Miracle-dz/UniFAR-FTUs huggingface dataset:
Differentiable Artificial Reverberation

Artificial reverberation (AR) models play a central role in various audio applications. Therefore, estimating the AR model parameters (ARPs) of a reference reverberation is a crucial task. Although a few recent deep-learning-based approaches have shown promising performance, their non-end-to-end training scheme prevents them from fully exploiting the potential of deep neural networks. This motivates the introduction of differentiable artificial reverberation (DAR) models, allowing loss gradients to be back-propagated end-to-end. However, implementing the AR models with their difference equations “as is” in the deep learning framework severely bottlenecks the training speed when executed with a parallel processor like GPU due to their infinite impulse response (IIR) components. We tackle this problem by replacing the IIR filters with finite impulse response (FIR) approximations with the frequency-sampling method. Using this technique, we implement three DAR models—differentiable Filtered Velvet Noise (FVN), Advanced Filtered Velvet Noise (AFVN), and Delay Network (DN). For each AR model, we train its ARP estimation networks for analysis-synthesis (RIR-to-ARP) and blind estimation (reverberant-speech-to-ARP) task in an end-to-end manner with its DAR model counterpart. Experiment results show that the proposed method achieves consistent performance improvement over the non-end-to-end approaches in both objective metrics and subjective listening test results.
Accelerating JavaScript static analysis via dynamic shortcuts

JavaScript has become one of the most widely used programming languages for web development, server-side programming, and even micro-controllers for IoT. However, its extremely functional and dynamic features degrade the performance and precision of static analysis. Moreover, the variety of built-in functions and host environments requires excessive manual modeling of their behaviors. To alleviate these problems, researchers have proposed various ways to leverage dynamic analysis during JavaScript static analysis. However, they do not fully utilize the high performance of dynamic analysis and often sacrifice the soundness of static analysis. In this paper, we present dynamic shortcuts, a new technique to flexibly switch between abstract and concrete execution during JavaScript static analysis in a sound way. It can significantly improve the analysis performance and precision by using highly-optimized commercial JavaScript engines and lessen the modeling efforts for opaque code. We actualize the technique via SAFEDS, an extended combination of SAFE and Jalangi, a static analyzer and a dynamic analyzer, respectively. We evaluated SAFEDS using 269 official tests of Lodash 4 library. Our experiment shows that SAFEDS is 7.81x faster than the baseline static analyzer, and it improves the precision to reduce failed assertions by 12.31% on average for 22 opaque functions.
Impact of conventional drying and thermal post-treatment on the residual stresses and shape deformations of larch lumber

ABSTRACT Quality evaluation and grading of thermally treated wood products are of fundamental importance to their commercial utilization. The combined impacts of conventional drying, thermal post-treatment, and transverse dimensions of lumber over the residual stresses and shape deformations of larch (Larix gmelinii) wood were examined. Larch specimens with two different thicknesses (25 and 40 mm) and three different widths (100, 150, and 200 mm) were dried using conventional technology and thereafter thermally treated at 200°C for 1 h at atmospheric superheated steam conditions. Drying and residual stresses and shape deformations of both kiln-dried and thermally post-treated lumbers were measured and statistically analyzed. The influential mechanisms of lumber thickness and width over the drying stresses and shape deformations of thermally post-treated lumbers were revealed. The drying and residual stress measurement based on the prong test was recommended as a potential quality evaluation strategy for the thermally post-treated larch lumbers. As the lumber width increased from 100 to 150 mm, and then to 200 mm, the cupping deformation of thermally post-treated larch lumber increased substantially. These results provided some practical foundations for the quality evaluation of thermally treated wood.
Recognition of orbital-angular-momentum modes with different topological charges and their unknown superpositions via machine learning

Light beams carrying orbital-angular-momentum (OAM) play an important role in optical manipulation and communication owing to their unbounded state space. However, it is still challenging to efficiently discriminate OAM modes with large topological charges and thus only a small part of the OAM states have been usually used. Here we demonstrate that neural networks can be trained to sort OAM modes with large topological charges and unknown superpositions. Using intensity images of OAM modes generalized in simulations and experiments as the input data, we illustrate that our neural network has great generalization power to recognize OAM modes of large topological charges beyond training areas with high accuracy. Moreover, the trained neural network can correctly classify and predict arbitrary superpositions of two OAM modes with random topological charges. Our machine learning approach only requires a small portion of experimental samples and significantly reduces the cost in experiments, which paves the way to study the OAM physics and increase the state space of OAM beams in practical applications.
Relation Matters in Sampling: A Scalable Multi-Relational Graph Neural Network for Drug-Drug Interaction Prediction

