Miracle-dz/UniFAR-FTUs huggingface dataset:
Multimodal Emotion Recognition Model using Physiological Signals

As an important field of research in Human-Machine Interactions, emotion recognition based on physiological signals has become research hotspots. Motivated by the outstanding performance of deep learning approaches in recognition tasks, we proposed a Multimodal Emotion Recognition Model that consists of a 3D convolutional neural network model, a 1D convolutional neural network model and a biologically inspired multimodal fusion model which integrates multimodal information on the decision level for emotion recognition. We use this model to classify four emotional regions from the arousal valence plane, i.e., low arousal and low valence (LALV), high arousal and low valence (HALV), low arousal and high valence (LAHV) and high arousal and high valence (HAHV) in the DEAP and AMIGOS dataset. The 3D CNN model and 1D CNN model are used for emotion recognition based on electroencephalogram (EEG) signals and peripheral physiological signals respectively, and get the accuracy of 93.53% and 95.86% with the original EEG signals in these two datasets. Compared with the single-modal recognition, the multimodal fusion model improves the accuracy of emotion recognition by 5% ~ 25%, and the fusion result of EEG signals (decomposed into four frequency bands) and peripheral physiological signals get the accuracy of 95.77%, 97.27% and 91.07%, 99.74% in these two datasets respectively. Integrated EEG signals and peripheral physiological signals, this model could reach the highest accuracy about 99% in both datasets which shows that our proposed method demonstrates certain advantages in solving the emotion recognition tasks.
SimpleBooks: Long-term dependency book dataset with simplified English vocabulary for word-level language modeling

With language modeling becoming the popular base task for unsupervised representation learning in Natural Language Processing, it is important to come up with new architectures and techniques for faster and better training of language models. However, due to a peculiarity of languages -- the larger the dataset, the higher the average number of times a word appears in that dataset -- datasets of different sizes have very different properties. Architectures performing well on small datasets might not perform well on larger ones. For example, LSTM models perform well on WikiText-2 but poorly on WikiText-103, while Transformer models perform well on WikiText-103 but not on WikiText-2. For setups like architectural search, this is a challenge since it is prohibitively costly to run a search on the full dataset but it is not indicative to experiment on smaller ones. In this paper, we introduce SimpleBooks, a small dataset with the average word frequency as high as that of much larger ones. Created from 1,573 Gutenberg books with the highest ratio of word-level book length to vocabulary size, SimpleBooks contains 92M word-level tokens, on par with WikiText-103 (103M tokens), but has the vocabulary of 98K, a third of WikiText-103's. SimpleBooks can be downloaded from this https URL.
LeRoP: A Learning-Based Modular Robot Photography Framework

We introduce a novel framework for automatic capturing of human portraits. The framework allows the robot to follow a person to the desired location using a Person Re-identification model. When composing is activated, the robot attempts to adjust its position to form the view that can best match the given template image, and finally takes a photograph. A template image can be predicted dynamically using an off-the-shelf photo evaluation model by the framework, or selected manually from a pre-defined set by the user. The template matching-based view adjustment is driven by a deep reinforcement learning network. Our framework lies on top of the Robot Operating System (ROS). The framework is designed to be modular so that all the models can be flexibly replaced based on needs. We show our framework on a variety of examples. In particular, we tested it in three indoor scenes and used it to take 20 photos of each scene: ten for the pre-defined template, ten for the dynamically generated ones. The average number of adjustment was $11.20$ for pre-defined templates and $12.76$ for dynamically generated ones; the average time spent was $22.11$ and $24.10$ seconds respectively.
From peculiar morphologies to Hubble-type spirals: the relation between galaxy dynamics and morphology in star-forming galaxies at z ∼ 1.5

