Miracle-dz/UniFAR-FTUs huggingface dataset:
Artificial intelligence system for continuous affect estimation from naturalistic human expressions

The analysis and automatic affect estimation system from human expression has been acknowledged as an active research topic in computer vision community. Most reported affect recognition systems, however, only consider subjects performing well-defined acted expression, in a very controlled condition, so they are not robust enough for real-life recognition tasks with subject variation, acoustic surrounding and illumination change. In this thesis, an artificial intelligence system is proposed to continuously (represented along a continuum e.g., from -1 to +1) estimate affect behaviour in terms of latent dimensions (e.g., arousal and valence) from naturalistic human expressions. To tackle the issues, feature representation and machine learning strategies are addressed. In feature representation, human expression is represented by modalities such as audio, video, physiological signal and text modality. Handcrafted features is extracted from each modality per frame, in order to match with consecutive affect label. However, the features extracted maybe missing information due to several factors such as background noise or lighting condition. Haar Wavelet Transform is employed to determine if noise cancellation mechanism in feature space should be considered in the design of affect estimation system. Other than hand-crafted features, deep learning features are also analysed in terms of the layer-wise; convolutional and fully connected layer. Convolutional Neural Network such as AlexNet, VGGFace and ResNet has been selected as deep learning architecture to do feature extraction on top of facial expression images. Then, multimodal fusion scheme is applied by fusing deep learning feature and hand-crafted feature together to improve the performance. In machine learning strategies, two-stage regression approach is introduced. In the first stage, baseline regression methods such as Support Vector Regression are applied to estimate each affect per time. Then in the second stage, subsequent model such as Time Delay Neural Network, Long Short-Term Memory and Kalman Filter is proposed to model the temporal relationships between consecutive estimation of each affect. In doing so, the temporal information employed by a subsequent model is not biased by high variability present in consecutive frame and at the same time, it allows the network to exploit the slow changing dynamic between emotional dynamic more efficiently. Following of two-stage regression approach for unimodal affect analysis, fusion information from different modalities is elaborated. Continuous emotion recognition in-the-wild is leveraged by investigating mathematical modelling for each emotion dimension. Linear Regression, Exponent Weighted Decision Fusion
GRANIITTI: A Monte Carlo Event Generator for High Energy Diffraction

We describe the physics and computational power of GRANIITTI Monte Carlo event generator, a new fully multithreaded engine designed for high energy diffraction, written in modern C++. The emphasis is especially on the low-mass domain of central exclusive processes of the S-matrix, where exotic QCD phenomena such as glueballs are expected and holographic dualities with gravity may be tested. The generator includes photon-photon, photon-pomeron, pomeron-pomeron, Durham QCD model and Tensor pomeron model type scattering amplitudes and advanced spin analysis tools. The generator combines a full parametric resonance spectrum with continuum interference and forward + central spin correlations together with event-by-event eikonal screening loop and forward proton excitation kinematics. We demonstrate the state-of-the-art capabilities and show how the enigmatic `glueball filter' observable is driven by spin polarization.
Comparing simulated 26Al maps to gamma-ray measurements

Context. The diffuse gamma-ray emission of 26Al at 1.8 MeV reflects ongoing nucleosynthesis in the Milky Way and traces massive-star feedback in the interstellar medium due to its 1 Myr radioactive lifetime. The morphology and dynamics of the interstellar medium are investigated in astrophysics through 3D hydrodynamic simulations in fine detail as there are few suitable astronomical probes available. Aims. We aim to compare a galactic-scale hydrodynamic simulation of the Galaxy’s interstellar medium, including feedback and nucleosynthesis, with gamma-ray data on 26Al emission in the Milky Way, extracting constraints that are only weakly dependent on the particular realisation of the simulation or Galaxy structure. Methods. Due to constraints and biases in both the simulations and the gamma-ray observations, such comparisons are not straightforward. For a direct comparison, we performed maximum likelihood fits of both simulated sky maps and observation-based maximum entropy maps to measurements using INTEGRAL/SPI. In order to study general morphological properties, we compare the scale heights of 26Al emission produced by the simulation to INTEGRAL/SPI measurements. Results. The direct comparison shows that the simulation describes the observed inner Galaxy well, however it differs significantly from the observed full-sky emission morphology. Comparing the scale height distribution, we see similarities for small-scale height features and a mismatch at larger-scale heights. We attribute this to prominent foreground emission sites which are not captured by the simulation.
The Bayesian Synthetic Control: Improved Counterfactual Estimation in the Social Sciences through Probabilistic Modeling

