Miracle-dz/UniFAR-FTUs huggingface dataset:
Drone mapping through multi-agent reinforcement learning

In recent years, the use of drones to map environments and survey them for items of interest such as forest fires, landslides or wild animals has gained traction in various research communities. However, the need for a human pilot or a pre-planned flight path severely limits the effectiveness of the drones, especially when a whole swarm is used. In this work, we propose a model of the drone survey problem and apply three well-known reinforcement learning strategies, showing that the performance loss due to the lack of explicit optimization and pre-programmed knowledge of the system statistics is negligible in the swarm scenario.
How Much Competence Is There in Performance? Assessing the Distributional Hypothesis in Word Bigrams

The field of Distributional Semantics (DS) is built on the ‘distributional hypothesis’, which states that meaning can be recovered from statistical information in observable language. It is however notable that the computations necessary to obtain ‘good’ DS representations are often very involved, implying that if meaning is derivable from linguistic data, it is not directly encoded in it. This prompts questions related to fundamental questions about language acquisition: if we regard text data as linguistic performance, what kind of ‘innate’ mechanisms must operate over that data to reach competence? In other words, how much of semantic acquisition is truly data-driven, and what must be hard-encoded in a system’s architecture? In this paper, we introduce a new methodology to pull those questions apart. We use state-of-the-art computational models to investigate the amount and nature of transformations required to perform particular semantic tasks. We apply that methodology to one of the simplest structures in language: the word bigram, giving insights into the specific contribution of that linguistic component.1
International Wheat Price Responses to ENSO Shocks: Modelling Transmissions Using Smooth Transitions

Climate anomalies affect agricultural production in different parts of the world and can impact price behavior of internationally traded commodities. This research examines the effect of a particular climate phenomenon, El Nino Southern Oscillation (ENSO), on wheat prices from the major exporting regions. The study adopts a smooth transition modelling framework to addresses nonlinear dynamics and asymmetric price transmissions in response to the climate anomalies. Results suggest that a positive ENSO shock, i.e. El Nino, reduces wheat prices, while a negative ENSO shock, i.e. La Nina, increases wheat prices. The price changes vary across the export regions, but on average are within the three percent magnitude. The price increase due to La Nina is of a larger magnitude as compared to the price decrease due to El Nino. Moreover, price responses to ENSO shocks are more amplified (up to seven percent) during the La Nina regime, as compared to the El Nino regime. These findings are indicative of and consistent with the economics of grain price volatility and the theory of storage.
Ecological Restoration of Abies religiosa Forests Using Nurse Plants and Assisted Migration in the Monarch Butterfly Biosphere Reserve, Mexico

Increasing disturbance events (forest fires, windstorms, pest outbreaks) associated with climate change are creating new ecological restoration challenges. Here, we examine the utility of assisted migration in combination with naturally established nurse plants in order to improve the success of afforestation with Abies religiosa (sacred fir), the overwintering host of the Monarch butterfly (Danaus plexippus). We established high-elevation assisted migration A. religiosa provenance field tests at two sites (Las Palomas and Los Ailes) in the core overwintering areas of the Monarch Butterfly Biosphere Reserve (MBBR), central Mexico. At each site, two-year-old seedlings were planted either in the open or under existing nurse plants. Three–and-a-half years after planting at the Las Palomas site (5.5 years from germination), A. religiosa seedling survival was 72% under the shade of nurse plants, but only 18% in open areas (without shade). At the Los Ailes site, a year-and-a-half after planting in the field (3.5 years from germination), survival was 94% and 10%, respectively. There were not significant differences in seedling height increment among populations at both sites. The results of our study and those published elsewhere suggest that A. religiosa benefits from shade protection of nurse plants and that population transfer 400 m upward in elevation (i.e., assisted migration) to compensate for future warmer climates does not appear to have any negative impacts on the seedlings, while potentially conferring closer alignment to future climates. If absent in planting sites, we recommend growing nurse shrub species (such as Baccharis conferta) alongside tree seedlings in forest nurseries so that these shrubs could be transplanted to the reforestation site two years before planting the tree seedlings.
System Performance of Indoor Office Millimeter Wave Communications

