Miracle-dz/UniFAR-FTUs huggingface dataset:
EFFECT OF DIFFERENT SHAPED DIMPLES ON AIRFOILS

The present work describes change in aerodynamic characteristics of an airfoil by applying certain surface modifications in form of dimples. At first surface modifications that are considered here have outward and inward dimples on the wing model. A comparative study of modified airfoil models showing variance in lift and drag at different angle of attacks (AOA). The surface modifications are done here by considering the different types and shapes of dimples. Dimples help in reduction of pressure drag when airfoil attains some angle of attack because that time, wake formation starts due to boundary layer separation. Application of dimples on aircraft wing works in same manner as vortex generators. They create turbulence which delays the boundary layer separation and reduces the wake and thereby reducing the pressure drag. It’s assist to enhance the lift and increase the Stall AOA. Dimples on the surface aircraft wing does not affect the pressure drag much since it is already aerodynamic in shape but it may affect its aerodynamics properties when the airfoil is at AOA that is one of the concern of this paper. A Subsonic wind tunnel (100×100×100 cm ) is used to investigate the effect of using dimples over the airfoil surface (NACA-4415) having uniform cross-section along span. For design purpose solid works software is used. Three wooden Airfoil Models with outward and inward dimples and without dimples have been constructed here. From this experimental investigation it has been observed that the flow separation on the airfoil can be delayed by using the dimples on the upper surface and the lift force and stall AOA are also increased remarkably.
Prefix-Free Coding for LQG Control

In this work, we develop quantization and variable-length source codecs for the feedback links in linear–quadratic–Gaussian (LQG) control systems. We prove that for any ﬁxed control performance, the approaches we propose nearly achieve lower bounds on communication cost that have been established in prior work. In particular, we reﬁne the analysis of a classical achievability approach with an eye towards more practical details. Notably, in the prior literature the source codecs used to demonstrate the (near) achievability of these lower bounds are often implicitly assumed to be time-varying. For SISO plants, we prove that it sufﬁces to consider time-invariant quantization and source coding. This result follows from analyzing the long-term stochastic behavior of the system’s quantized measurements and reconstruction errors. To our knowledge, this time-invariant achievability result is the ﬁrst in the literature.
Speech and the n-Back task as a lens into depression. How combining both may allow us to isolate different core symptoms of depression

Embedded in any speech signal is a rich combination of cognitive, neuromuscular and physiological information. This richness makes speech a powerful signal in relation to a range of different health conditions, including major depressive disorders (MDD). One pivotal issue in speech-depression research is the assumption that depressive severity is the dominant measurable effect. However, given the heterogeneous clinical profile of MDD, it may actually be the case that speech alterations are more strongly associated with subsets of key depression symptoms. This paper presents strong evidence in support of this argument. First, we present a novel large, cross-sectional, multi-modal dataset collected at Thymia. We then present a set of machine learning experiments that demonstrate that combining speech with features from an n-Back working memory assessment improves classifier performance when predicting the popular eight-item Patient Health Questionnaire depression scale (PHQ-8). Finally, we present a set of experiments that highlight the association between different speech and n-Back markers at the PHQ-8 item level. Specifically, we observe that somatic and psychomotor symptoms are more strongly associated with n-Back performance scores, whilst the other items: anhedonia, depressed mood, change in appetite, feelings of worthlessness and trouble concentrating are more strongly associated with speech changes.
From Statistical to Causal Learning

We describe basic ideas underlying research to build and understand artificially intelligent systems: from symbolic approaches via statistical learning to interventional models relying on concepts of causality. Some of the hard open problems of machine learning and AI are intrinsically related to causality, and progress may require advances in our understanding of how to model and infer causality from data.
Online Bayesian optimization for a recoil mass separator

The SEparator for CApture Reactions (SECAR) is a next-generation recoil separator system at the Facility for Rare Isotope Beams (FRIB) designed for the direct measurement of capture reactions on unstable nuclei in inverse kinematics. To maximize the performance of this system, stringent requirements on the beam alignment to the central beam axis and on the ion-optical settings need to be achieved. These can be difficult to attain through manual tuning by human operators without potentially leaving the system in a sub-optimal and irreproducible state. In this work, we present the first development of online Bayesian optimization with a Gaussian process model to tune an ion beam through a nuclear astrophysics recoil separator. We show that this method achieves small incoming angular deviations (\textless 1 mrad) in an efficient and reproducible manner that is at least three times faster than standard hand-tuning. Additionally, we present a Bayesian method for experimental optimization of the ion optics, and show that it validates the nominal theoretical ion-optical settings of the device, and improves the mass separation by 32\% for some beams.
Learning to Deblur using Light Field Generated and Real Defocus Images

