architecture sample evaluation, we can model the relation between the accuracy and the latency of a spectrum of models with graded complexity. We show that our method can accurately model the relative performance between models with different latencies and predict the performance of unseen models with good precision across different datasets.
Membrane Fusion-Based Transmitter Design for Static and Diffusive Mobile Molecular Communication Systems

This paper proposes a novel imperfect transmitter (TX) model, namely the membrane fusion (MF)-based TX, that adopts MF between a vesicle and the TX membrane to release molecules encapsulated within the vesicle. For the MF-based TX, the molecule release probability and the fraction of molecules released from the TX membrane are derived. Incorporating molecular degradation and a fully-absorbing receiver (RX), the channel impulse response (CIR) is derived for two scenarios: 1) Both TX and RX are static, and 2) both TX and RX are diffusion-based mobile. Moreover, a sequence of bits transmitted from the TX to the RX is considered. The average bit error rate (BER) is obtained for both scenarios, wherein the probability mass function (PMF) of the number of molecules absorbed in the mobile scenario is derived. Furthermore, a simulation framework is proposed for the MF-based TX, based on which the derived analytical expressions are validated. Simulation results show that a low MF probability or low vesicle mobility slows the release of molecules and reduces the molecule hitting probability at the RX. Simulation results also indicate the difference between the MF-based TX and an ideal point TX in terms of the inter-symbol interference (ISI).