Knuckle-walking in Sahelanthropus? Locomotor inferences from the ulnae of fossil hominins and other hominoids.

Because the ulna supports and transmits forces during movement, its morphology can signal aspects of functional adaptation. To test whether, like extant apes, some hominins habitually recruit the forelimb in locomotion, we separate the ulna shaft and ulna proximal complex for independent shape analyses via elliptical Fourier methods to identify functional signals. We examine the relative influence of locomotion, taxonomy, and body mass on ulna contours in Homo sapiens (n = 22), five species of extant apes (n = 33), two Miocene apes (Hispanopithecus and Danuvius), and 17 fossil hominin specimens including Sahelanthropus, Ardipithecus, Australopithecus, Paranthropus, and early Homo. Ulna proximal complex contours correlate with body mass but not locomotor patterns, while ulna shafts significantly correlate with locomotion. African apes' ulna shafts are more robust and curved than Asian apes and are unlike other terrestrial mammals (including other primates), curving ventrally rather than dorsally. Because this distinctive curvature is absent in orangutans and hylobatids, it is likely a function of powerful flexors engaged in wrist and hand stabilization during knuckle-walking, and not an adaptation to climbing or suspensory behavior. The OH 36 (purported Paranthropus boisei) and TM 266 (assigned to Sahelanthropus tchadensis) fossils differ from other hominins by falling within the knuckle-walking morphospace, and thus appear to show forelimb morphology consistent with terrestrial locomotion. Discriminant function analysis classifies both OH 36 and TM 266 with Pan and Gorilla with high posterior probability. Along with its associated femur, the TM 266 ulna shaft contours and its deep, keeled trochlear notch comprise a suite of traits signaling African ape-like quadrupedalism. While implications for the phylogenetic position and hominin status of S. tchadensis remain equivocal, this study supports the growing body of evidence indicating that S. tchadensis was not an obligate biped, but instead represents a late Miocene hominid with knuckle-walking adaptations.

Small Charging Energies and g-Factor Anisotropy in PbTe Quantum Dots.

PbTe is a semiconductor with promising properties for topological quantum computing applications. Here, we characterize electron quantum dots in PbTe nanowires selectively grown on InP. Charge stability diagrams at zero magnetic field reveal large even-odd spacing between Coulomb blockade peaks, charging energies below 140 µeV and Kondo peaks in odd Coulomb diamonds. We attribute the large even-odd spacing to the large dielectric constant and small effective electron mass of PbTe. By studying the Zeeman-induced level and Kondo splitting in finite magnetic fields, we extract the electron g-factor as a function of magnetic field direction. We find the g-factor tensor to be highly anisotropic with principal g-factors ranging from 0.9 to 22.4 and to depend on the electronic configuration of the devices. These results indicate strong Rashba spin-orbit interaction in our PbTe quantum dots.

Implicit Stochastic Gradient Descent Method for Cross-Domain Recommendation System.

The previous recommendation system applied the matrix factorization collaborative filtering (MFCF) technique to only single domains. Due to data sparsity, this approach has a limitation in overcoming the cold-start problem. Thus, in this study, we focus on discovering latent features from domains to understand the relationships between domains (called domain coherence). This approach uses potential knowledge of the source domain to improve the quality of the target domain recommendation. In this paper, we consider applying MFCF to multiple domains. Mainly, by adopting the implicit stochastic gradient descent algorithm to optimize the objective function for prediction, multiple matrices from different domains are consolidated inside the cross-domain recommendation system (CDRS). Additionally, we design a conceptual framework for CDRS, which applies to different industrial scenarios for recommenders across domains. Moreover, an experiment is devised to validate the proposed method. By using a real-world dataset gathered from Amazon Food and MovieLens, experimental results show that the proposed method improves 15.2% and 19.7% in terms of computation time and MSE over other methods on a utility matrix. Notably, a much lower convergence value of the loss function has been obtained from the experiment. Furthermore, a critical analysis of the obtained results shows that there is a dynamic balance between prediction accuracy and computational complexity.