Defocus deblurring is a challenging task due to the spatially varying nature of defocus blur. While deep learning approach shows great promise in solving image restoration problems, defocus deblurring demands accurate training data that consists of all-in-focus and defocus image pairs, which is difficult to collect. Naive two-shot capturing cannot achieve pixel-wise correspondence between the defocused and all-in-focus image pairs. Synthetic aperture of light fields is suggested to be a more reliable way to generate accurate image pairs. However, the defocus blur generated from light field data is different from that of the images captured with a traditional digital camera. In this paper, we propose a novel deep defocus deblurring network that leverages the strength and overcomes the shortcoming of light fields. We first train the network on a light field-generated dataset for its highly accurate image correspondence. Then, we fine-tune the network using feature loss on another dataset collected by the two-shot method to alleviate the differences between the defocus blur exists in the two domains. This strategy is proved to be highly effective and able to achieve the state-of-the-art performance both quantitatively and qualitatively on multiple test sets. Extensive ablation studies have been conducted to analyze the effect of each network module to the final performance.
A new Directional Algebraic Fast Multipole Method based iterative solver for the Lippmann-Schwinger equation accelerated with HODLR preconditioner

We present a fast iterative solver for scattering problems in 2D, where a penetrable object with compact support is considered. By representing the scattered field as a volume potential in terms of the Green's function, we arrive at the Lippmann-Schwinger equation in integral form, which is then discretized using an appropriate quadrature technique. The discretized linear system is then solved using an iterative solver accelerated by Directional Algebraic Fast Multipole Method (DAFMM). The DAFMM presented here relies on the directional admissibility condition of the 2D Helmholtz kernel. And the construction of low-rank factorizations of the appropriate low-rank matrix sub-blocks is based on our new Nested Cross Approximation (NCA)~\cite{ arXiv:2203.14832 [math.NA]}. The advantage of our new NCA is that the search space of so-called far-field pivots is smaller than that of the existing NCAs. Another significant contribution of this work is the use of HODLR based direct solver as a preconditioner to further accelerate the iterative solver. In one of our numerical experiments, the iterative solver does not converge without a preconditioner. We show that the HODLR preconditioner is capable of solving problems that the iterative solver can not. Another noteworthy contribution of this article is that we perform a comparative study of the HODLR based fast direct solver, DAFMM based fast iterative solver, and HODLR preconditioned DAFMM based fast iterative solver for the discretized Lippmann-Schwinger problem. To the best of our knowledge, this work is one of the first to provide a systematic study and comparison of these different solvers for various problem sizes and contrast functions. In the spirit of reproducible computational science, the implementation of the algorithms developed in this article is made available at \url{https://github.com/vaishna77/Lippmann_Schwinger_Solver}.
A Large-scale Dataset of (Open Source) License Text Variants

We introduce a large-scale dataset of the complete texts of free/open source software (FOSS) license variants. To assemble it we have collected from the Software Heritage archive-the largest publicly available archive of FOSS source code with accompanying development history-all versions of files whose names are commonly used to convey licensing terms to software users and developers. The dataset consists of 6.5 million unique license files that can be used to conduct empirical studies on open source licensing, training of automated license classifiers, natural language processing (NLP) analyses of legal texts, as well as historical and phylogenetic studies on FOSS licensing. Additional metadata about shipped license files are also provided, making the dataset ready to use in various contexts; they include: file length measures, detected MIME type, detected SPDX license (using ScanCode), example origin (e.g., GitHub repository), oldest public commit in which the license appeared. The dataset is released as open data as an archive file containing all deduplicated license files, plus several portable CSV files for metadata, referencing files via cryptographic checksums.
Perceval: A Software Platform for Discrete Variable Photonic Quantum Computing

We introduce Perceval, an open-source software platform for simulating and interfacing with discrete-variable photonic quantum computers, and describe its main features and components. Its Python front-end allows photonic circuits to be composed from basic photonic building blocks like photon sources, beam splitters, phase-shifters and detectors. A variety of computational back-ends are available and optimised for different use-cases. These use state-of-the-art simulation techniques covering both weak simulation, or sampling, and strong simulation. We give examples of Perceval in action by reproducing a variety of photonic experiments and simulating photonic implementations of a range of quantum algorithms, from Grover's and Shor's to examples of quantum machine learning. Perceval is intended to be a useful toolkit for experimentalists wishing to easily model, design, simulate, or optimise a discrete-variable photonic experiment, for theoreticians wishing to design algorithms and applications for discrete-variable photonic quantum computing platforms, and for application designers wishing to evaluate algorithms on available state-of-the-art photonic quantum computers.
Secure and Ultra-Reliable Provenance Recovery in Sparse Networks: Strategies and Performance Bounds