Sampling is an established technique to scale graph neural networks to large graphs. Current approaches however assume the graphs to be homogeneous in terms of relations and ignore relation types, critically important in biomedical graphs. Multi-relational graphs contain various types of relations that usually come with variable frequency and have different importance for the problem at hand. We propose an approach to modeling the importance of relation types for neighborhood sampling in graph neural networks and show that we can learn the right balance: relation-type probabilities that reflect both frequency and importance. Our experiments on drug-drug interaction prediction show that state-of-the-art graph neural networks profit from relation-dependent sampling in terms of both accuracy and efficiency.
2nd Place Solution for IJCAI-PRICAI 2020 3D AI Challenge: 3D Object Reconstruction from A Single Image

In this paper, we present our solution for the {\it IJCAI--PRICAI--20 3D AI Challenge: 3D Object Reconstruction from A Single Image}. We develop a variant of AtlasNet that consumes single 2D images and generates 3D point clouds through 2D to 3D mapping. To push the performance to the limit and present guidance on crucial implementation choices, we conduct extensive experiments to analyze the influence of decoder design and different settings on the normalization, projection, and sampling methods. Our method achieves 2nd place in the final track with a score of $70.88$, a chamfer distance of $36.87$, and a mean f-score of $59.18$. The source code of our method will be available at https://github.com/em-data/Enhanced_AtlasNet_3DReconstruction.
"Why Would I Trust Your Numbers?" On the Explainability of Expected Values in Soccer

In recent years, many different approaches have been proposed to quantify the performances of soccer players. Since player performances are challenging to quantify directly due to the low-scoring nature of soccer, most approaches estimate the expected impact of the players' on-the-ball actions on the scoreline. While effective, these approaches are yet to be widely embraced by soccer practitioners. The soccer analytics community has primarily focused on improving the accuracy of the models, while the explainability of the produced metrics is often much more important to practitioners. To help bridge the gap between scientists and practitioners, we introduce an explainable Generalized Additive Model that estimates the expected value for shots. Unlike existing models, our model leverages features corresponding to widespread soccer concepts. To this end, we represent the locations of shots by fuzzily assigning the shots to designated zones on the pitch that practitioners are familiar with. Our experimental evaluation shows that our model is as accurate as existing models, while being easier to explain to soccer practitioners.
Revitalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation

We propose a novel sparse constrained formulation and from it derive a real-time optimization method for 3D human pose and shape estimation. Our optimization method, SCOPE (Sparse Constrained Optimization for 3D human Pose and shapE estimation), is orders of magnitude faster (avg. 4ms convergence) than existing optimization methods, while being mathematically equivalent to their dense unconstrained formulation under mild assumptions. We achieve this by exploiting the underlying sparsity and constraints of our formulation to efficiently compute the Gauss-Newton direction. We show that this computation scales linearly with the number of joints and measurements of a complex 3D human model, in contrast to prior work where it scales cubically due to their dense unconstrained formulation. Based on our optimization method, we present a real-time motion capture framework that estimates 3D human poses and shapes from a single image at over 30 FPS. In benchmarks against state-of-the-art methods on multiple public datasets, our framework outperforms other optimization methods and achieves competitive accuracy against regression methods. Project page with code and videos: https://sites.google. com/view/scope-human/.
Synthetic dimension band structures on a Si CMOS photonic platform

Synthetic dimensions, which simulate spatial coordinates using nonspatial degrees of freedom, are drawing interest in topological science and other fields for modeling higher-dimensional phenomena on simple structures. We present the first realization of a synthetic frequency dimension on a silicon ring resonator integrated photonic device fabricated using a CMOS process. We confirm that its coupled modes correspond to a one-dimensional tight-binding model through acquisition of up to 280-GHz bandwidth optical frequency comb-like spectra and by measuring synthetic band structures. Furthermore, we realized two types of gauge potentials along the frequency dimension and probed their effects through the associated band structures. An electric field analog was produced via modulation detuning, whereas effective magnetic fields were induced using synchronized nearest- and second nearest–neighbor couplings. Creation of coupled mode lattices and two effective forces on a monolithic Si CMOS device represents a key step toward wider adoption of topological principles.
SafeAMC: Adversarial training for robust modulation recognition models

In communication systems, there are many tasks, like modulation recognition, which rely on Deep Neural Networks (DNNs) models. However, these models have been shown to be susceptible to adversarial perturbations, namely imperceptible additive noise crafted to induce misclassification. This raises questions about the security but also the general trust in model predictions. We propose to use adversarial training, which consists of fine-tuning the model with adversarial perturbations, to increase the robustness of automatic modulation recognition (AMC) models. We show that current state-of-the-art models benefit from adversarial training, which mitigates the robustness issues for some families of modulations. We use adversarial perturbations to visualize the features learned, and we found that in robust models the signal symbols are shifted towards the nearest classes in constellation space, like maximum likelihood methods. This confirms that robust models not only are more secure, but also more interpretable, building their decisions on signal statistics that are relevant to modulation recognition.
High Sensitivity and Full-circle Optical Rotary Sensor for Non-cooperatively Tracing Wrist Tremor with Nanoradian Resolution

