Miracle-dz/UniFAR-FTUs huggingface dataset:
Asynchronous Distributed Learning with Sparse Communications and Identification

In this paper, we present an asynchronous optimization algorithm for distributed learning, that efficiently reduces the communications between a master and working machines by randomly sparsifying the local updates. This sparsification allows to lift the communication bottleneck often present in distributed learning setups where computations are performed by workers on local data while a master machine coordinates their updates to optimize a global loss. We prove that despite its sparse asynchronous communications, our algorithm allows for a fixed stepsize and benefits from a linear convergence rate in the strongly convex case. Moreover, for $\ell_1$-regularized problems, this algorithm identifies near-optimal sparsity patterns, so that all communications eventually become sparse. We furthermore leverage on this identification to improve our sparsification technique. We illustrate on real and synthetic data that this algorithm converges faster in terms of data exchanges.
Saturation Technique for Optimizing Planned Acceptance Rates in Traffic Management Initiatives

Traffic Management Initiatives (TMIs), including Ground Delay Programs (GDP) and Collaborative Trajectory Options Programs (CTOP), are tools that air traffic managers use to balance demand and capacity in congested airports and airspace regions. In the current Collaborative Decision Making (CDM) paradigm, the Federal Aviation Administration (FAA) will set the Planned Acceptance Rates (PARs) for the constrained airspace resources, then run resource allocation algorithms to assign ground delays and/or reroutes to affected flights. In this paper, we have addressed a fundamental question in TMI PAR planning: do there exist optimal PARs which only depend on the physical airport or airspace capacity but not the demand? We show that this conjecture holds true in the deterministic capacity case but not in the general stochastic case. Several critical implications of this conclusion are discussed. We propose a new stochastic model and develop a heuristic saturation technique. We demonstrate that this technique can not only reveal the properties and limiting behaviors of GDP models but also could potentially be used as a robust PAR policy when facing demand uncertainty. We then show this ancillary saturation technique in GDP planning becomes an indispensable tool in CTOP optimization. The findings of this paper provide valuable insights in understanding the TMI rate-planning problem and a more robust algorithm for GDP optimization.
Anticipation of Uncertain Threat Modulates Subsequent Affective Responses and Covariation Bias

Uncertainty contributes to stress and anxiety-like behaviors by impairing the ability of participants to objectively estimate threat. Our study used the cue-picture paradigm in conjunction with the event-related potential (ERP) technique to explore the temporal dynamics of anticipation for and response to uncertain threat in healthy individuals. This task used two types of cue. While ‘certain’ cues precisely forecasted the valence of the subsequent pictures (negative or neutral), the valence of pictures following ‘uncertain’ cues was not predictable. ERP data showed that, during anticipation, uncertain cues elicited similar Stimulus-Preceding Negativity (SPN) to certain-negative cues, while both of them elicited larger SPN than certain-neutral cues. During affective processing, uncertainty enlarged the mean amplitude of late positive potential (LPP) for both negative and neutral pictures. Behavioral data showed that participants reported more negative mood ratings of uncertain-neutral pictures relative to certain-neutral pictures and overestimated the probability of negative pictures following uncertain cues. Importantly, the enlarged anticipatory activity evoked by uncertain cues relative to that evoked by certain-neutral cues positively modulated the more negative mood ratings of uncertain-neutral pictures relative to certain-neutral pictures. Further, this more negative mood ratings and the general arousal anticipation during anticipatory stage contributed to the covariation bias. These results can provide a novel insight into understanding the neural mechanism and pathological basis of anxiety.
Angle Domain Channel Estimation in Hybrid Millimeter Wave Massive MIMO Systems

This paper proposes a novel direction-of-arrival (DOA)-aided channel estimation for a hybrid millimeter-wave (mm-wave) massive multiple-input multiple-output system with a uniform planar array at the base station. To explore the physical characteristics of the antenna array in mm-wave systems, the parameters of each channel path are decomposed into the DOA information and the channel gain information. We first estimate the initial DOAs of each uplink path through the 2-D discrete Fourier transform and enhance the estimation accuracy via the angle rotation technique. We then estimate the channel gain information using a small amount of training resources, which significantly reduces the training overhead and the feedback cost. More importantly, to examine the estimation performance, we derive the theoretical bounds of the mean squared errors (MSEs) and the Cramér–Rao lower bounds (CRLBs) of the joint DOA and channel gain estimation. The simulation results show that the performances of the proposed methods are close to the theoretical MSEs’ analysis. Furthermore, the theoretical MSEs are also close to the corresponding CRLBs.
Giving the public a perspective into Unmanned Aircraft Systems' operations

