Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecules with ALMA at Planet-forming Scales (MAPS). XV. Tracing Protoplanetary Disk Structure within 20 au

Constraining the distribution of gas and dust in the inner 20 au of protoplanetary disks is difficult. At the same time, this region is thought to be responsible for most planet formation, especially around the water ice line at 3–10 au. Under the assumption that the gas is in a Keplerian disk, we use the exquisite sensitivity of the Molecules with ALMA at Planet-forming Scales (MAPS) ALMA large program to construct radial surface brightness profiles with a ∼3 au effective resolution for the CO isotopologue J = 2–1 lines using the line velocity profile. IM Lup reveals a central depression in 13CO and C18O that is ascribed to a pileup of ∼500 M ⊕ of dust in the inner 20 au, leading to a gas-to-dust ratio of around <10. This pileup is consistent with an efficient drift of grains (≳100 M ⊕ Myr−1) and a local gas-to-dust ratio that suggests that the streaming instability could be active. The CO isotopologue emission in the GM Aur disk is consistent with a small (∼15 au), strongly depleted gas cavity within the ∼40 au dust cavity. The radial surface brightness profiles for both the AS 209 and HD 163296 disks show a local minimum and maximum in the C18O emission at the location of a known dust ring (∼14 au) and gap (∼10 au), respectively. This indicates that the dust ring has a low gas-to-dust ratio (>10) and that the dust gap is gas-rich enough to have optically thick C18O. This paper is part of the MAPS special issue of the Astrophysical Journal Supplement.
Non-Parametric Unsupervised Domain Adaptation for Neural Machine Translation

Recently, $k$NN-MT has shown the promising capability of directly incorporating the pre-trained neural machine translation (NMT) model with domain-specific token-level $k$-nearest-neighbor ($k$NN) retrieval to achieve domain adaptation without retraining. Despite being conceptually attractive, it heavily relies on high-quality in-domain parallel corpora, limiting its capability on unsupervised domain adaptation, where in-domain parallel corpora are scarce or nonexistent. In this paper, we propose a novel framework that directly uses in-domain monolingual sentences in the target language to construct an effective datastore for $k$-nearest-neighbor retrieval. To this end, we first introduce an autoencoder task based on the target language, and then insert lightweight adapters into the original NMT model to map the token-level representation of this task to the ideal representation of translation task. Experiments on multi-domain datasets demonstrate that our proposed approach significantly improves the translation accuracy with target-side monolingual data, while achieving comparable performance with back-translation.
Taxonomic and functional diversity covary in rock pool microalgal communities despite their different drivers

Abstract Local biodiversity has traditionally been estimated with taxonomic diversity metrics such as species richness. Recently, the concept of biodiversity has been extended beyond species identity by ecological traits determining the functional role of a species in a community. This interspecific functional diversity typically responds more strongly to local environmental variation compared with taxonomic diversity, while taxonomic diversity may mirror more strongly dispersal processes compared with functional metrics. Several trait‐based indices have been developed to measure functional diversity for various organisms and habitat types, but studies of their applicability on aquatic microbial communities have been underrepresented. We examined the drivers and covariance of taxonomic and functional diversity among diatom rock pool communities on the Baltic Sea coast. We quantified three taxonomic (species richness, Shannon's diversity, and Pielou's evenness) and three functional (functional richness, evenness, and divergence) diversity indices and determined abiotic factors best explaining variation in these indices by generalized linear mixed models. The six diversity indices were highly collinear except functional evenness, which merely correlated significantly with taxonomic evenness. All diversity indices were always explained by water conductivity and temperature–sampling month interaction. Taxonomic diversity was further consistently explained by pool distance to the sea, and functional richness and divergence by pool location. The explained variance in regression models did not markedly differ between taxonomic and functional metrics. Our findings do not clearly support the superiority of neither set of diversity indices in explaining coastal microbial diversity, but rather highlight the general overlap among the indices. However, as individual metrics may be driven by different factors, the greatest advantage in assessing biodiversity is nevertheless probably achieved with a simultaneous application of the taxonomic and functional diversity metrics.
Variation-Incentive Loss Re-weighting for Regression Analysis on Biased Data

Both classification and regression tasks are susceptible to the biased distribution of training data. However, existing approaches are focused on the class-imbalanced learning and cannot be applied to the problems of numerical regression where the learning targets are continuous values rather than discrete labels. In this paper, we aim to improve the accuracy of the regression analysis by addressing the data skewness/bias during model training. We first introduce two metrics, uniqueness and abnormality, to reflect the localized data distribution from the perspectives of their feature (i.e., input) space and target (i.e., output) space. Combining these two metrics we propose a Variation-Incentive Loss re-weighting method (VILoss) to optimize the gradient descent-based model training for regression analysis. We have conducted comprehensive experiments on both synthetic and real-world data sets. The results show significant improvement in the model quality (reduction in error by up to 11.9%) when using VILoss as the loss criterion in training.
Overlap-Aware Low-Latency Online Speaker Diarization Based on End-to-End Local Segmentation