We present an analysis of the gas dynamics of star-forming galaxies at z ∼ 1.5 using data from the KMOS Galaxy Evolution Survey. We quantify the morphology of the galaxies using HSTcandels imaging parametrically and non-parametrically. We combine the H α dynamics from KMOS with the high-resolution imaging to derive the relation between stellar mass (M*) and stellar specific angular momentum (j*). We show that high-redshift star-forming galaxies at z ∼ 1.5 follow a power-law trend in specific stellar angular momentum with stellar mass similar to that of local late-type galaxies of the form j* ∝ M$_*^{0.53\, \pm \, 0.10}$. The highest specific angular momentum galaxies are mostly disc-like, although generally both peculiar morphologies and disc-like systems are found across the sequence of specific angular momentum at a fixed stellar mass. We explore the scatter within the j* – M* plane and its correlation with both the integrated dynamical properties of a galaxy (e.g. velocity dispersion, Toomre Qg, H α star formation rate surface density ΣSFR) and its parametrized rest-frame UV / optical morphology (e.g. Sérsic index, bulge to total ratio, clumpiness, asymmetry, and concentration). We establish that the position in the j* – M* plane is strongly correlated with the star-formation surface density and the clumpiness of the stellar light distribution. Galaxies with peculiar rest-frame UV / optical morphologies have comparable specific angular momentum to disc- dominated galaxies of the same stellar mass, but are clumpier and have higher star formation rate surface densities. We propose that the peculiar morphologies in high-redshift systems are driven by higher star formation rate surface densities and higher gas fractions leading to a more clumpy interstellar medium.
Macross: Urban Dynamics Modeling based on Metapath Guided Cross-Modal Embedding

As the ongoing rapid urbanization takes place with an ever-increasing speed, fully modeling urban dynamics becomes more and more challenging, but also a necessity for socioeconomic development. It is challenging because human activities and constructions are ubiquitous; urban landscape and life content change anywhere and anytime. It's crucial due to the fact that only up-to-date urban dynamics can enable governors to optimize their city planning strategy and help individuals organize their daily lives in a more efficient way. Previous geographic topic model based methods attempt to solve this problem but suffer from high computational cost and memory consumption, limiting their scalability to city level applications. Also, strong prior assumptions make such models fail to capture certain patterns by nature. To bridge the gap, we propose Macross, a metapath guided embedding approach to jointly model location, time and text information. Given a dataset of geo-tagged social media posts, we extract and aggregate location and time and construct a heterogeneous information network using the aggregated space and time. Metapath2vec based approach is used to construct vector representations for times, locations and frequent words such that co-occurrence pairs of nodes are closer in latent space. The vector representations will be used to infer related time, locations or keywords for a user query. Experiments done on enormous datasets show our model can generate comparable if not better quality query results compared to state of the art models and outperform some cutting-edge models for activity recovery and classification.
Effects of Probiotic Administration During Coccidiosis Vaccination on Performance and Lesion Development in Broilers

Effects of Probiotic Administration during Coccidiosis Vaccination on Performance and Lesion Development in Broilers. (August 2009) Anthony Emil Klein, Jr., B.S., Texas A&M University Chair of Advisory Committee: Dr. David J. Caldwell The principal objective of this investigation was to evaluate coccidiosis vaccination, with or without probiotic administration, for effects on broiler performance and clinical indices of infection due to field strain Eimeria challenge during pen trials of commercially applicable durations. During trials 1 and 2, body weights of vaccinated broilers were reduced (P<0.05) compared to other experimental groups during rearing through the grower phase. Final body weights, however, were not different among experimental groups at the termination of each trial. Similarly, feed conversion in trials 1 and 2 was increased (P<0.05) in vaccinated broilers during rearing through the grower phase when compared to non-vaccinated broilers. Significant improvements (P<0.05) in feed conversion were measured in trials 1 and 2 in vaccinated broilers during the withdrawal phase of grow-out. Probiotic administration significantly reduced (P<0.05) feed conversion during the withdrawal phase of trial 2. During trial 3, body weights of broilers in the vaccine with probiotic (water) group were higher (P<0.05) at termination (d 44) than all other experimental groups and equivalent to the ionophore alone and ionophore with probiotic groups. Similarly, cumulative mortality corrected feed conversion ratio (FCR) was lower (P<0.05) in broilers from the vaccine with probiotic
High-resolution spectroscopy of the high-velocity hot post-AGB star IRAS 18379–1707 (LS 5112)