Provenance embedding algorithms are well known for tracking the footprints of information ﬂow in wireless networks. Recently, low-latency provenance embedding algorithms have received traction in vehicular networks owing to strict deadlines on the delivery of packets. While existing low-latency provenance embedding methods focus on reducing the packet delay, they assume a complete graph on the underlying topology due to the mobility of the participating nodes. We identify that the complete graph assumption leads to sub-optimal performance in provenance recovery, especially when the vehicular network is sparse, which is usually observed outside peak-hour traﬃc conditions. As a result, we propose a two-part approach to design provenance embedding algorithms for sparse vehicular networks. In the ﬁrst part, we propose secure and practical topology-learning strategies, whereas in the second part, we design provenance embedding algorithms that guarantee ultra-reliability by incorporating the topology knowledge at the destination during the provenance recovery process. Besides the novel idea of using topology knowledge for provenance recovery, a distinguishing feature for achieving ultra-reliability is the use of hash-chains in the packet, which trade communication-overhead of the packet with the complexity-overhead at the destination. We derive tight upper bounds on the performance of our strategies, and show that the derived bounds, when optimized with appropriate constraints, deliver design parameters that outperform existing methods. Finally, we also implement our ideas on OMNeT++ based simulation environment to show that their latency beneﬁts indeed make them suitable for vehicular network applications.
Engineering Multi-Cloud Service-Oriented Applications

Cloud platforms are increasingly being used for hosting a broad diversity of services from traditional e-commerce applications to interactive web-based IDEs and crowdsourcing systems. However, the proliferation of offers by cloud providers raises several challenges. Developers will not only have to deploy applications for a specific cloud, but will also have to consider migrating services from one cloud to another, and to manage distributed applications spanning multiple clouds. In order to address these challenges, we present a federated multi-cloud PaaS infrastructure that is based on three foundations: i) an open service model used to design and implement both our multi-cloud PaaS and the SaaS applications running on top of it, ii) a configurable architecture of the federated PaaS, and iii) some infrastructure services for managing both our multi-cloud PaaS and the SaaS applications. We report on the deployment of this cloud-based infrastructure on top of thirteen existing IaaS/PaaS.
Improved Soft-Aided Decoding of Product Codes With Dynamic Reliability Scores

Products codes (PCs) are conventionally decoded with efficient iterative bounded-distance decoding (iBDD) based on hard-decision channel outputs which entails a performance loss compared to a soft-decision decoder. Recently, several hybrid algorithms have been proposed aimed to improve the performance of iBDD decoders via the aid of a certain amount of soft information while keeping the decoding complexity similarly low as in iBDD. We propose a novel hybrid low-complexity decoder for PCs based on error-and-erasure (EaE) decoding and dynamic reliability scores (DRSs). This decoder is based on a novel EaE component code decoder, which is able to decode beyond the designed distance of the component code but suffers from an increased miscorrection probability. The DRSs, reflecting the reliability of a codeword bit, are used to detect and avoid miscorrections. Simulation results show that this policy can reduce the miscorrection rate significantly and improves the decoding performance. The decoder requires only ternary message passing and a slight increase of computational complexity compared to iBDD, which makes it suitable for high-speed communication systems. Coding gains of up to 1.2 dB compared to the conventional iBDD decoder are observed.
VFDS: Variational Foresight Dynamic Selection in Bayesian Neural Networks for Efficient Human Activity Recognition

In many machine learning tasks, input features with varying degrees of predictive capability are acquired at varying costs. In order to optimize the performance-cost trade-off, one would select features to observe a priori. However, given the changing context with previous observations, the subset of predictive features to select may change dynamically. Therefore, we face the challenging new problem of foresight dynamic selection (FDS): finding a dynamic and light-weight policy to decide which features to observe next, before actually observing them, for overall performance-cost trade-offs. To tackle FDS, this paper proposes a Bayesian learning framework of Variational Foresight Dynamic Selection (VFDS). VFDS learns a policy that selects the next feature subset to observe, by optimizing a variational Bayesian objective that characterizes the trade-off between model performance and feature cost. At its core is an implicit variational distribution on binary gates that are dependent on previous observations, which will select the next subset of features to observe. We apply VFDS on the Human Activity Recognition (HAR) task where the performance-cost trade-off is critical in its practice. Extensive results demonstrate that VFDS selects different features under changing contexts, notably saving sensory costs while maintaining or improving the HAR accuracy. Moreover, the features that VFDS dynamically select are shown to be interpretable and associated with the different activity types. We will release the code.
Scalable Semi-Modular Inference with Variational Meta-Posteriors