We propose to address online speaker diarization as a combination of incremental clustering and local diarization applied to a rolling buffer updated every 500ms. Every single step of the proposed pipeline is designed to take full advantage of the strong ability of a recently proposed end-to-end overlap-aware segmentation to detect and separate overlapping speakers. In particular, we propose a modified version of the statistics pooling layer (initially introduced in the x-vector architecture) to give less weight to frames where the segmentation model predicts simultaneous speakers. Furthermore, we derive cannot-link constraints from the initial segmentation step to prevent two local speakers from being wrongfully merged during the incremental clustering step. Finally, we show how the latency of the proposed approach can be adjusted between 500ms and 5s to match the requirements of a particular use case, and we provide a systematic analysis of the influence of latency on the overall performance (on AMI, DIHARD and VoxConverse).
An Insect-Inspired Randomly, Weighted Neural Network with Random Fourier Features For Neuro-Symbolic Relational Learning

Insects, such as fruit flies and honey bees, can solve simple associative learning tasks and learn abstract concepts such as"sameness"and"difference", which is viewed as a higher-order cognitive function and typically thought to depend on top-down neocortical processing. Empirical research with fruit flies strongly supports that a randomized representational architecture is used in olfactory processing in insect brains. Based on these results, we propose a Randomly Weighted Feature Network (RWFN) that incorporates randomly drawn, untrained weights in an encoder that uses an adapted linear model as a decoder. The randomized projections between input neurons and higher-order processing centers in the input brain is mimicked in RWFN by a single-hidden-layer neural network that specially structures latent representations in the hidden layer using random Fourier features that better represent complex relationships between inputs using kernel approximation. Because of this special representation, RWFNs can effectively learn the degree of relationship among inputs by training only a linear decoder model. We compare the performance of RWFNs to LTNs for Semantic Image Interpretation (SII) tasks that have been used as a representative example of how LTNs utilize reasoning over first-order logic to surpass the performance of solely data-driven methods. We demonstrate that compared to LTNs, RWFNs can achieve better or similar performance for both object classification and detection of the part-of relations between objects in SII tasks while using much far fewer learnable parameters (1:62 ratio) and a faster learning process (1:2 ratio of running speed). Furthermore, we show that because the randomized weights do not depend on the data, several decoders can share a single randomized encoder, giving RWFNs a unique economy of spatial scale for simultaneous classification tasks.
Domain Adaptation by Maximizing Population Correlation with Neural Architecture Search

In Domain Adaptation (DA), where the feature distributions of the source and target domains are different, various distance-based methods have been proposed to minimize the discrepancy between the source and target domains to handle the domain shift. In this paper, we propose a new similarity function, which is called Population Correlation (PC), to measure the domain discrepancy for DA. Base on the PC function, we propose a new method called Domain Adaptation by Maximizing Population Correlation (DAMPC) to learn a domain-invariant feature representation for DA. Moreover, most existing DA methods use hand-crafted bottleneck networks, which may limit the capacity and flexibility of the corresponding model. Therefore, we further propose a method called DAMPC with Neural Architecture Search (DAMPC-NAS) to search the optimal network architecture for DAMPC. Experiments on several benchmark datasets, including Office-31, Office-Home, and VisDA-2017, show that the proposed DAMPC-NAS method achieves better results than state-of-the-art DA methods.
Vibrational excitation of molecules in the gas phase or embedded in matrices by ab initio molecular dynamics

Dans cette these nous avons travaille sur deux systemes prototypiques: en phase gazeuse et en phase condensee, pour suivre et quantifier l'energie vibrationnelle dans des modes normaux apres excitation vibrationnelle initiale.Pour la phase condensee, ce travail prend en compte l'immersion d'une molecule hote: l'acide formique immerge dans une matrice d'argon et pour la phase gazeuse avec un cluster de (Cl . N-Methyl Acetamide.Ar). Apres la preparation des conditions initiales utilisant un echantillonnage semi-classique de Wigner, plusieurs trajectoires DFT-MD ont ete executees pour analyser la relaxation vibrationnelle intra et intermoleculaire.Deux methodes theoriques ont ete utilisees pour suivre l'evolution avec le temps du nombre quantique vibrationnel: une nouvelle methode basee sur l'etude des spectres d'etat vibrationnels (VDOS) compare avec une methode plus traditionnelle basee sur l'etude de l'amplitude d'un mouvement specifique de la molecule (c'est-a-dire distance ou angle). Dans la phase condensee, le rendement quantique du processus d'isomerisation de la molecule d'acide formique a ete compare aux donnees experimentales. Nous avons montre que l'excitation vibrationnelle combinee de deux modes implique dans quelques cas l'isomerisation de la molecule d'acide formique. Cette excitation vibrationnelle initiale de l'acide formique a aussi un impact sur la matrice environnante: l'energie initialement contenue dans l'AF est redistribuee a la matrice, ce qui mene a la deformation et la fonte (transition solide-liquide) de la matrice d'argon.En ce qui concerne la phase gazeuse, nous avons montre que l'excitation vibrationnelle de l'etirement du mouvement N-H du (Cl. NMA) implique la perte (evaporation) systematique de l'atome d'argon.Finalement, les deux modeles (phase gaz ou condensee) ont montre qu'apres une excitation vibrationnelle initiale (n=1) d'un mode normal a t=0 de la dynamique, ce mode normal revient au cours de la dynamique dans son etat fondamental (n=0).
Automatic segmentation and grading of ankylosing spondylitis on MR images via lightweight hybrid multi-scale convolutional neural network with reinforcement learning