Social scientists often study how a policy reform impacted a single targeted country. Increasingly, this is done with the synthetic control method (SCM). SCM models the country's counterfactual (non-reform or untreated) trajectory as a weighted average of other countries' outcomes. The method struggles to quantify uncertainty; eg. it cannot produce confidence intervals. It is also suspect to overfit. We propose an alternative method, the Bayesian synthetic control (BSC), which lacks these flaws. Using MCMC sampling, we implement the method for two previously studied datasets. The proposed method outperforms SCM in a simple test of predictive accuracy and casts some doubt on significance of prior findings. The studied reforms are the German reunification of 1990 and the California tobacco legislation of 1988. BSC borrows its causal model, the linear latent factor model, from the SCM literature. Unlike SCM, BSC estimates the latent factors explicitly through a dimensionality reduction. All uncertainty is captured in the posterior distribution so that, unlike for SCM, credible intervals are easily derived. Further, BSC's reliability on the target panel dataset can be assessed through a posterior predictive check; SCM and its frequentist derivatives use up the required information while testing statistical significance.
eCNN: A Block-Based and Highly-Parallel CNN Accelerator for Edge Inference

Convolutional neural networks (CNNs) have recently demonstrated superior quality for computational imaging applications. Therefore, they have great potential to revolutionize the image pipelines on cameras and displays. However, it is difficult for conventional CNN accelerators to support ultra-high-resolution videos at the edge due to their considerable DRAM bandwidth and power consumption. Therefore, finding a further memory- and computation-efficient microarchitecture is crucial to speed up this coming revolution. In this paper, we approach this goal by considering the inference flow, network model, instruction set, and processor design jointly to optimize hardware performance and image quality. We apply a block-based inference flow which can eliminate all the DRAM bandwidth for feature maps and accordingly propose a hardware-oriented network model, ERNet, to optimize image quality based on hardware constraints. Then we devise a coarse-grained instruction set architecture, FBISA, to support power-hungry convolution by massive parallelism. Finally, we implement an embedded processor---eCNN---which accommodates to ERNet and FBISA with a flexible processing architecture. Layout results show that it can support high-quality ERNets for super-resolution and denoising at up to 4K Ultra-HD 30 fps while using only DDR-400 and consuming 6.94W on average. By comparison, the state-of-the-art Diffy uses dualchannel DDR3-2133 and consumes 54.3W to support lower-quality VDSR at Full HD 30 fps. Lastly, we will also present application examples of high-performance style transfer and object recognition to demonstrate the flexibility of eCNN.
How are attributes expressed in face DCNNs?

As deep networks become increasingly accurate at recognizing faces, it is vital to understand how these networks process faces. While these networks are solely trained to recognize identities, they also contain face related information such as sex, age, and pose of the face even when the networks are not trained to learn these attributes. We introduce expressivity as a measure of how much a feature vector informs us about an attribute, where a feature vector can be from internal or final layers of a network. Expressivity is computed by a second neural network whose inputs are features and attributes. The output of the second neural network approximates the mutual information between feature vectors and an attribute. We investigate the expressivity for two different deep convolutional neural network (DCNN) architectures: a Resnet-101 and an Inception Resnet v2. In the final fully connected layer of the networks, we found the order of expressivity for facial attributes to be Age > Sex > Yaw. Additionally, we studied the changes in the encoding of facial attributes over training iterations. We found that as training progresses, expressivities of yaw, sex, and age decrease. Our technique can be a tool for investigating the sources of bias in a network and a step towards explaining the network’s identity decisions.
Characterization and formation of on-disk spicules in the Ca II K and Mg II k spectral lines (Corrigendum)

We characterize, for the first time, type-II spicules in Ca II K 3934\AA\ using the CHROMIS instrument at the Swedish 1-m Solar Telescope. We find that their line formation is dominated by opacity shifts with the K$_{3}$ minimum best representing the velocity of the spicules. The K$_{2}$ features are either suppressed by the Doppler-shifted K$_{3}$ or enhanced via an increased contribution from the lower layers, leading to strongly enhanced but un-shifted K$_{2}$ peaks, with widening towards the line-core as consistent with upper-layer opacity removal via Doppler-shift. We identify spicule spectra in concurrent IRIS Mg II k 2796\AA\ observations with very similar properties. Using our interpretation of spicule chromospheric line-formation, we produce synthetic profiles that match observations.
The Search for Truth through Data: NP Decision Processes, ROC Functions, $P$-Functionals, Knowledge Updating and Sequential Learning