In order to achieve higher transmission rates and system capacity, fifth-generation cellular (SG) and 802.11ad/ay wireless systems will use higher frequency bands (24GHz-70GHz), the so-called millimeter wave frequencies. These systems rely on using large antenna arrays and narrow beamforming to counter the large path-loss experienced. Narrow beamforming maximizes array gain but results in a significant increase in beam management complexity, and the number of beams which are required to maintain the desired cell coverage as well. Hence, beam-broadening algorithms are suggested as a countermeasure, where a trade-off between high transmission quality and robustness to dynamics could be achieved. In this paper, a novel beam-steering algorithm is proposed to achieve improved coverage for a defined codebook size. Additionally, indoor office system performance is evaluated for different beamforming techniques. Numerical results show up to 1.5 dB improvement in system Signal-to-Interference-plus-Noise Ratio (SINR) via the proposed beam-steering approach when compared to conventional methods. The SINR gains could be converted to a 35% reduction in codebook size. These improvements are attained at zero additional cost.
Multi-view Moments Embedding Network for 3D Shape Recognition

Benefited from rapid developments of deep learning, 3D shape recognition has become a remarkable subject in computer vision systems.The existing methods of multi-perspective views have shown competitive performance in 3D shape recognition.However, they have not yet fully exploited the information among all views of projection.In this paper, we propose a novel Multi-view Moments Embedding Network(MMEN) for capturing multiple moments information.MMEN obtains the similarity between different views and retains the description of the original view by generating moments matrix for representing the general features of the 3D shape.Additionally, we apply the matrix square-root layer to perform a non-linear scaling to the eigenvalues of the moment embedding matrix.We compare the performance of our proposed network with several state-of-the-art models on the ModelNet datasets, and the results of the average instance/class accuracy demonstrate the promising performance of MMEN on 3D shape recognition.
Approximate Definitional Constructs as Lightweight Evidence for Detecting Classes Among Wikipedia Articles

A lightweight method applies a few extraction patterns to the task of distinguishing Wikipedia articles that are classes ("Walled garden", "Garden") from other articles ("High Hazels Park"). The method acquires a set of classes, based on patterns targeting phrases that likely refer to either concepts being introduced or defined ("a *walled garden* is a garden [.]"); or to concepts used to introduce or define other concepts ("a walled garden is a *garden* [.]"). Experimental results over multiple evaluation sets are better, when relying on defined phrases alone vs. defining phrases alone; and further improved, when combining complementary evidence from both.
BIOLOGY OF COLLETOTRICHUM SPP. AND EPIDEMIOLOGY OF THE ANTHRACNOSE IN TROPICAL FRUIT TREES

The anthracnose is important disease in the pre and postharvest phases. Several species of Colletotrichum (C. gloeosporioides, C. acutatum, C. musae e C. magna) are responsible for inciting this disease. The pathogen infects many fruit trees in tropical and temperate regions, causing considerable damage and loss in all phases of cultures. Characteristic symptoms are dark necrotic lesions depressed, subcircular or angular shaped, and there may be coalescing. Infections have a special feature: the phenomenon of quiescence. This process has important implications, particularly in post-harvest, because the damage from infections reflect only this phase. The intensity of the disease have been striking at temperatures from 24 to 28 °C and in the presence of high relative humidity. The understanding of some aspects of the biology of the pathogen (the process of quiescence) and the epidemiology of the disease is crucial, since much has not yet been fully clarified, especially when the aim is to achieve sustainable management.
Second Order Memristor Models for Neuromorphic Computing

Second order memristors have shown to be able to mimic some specific features of neuron synapses, specifically Spike-Timing-Dependent-Plasticity (STDP), and consequently to be good candidates for neuromorphic computing. In particular, memristor crossbar structures appear to be suitable to implement locally competitive algorithms (LCA) and to tackle classification problems, by exploiting temporal learning techniques. On the other hand neuromorphic studies and experiments have revealed different kinds of plasticity and have shown the effect of calcium concentration on synaptic changes. In this paper we firstly derive a simplified model of a second order memristor, only involving two variables, the mem-conductance and the temperature, directly ascribable to the synaptic efficacy and to the calcium concentration. Then we provide a comparison between second order memristors and neuromorphic models, with reference to some relevant plasticity models, including cycles of spike pairs, triplets and quadruplets at different frequencies. Preliminary results show that, through our almost analytical approach, a significant portion of synaptic behaviors in second order memristors can be easily studied and predicted.
Synthesis and characterization of functionalized polyacrylonitrile coated with iron oxide nanoparticles and its applicability in nitrate removal from aqueous solution