NASA is currently engaged in research to safely enable large-scale commercial applications of small Unmanned Aircraft Systems (UAS) in low altitude airspace. This research effort, referred to as UAS Traffic Management (UTM), encompasses the concepts and technologies needed to accommodate the projected demand of UAS operating in the national airspace. One aspect related to the successful implementation of UTM in the future is public acceptance. UTM transparency will heavily influence this acceptance, and a public portal will provide much of that transparency through ease of access to information about the operations - mainly why and where such operations are taking place. Related concerns to the public are individual privacy, security, and accountability of the operators. Providing the aforementioned information about operations can mitigate these concerns, but a balance will have to be achieved between the need for transparency from the public and the privacy of the operators. The proper balance and the needs of the various UTM stakeholders with regard to information access is being explored through the development and testing of a public portal alongside NASA's development and flight testing of the UTM system. Additionally, various approaches to the display of information and user interfaces were surveyed through the development of different instantiations of a public portal by multiple UTM industry partners across different test sites.
Mobile Money

Access to financial institutions is difficult in developing economies and especially for the poor. However, the widespread adoption of mobile phones has enabled the development of mobile money systems that deliver financial services through the mobile phone network. Despite the success of mobile money, there is a lack of quantitative studies that unveil which factors contribute to the adoption and sustained usage of such services. In this paper, we describe the results of a quantitative study that analyzes data from the world's leading mobile money service, M-Pesa. We analyzed millions of anonymized mobile phone communications and M-Pesa transactions in an African country. Our contributions are threefold: (1) we analyze the customers' usage of M-Pesa and report large-scale patterns of behavior; (2) we present the results of applying machine learning models to predict mobile money adoption (AUC=0.691), and mobile money spending (AUC=0.619) using multiple data sources: mobile phone data, M-Pesa agent information, the number of M-Pesa friends in the user's social network, and the characterization of the user's geographic location; (3) we discuss the most predictive features in both models and draw key implications for the design of mobile money services in a developing country. We find that the most predictive features are related to mobile phone activity, to the presence of M-Pesa users in a customer's ego-network and to mobility. We believe that our work will contribute to the understanding of the factors playing a role in the adoption and sustained usage of mobile money services in developing economies.
Comments on “A Study of Magnitude and Psychological Correlates of Smartphone Use in Medical Students: A Pilot Study with a Novel Telemetric Approach”

596 Indian Journal of Psychological Medicine | Volume 40 | Issue 6 | November-December 2018 Sir, The telemetric approach carried out by Prasad et al. needs to be commended. [1] The literature has shown that self‐reported Internet or mobile phone use gives a lot of subjectivity and is often unreliable. Using phone‐based apps, it is possible to record the total mobile phone use and to ascertain individual app usage. [2,3] This method can provide a lot of objectivity and can be a useful tool to correlate such usage with psychological measures.
Judging Others by Your Own Standards: Attractiveness of Primate Faces as Seen by Human Respondents

The aspects of facial attractiveness have been widely studied, especially within the context of evolutionary psychology, which proposes that aesthetic judgements of human faces are shaped by biologically based standards of beauty reflecting the mate quality. However, the faces of primates, who are very similar to us yet still considered non-human, remain neglected. In this paper, we aimed to study the facial attractiveness of non-human primates as judged by human respondents. We asked 286 Czech respondents to score photos of 107 primate species according to their perceived “beauty. ” Then, we analyzed factors affecting the scores including morphology, colors, and human-likeness. We found that the three main primate groups were each scored using different cues. The proportions of inner facial features and distinctiveness are cues widely reported to affect human facial attractiveness. Interestingly, we found that these factors also affected the attractiveness scores of primate faces, but only within the Catarrhines, i.e., the primate group most similar to humans. Within this group, human-likeness positively affected the attractiveness scores, and facial extremities such as a prolonged nose or exaggerated cheeks were considered the least attractive. On the contrary, the least human-like prosimians were scored as the most attractive group. The results are discussed in the context of the “uncanny valley,” the widely discussed empirical rule.
Rhizosphere Microbial Communities of Spartina alterniflora and Juncus roemerianus From Restored and Natural Tidal Marshes on Deer Island, Mississippi

The U. S. Gulf of Mexico is experiencing a dramatic increase in tidal marsh restoration actions, which involves planting coastal areas with smooth cordgrass (Spartina alterniflora) and black needlerush (Juncus roemerianus) for erosion control and to provide habitat for fish and wildlife. It can take decades for sedimentary cycles in restored marshes to approach reference conditions, and the contribution of the sediment microbial communities to these processes is poorly elucidated. In this study, we addressed this gap by comparing rhizosphere microbiomes of S. alterniflora and J. roemerianus from two restored marshes and a natural reference marsh located at Deer Island, MS. Our results revealed that plants from the restored and reference areas supported similar microbial diversity indicating the rapid colonization of planted grasses with indigenous soil microbiota. Although close in composition, the microbial communities from the three studied sites differed significantly in the relative abundance of specific taxa. The observed differences are likely driven by the host plant identity and properties of sediment material used for the creation of restored marshes. Some of the differentially distributed groups of bacteria include taxa involved in the cycling of carbon, nitrogen, and sulfur, and may influence the succession of vegetation at the restored sites to climax condition. We also demonstrated that plants from the restored and reference sites vary in the frequency of culturable rhizobacteria that exhibit traits commonly associated with the promotion of plant growth and suppression of phytopathogenic fungi. Our findings will contribute to the establishment of benchmarks for the assessment of the outcome of coastal restoration projects in the Gulf of Mexico and better define factors that affect the long-term resiliency of tidal marshes and their vulnerability to climate change.
Deep Learning Power Allocation in Massive MIMO