Objective. Ankylosing spondylitis (AS) is a disabling systemic disease that seriously threatens the patient’s quality of life. Magnetic resonance imaging (MRI) is highly preferred in clinical diagnosis due to its high contrast and tissue resolution. However, since the uncertainty and intensity inhomogeneous of the AS lesions in MRI, it is still challenging and time-consuming for doctors to quantify the lesions to determine the grade of the patient’s condition. Thus, an automatic AS grading method is presented in this study, which integrates the lesion segmentation and grading in a pipeline. Approach. To tackle the large variations in lesion shapes, sizes, and intensity distributions, a lightweight hybrid multi-scale convolutional neural network with reinforcement learning (LHR-Net) is proposed for the AS lesion segmentation. Specifically, the proposed LHR-Net is equipped with the newly proposed hybrid multi-scale module, which consists of multiply convolution layers with different kernel sizes and dilation rates for extracting sufficient multi-scale features. Additionally, a reinforcement learning-based data augmentation module is utilized to deal with the subjects with diffuse and fuzzy lesions that are difficult to segment. Furthermore, to resolve the incomplete segmentation results caused by the inhomogeneous intensity distributions of the AS lesions in MR images, a voxel constraint strategy is proposed to weigh the training voxel labels in the lesion regions. With the accurately segmented AS lesions, automatic AS grading is then performed by a ResNet-50-based classification network. Main results. The performance of the proposed LHR-Net was extensively evaluated on a clinically collected AS MRI dataset, which includes 100 subjects. Dice similarity coefficient (DSC), average surface distance, Hausdorff Distance at 95th percentile (HD95), predicted positive volume, and sensitivity were employed to quantitatively evaluate the segmentation results. The average DSC of the proposed LHR-Net on the AS dataset reached 0.71 on the test set, which outperforms the other state-of-the-art segmentation method by 0.04. Significance. With the accurately segmented lesions, 31 subjects in the test set (38 subjects) were correctly graded, which demonstrates that the proposed LHR-Net might provide a potential automatic method for reproducible computer-assisted diagnosis of AS grading.
The correlation coefficient between citation metrics and winning a Nobel or Abel Prize

Computing such correlation coefficient would be straightforward had we had available the rankings given by the prize committee to all scientists in the pool. In reality we only have citation rankings for all scientists. This means, however, that we have the ordinal rankings of the prize winners with regard to citation metrics. I use maximum likelihood method to infer the most probable correlation coefficient to produce the observed pattern of ordinal ranks of the prize winners. I get the correlation coefficients of 0.47 and 0.59 between the composite citation indicator and getting Abel Prize and Fields Medal, respectively. The correlation coefficient between getting a Nobel Prize and the Q-factor is 0.65. These coefficients are of the same magnitude as the correlation coefficient between Elo ratings of the chess players and their popularity measured as numbers of webpages mentioning the players.
Alliance Network Diversity and Social Capital Mobilization an Empirical Study of the Global Airline Industry

Firms establish a presence in a wide range of alliances to access the critical resources that they may need at any one time. Although inter-organizational relationships constitute the channels through which social capital flows, firms should consider which mechanisms or characteristics of the relations facilitate their actual mobilization. Accordingly, the final purpose of alliances cannot simply be potential access to partner resources, it must also be their real use. A definition of alliance types yielded the parameters for an ordinary least squares regression of a sample from top international airline databases. The results showed that certain kind of alliances favored the actual mobilization of social capital. Accordingly, cooperation as a structural organizational form, and in particular certain types of alliances such as global or multilateral, makes it easier to cope with increasingly volatile, uncertain, complex and ambiguous environments -VUCA conditions.
AdaPruner: Adaptive Channel Pruning and Effective Weights Inheritance