Learning Hierarchical Representations of Stories by Using Multi-layered Structures in Narrative Multimedia.

Narrative works (e.g., novels and movies) consist of various utterances (e.g., scenes and episodes) with multi-layered structures. However, the existing studies aimed to embed only stories in a narrative work. By covering other granularity levels, we can easily compare narrative utterances that are coarser (e.g., movie series) or finer (e.g., scenes) than a narrative work. We apply the multi-layered structures on learning hierarchical representations of the narrative utterances. To represent coarser utterances, we consider adjacency and appearance of finer utterances in the coarser ones. For the movies, we suppose a four-layered structure (character roles ∈ characters ∈ scenes ∈ movies) and propose three learning methods bridging the layers: Char2Vec, Scene2Vec, and Hierarchical Story2Vec. Char2Vec represents a character by using dynamic changes in the character's roles. To find the character roles, we use substructures of character networks (i.e., dynamic social networks of characters). A scene describes an event. Interactions between characters in the scene are designed to describe the event. Scene2Vec learns representations of a scene from interactions between characters in the scene. A story is a series of events. Meanings of the story are affected by order of the events as well as their content. Hierarchical Story2Vec uses sequential order of scenes to represent stories. The proposed model has been evaluated by estimating the similarity between narrative utterances in real movies.

The Cooling Rate- and Volatility-Dependent Glass-Forming Properties of Organic Aerosols Measured by Broadband Dielectric Spectroscopy.

Glass transitions of secondary organic aerosols (SOA) from liquid/semisolid to solid phase states have important implications for aerosol reactivity, growth, and cloud formation properties. In the present study, glass transition temperatures (Tg) of isoprene SOA components, including isoprene hydroxy hydroperoxide (ISOPOOH), isoprene-derived epoxydiols (IEPOX), 2-methyltetrols, and 2-methyltetrol sulfates, were measured at atmospherically relevant cooling rates (2-10 K/min) by thin film broadband dielectric spectroscopy. The results indicate that 2-methyltetrol sulfates have the highest glass transition temperature, while ISOPOOH has the lowest glass transition temperature. By varying the cooling rate of the same compound from 2 to 10 K/min, the Tg of these compounds increased by 4-5 K. This temperature difference leads to a height difference of 400-800 m in the atmosphere for the corresponding updraft induced cooling rates, assuming a hygroscopicity value (κ) of 0.1 and relative humidity less than 95%. The Tg of the organic compounds was found to be strongly correlated with volatility, and a semiempirical formula between glass transition temperatures and volatility was derived. The Gordon-Taylor equation was applied to calculate the effect of relative humidity (RH) and water content at five mixing ratios on the Tg of organic aerosols. The model shows that Tg could drop by 15-40 K as the RH changes from <5 to 90%, whereas the mixing ratio of water in the particle increases from 0 to 0.5. These results underscore the importance of chemical composition, updraft rates, and water content (RH) in determining the phase states and hygroscopic properties of organic particles.

Consensual Negotiation-Based Decision Making for Connected Appliances in Smart Home Management Systems.

Recently, the concept of Internet of Agent has been introduced as a potential technology that pushes intelligence, data processing, analytics and communication capabilities down to the point where the data originates. In this paper, we introduce a novel approach for a Decentralized Home Energy Management System by applying the Internet of Agent concept. In particular, we first present an Internet of Agent framework in terms of sensing, communicating and collaborating among connected appliances. Then, the decentralized management based on consensual negotiation mechanism with several intelligent techniques are proposed for dynamic scheduling connected appliance. Specifically, by applying the Internet of Agent framework, connected appliances are regarded as smart agents that are able to make individual decisions by reaching agreement over the exchange of operations on competitive resources. Furthermore, in this study, the load balancing problem in which load shifting is able to reduce the electricity demand during peak hours is taken into account in order to emphasize the effectiveness of our approach. For the experiment, we develop a simulation of smart home environment to evaluate our approach using NetLogo, a tool which provides real-time analysis in the modeling and simulation domain of complex systems.

