Miracle-dz/UniFAR-FTUs huggingface dataset:
A Generic Network Compression Framework for Sequential Recommender Systems

Sequential recommender systems (SRS) have become the key technology in capturing user's dynamic interests and generating high-quality recommendations. Current state-of-the-art sequential recommender models are typically based on a sandwich-structured deep neural network, where one or more middle (hidden) layers are placed between the input embedding layer and output softmax layer. In general, these models require a large number of parameters to obtain optimal performance. Despite the effectiveness, at some point, further increasing model size may be harder for model deployment in resource-constraint devices. To resolve the issues, we propose a compressed sequential recommendation framework, termed as CpRec, where two generic model shrinking techniques are employed. Specifically, we first propose a block-wise adaptive decomposition to approximate the input and softmax matrices by exploiting the fact that items in SRS obey a long-tailed distribution. To reduce the parameters of the middle layers, we introduce three layer-wise parameter sharing schemes. We instantiate CpRec using deep convolutional neural network with dilated kernels given consideration to both recommendation accuracy and efficiency. By the extensive ablation studies, we demonstrate that the proposed CpRec can achieve up to 4~8 times compression rates in real-world SRS datasets. Meanwhile, CpRec is faster during training & inference, and in most cases outperforms its uncompressed counterpart.
Conversational Word Embedding for Retrieval-Based Dialog System

Human conversations contain many types of information, e.g., knowledge, common sense, and language habits. In this paper, we propose a conversational word embedding method named PR-Embedding, which utilizes the conversation pairs to learn word embedding. Different from previous works, PR-Embedding uses the vectors from two different semantic spaces to represent the words in post and reply.To catch the information among the pair, we first introduce the word alignment model from statistical machine translation to generate the cross-sentence window, then train the embedding on word-level and sentence-level.We evaluate the method on single-turn and multi-turn response selection tasks for retrieval-based dialog systems.The experiment results show that PR-Embedding can improve the quality of the selected response.
Exploring the Contextual Dynamics of Multimodal Emotion Recognition in Videos

Emotional expressions form a key part of user behavior on today's digital platforms. While multimodal emotion recognition techniques are gaining research attention, there is a lack of deeper understanding on how visual and non-visual features can be used in better recognizing emotions for certain contexts, but not others. This study analyzes the interplay between the effects of multimodal emotion features derived from facial expressions, tone and text in conjunction with two key contextual factors: 1) the gender of the speaker, and 2) the duration of the emotional episode. Using a large dataset of more than 2500 manually annotated videos from YouTube, we found that while multimodal features consistently outperformed bimodal and unimodal features, their performances varied significantly for different emotions, gender and duration contexts. Multimodal features were found to perform particularly better for male than female speakers in recognizing most emotions except for fear. Furthermore, multimodal features performed particularly better for shorter than for longer videos in recognizing neutral, happiness, and surprise, but not sadness, anger, disgust and fear. These findings offer new insights towards the development of more context-aware emotion recognition and empathetic systems.
Scheduled DropHead: A Regularization Method for Transformer Models

We introduce DropHead, a structured dropout method specifically designed for regularizing the multi-head attention mechanism which is a key component of transformer. In contrast to the conventional dropout mechanism which randomly drops units or connections, DropHead drops entire attention heads during training to prevent the multi-head attention model from being dominated by a small portion of attention heads. It can help reduce the risk of overfitting and allow the models to better benefit from the multi-head attention. Given the interaction between multi-headedness and training dynamics, we further propose a novel dropout rate scheduler to adjust the dropout rate of DropHead throughout training, which results in a better regularization effect. Experimental results demonstrate that our proposed approach can improve transformer models by 0.9 BLEU score on WMT14 En-De translation task and around 1.0 accuracy for various text classification tasks.
SN 2013aa and SN 2017cbv: Two Sibling Type Ia Supernovae in the Spiral Galaxy NGC 5643