Channel pruning is one of the major compression approaches for deep neural networks. While previous pruning methods have mostly focused on identifying unimportant channels, channel pruning is considered as a special case of neural architecture search in recent years. However, existing methods are either complicated or prone to sub-optimal pruning. In this paper, we propose a pruning framework that adaptively determines the number of each layer's channels as well as the wights inheritance criteria for sub-network. Firstly, evaluate the importance of each block in the network based on the mean of the scaling parameters of the BN layers. Secondly, use the bisection method to quickly find the compact sub-network satisfying the budget. Finally, adaptively and efficiently choose the weight inheritance criterion that fits the current architecture and fine-tune the pruned network to recover performance. AdaPruner allows to obtain pruned network quickly, accurately and efficiently, taking into account both the structure and initialization weights. We prune the currently popular CNN models (VGG, ResNet, MobileNetV2) on different image classification datasets, and the experimental results demonstrate the effectiveness of our proposed method. On ImageNet, we reduce 32.8% FLOPs of MobileNetV2 with only 0.62% decrease for top-1 accuracy, which exceeds all previous state-of-the-art channel pruning methods. The code will be released.
Obstacle Avoidance for Autonomous Mobile Robots Based on Mapping Method

In recent years, the mobile robot has been considerable attention to researchers for its application in various environments. For a mobile robot navigating its way from starting point to a goal point while traversing through deterrents, needs to recognize the obstacles and generate new trajectories to reach the destination. This paper presents an obstacle avoidance method for mobile robots using an open-source in robot operation system (ROS) combining with the dynamic window approach (DWA) algorithm. The experiment is carried out using a mobile robot in which the navigation data is based on data collecting by a laser scanner. The experimental results show that the robot could work well in environments containing static and dynamic obstacles.
Incentivizing Mobile Edge Caching and Sharing: An Evolutionary Game Approach

Mobile Edge Caching is a promising technique to enhance the content delivery quality and reduce the backhaul link congestion, by storing popular contents at the network edge or mobile devices (e.g. base stations and smartphones) that are proximate to content requesters. In this work, we study a novel mobile edge caching framework, which enables mobile devices to cache and share popular contents with each other via device-to-device (D2D) links. We are interested in the incentive-related problem of mobile device users: whether and which users are willing to cache and share what contents, taking the user mobility and cost/reward into consideration. The problem is challenging in a large-scale network. We introduce the evolutionary game theory, an effective tool for analyzing large-scale dynamic systems, to analyze the mobile users' content caching and sharing strategies. Specifically, we first derive the users' best caching and sharing strategies, and then analyze how these best strategies change dynamically over time. Based on the above, we further characterize the system equilibrium systematically. Simulation results show that the proposed scheme outperforms the existing schemes in terms of the total transmission cost and the cellular load. In particular, in our simulations, the total transmission cost can be reduced by 42.5%~55.2% and the cellular load can be reduced by 21.5%~56.4%.
Tribrid: Stance Classification with Neural Inconsistency Detection

We study the problem of performing automatic stance classification on social media with neural architectures such as BERT. Although these architectures deliver impressive results, their level is not yet comparable to the one of humans and they might produce errors that have a significant impact on the downstream task (e.g., fact-checking). To improve the performance, we present a new neural architecture where the input also includes automatically generated negated perspectives over a given claim. The model is jointly learned to make simultaneously multiple predictions, which can be used either to improve the classification of the original perspective or to filter out doubtful predictions. In the first case, we propose a weakly supervised method for combining the predictions into a final one. In the second case, we show that using the confidence scores to remove doubtful predictions allows our method to achieve human-like performance over the retained information, which is still a sizable part of the original input.
Music Waveform Analysis Based on SOM Neural Network and Big Data

Music is an indispensable part of our life and study and is one of the most important forms of multimedia applications. With the development of deep learning and neural network in recent years, how to use cutting-edge technology to study and apply music has become a research hotspot. Music waveform is not only the main form of music frequency but also the basis of music feature extraction. This paper first designs a method of note extraction based on the fast Fourier transform principle of the audio signal packet route under the self-organizing map (SOM neural network) which can accurately extract the musical features of the note, such as amplitude, loudness, period, and so on. Secondly, the audio segments are divided into summary by adding window moving matching method, and the music features such as amplitude, loudness, and period of each bar are obtained according to the performance of audio signal in each bar. Finally, according to the similarity of the audio music theory of the adjacent summary of each bar, the audio segments are divided, and the music features of each segment are obtained. The traditional recurrent neural network (RNN) is improved, and the SOM neural network is used to recognize the audio emotion features. The final experimental results show that the proposed method based on SOM neural network and big data can effectively extract and analyze music waveform features. Compared with previous studies, this paper creatively proposed a new algorithm, which can more accurately and quickly extract and analyze the data sound waveform, and used SOM neural network to analyze the emotion model contained in music for the first time.