Miracle-dz/UniFAR-FTUs huggingface dataset:
Dust properties and star formation of approximately a thousand local galaxies

Aims. We derived the dust properties for 753 local galaxies and examine how these relate to some of their physical properties. We present the derived dust emission properties, including model spectral energy distribution (SEDs), star formation rates (SFRs) and stellar masses, as well as their relations. Methods. We modelled the global dust-SEDs for 753 galaxies, treated statistically as an ensemble within a hierarchical Bayesian dust-SED modelling approach, so as to derive their infrared (IR) emission properties. To create the observed dust-SEDs, we used a multi-wavelength set of observations, ranging from near-IR to far-IR-to-submillimeter wavelengths. The model-derived properties are the dust masses (Mdust), the average interstellar radiation field intensities (Uav), the mass fraction of very small dust grains (“QPAH” fraction), as well as their standard deviations. In addition, we used mid-IR observations to derive SFR and stellar masses, quantities independent of the dust-SED modelling. Results. We derive distribution functions of the properties for the galaxy ensemble and as a function of galaxy type. The mean value of Mdust for the early-type galaxies (ETGs) is lower than that for the late-type and irregular galaxies (LTGs and Irs, respectively), despite ETGs and LTGs having stellar masses spanning across the whole range observed. The Uav and “QPAH” fraction show no difference among different galaxy types. When fixing Uav to the Galactic value, the derived “QPAH” fraction varies across the Galactic value (0.071). The specific SFR increases with galaxy type, while this is not the case for the dust-specific SFR (SFR/Mdust), showing an almost constant star formation efficiency per galaxy type. The galaxy sample is characterised by a tight relationship between the dust mass and the stellar mass for the LTGs and Irs, while ETGs scatter around this relation and tend towards smaller dust masses. While the relation indicates that Mdust may fundamentally be linked to M⋆, metallicity and Uav are the second parameter driving the scatter, which we investigate in a forthcoming work. We used the extended Kennicutt–Schmidt (KS) law to estimate the gas mass and the gas-to-dust mass ratio (GDR). The gas mass derived from the extended KS law is on average ∼20% higher than that derived from the KS law, and a large standard deviation indicates the importance of the average star formation present to regulate star formation and gas supply. The average GDR for the LTGs and Irs is 370, and including the ETGs gives an average of 550.
Strong particle-hole asymmetry in a 200 Kelvin superconductor

The superconducting state of metals has long provided a classic example of particle-hole symmetry (PHS) at low energy. Fermionic self-energy results based on first principles theory for the electron-phonon coupling in H$_3$S presented here illustrate strong PHS-breaking dynamics arising from the underlying sharp structure in the fermionic density of states. Thus H$_3$S is not only the superconductor with the highest critical temperature $T_c$ (through 2018), but its low energy, low temperature properties deviate strongly from textbook behavior. The minor momentum and band dependence of the fermionic self-energy allows evaluation of the momentum-resolved and zone-averaged spectral densities and interacting thermal distribution function, all of which clearly illustrate strong particle-hole asymmetry.
From Receptive to Productive: Learning to Use Confusing Words through Automatically Selected Example Sentences

Knowing how to use words appropriately has been a key to improving language proficiency. Previous studies typically discuss how students learn receptively to select the correct candidate from a set of confusing words in the fill-in-the-blank task where specific context is given. In this paper, we go one step further, assisting students to learn to use confusing words appropriately in a productive task: sentence translation. We leverage the GiveMe-Example system, which suggests example sentences for each confusing word, to achieve this goal. In this study, students learn to differentiate the confusing words by reading the example sentences, and then choose the appropriate word(s) to complete the sentence translation task. Results show students made substantial progress in terms of sentence structure. In addition, highly proficient students better managed to learn confusing words. In view of the influence of the first language on learners, we further propose an effective approach to improve the quality of the suggested sentences.
MaR-T: Designing a Projec3on-Based Mixed Reality System for Nonsymbolic Math Development of Preschoolers Guided by Theories of Cogni2on and Learning

Recent developmental studies state that nonsymbolic number representation (i.e., more-less comparisons) is important for math development, and children’s judgment about such non-numerical magnitudes can be affected by sensory properties (i.e., volume, space). Yet, to our knowledge, there are no tangible based systems for training this math concept. Building on theories of cognition and learning, we developed MaR-T, a projector-camera setup. This paper is a step towards investigating the effects of projection-based mixed-reality (MR) system with tangibles on nonsymbolic number representation of 3-to 5-year-old children. We present our user studies with a total of 14 participants, conducted to observe their interaction with the setup and the possible effects of our design on learning. The results indicate that MaR-T can provide active, engaging, and social learning, and our insights can inspire other interaction design and education studies. CCS CONCEPTS
Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation

The simplest and most widely applied method for guaranteeing differential privacy is to add instance-independent noise to a statistic of interest that is scaled to its global sensitivity. However, global sensitivity is a worst-case notion that is often too conservative for realized dataset instances. We provide methods for scaling noise in an instance-dependent way and demonstrate that they provide greater accuracy under average-case distributional assumptions. Specifically, we consider the basic problem of privately estimating the mean of a real distribution from i.i. d. ~samples. The standard empirical mean estimator can have arbitrarily-high global sensitivity. We propose the trimmed mean estimator, which interpolates between the mean and the median, as a way of attaining much lower sensitivity on average while losing very little in terms of statistical accuracy. To privately estimate the trimmed mean, we revisit the smooth sensitivity framework of Nissim, Raskhodnikova, and Smith (STOC 2007), which provides a framework for using instance-dependent sensitivity. We propose three new additive noise distributions which provide concentrated differential privacy when scaled to smooth sensitivity. We provide theoretical and experimental evidence showing that our noise distributions compare favorably to others in the literature, in particular, when applied to the mean estimation problem.
NICO: A Dataset Towards Non-I.I.D. Image Classification

The I.I. D. hypothesis between training data and testing data is the basis of a large number of image classification methods. Such a property can hardly be guaranteed in practical cases where the Non-IIDness is common, leading to instable performances of these models. In literature, however, the Non-I.I. D. image classification problem is largely understudied. A key reason is the lacking of a well-designed dataset to support related research. In this paper, we construct and release a Non-I.I. D. image dataset called NICO, which makes use of contexts to create Non-IIDness consciously. Extended experimental results and anslyses demonstrate that the NICO dataset can well support the training of a ConvNet model from scratch, and NICO can support various Non-I.I. D. situations with sufficient flexibility compared to other datasets.
Multimodal optimization using particle swarm optimization algorithms: CEC 2015 competition on single objective multi-niche optimization

The aim of multimodal optimization is to locate multiple peaks/optima in a single run and to maintain these found optima until the end of a run. The results of seven variants of particle swarm optimization (PSO) algorithms on IEEE Congress on Evolutionary Computation (CEC) 2015 single objective multi-niche optimization problems are reported in this paper. The PSO algorithms include PSO with star structure, PSO with ring structure, PSO with four clusters structure, PSO with Von Neumann structure, social-only PSO with star structure, social-only PSO with ring structure, and cognition-only PSO. The experimental tests are conducted on fifteen benchmark functions. Based on the experimental results, the conclusions could be made that the PSO with ring structure performs better than the other PSO variants on multimodal optimization. To obtain good performance on the multimodal optimization problems, an algorithm needs to converge the candidate solutions to the global optima while keep the population diversity during whole search process.
Learning Representations of Graph Data - A Survey

Deep Neural Networks have shown tremendous success in the area of object recognition, image classification and natural language processing. However, designing optimal Neural Network architectures that can learn and output arbitrary graphs is an ongoing research problem. The objective of this survey is to summarize and discuss the latest advances in methods to Learn Representations of Graph Data. We start by identifying commonly used types of graph data and review basics of graph theory. This is followed by a discussion of the relationships between graph kernel methods and neural networks. Next we identify the major approaches used for learning representations of graph data namely: Kernel approaches, Convolutional approaches, Graph neural networks approaches, Graph embedding approaches and Probabilistic approaches. A variety of methods under each of the approaches are discussed and the survey is concluded with a brief discussion of the future of learning representation of graph data.
Cool La Niña During the Warmth of the Pliocene?

The role of El Niño–Southern Oscillation (ENSO) in greenhouse warming and climate change remains controversial. During the warmth of the early-mid Pliocene, we find evidence for enhanced thermocline tilt and cold upwelling in the equatorial Pacific, consistent with the prevalence of a La Niña–like state, rather than the proposed persistent warm El Niño–like conditions. Our Pliocene paleothermometer supports the idea of a dynamic “ocean thermostat” in which heating of the tropical Pacific leads to a cooling of the east equatorial Pacific and a La Niña–like state, analogous to observations of a transient increasing east-west sea surface temperature gradient in the 20th-century tropical Pacific.
Online Heterogeneous Transfer Learning by Knowledge Transition

