Miracle-dz/UniFAR-FTUs huggingface dataset:
Learning representations in Bayesian Confidence Propagation neural networks

Unsupervised learning of hierarchical representations has been one of the most vibrant research directions in deep learning during recent years. In this work we study biologically inspired unsupervised strategies in neural networks based on local Hebbian learning. We propose new mechanisms to extend the Bayesian Confidence Propagating Neural Network (BCPNN) architecture, and demonstrate their capability for unsupervised learning of salient hidden representations when tested on the MNIST dataset.
Guardauto: A Decentralized Runtime Protection System for Autonomous Driving

Due to the broad attack surface and the lack of runtime protection, potential safety and security threats hinder the real-life adoption of autonomous vehicles. Although efforts have been made to mitigate some specific attacks, there are few works on the protection of the autonomous driving system, i.e., the control software system performing such as perception, decision making, and motion tracking. This article presents a decentralized self-protection framework called Guardauto to protect the autonomous driving system against runtime threats. First, Guardauto proposes an isolation model to decouple the autonomous driving system and isolate its components with a set of partitions. Second, Guardauto provides self-protection mechanisms for each target component, which combines different methods to monitor the target execution and plan adaption actions accordingly. Third, Guardauto provides cooperation among local self-protection mechanisms to identify the root-cause component in the case of cascading failures affecting multiple components. A prototype has been implemented and evaluated on the open-source autonomous driving system Autoware. Results show that Guardauto could effectively mitigate runtime failures and attacks, and protect the control system with acceptable performance overhead.
On the role of surrogates in the efficient estimation of treatment effects with limited outcome data

We study the problem of estimating treatment effects when the outcome of primary interest (e.g., long-term health status) is only seldom observed but abundant surrogate observations (e.g., short-term health outcomes) are available. To investigate the role of surrogates in this setting, we derive the semiparametric efficiency lower bounds of average treatment effect (ATE) both with and without presence of surrogates, as well as several intermediary settings. These bounds characterize the best-possible precision of ATE estimation in each case, and their difference quantifies the efficiency gains from optimally leveraging the surrogates in terms of key problem characteristics when only limited outcome data are available. We show these results apply in two important regimes: when the number of surrogate observations is comparable to primary-outcome observations and when the former dominates the latter. Importantly, we take a missing-data approach that circumvents strong surrogate conditions which are commonly assumed in previous literature but almost always fail in practice. To show how to leverage the efficiency gains of surrogate observations, we propose ATE estimators and inferential methods based on flexible machine learning methods to estimate nuisance parameters that appear in the influence functions. We show our estimators enjoy efficiency and robustness guarantees under weak conditions.
Weakly-Supervised Action Localization by Generative Attention Modeling

Weakly-supervised temporal action localization is a problem of learning an action localization model with only video-level action labeling available. The general framework largely relies on the classification activation, which employs an attention model to identify the action-related frames and then categorizes them into different classes. Such method results in the action-context confusion issue: context frames near action clips tend to be recognized as action frames themselves, since they are closely related to the specific classes. To solve the problem, in this paper we propose to model the class-agnostic frame-wise probability conditioned on the frame attention using conditional Variational Auto-Encoder (VAE). With the observation that the context exhibits notable difference from the action at representation level, a probabilistic model, i.e., conditional VAE, is learned to model the likelihood of each frame given the attention. By maximizing the conditional probability with respect to the attention, the action and non-action frames are well separated. Experiments on THUMOS14 and ActivityNet1.2 demonstrate advantage of our method and effectiveness in handling action-context confusion problem. Code is now available on GitHub.
CurlingNet: Compositional Learning between Images and Text for Fashion IQ Data

We present an approach named CurlingNet that can measure the semantic distance of composition of image-text embedding. In order to learn an effective image-text composition for the data in the fashion domain, our model proposes two key components as follows. First, the Delivery makes the transition of a source image in an embedding space. Second, the Sweeping emphasizes query-related components of fashion images in the embedding space. We utilize a channel-wise gating mechanism to make it possible. Our single model outperforms previous state-of-the-art image-text composition models including TIRG and FiLM. We participate in the first fashion-IQ challenge in ICCV 2019, for which ensemble of our model achieves one of the best performances.
Non-equilibrium dynamics of three-dimensional initially spherical vesicles in general flow