The Cut posterior and related Semi-Modular Inference (SMI) are Generalised Bayes methods for Modular Bayesian evidence combination. Analysis is broken up over modular sub-models of the joint posterior distribution. Model-misspeciﬁcation in multi-modular models can be hard to ﬁx by model elaboration alone and the Cut posterior and SMI oﬀer a way round this. Information entering the analysis from misspeciﬁed modules is controlled by an inﬂuence parameter η related to the learning rate. This paper contains two substantial new methods. First, we give variational methods for approximating the Cut and SMI posteriors which are adapted to the inferential goals of evidence combination. We parameterise a family of variational posteriors using a Normalizing Flow for accurate approximation and end-to-end training. Secondly, we show that analysis of models with multiple cuts is feasible using a new Variational Meta-Posterior. This approximates a family of SMI posteriors indexed by η using a single set of variational parameters.
PogoDrone: Design, Model, and Control of a Jumping Quadrotor

We present a design, model, and control for a novel jumping-flying robot that is called PogoDrone. The robot is composed of a quadrotor with a passive mechanism for jumping. The robot can continuously jump in place or fly like a normal quadrotor. Jumping in place allows the robot to quickly move and operate very close to the ground. For instance, in agricultural applications, the jumping mechanism allows the robot to take samples of soil. We propose a hybrid controller that switches from attitude to position control to allow the robot to fall horizontally and recover to the original position. We compare the jumping mode with the hovering mode to analyze the energy consumption. In simulations, we evaluate the effect of different factors on energy consumption. In real experiments, we show that our robot can repeatedly impact the ground, jump, and fly in a physical environment.
Multi-sequence Intermediate Conditioning for CTC-based ASR

End-to-end automatic speech recognition (ASR) directly maps input speech to a character sequence without using pronunciation lexica. However, in languages with thousands of characters, such as Japanese and Mandarin, modeling all these characters is problematic due to data scarcity. To alleviate the problem, we propose a multi-task learning model with explicit interaction between characters and syllables by utilizing Self-conditioned connectionist temporal classiﬁcation (CTC) technique. While the original Self-conditioned CTC estimates character-level intermediate predictions by applying auxiliary CTC losses to a set of intermediate layers, the proposed method additionally estimates syllable-level intermediate predictions in another set of intermediate layers. The character-level and syllable-level predictions are alternately used as conditioning features to deal with mutual dependency between characters and syllables. Experimental results on Japanese and Mandarin datasets show that the proposed multi-sequence intermediate conditioning outperformed the conventional multi-task-based and Self-conditioned CTC-based methods.
Fashion Style Generation: Evolutionary Search with Gaussian Mixture Models in the Latent Space

This paper presents a novel approach for guiding a Generative Adversarial Network trained on the FashionGen dataset to generate designs corresponding to target fashion styles. Finding the latent vectors in the generator's latent space that correspond to a style is approached as an evolutionary search problem. A Gaussian mixture model is applied to identify fashion styles based on the higher-layer representations of outfits in a clothing-specific attribute prediction model. Over generations, a genetic algorithm optimizes a population of designs to increase their probability of belonging to one of the Gaussian mixture components or styles. Showing that the developed system can generate images of maximum fitness visually resembling certain styles, our approach provides a promising direction to guide the search for style-coherent designs.
Mutual Scene Synthesis for Mixed Reality Telepresence

Remote telepresence via next-generation mixed reality platforms can provide higher levels of immersion for computer-mediated communications, allowing participants to engage in a wide spectrum of activities, previously not possible in 2D screen-based communication methods. However, as mixed reality experiences are limited to the local physical surrounding of each user, finding a common virtual ground where users can freely move and interact with each other is challenging. In this paper, we propose a novel mutual scene synthesis method that takes the participants' spaces as input, and generates a virtual synthetic scene that corresponds to the functional features of all participants' local spaces. Our method combines a mutual function optimization module with a deep-learning conditional scene augmentation process to generate a scene mutually and physically accessible to all participants of a mixed reality telepresence scenario. The synthesized scene can hold mutual walkable, sittable and workable functions, all corresponding to physical objects in the users' real environments. We perform experiments using the MatterPort3D dataset and conduct comparative user studies to evaluate the effectiveness of our system. Our results show that our proposed approach can be a promising research direction for facilitating contextualized telepresence systems for next-generation spatial computing platforms.