In this article, we study the problem of online heterogeneous transfer learning, where the objective is to make predictions for a target data sequence arriving in an online fashion, and some offline labeled instances from a heterogeneous source domain are provided as auxiliary data. The feature spaces of the source and target domains are completely different, thus the source data cannot be used directly to assist the learning task in the target domain. To address this issue, we take advantage of unlabeled co-occurrence instances as intermediate supplementary data to connect the source and target domains, and perform knowledge transition from the source domain into the target domain. We propose a novel online heterogeneous transfer learning algorithm called Online Heterogeneous Knowledge Transition (OHKT) for this purpose. In OHKT, we first seek to generate pseudo labels for the co-occurrence data based on the labeled source data, and then develop an online learning algorithm to classify the target sequence by leveraging the co-occurrence data with pseudo labels. Experimental results on real-world data sets demonstrate the effectiveness and efficiency of the proposed algorithm.
Disentangled State Space Representations

Sequential data often originates from diverse domains across which statistical regularities and domain specifics exist. To specifically learn cross-domain sequence representations, we introduce disentangled state space models (DSSM) -- a class of SSM in which domain-invariant state dynamics is explicitly disentangled from domain-specific information governing that dynamics. We analyze how such separation can improve knowledge transfer to new domains, and enable robust prediction, sequence manipulation and domain characterization. We furthermore propose an unsupervised VAE-based training procedure to implement DSSM in form of Bayesian filters. In our experiments, we applied VAE-DSSM framework to achieve competitive performance in online ODE system identification and regression across experimental settings, and controlled generation and prediction of bouncing ball video sequences across varying gravitational influences.
Trajectory sampling for direct traffic observation

Traffic measurement is a critical component for the control and engineering of communication networks. We argue that traffic measurement should make it possible to obtain the spatial flow of traffic through the domain, i.e., the paths followed by packets between any ingress and egress point of the domain. Most resource allocation and capacity planning tasks can benefit from such information. Also, traffic measurements should be obtained without a routing model and without knowledge of network state. This allows the traffic measurement process to be resilient to network failures and state uncertainty. We propose a method that allows the direct inference of traffic flows through a domain by observing the trajectories of a subset of all packets traversing the network. The key advantages of the method are that (i) it does not rely on routing state, (ii) its implementation cost is small, and (iii) the measurement reporting traffic is modest and can be controlled precisely. The key idea of the method is to sample packets based on a hash function computed over the packet content. Using the same hash function will yield the same sample set of packets in the entire domain, and enables us to reconstruct packet trajectories.
Increased expression of neuronal nitric oxide synthase mRNA in the accessory olfactory bulb during the formation of olfactory recognition memory in mice

The mouse accessory olfactory bulb contains a high density of nitric oxide synthase and, in females, is involved in the formation of a mating and pheromone‐specific recognition memory. The exact role of nitric oxide in this memory model is not yet clearly understood. In this study, in situ hybridization was used to assess neuronal nitric oxide synthase mRNA expression during the critical interval associated with synaptic plasticity in the accessory olfactory bulb of female mice leading to the formation of a recognition memory for the stud male pheromones present following mating. Nitric oxide synthase mRNA was significantly increased following mating and 120‐min stud male exposure compared with oestrus mice. The mRNA expression was more predominant in the anterior than the posterior regions of the bulb. These observations indicate a stimulus‐specific activation of nitric oxide gene expression in the female mouse accessory olfactory bulb and support the hypothesis that nitric oxide may modulate intermediate synaptic pathways during the formation of a pheromone‐dependent olfactory recognition for stud males.
Timeless Homes: Exploring Genealogy and Family Histories through Co-Design with Children

Genealogy and family history build the narratives of our family. By exploring these topics, children can make concrete and personal connections to larger historical themes. However, little is known about how to design genealogy and family history interactive applications for children. In this exploratory study, we consider what children want to know about these topics and how children want to interact with genealogical tools. We conducted 2 co-design sessions using the Cooperative Inquiry approach, Big Paper and Layered Elaboration, with an intergenerational design team that included 8 children ages 7-11. Our exploratory results indicate that 1) children are more interested in "fun facts" than kinship; 2) they are interested in geographical aspects of genealogy and family history such as migration history; and 3) they express and develop interests in broader historical topics such as artifacts from ancestors' eras during the design process.
Joint Emotional State of Children and Perceived Collaborative Experience in Coding Activities