Many vesicles have a spherical resting shape and exposure to fluid flows induces an exchange between sub-optical fluctuations and visible (systematic) deformation, while the total area is conserved. These dynamics can be used to determine membrane properties such as the resting surface tension and the membrane bending rigidity. In this work a general numerical model is extended to account for the exchange between sub-optical fluctuations and visible area. Compared to many prior methods, assumptions about the underlying flow field or vesicle shape are not made. It is thus possible to model a wide number of experimental conditions. The methods shown here are compared against several published experimental results and show excellent agreement. It is then used to explore how system properties, such as the viscosity ratio between the inner and outer fluid, influence experimentally measurable parameters. Such information is critical when designing experiments to extract membrane properties.
GPVAD: Towards noise robust voice activity detection via weakly supervised sound event detection

Traditional voice activity detection (VAD) methods work well in clean and controlled scenarios, with performance severely degrading in real-world applications. One possible bottleneck for such supervised VAD training is its requirement for clean training data and frame-level labels. In contrast, we propose the GPVAD framework, which can be easily trained from noisy data in a weakly supervised fashion, requiring only clip-level labels. We proposed two GPVAD models, one full (GPV-F), which outputs all possible sound events and one binary (GPV-B), only splitting speech and noise. We evaluate the two GPVAD models and a CRNN based standard VAD model (VAD-C) on three different evaluation protocols (clean, synthetic noise, real). Results show that the GPV-F demonstrates competitive performance in clean and noisy scenarios compared to traditional VAD-C. Interestingly, in real-world evaluation, GPV-F largely outperforms VAD-C in terms of frame-level evaluation metrics as well as segment-level ones. With a much lower request for data, the naive binary clip-level GPV-B model can still achieve a comparable performance to VAD-C in real-world scenarios.
DHA and improvement of memory function: evaluation of a health claim pursuant to Article 13(5) of Regulation (EC) No 1924/2006

Following an application from DSM Nutritional Products, submitted for authorisation of a health claim pursuant to Article 13(5) of Regulation (EC) No 1924/2006 via the Competent Authority of the United Kingdom, the EFSA Panel on Dietetic Products, Nutrition and Allergies (NDA) was asked to deliver an opinion on the scientific substantiation of a health claim related to docosahexaenoic acid (DHA) and improvement of memory function. DHA is sufficiently characterised. An improvement of memory function is a beneficial physiological effect. In weighing the evidence, the Panel took into account that, out of the 11 human intervention studies from which conclusions can be drawn for the scientific substantiation of the claim, two studies showed a beneficial effect of DHA supplementation on memory function, one study showed inconsistent results, one study showed a negative effect of DHA on memory function and seven studies did not show an effect of DHA on memory outcomes. The Panel considers that the majority of the human intervention studies provided did not show an effect of DHA supplementation on memory, and that the conflicting results across studies cannot be explained by differences in the study design, the source of DHA, the DHA dose, the baseline characteristics of the subjects recruited, or the duration of the studies. The Panel also took into account that the meta-analyses of intervention studies submitted by the applicant do not provide evidence for an effect of DHA on memory function, and that the three observational studies from which conclusions could be drawn for the scientific substantiation of the claim do not provide evidence for an association between dietary DHA and memory function. The Panel concludes that a cause and effect relationship has not been established between the consumption of DHA and an improvement of memory function.
Iterative Cauchy Thresholding: Regularisation With A Heavy-Tailed Prior

In the machine learning era, sparsity continues to attract significant interest due to the benefits it provides to learning models. Algorithms aiming to optimise the $\ell_{0}$- and $\ell_{1}$-norm are the common choices to achieve sparsity. In this work, an alternative algorithm is proposed, which is derived based on the assumption of a Cauchy distribution characterising the coefficients in sparse domains. The Cauchy distribution is known to be able to capture heavy-tails in the data, which are linked to sparse processes. We begin by deriving the Cauchy proximal operator and subsequently propose an algorithm for optimising a cost function which includes a Cauchy penalty term. We have coined our contribution as Iterative Cauchy Thresholding (ICT). Results indicate that sparser solutions can be achieved using ICT in conjunction with a fixed overcomplete discrete cosine transform dictionary under a sparse coding methodology.
A General Approach for Using Deep Neural Network for Digital Watermarking

Technologies of the Internet of Things (IoT) facilitate digital contents such as images being acquired in a massive way. However, consideration from the privacy or legislation perspective still demands the need for intellectual content protection. In this paper, we propose a general deep neural network (DNN) based watermarking method to fulfill this goal. Instead of training a neural network for protecting a specific image, we train on an image set and use the trained model to protect a distinct test image set in a bulk manner. Respective evaluations both from the subjective and objective aspects confirm the supremacy and practicability of our proposed method. To demonstrate the robustness of this general neural watermarking mechanism, commonly used manipulations are applied to the watermarked image to examine the corresponding extracted watermark, which still retains sufficient recognizable traits. To the best of our knowledge, we are the first to propose a general way to perform watermarking using DNN. Considering its performance and economy, it is concluded that subsequent studies that generalize our work on utilizing DNN for intellectual content protection is a promising research trend.
On the utilization of Macroscopic Information for String Stability of a Vehicular Platoon