The high-resolution ($R\sim48\,000$) optical spectrum of the B-type supergiant LS 5112, identified as the optical counterpart of the post-AGB candidate IRAS 18379-1707, is analysed. We report the detailed identifications of the observed absorption and emission features in the wavelength range 3700-9200 A for the first time. The absorption line spectrum has been analysed using non-LTE model atmosphere techniques to determine stellar atmospheric parameters and chemical composition. We estimate $T_\text{eff}=18~000\pm1000$ K, $\log g=2.25\pm0.08$, $\xi_{\rm t}=10\pm4$ km/s and $v \sin i=37\pm6$ km/s, and the derived abundances indicate a metal-deficient ([M/H]$\approx-0.6)$ post-AGB star. Chemical abundances of eight different elements were obtained. The estimates of the CNO abundances in IRAS 18379-1707 indicate that these elements are overabundant with [(C+N+O)/S]=+0.5$\pm$0.2 suggesting that the products of helium burning have been brought to the surface as a result of third dredge-up on the AGB. From the absorption lines, we derived heliocentric radial velocity of $V_\text{r}=-124.0\pm0.4$ km/s. We have identified permitted emission lines of O I, N I, Na I, S II, Si II, C II, Mg II and Fe III. The nebula forbidden lines of [N I], [O I], [Fe II], [N II], [S II], [Ni II] and [Cr II] have also been identified. The Balmer lines H$\alpha$, H$\beta$ and H$\gamma$ show P-Cygni behaviour clearly indicating post-AGB mass-loss process in the object with the wind velocity up to 170 km/s.
How Can We Know What Language Models Know?

Abstract Recent work has presented intriguing results examining the knowledge contained in language models (LMs) by having the LM fill in the blanks of prompts such as “Obama is a __ by profession”. These prompts are usually manually created, and quite possibly sub-optimal; another prompt such as “Obama worked as a __ ” may result in more accurately predicting the correct profession. Because of this, given an inappropriate prompt, we might fail to retrieve facts that the LM does know, and thus any given prompt only provides a lower bound estimate of the knowledge contained in an LM. In this paper, we attempt to more accurately estimate the knowledge contained in LMs by automatically discovering better prompts to use in this querying process. Specifically, we propose mining-based and paraphrasing-based methods to automatically generate high-quality and diverse prompts, as well as ensemble methods to combine answers from different prompts. Extensive experiments on the LAMA benchmark for extracting relational knowledge from LMs demonstrate that our methods can improve accuracy from 31.1% to 39.6%, providing a tighter lower bound on what LMs know. We have released the code and the resulting LM Prompt And Query Archive (LPAQA) at https://github.com/jzbjyb/LPAQA.
Online Structured Sparsity-Based Moving-Object Detection From Satellite Videos

Inspired by the recent developments in computer vision, low-rank and structured sparse matrix decomposition can be potentially be used for extract moving objects in satellite videos. This set of approaches seeks for rank minimization on the background that typically requires batch-based optimization over a sequence of frames, which causes delays in processing and limits their applications. To remedy this delay, we propose an online low-rank and structured sparse decomposition (O-LSD). O-LSD reformulates the batch-based low-rank matrix decomposition with the structured sparse penalty to its equivalent framewise separable counterpart, which then defines a stochastic optimization problem for online subspace basis estimation. In order to promote online processing, O-LSD conducts the foreground and background separations and the subspace basis update alternatingly for every frame in a video. We also show the convergence of O-LSD theoretically. Experimental results on two satellite videos demonstrate the performance of O-LSD in terms of accuracy, and the time consumption is comparable with the batch-based approaches with significantly reduced delay in processing.
First-principles study of the nontrivial topological phase in chains of 
3d
 transition metals