This paper re-visits the problem of deciding between two simple hypotheses, the setting considered by Neyman and Pearson in developing their fundamental lemma. It studies the decision process induced by the most powerful test and the receiver operating characteristic function associated with this decision process. It addresses the question of how to report the decision arising from the decision function. It also examines the P-functional (the P-value statistic) and its role in the decision-making process. The impetus of this work is the continuing criticisms of statistical decision-making procedures that uses the P-functional and a level of significance (LoS) of 0.05. A point made is that if one is going to use the value of the P-functional, then it should be used in an equivalent manner as the most powerful decision function, but if one wants to obtain from its value the degree of support for either hypotheses, then the value of its density under the alternative is the proper quantity to use. Replicability of results are discussed. Knowledge updating through Bayes theorem when given the decision or the value of the P-functional is also discussed, and it is argued that sequential learning is a coherent way of finding the truth. But the impact of publication bias is also demonstrated to be quite serious in the search for truth. It is argued that decision-makers are free to choose their own LoS, since the additional summary measures will automatically take their LoS choices into consideration. Three approaches for choosing an optimal LoS are discussed and a procedure for sample size determination is described. Ideas are illustrated by concrete problems and by the lady tea-tasting experiment of Fisher which ushered null hypothesis significance testing. It is hoped that by considering this fundamental setting of simple hypotheses, a better understanding of more complex settings will ensue.
Resource Allocation for Multi-User Downlink MISO OFDMA-URLLC Systems

This article considers the resource allocation algorithm design for downlink multiple-input single-output (MISO) orthogonal frequency division multiple access (OFDMA) ultra-reliable low latency communication (URLLC) systems. To meet the stringent delay requirements of URLLC, short packet transmission is adopted and taken into account for resource allocation algorithm design. The resource allocation is optimized for maximization of the weighted system sum throughput subject to quality-of-service (QoS) constraints regarding the URLLC users’ number of transmitted bits, packet error probability, and delay. Despite the non-convexity of the resulting optimization problem, the optimal solution is found via monotonic optimization. The corresponding optimal resource allocation policy can serve as a performance upper bound for sub-optimal low-complexity solutions. We develop such a low-complexity sub-optimal resource allocation algorithm based on successive convex approximation and difference of convex programming. Our simulation results reveal the importance of using multiple antennas for reducing the latency and improving the reliability of URLLC systems. Moreover, the proposed sub-optimal algorithm is shown to closely approach the performance of the proposed optimal algorithm and outperforms two baseline schemes by a considerable margin, especially when the users have heterogeneous delay requirements. Finally, conventional resource allocation designs based on Shannon’s capacity formula are shown to be not applicable in MISO OFDMA-URLLC systems as they are not able to guarantee the users’ delay constraints.
Effects of Depth, Width, and Initialization: A Convergence Analysis of Layer-wise Training for Deep Linear Neural Networks

Deep neural networks have been used in various machine learning applications and achieved tremendous empirical successes. However, training deep neural networks is a challenging task. Many alternatives have been proposed in place of end-to-end back-propagation. Layer-wise training is one of them, which trains a single layer at a time, rather than trains the whole layers simultaneously. In this paper, we study a layer-wise training using a block coordinate gradient descent (BCGD) for deep linear networks. We establish a general convergence analysis of BCGD and found the optimal learning rate, which results in the fastest decrease in the loss. We identify the effects of depth, width, and initialization. When the orthogonal-like initialization is employed, we show that the width of intermediate layers plays no role in gradient-based training beyond a certain threshold. Besides, we found that the use of deep networks could drastically accelerate convergence when it is compared to those of a depth 1 network, even when the computational cost is considered. Numerical examples are provided to justify our theoretical findings and demonstrate the performance of layer-wise training by BCGD.
Accelerometer-Based Gait Segmentation: Simultaneously User and Adversary Identification

In this paper, we introduce a new gait segmentation method based on accelerometer data and develop a new distance function between two time series, showing novel and effectiveness in simultaneously identifying user and adversary. Comparing with the normally used Neural Network methods, our approaches use geometric features to extract walking cycles more precisely and employ a new similarity metric to conduct user-adversary identification. This new technology for simultaneously identify user and adversary contributes to cybersecurity beyond user-only identification. In particular, the new technology is being applied to cell phone recorded walking data and performs an accuracy of $98.79\%$ for 6 classes classification (user-adversary identification) and $99.06\%$ for binary classification (user only identification). In addition to walking signal, our approach works on walking up, walking down and mixed walking signals. This technology is feasible for both large and small data set, overcoming the current challenges facing to Neural Networks such as tuning large number of hyper-parameters for large data sets and lacking of training data for small data sets. In addition, the new distance function developed here can be applied in any signal analysis.
Constraining the magnetic field structure in collisionless relativistic shocks with a radio afterglow polarization upper limit in GW 170817