We present photometric and spectroscopic observations of SN 2013aa and SN 2017cbv, two nearly identical type Ia supernovae (SNe Ia) in the host galaxy NGC 5643. The optical photometry has been obtained using the same telescope and instruments used by the Carnegie Supernova Project. This eliminates most instrumental systematics and provides light curves in a stable and well-understood photometric system. Having the same host galaxy also eliminates systematics due to distance and peculiar velocity, providing an opportunity to directly test the relative precision of SNe Ia as standard candles. The two SNe have nearly identical decline rates, negligible reddenings, and remarkably similar spectra, and, at a distance of ∼20 Mpc, they are ideal potential calibrators for the absolute distance using primary indicators such as Cepheid variables. We discuss to what extent these two SNe can be considered twins and compare them with other supernova “siblings” in the literature and their likely progenitor scenarios. Using 12 galaxies that hosted two or more SNe Ia, we find that when using SNe Ia, and after accounting for all sources of observational error, one gets consistency in distance to 3%.
Fast and Memory-Efficient Neural Code Completion

Code completion is one of the most widely used features of modern integrated development environments (IDEs). While deep learning has made significant progress in the statistical prediction of source code, state-of-the-art neural network models consume hundreds of megabytes of memory, bloating the development environment. We address this in two steps: first we present a modular neural framework for code completion. This allows us to explore the design space and evaluate different techniques. Second, within this framework we design a novel reranking neural completion model that combines static analysis with granular token encodings. The best neural reranking model consumes just 6 MB of RAM, — 19x less than previous models — computes a single completion in 8 ms, and achieves 90% accuracy in its top five suggestions.
Comments on the Bellman functional for linear time‐delay systems

In this note, we present some complementary results on the infinite horizon optimal control for linear time‐delay systems. We formally establish some properties of the matrices arising in the Bellman functional, and we prove that any admissible linear control law generates only a class of Bellman functional for addressed systems. Additionally, experimental results are presented, which illustrate a satisfactory control performance when is compared with an optimal proportional integral industrial control.
Analytical modeling and experimentally optimizing synergistic effect on thermal conductivity enhancement of polyurethane nanocomposites with hybrid carbon nanofillers

Combining various carbon nanofillers with different dimensions can lead to a synergistic effect through the formation of an efficient conductive network. Hybrid polyurethane (PU) nanocomposites containing multi-walled carbon nanotubes (MWCNTs) and graphene nanoplatelets (GNPs) were fabricated to study experimental and theoretical aspects of thermal conductivity (TC) enhancement. The optimization of hybrid nanofillers combinations was done to synergically enhance the TC using various types of GNPs, different nanofillers concentrations and varied ratios. A synergistic thermal conductivity improvement with MWCNTs and GNPs was confirmed at low nanofillers contents. The TC of hybrid nanocomposite at 0.25 wt% is approximately equivalent to the TC of individual nanofillers at 0.75 wt%. An analytical model for the effective thermal conductivity of single and hybrid nanocomposites was considered with variables of volume fraction, interfacial thermal resistance, straightness of the nanofillers and the percolation effect, in which the predictions of the modified models agree with the experimental results.
Denise: Deep Learning based Robust PCA for Positive Semidefinite Matrices

The robust PCA of high-dimensional matrices plays an essential role when isolating key explanatory features. The currently available methods for performing such a low-rank plus sparse decomposition are matrix specific, meaning, the algorithm must re-run each time a new matrix should be decomposed. Since these algorithms are computationally expensive, it is preferable to learn and store a function that instantaneously performs this decomposition when evaluated. Therefore, we introduce Denise, a deep learning-based algorithm for robust PCA of symmetric positive semidefinite matrices, which learns precisely such a function. Theoretical guarantees that Denise's architecture can approximate the decomposition function, to arbitrary precision and with arbitrarily high probability, are obtained. The training scheme is also shown to convergence to a stationary point of the robust PCA's loss-function. We train Denise on a randomly generated dataset, and evaluate the performance of the DNN on synthetic and real-world covariance matrices. Denise achieves comparable results to several state-of-the-art algorithms in terms of decomposition quality, but as only one evaluation of the learned DNN is needed, Denise outperforms all existing algorithms in terms of computation time.
Microscopic Mechanism of Van der Waals Heteroepitaxy in the Formation of MoS2/hBN Vertical Heterostructures