AbstractA novel adsorbent, Polyacrylonitrile (PAN)-oxime-nano-Fe2O3, was developed to remove nitrates from water. The properties of the adsorbent were characterized by transmission electron microscopy, X-ray diffraction, and Fourier transform infrared spectroscopy. Experiments were carried out to investigate the adsorption kinetics and desorption behaviour of the adsorbent. The Langmuir, Freundlich, and Dubinin–Radushkevich (D–R) isotherms were determined and the results revealed that the adsorption was well explained by the (D–R) model. The experimental data fitted very well the pseudo-second-order kinetic model. Intra-particle diffusion affects nitrate uptake. The experiments showed that the maximum amount of nitrate released after desorption processes was about 50%, indicating that a large portion of nitrate was irreversibly retained by the PAN-oxime-nano-Fe2O3.
Dynamic Computation Offloading in Mobile-Edge-Cloud Computing Systems

The proliferation of advanced mobile devices has enabled the wide-spread adoption of computation-intensive mobile applications. Nevertheless, the execution of these applications is still constrained by the limited battery and processing capacity of the devices. These limitations have led to the mobile cloud/edge computation offloading paradigm, and several such architectures have been proposed in recent years. However, we currently lack a clear understanding of the benefits of these diverse offloading solutions in terms of computation delay and energy cost savings. In this paper, we implement a hierarchical mobile-edge-cloud computing system that is comprised of a mobile device, an edge server and a cloud server, and conduct a series of experiments to measure the device's energy consumption, as well as the computation and transmission delay for different tasks. Our experiments reveal an interesting relation between the mobile's CPU clock frequency and the transmission delay for the offloaded tasks. Based on this finding, we propose a dynamic greedy algorithm that selects the CPU frequency, the tasks to be offloaded, and the network path (cellular or WiFi), in order to reduce the total energy cost and execution delay. We fully implement this multi-tier architecture and verify experimentally that the proposed algorithm can save up to 50% of the device battery energy, at the expense of transmission delay. Our results motivate the design of offloading policies that optimize jointly the CPU clock and network capacity.
Optimal Power Allocation for Multiple D2D Users in HetNets with Channel Estimation Error

Heterogeneous network (HetNets) and Device-to-device (D2D) communication are two promising techniques to be introduced in future generation networks to meet the requirements of explosive data traffic. D2D is a promising technique to communicate between user equipment underlying a cellular network by re-using cellular resources within the cell. Although, this can help in offloading the main cellular network, however one of the major challenges is to to control the interference generated by reusing the same resources. In this paper, the problem of interference management for underlay D2D users is studied in a two-tier heterogeneous macro-small cell network assuming imperfect channel state information (CSI). The network consists of one macro-cell with its macro-cell base station (MBS) and one small-cell cellular user equipment (SUE) linked to a small-cell base station (SCBS). The network has N D2D pairs belonging to the macro-cell tier outside the range of the small-cell. The N D2D pairs use the same resources of the SUE to increase the spectral efficiency of the network. The optimal power of D2D users that maximize the signal-to-interference-and-noise (SINR) ratio is derived in presence of imperfect CSI maintaining the signal to interference-plus-noise ratio (SINR) of the SUE greater than certain threshold to guarantee by quality-of-service (QoS).
Selectively Enhanced Development of Working Memory in Musically Trained Children and Adolescents

In the current longitudinal study, we investigated the development of working memory in musically trained and nontrained children and adolescents, aged 9–20. We measured working memory with the Digit Span (DS) forwards and backwards tests (N = 106) and the Trail-Making A and B (TMT-A and B; N = 104) tests three times, in 2011, 2013, and 2016. We expected that musically trained participants would outperform peers with no musical training. Indeed, we found that the younger musically trained participants, in particular, outperformed their nontrained peers in the TMT-A, TMT-B and DS forwards tests. These tests all primarily require active maintenance of a rule in memory or immediate recall. In contrast, we found no group differences in the backwards test that requires manipulation and updating of information in working memory. These results suggest that musical training is more strongly associated with heightened working memory capacity and maintenance than enhanced working memory updating, especially in late childhood and early adolescence.
Self-Organization and Self-Governance

The intuitive difference between a system that choreographs the motion of its parts in the service of goals of its own formulation and a system composed of a collection of parts doing their own thing without coordination has been shaken by now familiar examples of self-organization. There is a broad and growing presumption in parts of philosophy and across the sciences that the appearance of centralized information-processing and control in the service of system-wide goals is mere appearance, i.e., an explanatory heuristic we have evolved to predict behavior, but one that will eventually get swept away in the advancing tide of self-organization. I argue that there is a distinction of central importance here, and that no adequate science of complex systems can dispense with it.
The Development of the Chinese Sentiment Lexicon for Internet