The use of macroscopic information for the control of a vehicular platoon composed of autonomous vehicles is investigated. A mesoscopic control law is provided, and String Stability is proved by Lyapunov functions and Input-to-State Stability (ISS) concepts. Simulations are implemented in order to validate the controller and to show the efficacy of the proposed approach for mitigating traffic oscillations.
A light neural network for modulation detection under impairments

We present a neural network architecture able to efficiently detect modulation scheme in a portion of I/Q signals. This network is lighter by up to two orders of magnitude than other state-of-the-art architectures working on the same or similar tasks. Moreover, the number of parameters does not depend on the signal duration, which allows processing stream of data, and results in a signal-length invariant network. In addition, we have generated a dataset based on the simulation of impairments that the propagation channel and the demodulator can bring to recorded I/Q signals: random phase shifts, delays, roll-off, sampling rates, and frequency offsets. We benefit from this dataset to train our neural network to be invariant to impairments and quantify its accuracy at disentangling between modulations under realistic real-life conditions. Data and code to reproduce the results are made publicly available.
Generic bivariate multi-point evaluation, interpolation and modular composition with precomputation

Suppose K is a large enough field and P ⊂ K2 is a fixed, generic set of points which is available for precomputation. We introduce a technique called reshaping which allows us to design quasi-linear algorithms for both: computing the evaluations of an input polynomial f ∈ K [x, y] at all points of P and computing an interpolant f ∈ K[x, y] which takes prescribed values on P and satisfies an input y-degree bound. Our genericity assumption is explicit and we prove that it holds for most point sets over a large enough field. If P violates the assumption, our algorithms still work and the performance degrades smoothly according to a distance from being generic. To show that the reshaping technique may have an impact on other related problems, we apply it to modular composition: suppose generic polynomials M ∈ K[x] and A ∈ K[x] are available for precomputation, then given an input f ∈ K[x, y] we show how to compute f (x, A(x)) rem M(x) in quasi-linear time.
AirRL: A Reinforcement Learning Approach to Urban Air Quality Inference

Urban air pollution has become a major environmental problem that threatens public health. It has become increasingly important to infer fine-grained urban air quality based on existing monitoring stations. One of the challenges is how to effectively select some relevant stations for air quality inference. In this paper, we propose a novel model based on reinforcement learning for urban air quality inference. The model consists of two modules: a station selector and an air quality regressor. The station selector dynamically selects the most relevant monitoring stations when inferring air quality. The air quality regressor takes in the selected stations and makes air quality inference with deep neural network. We conduct experiments on a real-world air quality dataset and our approach achieves the highest performance compared with several popular solutions, and the experiments show significant effectiveness of proposed model in tackling problems of air quality inference.
The Mira-Titan Universe. III. Emulation of the Halo Mass Function

We construct an emulator for the halo mass function over group and cluster mass scales for a range of cosmologies, including the effects of dynamical dark energy and massive neutrinos. The emulator is based on the recently completed Mira-Titan Universe suite of cosmological N-body simulations. The main set of simulations spans 111 cosmological models with 2.1 Gpc boxes. We extract halo catalogs in the redshift range z = [0.0, 2.0] and for masses . The emulator covers an eight-dimensional hypercube spanned by { , , , σ8, h, ns, w0, wa}; spatial flatness is assumed. We obtain smooth halo mass functions by fitting piecewise second-order polynomials to the halo catalogs and employ Gaussian process regression to construct the emulator while keeping track of the statistical noise in the input halo catalogs and uncertainties in the regression process. For redshifts z ≲ 1, the typical emulator precision is better than 2% for and <10% for . For comparison, fitting functions using the traditional universal form for the halo mass function can be biased at up to 30% at for z = 0. Our emulator is publicly available at https://github.com/SebastianBocquet/MiraTitanHMFemulator.
Evolutionary bin packing for memory-efficient dataflow inference acceleration on FPGA