Recent experiments have shown the signatures of Majorana bound states at the ends of magnetic chains deposited on a superconducting substrate. Here, we employ first-principles calculations to directly investigate the topological properties of $3d$ transition metal nanochains (i.e., Mn, Cr, Fe and Co). In contrast to the previous studies [Nadj-Perge et al., Science 346, 602 (2014) and Ruby et al., Nano Lett. 17, 4473 (2017)], we found the exact tight-binding models in the Wannier orbital basis for the isolated chains as well as for the surface--deposited wires. Based on these models, we calculate the topological invariant of ${\mathbb{Z}}_{2}$ phases for all systems. Our results for the isolated chains demonstrate the existence of the topological phase only in Mn and Co systems. We also considered a noncollinear magnetic order as a source of the nontrivial topological phase and found that this type of magnetic order is not a stable ground state in the Fe and Co isolated chains. Further studies showed that a coupling between the chain and substrate leads to strong modification of the band structure. Moreover, the analysis of the topological invariant indicates the possibility of emergence of the topological phase in all studied nanochains deposited on the Pb surface. Therefore, our results demonstrate an important role of the coupling between deposited atoms and a substrate for topological properties of nanosystems.
A Dimensionality-Reduction Strategy to Compute Shortest Paths in Urban Water Networks

The efficient computation of shortest paths in complex networks is essential to face new challenges related to critical infrastructures such as a near real-time monitoring and control and the management of big size systems. In particular, using information on the minimum paths in water distribution networks (WDNs) allows to track the diffusion of contaminants and to quantify the resilience and criticality of the system. This is, ultimately, approached by considering dynamically changing path-weights that depend on the flow or on other information available at run-time. These analyses tipically include all the WDN assets but reducing the high degree of physical details with a minimum lost of key information for their performance assessment. This paper proposes a strategy to compute minimum paths that is based on a dimensionality-reduction process. Specifically, the network is partitioned into communities and suitably modified to obtain a reduced complexity representation (e.g., in terms of number of nodes and links). The paper shows how this novel, reduced representation is equivalent to the traditional network on computing the shortest paths. The proposed approach is validated considering two utility networks as case studies. The results show that the proposed method provides the exact solution for the shortest path with a computational-time reduction consistently over 50\% and up to 90\% for some cases. Furthermore, the application of the proposal on WDNs partitioning shows both hydraulic and economic advantages thanks to their monitoring and controlling at near real-time.
Safe Walking In VR using Augmented Virtuality

New technologies allow ordinary people to access Virtual Reality at affordable prices in their homes. One of the most important tasks when interacting with immersive Virtual Reality is to navigate the virtual environments (VEs). Arguably, the best methods to accomplish this use of direct control interfaces. Among those, natural walking (NW) makes for enjoyable user experience. However, common techniques to support direct control interfaces in VEs feature constraints that make it difficult to use those methods in cramped home environments. Indeed, NW requires unobstructed and open space. To approach this problem, we propose a new virtual locomotion technique, Combined Walking in Place (CWIP). CWIP allows people to take advantage of the available physical space and empowers them to use NW to navigate in the virtual world. For longer distances, we adopt Walking in Place (WIP) to enable them to move in the virtual world beyond the confines of a cramped real room. However, roaming in immersive alternate reality, while moving in the confines of a cluttered environment can lead people to stumble and fall. To approach these problems, we developed Augmented Virtual Reality (AVR), to inform users about real-world hazards, such as chairs, drawers, walls via proxies and signs placed in the virtual world. We propose thus CWIP-AVR as a way to safely explore VR in the cramped confines of your own home. To our knowledge, this is the first approach to combined different locomotion modalities in a safe manner. We evaluated it in a user study with 20 participants to validate their ability to navigate a virtual world while walking in a confined and cluttered real space. Our results show that CWIP-AVR allows people to navigate VR safely, switching between locomotion modes flexibly while maintaining a good immersion.
Augmented Random Search for Quadcopter Control: An alternative to Reinforcement Learning