Hunk negatively regulates c-myc to promote Akt-mediated cell survival and mammary tumorigenesis induced by loss of Pten.

The protooncogenes Akt and c-myc each positively regulate cell growth and proliferation, but have opposing effects on cell survival. These oncogenes cooperate to promote tumorigenesis, in part because the prosurvival effects of Akt offset the proapoptotic effects of c-myc. Akt's ability to counterbalance c-myc's proapoptotic effects has primarily been attributed to Akt-induced stimulation of prosurvival pathways that indirectly antagonize the effects of c-myc. We report a more direct mechanism by which Akt modulates the proapoptotic effects of c-myc. Specifically, we demonstrate that Akt up-regulates the adenosine monophosphate-associated kinase (AMPK)-related protein kinase, Hormonally up-regulated neu-associated kinase (Hunk), which serves as an effector of Akt prosurvival signaling by suppressing c-myc expression in a kinase-dependent manner to levels that are compatible with cell survival. Consequently, Akt pathway activation in the mammary glands of Hunk(-/-) mice results in induction of c-myc expression to levels that induce apoptosis. c-myc knockdown rescues the increase in apoptosis induced by Hunk deletion in cells in which Akt has been activated, indicating that repression of c-myc is a principal mechanism by which Hunk mediates the prosurvival effects of Akt. Consistent with this mechanism of action, we find that Hunk is required for c-myc suppression and mammary tumorigenesis induced by phosphatase and tensin homolog (Pten) deletion in mice. Together, our findings establish a prosurvival function for Hunk in tumorigenesis, define an essential mechanism by which Akt suppresses c-myc-induced apoptosis, and identify Hunk as a previously unrecognized link between the Akt and c-myc oncogenic pathways.

Social big data: Recent achievements and new challenges.

Big data has become an important issue for a large number of research areas such as data mining, machine learning, computational intelligence, information fusion, the semantic Web, and social networks. The rise of different big data frameworks such as Apache Hadoop and, more recently, Spark, for massive data processing based on the MapReduce paradigm has allowed for the efficient utilisation of data mining methods and machine learning algorithms in different domains. A number of libraries such as Mahout and SparkMLib have been designed to develop new efficient applications based on machine learning algorithms. The combination of big data technologies and traditional machine learning algorithms has generated new and interesting challenges in other areas as social media and social networks. These new challenges are focused mainly on problems such as data processing, data storage, data representation, and how data can be used for pattern mining, analysing user behaviours, and visualizing and tracking data, among others. In this paper, we present a revision of the new methodologies that is designed to allow for efficient data mining and information fusion from social media and of the new applications and frameworks that are currently appearing under the "umbrella" of the social networks, social media and big data paradigms.

Intelligent advisory speed limit dedication in highway using VANET.

Variable speed limits (VSLs) as a mean for enhancing road traffic safety are studied for decades to modify the speed limit based on the prevailing road circumstances. In this study the pros and cons of VSL systems and their effects on traffic controlling efficiency are summarized. Despite the potential effectiveness of utilizing VSLs, we have witnessed that the effectiveness of this system is impacted by factors such as VSL control strategy used and the level of driver compliance. Hence, the proposed approach called Intelligent Advisory Speed Limit Dedication (IASLD) as the novel VSL control strategy which considers the driver compliance aims to improve the traffic flow and occupancy of vehicles in addition to amelioration of vehicle's travel times. The IASLD provides the advisory speed limit for each vehicle exclusively based on the vehicle's characteristics including the vehicle type, size, and safety capabilities as well as traffic and weather conditions. The proposed approach takes advantage of vehicular ad hoc network (VANET) to accelerate its performance, in the way that simulation results demonstrate the reduction of incident detection time up to 31.2% in comparison with traditional VSL strategy. The simulation results similarly indicate the improvement of traffic flow efficiency, occupancy, and travel time in different conditions.

Self-clustered GAN for precipitation nowcasting.