Recent studies have revealed that van der Waals (vdW) heteroepitaxial growth of 2D materials on crystalline substrates, such as hexagonal boron nitride (hBN), leads to the formation of self-aligned grains, which results in defect-free stitching between the grains. However, how the weak vdW interaction causes a strong limitation on the crystal orientation of grains is still not understood yet. In this work, we have focused on investigating the microscopic mechanism of the self-alignment of MoS2 grains in vdW epitaxial growth on hBN. Using the density functional theory and the Lennard–Jones potential, we found that the interlayer energy between MoS2 and hBN strongly depends on the size and crystal orientation of MoS2. We also found that, when the size of MoS2 is several tens of nanometers, the rotational energy barrier can exceed ∼1 eV, which should suppress rotation to align the crystal orientation of MoS2 even at the growth temperature.
A Sample of Edge-on H i-rich Low Surface Brightness Galaxy Candidates in the 40% ALFALFA Catalog

Low-surface-brightness galaxies(LSBGs) are defined as galaxies that are fainter than dark night sky and are important for studying our universe. Particularly, edge-on galaxies are useful for the study of rotational velocity and dynamical properties of galaxies. Hence here we focus on searching for edge-on LSBGs. In order to find these edge-on dim galaxies, a series of effects caused by inclination, including the surface brightness profile, internal extinction, and scale length, have been corrected. In this work, we present a catalog of 281 edge-on LSBG candidates, which are selected from the cross-match between SDSS DR7 and the 40% ALFALFA catalog. We also present the properties of these edge-on LSBG candidates including absolute magnitude, central surface brightness, B-V color, scale length, and relative thickness. Our result suggests that the correction of inclination effects is very important for obtaining a complete sample of LSBGs.
Deep Learning for ECG Analysis: Benchmarks and Insights from PTB-XL

Electrocardiography (ECG) is a very common, non-invasive diagnostic procedure and its interpretation is increasingly supported by algorithms. The progress in the field of automatic ECG analysis has up to now been hampered by a lack of appropriate datasets for training as well as a lack of well-defined evaluation procedures to ensure comparability of different algorithms. To alleviate these issues, we put forward first benchmarking results for the recently published, freely accessible clinical 12-lead ECG dataset PTB-XL, covering a variety of tasks from different ECG statement prediction tasks to age and sex prediction. Among the investigated deep-learning-based timeseries classification algorithms, we find that convolutional neural networks, in particular resnet- and inception-based architectures, show the strongest performance across all tasks. We find consistent results on the ICBEB2018 challenge ECG dataset and discuss prospects of transfer learning using classifiers pretrained on PTB-XL. These benchmarking results are complemented by deeper insights into the classification algorithm in terms of hidden stratification, model uncertainty and an exploratory interpretability analysis, which provide connecting points for future research on the dataset. Our results emphasize the prospects of deep-learning-based algorithms in the field of ECG analysis, not only in terms of quantitative accuracy but also in terms of clinically equally important further quality metrics such as uncertainty quantification and interpretability. With this resource, we aim to establish the PTB-XL dataset as a resource for structured benchmarking of ECG analysis algorithms and encourage other researchers in the field to join these efforts.
Plant raw materials and natural substances influencing the immune system

the herb and the root of purple coneflower, the root of pale coneflower as well as narrowleaved coneflower have a very well-documented immunostimulating activity. among the other medicinal plants influencing the immune system there are also the herb of pale coneflower, the leaf of Aloe arborescens and the mistletoe herb. Both macromolecular compounds such as glycoproteins and polysaccharides and the micromolecular derivatives of caffeic acid, alkamides and unsaturated alkyl ketones, are listed among active immunostimulators.
Mineral snowflakes on exoplanets and brown dwarfs