Model-based reinforcement learning strategies are believed to exhibit more significant sample complexity than model-free strategies to control dynamical systems,such as quadcopters.This belief that Model-based strategies that involve the use of well-trained neural networks for making such high-level decisions always give better performance can be dispelled by making use of Model-free policy search methods.This paper proposes the use of a model-free random searching strategy,called Augmented Random Search(ARS),which is a better and faster approach of linear policy training for continuous control tasks like controlling a Quadcopters flight.The method achieves state-of-the-art accuracy by eliminating the use of too much data for the training of neural networks that are present in the previous approaches to the task of Quadcopter control.The paper also highlights the performance results of the searching strategy used for this task in a strategically designed task environment with the help of simulations.Reward collection performance over 1000 episodes and agents behavior in flight for augmented random search is compared with that of the behavior for reinforcement learning state-of-the-art algorithm,called Deep Deterministic policy gradient(DDPG). Our simulations and results manifest that a high variability in performance is observed in commonly used strategies for sample efficiency of such tasks but the built policy network of ARS-Quad can react relatively accurately to step response providing a better performing alternative to reinforcement learning strategies.
LL(1) Parsing with Derivatives and Zippers

In this paper, we present an efficient, functional, and formally verified parsing algorithm for LL(1) context-free expressions based on the concept of derivatives of formal languages. Parsing with derivatives is an elegant parsing technique, which, in the general case, suffers from cubic worst-case time complexity and slow performance in practice. We specialise the parsing with derivatives algorithm to LL(1) context-free expressions, where alternatives can be chosen given a single token of lookahead. We formalise the notion of LL(1) expressions and show how to efficiently check the LL(1) property. Next, we present a novel linear-time parsing with derivatives algorithm for LL(1) expressions operating on a zipper-inspired data structure. We prove the algorithm correct in Coq and present an implementation as a parser combinators framework in Scala, with enumeration and pretty printing capabilities.
Non-Intrusive Electrical Appliances Monitoring and Classification using K-Nearest Neighbors

Non-Intrusive Load Monitoring (NILM) is the method of detecting an individual device’s energy signal from an aggregated energy consumption signature [1]. As existing energy meters provide very little to no information regarding the energy consumptions of individual appliances apart from the aggregated power rating, the spotting of individual appliances’ energy usages by NILM will not only provide consumers the feedback of appliance-specific energy usage but also lead to the changes of their consumption behavior which facilitate energy conservation. B Neenan et al. [2] have demonstrated that direct individual appliance-specific energy usage signals lead to consumers’ behavioral changes which improves energy efficiency by as much as 15%. Upon disaggregation of an energy signal, the signal needs to be classified according to the appropriate appliance. Hence, the goal of this paper is to disaggregate total energy consumption data to individual appliance signature and then classify appliance-specific energy loads using a prominent supervised classification method known as K-Nearest Neighbors (KNN). To perform this operation, we have used a publicly accessible dataset of power signals from several houses known as the REDD dataset. Before applying KNN, data is preprocessed for each device. Then KNN is applied to check whether their energy consumption signature is separable or not. KNN is applied with K=5.
J-Net: Randomly weighted U-Net for audio source separation

Several results in the computer vision literature have shown the potential of randomly weighted neural networks. While they perform fairly well as feature extractors for discriminative tasks, a positive correlation exists between their performance and their fully trained counterparts. According to these discoveries, we pose two questions: what is the value of randomly weighted networks in difficult generative audio tasks such as audio source separation and does such positive correlation still exist when it comes to large random networks and their trained counterparts? In this paper, we demonstrate that the positive correlation still exists. Based on this discovery, we can try out different architecture designs or tricks without training the whole model. Meanwhile, we find a surprising result that in comparison to the non-trained encoder (down-sample path) in Wave-U-Net, fixing the decoder (up-sample path) to random weights results in better performance, almost comparable to the fully trained model.