This paper examines the development of the Chinese Sentiment Lexicon for Internet (CSLI), a sentiment lexicon for capturing the valence and arousal in Chinese online social media texts. We first review the current sentiment lexicons and their building process, including the collection of words, judging the emotionality of words, and testing reliability and validity. In Study 1, we develop CSLI and test its initial reliability and validity. In Study 2, we further test the convergent validity of CSLI by examining its correlations with human judgment in 429 aggregated Weibo comments. In Study 3, the predictive validity of CSLI is examined by linking its results to personality traits among 52 undergraduates. Two replication studies are also conducted to verify the findings in Study 2 and 3. The results have generally supported the reliability and validity of CSLI. Therefore, CSLI can be used as a research tool to capture the degree of valence and arousal in Chinese online social media texts. Its potential to promote human well-being is also discussed.
What You Look Matters?: Offline Evaluation of Advertising Creatives for Cold-start Problem

Modern online auction-based advertising systems combine item and user features to promote ad creatives with the most revenue.However, new ad creatives have to display for certain initial users before enough click statistics could collected and utilized in later ads ranking and bidding processes. This leads to a well-known challenging cold start problem.In this paper, we argue that the content of the creatives intrinsically determines their performance (e.g. ctr, cvr), and we add a pre-ranking stage based on the content. The stage prunes inferior creatives and thus makes online impressions more effective. Since the pre-ranking stage can be executed offline, we can use deep features and take their well generalization to navigate the cold start problem.Specifically, we propose Pre Evaluation Ad Creation Model (PEAC), a novel method to evaluate creatives even before they were shown in the online ads system. Our proposed PEAC only utilizes ads information such as verbal and visual content, but requires no user data as features. During the online A/B testing, PEAC shows significant improvement in revenue. The method has been implemented and deployed in the large scale online advertising system at ByteDance. Furthermore, we provide detailed analysis on what the model learns, which also gives suggestions for ad creative design.
Slope of the beta function at the fixed point of SU(2) gauge theory with six or eight flavors

We consider measurement of the leading irrelevant scaling exponent $\gamma_g^\ast$, given by the slope of the beta function, at the fixed point of SU(2) gauge theory with six or eight flavors. We use the running coupling measured using the gradient flow method and perform the continuum extrapolation by interpolating the measured beta function. We study also the dependence of the results on different discretization of the flow. For the eight flavor theory we find $\gamma_g^\ast=0.19(8)_{-0.09}^{+0.21}$. Applying the same analysis also for the six flavor theory, we find $\gamma_g^\ast=0.648(97)_{-0.1}^{+0.16}$ consistently with the earlier analysis.
Dribble: A learn-based timer scheme selector for mobility management in IoT

In this work, we present Dribble a learn-based timer scheme selector to manage topology changes caused by mobility in the Internet of Things (IoT) context. IoT has turned smart devices part of our everyday lives. They are in everywhere with many shapes, sizes, and capabilities. For IoT to become even more ubiquitous, it is necessary to overcome the challenges posed by mobility. One of them is the management of topology changes, especially at the network layer. Currently, routing protocols check the topology through an advertisement timer scheme. Such schemes face a basic trade-off between being fast to find topology problems and concurrently be energy and overhead control saver. Although there are timer schemes designed to mobile context, all devices are governed by the same one, which is a hard assumption since IoT is heterogeneous and naturally, devices have different behaviors. Thus, Dribble learns the devices' mobility pattern and then it assigns a custom-made timer scheme conveniently for each device. Our results show that personalized timer schemes present better performance than single traditional timer schemes such as Trickle Timer (TT) and Reverse Trickle Timer (RevTT).
Gerschenkron Revisited: The New Corporate Russia Abstract Based on firm-specific data compiled from the Russian Trading System stock exchange and SKRIN database, this paper seeks to identify the factors behind Russia’s dramatically improved corporate sector performance approximately from the beginni

Based on firm-specific data compiled from the Russian Trading System stock exchange and SKRIN database, this paper seeks to identify the factors behind Russia’s dramatically improved corporate sector performance approximately from the beginning of the new millennium through December 2007. We argue that improved corporate long-term performance was a consequence of several policy initiatives associated with the state-dominated banking sector that enabled statesubsidized investment funds to be channelled from a structurally re-engineered energy sector to targeted investment projects located in other industries. We argue that Russia’s industrial strategy closely conforms to Gerschenkron’s catch-up theory.