This paper proposes a novel GAN framework with self-clustering approach for precipitation nowcasting (ClusterCast). Previous studies have primarily captured the motion vector using only a single latent space, making the models difficult to adapt to disparate space-time distribution of precipitation. Environmental factors (e.g., regional characteristics and precipitation scale) have an impact on precipitation systems and can cause non-stationary distribution. To tackle this problem, our key idea is to train a generator network to predict future radar frames by learning a sub-network that automatically labels precipitation types from a generative model. The training process consists of (i) clustering the hierarchical features derived from the generator stem using a sub-network and (ii) predicting future radar frames according to the self-supervised labels, enabling heterogeneous latent representation. Additionally, we attempt an ensemble forecast that prescribes random perturbations to improve performance. With the flexibility of representation learning, ClusterCast enables the model to learn precipitation distribution more accurately. Results indicate that our method generates non-blurry future frames by preventing mode collapse, and the proposed method demonstrates robustness across various precipitation scenarios. Extensive experiments demonstrate that our method outperforms four benchmarks on a 2-h prediction basis with a mean squared error (MSE) of 8.9% on unseen datasets.

Parity-conserving Cooper-pair transport and ideal superconducting diode in planar germanium.

Superconductor/semiconductor hybrid devices have attracted increasing interest in the past years. Superconducting electronics aims to complement semiconductor technology, while hybrid architectures are at the forefront of new ideas such as topological superconductivity and protected qubits. In this work, we engineer the induced superconductivity in two-dimensional germanium hole gas by varying the distance between the quantum well and the aluminum. We demonstrate a hard superconducting gap and realize an electrically and flux tunable superconducting diode using a superconducting quantum interference device (SQUID). This allows to tune the current phase relation (CPR), to a regime where single Cooper pair tunneling is suppressed, creating a [Formula: see text] CPR. Shapiro experiments complement this interpretation and the microwave drive allows to create a diode with ≈ 100% efficiency. The reported results open up the path towards integration of spin qubit devices, microwave resonators and (protected) superconducting qubits on  the same silicon technology compatible platform.

Understanding asymmetric synergistic effect between movie actors.

Synergistic effects between movie actors have been regarded as an important indicator when they are casted in a new movie. People simply assume that synergistic effect is symmetric. The aim of this study is to understand the asymmetric synergy between actors. We propose an asymmetric synergy measurement method for actor's star-power-based costarring movies to understand the synergistic effect. When measuring the synergy, we designed it so that it would be possible to measure the synergy that varies with the time of the costarring movie's release and the synergy of new actors. Measured synergies were analyzed on an actor's synergy and asymmetric synergy between actors to examine the characteristics of highly synergistic actors and the asymmetric synergy between actors. Moreover, we confirmed that measuring synergies asymmetrically demonstrated better prediction performance in various evaluation metrics (accuracy, precision, recall, and F1-score) than measuring synergies symmetrically through the synergy prediction experiment using synergy and asymmetric synergy.

DaGzang: a synthetic data generator for cross-domain recommendation services.

Research on cross-domain recommendation systems (CDRS) has shown efficiency by leveraging the overlapping associations between domains in order to generate more encompassing user models and better recommendations. Nonetheless, if there is no dataset belonging to a specific domain, it is a challenge to generate recommendations in CDRS. In addition, finding these overlapping associations in the real world is generally tricky, and it makes its application to actual services hard. Considering these issues, this study aims to present a synthetic data generation platform (called DaGzang) for cross-domain recommendation systems. The DaGzang platform works according to the complete loop, and it consists of the following three steps: (i) detecting the overlap association (data distribution pattern) between the real-world datasets, (ii) generating synthetic datasets based on these overlap associations, and (iii) evaluating the quality of the generated synthetic datasets. The real-world datasets in our experiments were collected from Amazon's e-commercial website. To validate the usefulness of the synthetic datasets generated from DaGzang, we embed these datasets into our cross-domain recommender system, called DakGalBi. We then evaluate the recommendations generated from DakGalBi with collaborative filtering (CF) algorithms, user-based CF, and item-based CF. Mean absolute error (MAE) and root mean square error (RMSE) metrics are measured to evaluate the performance of collaborative filtering (CF) CDRS. In particular, the highest performance of the three recommendation methods is user-based CF when using 10 synthetic datasets generated from DaGzang (0.437 at MAE and 0.465 at RMSE).