This work advocates the use of deep learning to perform max-min and max-prod power allocation in the downlink of Massive MIMO networks. More precisely, a deep neural network is trained to learn the map between the positions of user equipments (UEs) and the optimal power allocation policies, and then used to predict the power allocation profiles for a new set of UEs’ positions. The use of deep learning significantly improves the complexity-performance trade-off of power allocation, compared to traditional optimization-oriented methods. Particularly, the proposed approach does not require the computation of any statistical average, which would be instead necessary by using standard methods, and is able to guarantee near-optimal performance.
Exploring the Intention to Invest in Cryptocurrency: The Case of Bitcoin

The explosion of virtual currency investment has hit the financial system and brought the cryptocurrency to the global spotlight in recent years. Hitherto, no research has yet systematically investigated the factors that influencing users' intention to invest in cryptocurrency. Thus, this study is aimed to develop an understanding regarding cryptocurrency investment by investigating the intention to invest in cryptocurrency; in this case is Bitcoin. This paper is a conceptual paper that extends the applicability of Theory of Reason Action (TRA) by incorporating perceived risk and perceived benefits as the additional variables from cryptocurrency investment context. Being limited to conceptual evidence, this study lacks the empirical evidence. Future works are needed to be done to establish the empirical support on the association of the studied variables.
An Enhanced Symmetric Cryptosystem and Biometric-Based Anonymous User Authentication and Session Key Establishment Scheme for WSN

Wireless sensor networks (WSNs) are progressive ad hoc networks that comprise of distributed sensors that are typically and randomly deployed over the target region. The valuable information from the sensor nodes is allowed to access with the help of gateway node by the registered user. To ensure secure communication, a session key is exchanged between the participants over the insecure channel. In this paper, we identified some deficiencies in Jung et al.’s scheme and then devised an enhanced biometric-based anonymous user authentication and the key agreement scheme that is also embedded with symmetric cryptosystem for WSNs. The advantage of using biometric login is to ensure the legal user’s efficient login. We conferred about preserving the security of our proposed scheme, we primarily applied formal verification BAN-logic method to check the exactness of mutual authentication. Furthermore, used automated validation of internet security protocols and applications software that is widely accepted and its results confirm that our scheme is secure against active and passive attacks, including forgery, replay, and man-in-the-middle attack. In addition, an informal analysis proves our scheme can withstand various possible attacks on authentication protocols over the insecure channel. Furthermore, our scheme is more appropriate for WSNs based upon the comparison of computational efficiency and security requirements with recent results.
Auto-Encoding Scene Graphs for Image Captioning

We propose Scene Graph Auto-Encoder (SGAE) that incorporates the language inductive bias into the encoder-decoder image captioning framework for more human-like captions. Intuitively, we humans use the inductive bias to compose collocations and contextual inference in discourse. For example, when we see the relation ``person on bike'', it is natural to replace ``on'' with ``ride'' and infer ``person riding bike on a road'' even the ``road'' is not evident. Therefore, exploiting such bias as a language prior is expected to help the conventional encoder-decoder models less likely to overfit to the dataset bias and focus on reasoning. Specifically, we use the scene graph --- a directed graph (G) where an object node is connected by adjective nodes and relationship nodes --- to represent the complex structural layout of both image (I) and sentence (S). In the textual domain, we use SGAE to learn a dictionary (D) that helps to reconstruct sentences in the S -> G -> D -> S pipeline, where D encodes the desired language prior; in the vision-language domain, we use the shared D to guide the encoder-decoder in the I -> G -> D -> S pipeline. Thanks to the scene graph representation and shared dictionary, the inductive bias is transferred across domains in principle. We validate the effectiveness of SGAE on the challenging MS-COCO image captioning benchmark, \eg, our SGAE-based single-model achieves a new state-of-the-art 127.8 CIDEr-D on the Karpathy split, and a competitive 125.5 CIDEr-D (c40) on the official server even compared to other ensemble models. Code has been made available at: https://github.com/yangxuntu/SGAE.
On the Price of European Call Option Based on the Black Scholes Model with Fuzzy Number Coefficients