This paper employs facial features to recognize emotions during a coding activity with 50 children. Extracting group-level emotional states via facial features, allows us to understand how emotions of a group affect collaboration. To do so, we captured joint emotional state using videos and collaborative experience using questionnaires, from collaborative coding sessions. We define groups' emotional state using a method inspired from dynamic systems, utilizing a measure called cross-recurrence. We also define a collaborative emotional profile using the different measurements from facial features of children. The results show that the emotional cross recurrence (coming from the videos) is positively related with the collaborative experience (coming from the surveys). We also show that the groups with better experience than the others showcase more positive and a consistent set of emotions during the coding activity. The results inform the design of an emotion-aware collaborative support system.
HPILN: A feature learning framework for cross-modality person re-identification

Most video surveillance systems use both RGB and infrared cameras, making it a vital technique to re-identify a person cross the RGB and infrared modalities. This task can be challenging due to both the cross-modality variations caused by heterogeneous images in RGB and infrared, and the intra-modality variations caused by the heterogeneous human poses, camera views, light brightness, etc. To meet these challenges a novel feature learning framework, HPILN, is proposed. In the framework existing single-modality re-identification models are modified to fit for the cross-modality scenario, following which specifically designed hard pentaplet loss and identity loss are used to improve the performance of the modified cross-modality re-identification models. Based on the benchmark of the SYSU-MM01 dataset, extensive experiments have been conducted, which show that the proposed method outperforms all existing methods in terms of Cumulative Match Characteristic curve (CMC) and Mean Average Precision (MAP).
A Comparative Study of Repeat Buyer Prediction: Kaggle Acquired Value Shopper Case Study

Many consumer brands try their best to offer promotions that attract new customers with that hope the customer will remain loyal to the brand and come back to buy more. However, only a fraction of customers who use these promotions actually remained loyal after the promotion period. This type of person typically bought products because they had a promotion (one-time deal hunter) As a result, promotional campaigns have become less effective. Thus, the prediction model is now a popular tool to make predictions about customers who remained loyal after the promotional period to make it more targeted and maximize its effectiveness. In this paper, a solution was proposed on repeat buyer prediction in order to identify buyers with the potential to come back to buy more products. This solution would help companies reduce their budgets in terms of distributing promotional offers to customers, not just a one-time deal hunter. In this study, the dataset used was called "Acquire Value Shopper Challenge", which focused on customers transactions and used a promotional offering information on the Kaggle website. The aspect of feature engineering was based on a summary and the aggregation of customers transaction over a few months. The classifier and regressor techniques for creating prediction models, which included a random forest classifier, a random forest regressor, an XGBoost classifier, and a gradient boost regressor which incorporated with leave one out technique. To evaluate the models, the area under the operating characteristic curve (AUC) was used. The final result was 0.60936 which is equivalent to twentieth place in the contest. The first place in the contest was 0.62703.
Predictive Online Server Provisioning for Cost-Efficient IoT Data Streaming Across Collaborative Edges

Edge computing is envisioned to be the de-facto paradigm of hosting emerging low latency Internet-of-Things (IoT) data streaming services.For IoT data streaming in edge computing, cost management is of strategic significance, due to the low cost-efficiency of edge servers. While existing literature adopts a reactive approach to dynamically provisioning edge servers to reduce cost, the delay of server activation and instantiation has been mostly ignored. In this paper, we target a proactive approach to dynamic edge server provisioning for real-time IoT data streaming across edge nodes, which adjusts server provisioning ahead of time, based on prediction of the upcoming workload. To effectively predict upcoming workload, a learning-based method online gradient descent is applied. We further combine the online learning method with an online optimization algorithm for server provisioning in a joint online optimization framework, through (1) minimizing of the regret incurred by inaccurate workload prediction, and (2) minimizing the cost incurred by near-optimal online decisions. The resulting predictive online algorithm can well leverage the power of prediction and achieve a good performance guarantee, as verified by both rigorous theoretical analysis and extensive trace-driven evaluations.
An Artificial Intelligence-Based System for Nutrient Intake Assessment of Hospitalised Patients*

Regular nutrient intake monitoring in hospitalised patients plays a critical role in reducing the risk of disease-related malnutrition (DRM). Although several methods to estimate nutrient intake have been developed, there is still a clear demand for a more reliable and fully automated technique, as this could improve the data accuracy and reduce both the participant burden and the health costs. In this paper, we propose a novel system based on artificial intelligence to accurately estimate nutrient intake, by simply processing RGB depth image pairs captured before and after a meal consumption. For the development and evaluation of the system, a dedicated and new database of images and recipes of 322 meals was assembled, coupled to data annotation using innovative strategies. With this database, a system was developed that employed a novel multi-task neural network and an algorithm for 3D surface construction. This allowed sequential semantic food segmentation and estimation of the volume of the consumed food, and permitted fully automatic estimation of nutrient intake for each food type with a 15% estimation error.