SocioPedia+: a visual analytics system for social knowledge graph-based event exploration.

In the recent era of information explosion, exploring event from social networks has recently been a crucial task for many applications. To derive valuable comprehensive and thorough insights on social events, visual analytics (VA) system have been broadly used as a promising solution. However, due to the enormous social data volume with highly diversity and complexity, the number of event exploration tasks which can be enabled in a conventional real-time visual analytics systems has been limited. In this article, we introduce SocioPedia+, a real-time visual analytics system for social event exploration in time and space domains. By introducing the dimension of social knowledge graph analysis into the system multivariate analysis, the process of event explorations in SocioPedia+ can be significantly enhanced and thus enabling system capability on performing full required tasks of visual analytics and social event explorations. Furthermore, SocioPedia+ has been optimized for visualizing event analysis on different levels from macroscopic (events level) to microscopic (knowledge level). The system is then implemented and investigated with a detailed case study for evaluating its usefulness and visualization effectiveness for the application of event explorations.

Lin-28B transactivation is necessary for Myc-mediated let-7 repression and proliferation.

Direct control of microRNA (miRNA) expression by oncogenic and tumor suppressor networks results in frequent dysregulation of miRNAs in cancer cells and contributes to tumorigenesis. We previously demonstrated that activation of the c-Myc oncogenic transcription factor (Myc) broadly influences miRNA expression and in particular leads to widespread miRNA down-regulation. miRNA transcripts repressed by Myc include several with potent tumor suppressor activity such as miR-15a/16-1, miR-34a, and let-7 family members. In this study, we have investigated mechanisms downstream of Myc that contribute to miRNA repression. Consistent with transcriptional down-regulation, Myc activity results in the decreased abundance of multiple miRNA primary transcripts. Surprisingly, however, primary transcripts encoding several let-7 miRNAs are not reduced in the high Myc state, suggesting a posttranscriptional mechanism of repression. The Lin-28 and Lin-28B RNA binding proteins were recently demonstrated to negatively regulate let-7 biogenesis. We now show that Myc induces Lin-28B expression in multiple human and mouse tumor models. Chromatin immunoprecipitation and reporter assays reveal direct association of Myc with the Lin-28B promoter resulting in transcriptional transactivation. Moreover, we document that activation of Lin-28B is necessary and sufficient for Myc-mediated let-7 repression. Accordingly, Lin-28B loss-of-function significantly impairs Myc-dependent cellular proliferation. These findings highlight an important role for Lin-28B in Myc-driven cellular phenotypes and uncover an orchestration of transcriptional and posttranscriptional mechanisms in Myc-mediated reprogramming of miRNA expression.

DNN-based multi-output model for predicting soccer team tactics.

In modern sports, strategy and tactics are important in determining the game outcome. However, many coaches still base their game tactics on experience and intuition. The aim of this study is to predict tactics such as formations, game styles, and game outcome based on soccer dataset. In this paper, we propose to use Deep Neural Networks (DNN) based on Multi-Layer Perceptron (MLP) and feature engineering to predict the soccer tactics of teams. Previous works adopt simple machine learning techniques, such as Support Vector Machine (SVM) and decision tree, to analyze soccer dataset. However, these often have limitations in predicting tactics using soccer dataset. In this study, we use feature selection, clustering techniques for the segmented positions and Multi-Output model for Soccer (MOS) based on DNN, wide inputs and residual connections. Feature selection selects important features among features of soccer player dataset. Each position is segmented by applying clustering to the selected features. The segmented positions and game appearance dataset are used as training dataset for the proposed model. Our model predicts the core of soccer tactics: formation, game style and game outcome. And, we use wide inputs and embedding layers to learn sparse, specific rules of soccer dataset, and use residual connections to learn additional information. MLP layers help the model to generalize features of soccer dataset. Experimental results demonstrate the superiority of the proposed model, which obtain significant improvements comparing to baseline models.