Convolutional Neural Network (CNN) dataflow inference accelerators implemented in Field-Programmable Gate Arrays (FPGAs) have demonstrated increased energy efficiency and lower latency compared to CNN execution on CPUs or GPUs. However, the complex shapes of CNN parameter memories do not typically map well to FPGA On-Chip Memories (OCM), which results in poor OCM utilization and ultimately limits the size and types of CNNs which can be effectively accelerated on FPGAs. In this work, we present a design methodology that improves the mapping efficiency of CNN parameters to FPGA OCM. We frame the mapping as a bin packing problem and determine that traditional bin packing algorithms are not well suited to solve the problem within FPGA- and CNN-specific constraints. We hybridize genetic algorithms and simulated annealing with traditional bin packing heuristics to create flexible mappers capable of grouping parameter memories such that each group optimally fits FPGA on-chip memories. We evaluate these algorithms on a variety of FPGA inference accelerators. Our hybrid mappers converge to optimal solutions in a matter of seconds for all CNN use-cases, achieve an increase of up to 65% in OCM utilization efficiency for deep CNNs, and are up to 200× faster than current state-of-the-art simulated annealing approaches.
Generalizable Semantic Segmentation via Model-agnostic Learning and Target-specific Normalization

Semantic segmentation methods in the supervised scenario have achieved significant improvement in recent years. However, when directly deploying the trained model to segment the images of unseen (or new coming) domains, its performance usually drops dramatically due to the data-distribution discrepancy between seen and unseen domains. To overcome this limitation, we propose a novel domain generalization framework for the generalizable semantic segmentation task, which enhances the generalization ability of the model from two different views, including the training paradigm and the data-distribution discrepancy. Concretely, we exploit the model-agnostic learning method to simulate the domain shift problem, which deals with the domain generalization from the training scheme perspective. Besides, considering the data-distribution discrepancy between source domains and unseen target domains, we develop the target-specific normalization scheme to further boost the generalization ability in unseen target domains. Extensive experiments highlight that the proposed method produces state-of-the-art performance for the domain generalization of semantic segmentation on multiple benchmark segmentation datasets (i.e., Cityscapes, Mapillary). Furthermore, we gain an interesting observation that the target-specific normalization can benefit from the model-agnostic learning scheme.
Gryffin: An algorithm for Bayesian optimization for categorical variables informed by physical intuition with applications to chemistry

Designing functional molecules and advanced materials requires complex interdependent design choices: tuning continuous process parameters such as temperatures or flow rates, while simultaneously selecting categorical variables like catalysts or solvents. To date, the development of data-driven experiment planning strategies for autonomous experimentation has largely focused on continuous process parameters despite the urge to devise efficient strategies for the selection of categorical variables to substantially accelerate scientific discovery. We introduce Gryffin, as a general purpose optimization framework for the autonomous selection of categorical variables driven by expert knowledge. Gryffin augments Bayesian optimization with kernel density estimation using smooth approximations to categorical distributions. Leveraging domain knowledge from physicochemical descriptors to characterize categorical options, Gryffin can significantly accelerate the search for promising molecules and materials. Gryffin can further highlight relevant correlations between the provided descriptors to inspire physical insights and foster scientific intuition. In addition to comprehensive benchmarks, we demonstrate the capabilities and performance of Gryffin on three examples in materials science and chemistry: (i) the discovery of non-fullerene acceptors for organic solar cells, (ii) the design of hybrid organic-inorganic perovskites for light-harvesting, and (iii) the identification of ligands and process parameters for Suzuki-Miyaura reactions. Our observations suggest that Gryffin, in its simplest form without descriptors, constitutes a competitive categorical optimizer compared to state-of-the-art approaches. However, when leveraging domain knowledge provided via descriptors, Gryffin can optimize at considerable higher rates and refine this domain knowledge to spark scientific understanding.
Digitizing analogic spectrograms recorded by the Nan\c{c}ay Decameter Array on 35 mm film rolls from 1970 to 1990.

The Nancay Decameter Array (NDA), which has now passed 40 years old, acquires daily observations of Jovian and Solar low frequency radio emissions over a continuous spectrum ranging from 10 up to 100MHz, forming the largest database of LW radio observations of these two bodies. It also intermittently observed intense radio sources since its opening in 1977. Before that date, decametric observations were conducted on the same site with an interferometer formed of a pair of log-periodic Yagi antennas mounted on mobile booms. These observations have been recorded with a series of analogic recorders (before 1990) and then digital receivers (after 1990), with increasing performances and sensitivities. The NDA scientific team recently retrieved and inventoried the archives of analogic data (35mm film rolls) covering two decades (1970 to 1990). We now plan to digitize those observations, in order to recover their scientific value and to include them into the currently operational database covering a time span starting in 1990 up to now, still adding new files every day. This modern and interoperable database has virtual observatory interfaces. It is a required element to foster scientific data exploitation, including Jovian and Solar data analysis over long timescales. We present the status of this project.