In this paper, a novel optical rotary sensor based on laser self-mixing interferometry is developed for the full-circle rotation measurement. The proposed sensor is convenient to use for it does not need any contact with the target or a cooperative mirror. A prototype is fabricated and tested. The measured results demonstrate a good performance compared with other optical rotary sensors, in terms of the 0.1 rad resolution, the 2.33104 linearity and 2 rad stability over one hour. Additionally, the repeatability error is below 14.66 mrad under 9-group full-circle tests, which exhibits the potential to be instrumentalized reliably. Error analysis and limitation discussion have been also carried out. Although the accuracy needs further improvement compared with the best rotary sensor, this method has its unique advantages of high resolution, non-cooperative target sensing and electromagnetic immunity. Hence, the proposed optical rotary sensor provides a promising alternative in precise rotation measurement, tremor tracing and nano-motion monitoring.
Saudi Parents' Security and Privacy Concerns about their Children's Smart Device Applications

—In this paper, we investigate Saudi parents’ security and privacy concerns regarding their children’s smart device applications (apps). To this end, we conducted a survey and analysed 119 responses. Our results show that Saudi parents expressed a high level of concern regarding their children’s security and privacy when using smart device apps. However, they expressed higher concerns about apps’ content than privacy issues such as apps’ requests to access sensitive data. Furthermore, parents’ concerns are not in line with most of the children’s installed apps, which contain apps inappropriate for their age, require parental guidance, and request access to sensitive data such as location. We also compare Saudi parents’ practices and concerns with those reported by Western (mainly from the UK) and Chinese parents in previous reports. We ﬁnd interesting patterns and establish new relationships. For example, Saudi and Chinese children have a higher level of autonomy in installing apps in their devices than Western children who mostly request apps installation from their parents. However, there are more Saudi and Chinese parents who end up uninstalling apps from their children’s devices than Western parents. This suggests that parents’ involvement in installing children’s apps reduces the number of uninstallation incidents which are mostly negatively received by children. Furthermore, Saudi and Western parents show higher levels of privacy concerns than Chinese parents. The low level of privacy concerns expressed by Chinese parents even after being informed about possible privacy implications could be related cultural or political reasons. Finally, we tested 14 security and privacy practices and concerns against high vs. low socioeconomic classes (parents’ education, technical background, and income) to ﬁnd whether there are signiﬁcant differences between high and low classes (we denote these differences by “digital divide”). Out of 42 tests (14 properties × 3 classes) we ﬁnd signiﬁcant differences between high and low classes in 7 tests only. While this is a positive trend overall, it is important to work on bridging these gaps. The results of this paper provide key ﬁndings to identify areas of improvements and recommendations, especially for Saudis, which can be used by parents, developers, researchers, regulators, and policy makers.
Learning Model-Based Vehicle-Relocation Decisions for Real-Time Ride-Sharing: Hybridizing Learning and Optimization

Large-scale ride-sharing systems combine real-time dispatching and routing optimization over a rolling time horizon with a model predictive control (MPC) component that relocates idle vehicles to anticipate the demand. The MPC optimization operates over a longer time horizon to compensate for the inherent myopic nature of the real-time dispatching. These longer time horizons are beneficial for the quality of relocation decisions but increase computational complexity. Consequently, the ride-sharing operators are often forced to use a relatively short time horizon. To address this computational challenge, this paper proposes a hybrid approach that combines machine learning and optimization. The machine-learning component learns the optimal solution to the MPC on the aggregated level to overcome the sparsity and high-dimensionality of the solution. The optimization component transforms the machine-learning prediction back to the original granularity through a tractable transportation model. As a consequence, the original NP-hard MPC problem is reduced to a polynomial time prediction and optimization, which allows the ride-sharing operators to consider a longer time horizon. Experimental results show that the hybrid approach achieves significantly better service quality than the MPC optimization in terms of average rider waiting time, due to its ability to model a longer horizon.
A direct comparison between the mixing time of the interchange process with"few"particles and independent random walks