Electronic Structure and Epitaxy of CdTe Shells on InSb Nanowires.

Indium antimonide (InSb) nanowires are used as building blocks for quantum devices because of their unique properties, that is, strong spin-orbit interaction and large Landé g-factor. Integrating InSb nanowires with other materials could potentially unfold novel devices with distinctive functionality. A prominent example is the combination of InSb nanowires with superconductors for the emerging topological particles research. Here, the combination of the II-VI cadmium telluride (CdTe) with the III-V InSb in the form of core-shell (InSb-CdTe) nanowires is investigated and potential applications based on the electronic structure of the InSb-CdTe interface and the epitaxy of CdTe on the InSb nanowires are explored. The electronic structure of the InSb-CdTe interface using density functional theory is determined and a type-I band alignment is extracted with a small conduction band offset ( ⩽0.3 eV). These results indicate the potential application of these shells for surface passivation or as tunnel barriers in combination with superconductors. In terms of structural quality, it is demonstrated that the lattice-matched CdTe can be grown epitaxially on the InSb nanowires without interfacial strain or defects. These shells do not introduce disorder to the InSb nanowires as indicated by the comparable field-effect mobility measured for both uncapped and CdTe-capped nanowires.

Entropy-based dynamic graph embedding for anomaly detection on multiple climate time series.

Abnormal climate event is that some meteorological conditions are extreme in a certain time interval. The existing methods for detecting abnormal climate events utilize supervised learning models to learn the abnormal patterns, but they cannot detect the untrained patterns. To overcome this problem, we construct a dynamic graph by discovering the correlation among the climate time series and propose a novel dynamic graph embedding model based on graph entropy called EDynGE to discriminate anomalies. The graph entropy measurement quantifies the information of the graphs and constructs the embedding space. We conducted experiments on synthetic datasets and real-world meteorological datasets. The results showed that EdynGE model achieved a better F1-score than the baselines by 43.2%, and the number of days of abnormal climate events has increased by 304.5 days in the past 30 years.

Seizure detection from multi-channel EEG using entropy-based dynamic graph embedding.

An epileptic seizure is a chronic disease with sudden abnormal discharge of brain neurons, which leads to transient brain dysfunction. To detect epileptic seizures, we propose a novel idea based on a dynamic graph embedding model. The dynamic graph is built by identifying the correlation among the multi-channel EEG signals. Graph entropy measurement is exploited to calculate the similarity among the graph at each time interval and construct the graph embedding space. Since the abnormal electrical brain activity causes the epileptic seizure, the graph entropy during the seizure time interval is different from other time intervals. Therefore, we propose an entropy-based dynamic graph embedding model to cluster the graphs, and the graphs with epileptic seizures are discriminated. We applied the proposed approach to the Children Hospital Boston-Massachusetts Institute of Technology Scalp EEG database. The results have shown that the proposed approach outperformed the baselines by 1.4% with respect to accuracy.

Dynamic graph embedding for outlier detection on multiple meteorological time series.

Existing dynamic graph embedding-based outlier detection methods mainly focus on the evolution of graphs and ignore the similarities among them. To overcome this limitation for the effective detection of abnormal climatic events from meteorological time series, we proposed a dynamic graph embedding model based on graph proximity, called DynGPE. Climatic events are represented as a graph where each vertex indicates meteorological data and each edge indicates a spurious relationship between two meteorological time series that are not causally related. The graph proximity is described as the distance between two graphs. DynGPE can cluster similar climatic events in the embedding space. Abnormal climatic events are distant from most of the other events and can be detected using outlier detection methods. We conducted experiments by applying three outlier detection methods (i.e., isolation forest, local outlier factor, and box plot) to real meteorological data. The results showed that DynGPE achieves better results than the baseline by 44.3% on average in terms of the F-measure. Isolation forest provides the best performance and stability. It achieved higher results than the local outlier factor and box plot methods, namely, by 15.4% and 78.9% on average, respectively.