In this paper, some problems about Black-Scholes model with fuzzy number coefficients is studied. Firstly, by using the Itô formula, the asset prices at $T$ or $t$ times are obtained from the Black-Scholes model with fuzzy number coefficients. Then, by operation rules of fuzzy numbers, the expression of the price of European call option in form of level set of fuzzy number is obtained. At last, an example is given to show the feasibility of the method.
T Lymphocytes Contribute to the Control of Baseline Neural Precursor Cell Proliferation but Not the Exercise-Induced Up-Regulation of Adult Hippocampal Neurogenesis

Cross-talk between the peripheral immune system and the central nervous system is important for physiological brain health. T cells are required to maintain normal baseline levels of neural precursor proliferation in the hippocampus of adult mice. We show here that neither T cells, B cells, natural killer cells nor natural killer T cells are required for the increase in hippocampal precursor proliferation that occurs in response to physical exercise. In addition, we demonstrate that a subpopulation of T cells, regulatory T cells, is not involved in maintaining baseline levels of neural precursor proliferation. Even when applied at supraphysiological numbers, populations of both naive and stimulated lymphocytes had no effect on hippocampal precursor proliferation in vitro. In addition, physical activity had no effect on peripheral immune cells in terms of distribution in the bone marrow, lymph nodes or spleen, activation state or chemokine receptor (CXCR4 and CCR9) expression. Together these results suggest that lymphocytes are not involved in translating the peripheral effects of exercise to the neurogenic niche in the hippocampus and further support the idea that the exercise-induced regulation of adult neurogenesis is mechanistically distinct from its baseline control.
Trait Emotional Intelligence as a Predictor of Adaptive Responses to Positive and Negative Affect During Adolescence

Introduction and aim : The examination of trait emotional intelligence as an important component of adolescent psychological adjustment and coping has received a great deal of attention. Trait emotional intelligence is expected to reduce the vulnerability to emotional problems by reducing mood deterioration in adverse situations. Most research to date has addressed the regulation of negative affective states, with less attention paid to the responses to positive affect. Thus, the aim of this research was to examine the cross-sectional and prospective associations between trait emotional intelligence dimensions (i.e., trait emotional attention, trait emotional clarity, and trait emotional repair), response styles to negative affect (i.e., depressive rumination and distraction) and response to positive affect (i.e., emotion-focused and self-focused positive rumination and dampening) in adolescence. Methods: A 1-year follow-up study was conducted with a sample of 880 adolescents (52.4% girls) aged 14–17 years old (M = 14.74, SD = 0.68) who were enrolled in 18 high schools in Andalusia (Spain). Participants completed self-report measures of trait emotional intelligence, response to negative affect and response styles to positive affect. To analyse the data, hierarchical regression analyses and path analysis were performed. Results: Our results showed that high trait emotional attention was cross-sectionally and longitudinally associated with more dampening of positive affect and more depressive rumination. Furthermore, high trait emotional repair was cross-sectionally and longitudinally related to more distraction to negative affect and more self-focused positive rumination. Some gender differences were also found; girls reported higher trait emotional attention, higher dampening, and higher depressive rumination. Furthermore, boys reported higher trait emotional repair, higher self-focused positive rumination and higher distraction to negative affect. Conclusions and discussion: Our findings provide longitudinal evidence of the relationships between trait emotional intelligence and responses to both positive and negative affect during adolescence. Consequently, interventions designed to promote resilience during adolescence could target the development of more adaptive responses to both negative and positive affect within the framework of school-based emotional education programmes.
Subband CQI Feedback-Based Multicast Resource Allocation in MIMO-OFDMA Networks

Multicast transmission is one of the key enablers toward a more spectral- and energy-efficient distribution of multimedia content in current and envisaged cellular networks. In order to ensure that all users in a multicast group are able to correctly decode the received data, most multicast techniques adopt rather conservative strategies that select a very robust modulation and coding scheme (MCS) whose characteristics are determined by the propagation conditions experienced by the worst user in the group. Obviously, this robustness comes at the prize of a low spectral efficiency. Moreover, in the specific context of wideband communication systems, the selection of the multicast MCS has often relied on the use of wideband channel quality indicators (CQIs) providing rather imprecise information regarding the potential capacity of the multicast channel. Only recently has the per-subband CQI been used to improve the spectral efficiency of the system without compromising the link robustness. However, most subband-CQI multicast schemes proposed so far rely on overpessimistic assumptions that preclude the achievement of high data rates. In this paper, novel subband CQI-based multicast strategies are proposed that, relying on the selection of more spectrally efficient transmission modes, lead to increased data rates while still being able to fulfill prescribed quality of service metrics. To this end, a constrained optimization problem is posed that seeks to maximize the data rate of the whole multicast group while ensuring that the average block error rate for all users remains below a threshold and that a minimum data rate is guaranteed for all users in the group.