Gamma-ray burst (GRB) afterglow arises from a relativistic shock driven into the ambient medium, which generates tangled magnetic fields and accelerates relativistic electrons that radiate the observed synchrotron emission. In relativistic collisionless shocks the post-shock magnetic field $\boldsymbol {B}$ is produced by the two-stream and/or Weibel instabilities on plasma skin-depth scales (c/ωp), and is oriented predominantly within the shock plane (B⊥; transverse to the shock normal, $\hat{\boldsymbol {n}}_{\rm {sh}}$), and is often approximated to be completely within it ($B_\parallel \equiv \hat{\boldsymbol {n}}_{\rm {sh}}\, \cdot \, \boldsymbol {B}=0$). Current 2D/3D particle-in-cell simulations are limited to short time-scales and box sizes ≲104(c/ωp) ≪ R/Γsh much smaller than the shocked region’s comoving width, and hence cannot probe the asymptotic downstream $\boldsymbol {B}$ structure. We constrain the latter using the linear polarization upper limit, $\vert \Pi \vert \lt 12{{\ \rm per\ cent}}$, on the radio afterglow of GW $170817$ / GRB 170817A. Afterglow polarization depends on the jet’s angular structure, our viewing angle, and the $\boldsymbol {B}$ structure. In GW $170817$ / GRB 170817A the latter can be tightly constrained since the former two are well-constrained by its exquisite observations. We model $\boldsymbol {B}$ as an isotropic field in 3D that is stretched along $\hat{\boldsymbol {n}}_{\rm {sh}}$ by a factor ξ ≡ $B_\parallel $/B⊥, whose initial value ξf ≡ $B_\parallel,$f/B⊥, f describes the field that survives downstream on plasma scales ≪R/Γsh. We calculate Π(ξf) by integrating over the entire shocked volume for a Gaussian or power-law core-dominated structured jet, with a local Blandford-McKee self-similar radial profile (used for evolving ξ downstream). We find that independent of the exact jet structure, $\boldsymbol {B}$ has a finite, but initially sub-dominant, parallel component: 0.57 ≲ ξf ≲ 0.89, making it less anisotropic. While this motivates numerical studies of the asymptotic $\boldsymbol {B}$ structure in relativistic collisionless shocks, it may be consistent with turbulence amplified magnetic field.
A relative 2–nerve

In this work, we introduce a 2-categorical variant of Lurie's relative nerve functor. We prove that it defines a right Quillen equivalence which, upon passage to $\infty$-categorical localizations, corresponds to Lurie's scaled unstraightening equivalence. In this $\infty$-bicategorical context, the relative 2-nerve provides a computationally tractable model for the Grothendieck construction which becomes equivalent, via an explicit comparison map, to Lurie's relative nerve when restricted to 1-categories.
Face flips in origami tessellations

Given a flat-foldable origami crease pattern $G=(V,E)$ (a straight-line drawing of a planar graph on a region of the plane) with a mountain-valley (MV) assignment $\mu:E\to\{-1,1\}$ indicating which creases in $E$ bend convexly (mountain) or concavely (valley), we may \emph{flip} a face $F$ of $G$ to create a new MV assignment $\mu_F$ which equals $\mu$ except for all creases $e$ bordering $F$, where we have $\mu_F(e)=-\mu(e)$. In this paper we explore the configuration space of face flips for a variety of crease patterns $G$ that are tilings of the plane, proving examples where $\mu_F$ results in a MV assignment that is either never, sometimes, or always flat-foldable for various choices of $F$. We also consider the problem of finding, given two foldable MV assignments $\mu_1$ and $\mu_2$ of a given crease pattern $G$, a minimal sequence of face flips to turn $\mu_1$ into $\mu_2$. We find polynomial-time algorithms for this in the cases where $G$ is either a square grid or the Miura-ori, and show that this problem is NP-hard in the case where $G$ is the triangle lattice.
Quantity and Diversity of Preliteracy Language Exposure Both Affect Literacy Development: Evidence from a Computational Model of Reading

ABSTRACT Diversity of vocabulary knowledge and quantity of language exposure prior to literacy are key predictors of reading development. However, diversity and quantity of exposure are difficult to distinguish in behavioural studies, and so the causal relations with literacy are not well known. We tested these relations by training a connectionist triangle model of reading that learned to map between semantic; phonological; and, later, orthographic forms of words. The model first learned to map between phonology and semantics, where we manipulated the quantity and diversity of this preliterate language experience. Then the model learned to read. Both diversity and quantity of exposure had unique effects on reading performance, with larger effects for written word comprehension than for reading fluency. The results further showed that quantity of preliteracy language exposure was beneficial only when this was to a varied vocabulary and could be an impediment when exposed to a limited vocabulary.
The scalar particle hai in Chinese

La particule hai a en mandarin divers emplois. Elle peut avoir le sens de 'toujours', 'encore', 'aussi', et 'meme'. Jusqu'a present, il n'y a pas eu d'etude regroupant ces divers emplois sous une seule acception. Dans cet article, m'appuyant sur le modele scalaire de Fillmore, Kay et O'Conner (1988), j'avance que tous ces emplois ont un point commun fondamental. Invariablement hai evoque une relation entre deux propositions, et est associe a une proposition plus forte. En tant que particule de focus, hai est scalaire et additif ; toutefois, son focus n'est pas toujours exprime dans la phrase.