Exoplanet atmosphere characterisation has become an important tool in understanding exoplanet formation, evolution. However, clouds remain a key challenge for characterisation: upcoming space telescopes (e.g. JWST, ARIEL) and ground-based high-resolution spectrographs (e.g. CRIRES+) will produce data requiring detailed understanding of cloud formation and cloud effects. We aim to understand how the micro-porosity of cloud particles affects the cloud structure, particle size, and material composition. We examine the spectroscopic effects of micro-porous particles, the particle size distribution, and non-spherical cloud particles. We expanded our kinetic non-equilibrium cloud formation model and use a grid of prescribed 1D (T_gas-p_gas) DRIFT-PHOENIX profiles. We applied the effective medium theory and the Mie theory to model the spectroscopic properties of cloud particles with micro-porosity and a derived particle size distribution. We used a statistical distribution of hollow spheres to represent the effects of non-spherical cloud particles. Highly micro-porous cloud particles (90% vacuum) have a larger surface area, enabling efficient bulk growth higher in the atmosphere than for compact particles. Increases in single-scattering albedo and cross-sectional area for these mineral snowflakes cause the cloud deck to become optically thin only at a wavelength of ~100 ${\rm \mu m}$ instead of at the ~20 ${\rm \mu m}$ for compact cloud particles. A significant enhancement in albedo is also seen when cloud particles occur with a locally changing Gaussian size distribution. Non-spherical particles increase the opacity of silicate spectral features, which further increases the wavelength at which the clouds become optically thin. JWST MIRI will be sensitive to signatures of micro-porous and non-spherical cloud particles based on the wavelength at which clouds are optically thin.
CANU-ReID: A Conditional Adversarial Network for Unsupervised person Re-IDentification

Unsupervised person re-ID is the task of identifying people on a target data set for which the ID labels are unavailable during training. In this paper, we propose to unify two trends in unsupervised person re-ID: clustering & fine-tuning and adversarial learning. On one side, clustering groups training images into pseudo-ID labels, and uses them to fine-tune the feature extractor. On the other side, adversarial learning is used, inspired by domain adaptation, to match distributions from different domains. Since target data is distributed across different camera viewpoints, we propose to model each camera as an independent domain, and aim to learn domain-independent features. Straightforward adversarial learning yields negative transfer, we thus introduce a conditioning vector to mitigate this undesirable effect. In our framework, the centroid of the cluster to which the visual sample belongs is used as conditioning vector of our conditional adversarial network, where the vector is permutation invariant (clusters ordering does not matter) and its size is independent of the number of clusters. To our knowledge, we are the first to propose the use of conditional adversarial networks for unsupervised person re-ID. We evaluate the proposed architecture on top of two state-of-the-art clustering-based unsupervised person re-identification (re-ID) methods on four different experimental settings with three different data sets and set the new state-of-the-art performance on all four of them. Our code and model is publicly available at https://team.inria. fr/perception/research/canu-reid/.
Evolution and role of mergers in the BCG–cluster alignment. A view from cosmological hydrosimulations

Contradictory results have been reported on the time evolution of the alignment between clusters and their Brightest Cluster Galaxy (BCG). We study this topic by analyzing cosmological hydro-simulations of 24 massive clusters with $M_{200}|_{z=0} \gtrsim 10^{15}\, M_\odot$, plus 5 less massive with $1 \times 10^{14} \lesssim M_{200}|_{z=0} \lesssim 7 \times 10^{14}\, M_\odot$, which have already proven to produce realistic BCG masses. We compute the BCG alignment with both the distribution of cluster galaxies and the dark matter (DM) halo. At redshift $z=0$, the major axes of the simulated BCGs and their host cluster galaxy distributions are aligned on average within 20$^\circ$. The BCG alignment with the DM halo is even tighter. The alignment persists up to $z\lesssim2$ with no evident evolution. This result continues, although with a weaker signal, when considering the projected alignment. The cluster alignment with the surrounding distribution of matter ($3R_{200}$) is already in place at $z\sim4$ with a typical angle of $35^\circ$, before the BCG-Cluster alignment develops. The BCG turns out to be also aligned with the same matter distribution, albeit always to a lesser extent. These results taken together might imply that the BCG-Cluster alignment occurs in an outside-in fashion. Depending on their frequency and geometry, mergers can promote, destroy or weaken the alignments. Clusters that do not experience recent major mergers are typically more relaxed and aligned with their BCG. In turn, accretions closer to the cluster elongation axis tend to improve the alignment as opposed to accretions closer to the cluster minor axis.
Would You Like Sashimi Even If It's Sliced Too Thin? Selective Neural Attention for Aspect Targeted Sentiment Analysis (SNAT)