We consider the interchange process with k particles (denoted IP(k)) on n-vertex hypergraphs in which each hyperedge e rings at rate re. When e rings, the particles occupying it are permuted according to a random permutation from some arbitrary law, where our only assumption is that IP(2) has uniform stationary distribution. We show that t IP(k) mix (ε) = Ob(t IP(2) mix (ε/k)), where t IP(i) mix (ε) is the ε total-variation mixing time of IP(i), provided that knRt IP(2) mix (ε/k) = O((ε/k) ) for some b > 0, where R = ∑ e re|e|(|e| − 1) is n(n− 1) times the particle-particle interaction rate at equilibrium. This has some consequences concerning the validity (in this regime) of conjectures of Oliveira about comparison of the εmixing time of IP(k) to that of k independent particles, each evolving according to IP(1), denoted RW(k), and of Caputo about comparison of the spectral-gap of IP(k) to that of a single particle IP(1) = RW(1). We also show that t IP(k) mix (ε) ≍ t RW(1) mix (ε) ≍ t RW(k) mix (εk/4) for all k . n 1−Ω(1) and all ε ≤ 1 k ∧ 14 for vertex-transitive graphs of constant degree, as well as for general graphs satisfying a mild (“transience-like”) heat-kernel condition. In the special case where the particles occupying a hyperedge e are permuted uniformly at random (in e) when e rings, we obtain results bounding the spectral gap of IP(k) in terms of that RW(1). In contrast to recent works on mixing times of IP(k), the proof does not use Morris’ chameleon process. It can be seen as a rigorous and direct way of arguing that when the number of particles is fairly small, the system behaves similarly to k independent particles, due to the small amount of interaction between particles.
Deception Detection in Videos using the Facial Action Coding System

Facts are important in decision making in every situation, which is why it is important to catch deceptive information before they are accepted as facts. Deception detection in videos has gained traction in recent times for its various real-life application. In our approach, we extract facial action units using the facial action coding system which we use as parameters for training a deep learning model. We specifically use long short-term memory (LSTM) which we trained using the real-life trial dataset and it provided one of the best facial only approaches to deception detection. We also tested cross-dataset validation using the Real-life trial dataset, the Silesian Deception Dataset, and the Bag-of-lies Deception Dataset which has not yet been attempted by anyone else for a deception detection system. We tested and compared all datasets amongst each other individually and collectively using the same deep learning training model. The results show that adding different datasets for training worsen the accuracy of the model. One of the primary reasons is that the nature of these datasets vastly differs from one another.
Elemental Topological Dirac Semimetal α‐Sn with High Quantum Mobility

α‐Sn provides an ideal avenue to investigate novel topological properties owing to its rich diagram of topological phases and simple elemental material structure. Thus far, however, the realization of high‐quality α‐Sn remains a challenge, which limits the understanding of its quantum transport properties and device applications. Here, epitaxial growth of α‐Sn on InSb (001) with the highest quality thus far is presented. The studied samples exhibit unprecedentedly high quantum mobilities of both the surface state (30 000 cm2 V−1 s−1), which is ten times higher than the previously reported values, and the bulk heavy‐hole state (1800 cm2 V−1 s−1), which is never obtained experimentally. These excellent features allow quantitative characterization of the nontrivial interfacial and bulk band structure of α‐Sn via a thorough investigation of Shubnikov–de Haas oscillations combined with first‐principles calculations. The results firmly identify that α‐Sn grown on InSb (001) is a topological Dirac semimetal (TDS). Furthermore, a crossover from the TDS to a 2D topological insulator and a subsequent phase transition to a trivial insulator when varying the thickness of α‐Sn are demonstrated. This work indicates that α‐Sn is an excellent model system to study novel topological phases and a prominent material candidate for topological devices.
On the Unconditional Validity of J. von Neumann's Proof of the Impossibility of Hidden Variables in Quantum Mechanics

The impossibility of theories with hidden variables as an alternative and replacement for quantum mechanics was discussed by J. von Neumann in 1932. His proof was criticized as being logically circular, by Grete Hermann soon after, and as fundamentally flawed, by John Bell in 1964. Bell’s severe criticism of Neumann’s proof and the explicit (counter) example of a hidden variable model for the measurement of a quantum spin are considered by most researchers, though not all, as the definitive demonstration that Neumann’s proof is inadequate. Despite being an argument of mathematical physics, an ambiguity of decision remains to this day. I show that Neumann’s assumption of the linear additivity of the expectation values, even for incompatible (noncommuting) observables, is a necessary constraint related to the nature of observable physical variables and to the conservation laws. Therefore, any theory should necessarily obey it to qualify as a physically valid theory. Then, obviously, the hidden variable theories with dispersion-free ensembles that violate this assumption are ruled out. I show that it is Bell’s counter-example that is fundamentally flawed, being inconsistent with the factual mechanics. Further, it is shown that the local hidden variable theories, for which the Bell’s inequalities were derived, are grossly incompatible with the fundamental conservation laws. I identify the intrinsic uncertainty in the action as the reason for the irreducible dispersion, which implies that there are no dispersion-free ensembles at any scale of mechanics. With the unconditional validity of its central assumption shown, Neumann’s proof is fully resurrected. Note: A French translation of the title, abstract and a slightly condensed version of the introductory section with a summary of the main results are included in the manuscript (pp 3-6), between the table of contents and the main text in English.