Sentiments in opinionated text are often determined by both aspects and target words (or targets). We observe that targets and aspects interrelate in subtle ways, often yielding conflicting sentiments. Thus, a naive aggregation of sentiments from aspects and targets treated separately, as in existing sentiment analysis models, impairs performance. We propose SNAT, an approach that jointly considers aspects and targets when inferring sentiments. To capture and quantify relationships between targets and context words, SNAT uses a selective self-attention mechanism that handles implicit or missing targets. Specifically, SNAT involves two layers of attention mechanisms, respectively, for selective attention between targets and context words and attention over words based on aspects. On benchmark datasets, SNAT outperforms leading models by a large margin, yielding (absolute) gains in accuracy of 1.8% to 5.2%.
Evolution of Flare-Accelerated Electrons Quantified by Spatially Resolved Analysis

Non–thermal electrons accelerated in solar flares produce electromagnetic emission in two distinct, highly complementary domains—hard X-rays (HXRs) and microwaves (MWs). This paper reports MW imaging spectroscopy observations from the Expanded Owens Valley Solar Array of an M1.2 flare that occurred on 2017 September 9, from which we deduce evolving coronal parameter maps. We analyze these data jointly with the complementary Reuven Ramaty High-Energy Solar Spectroscopic Imager HXR data to reveal the spatially-resolved evolution of the non-thermal electrons in the flaring volume. We find that the high-energy portion of the non-thermal electron distribution, responsible for the MW emission, displays a much more prominent evolution (in the form of strong spectral hardening) than the low-energy portion, responsible for the HXR emission. We show that the revealed trends are consistent with a single electron population evolving according to a simplified trap-plus-precipitation model with sustained injection/acceleration of non-thermal electrons, which produces a double-powerlaw with steadily increasing break energy.
The systematics of Australopithecus garhi

Cladistic analysis was used to test the hypothesis that Australopithecus garhi is ancestral to the genus Homo. The data set of a prior cladistic analysis (Strait et al., 1997) was updated to account for recent fossil hominid discoveries, and reanalyzed. Current evidence suggests that A. garhi is the sister taxon of a clade that includes Homo, Paranthropus and A. africanus. Such a result is inconsistent with the hypothesis that A. garhi is the direct ancestor of the genus Homo. Conditions are specified under which future fossil finds might necessitate a revision of this conclusion. Key words: Australopithecus, Paranthropus, Homo, hominid phylogeny, cladistics, taxonomy.
Living with constraints - food quality effects on zooplankton

Copepods and daphniids exhibit different grazing patterns, which affect the seston size distribution. I have studied whether the grazing of one guild could be beneficial for the other guild. Daphniids grew faster on seston previous manipulated by copepods than on seston which was not manipulated. Seston manipulated by daphniids was a poor food source for Daphnia and thus, growth declined. When marine copepods were feeding on plankton previously manipulated by copepods their growth potential (RNA:DNA) decreased with increasing copepod densities. In the laboratory I studied the effect of fatty acids and phosphorus (P) on Daphnia magna life-history and stoichiometry. D. magna had low EPA saturation threshold for growth (0.04 µg EPA L-1) and high storage capacity. However for P, daphniids had high requirements but only small storage capacity. P storage could not be utilised, whereas EPA storage could compensate periods of poor food quality.