Miracle-dz/UniFAR-FTUs huggingface dataset:
Surface Reconstruction from 3D Line Segments

In man-made environments such as indoor scenes, when point-based 3D reconstruction fails due to the lack of texture, lines can still be detected and used to support surfaces. We present a novel method for watertight piecewise-planar surface reconstruction from 3D line segments with visibility information. First, planes are extracted by a novel RANSAC approach for line segments that allows multiple shape support. Then, each 3D cell of a plane arrangement is labeled full or empty based on line attachment to planes, visibility and regularization. Experiments show the robustness to sparse input data, noise and outliers.
Functional Characterization of the Grapevine γ-Glutamyl Transferase/Transpeptidase (E.C. 2.3.2.2) Gene Family Reveals a Single Functional Gene Whose Encoded Protein Product Is Not Located in Either the Vacuole or Apoplast

γ-glutamyl transferases/transpeptidases (E.C. 2.3.2.2, GGTs) are involved in the catabolism of many compounds that are conjugated to glutathione (GSH), which have a variety of roles. GSH can act as storage and transport vehicle for reduced sulfur; it is involved in the detoxification of xenobiotics and also acts as a redox buffer by utilizing its thiol residue to protect against reactive oxygen species, which accumulate in response to biotic and abiotic stress. Furthermore, many distinctive flavor and aroma compounds in Sauvignon blanc wines originate from odorless C5- and C6-GSH conjugates or their GGT catabolized derivatives. These precursors are then processed into their volatile forms by yeast during fermentation. In many plant species, two or more isoforms of GGTs exist that target GSH-conjugates to either the apoplast or the vacuole. A bioinformatics approach identified multiple GGT candidates in grapevine (Vitis vinifera). However, only a single candidate, VvGGT3, has all the conserved residues needed for GGT activity. This is intriguing given the variety of roles of GSH and GGTs in plant cells. Characterization of VvGGT3 from cv. Sauvignon blanc was then undertaken. The VvGGT3 transcript is present in roots, leaves, inflorescences, and tendril and at equal abundance in the skin, pulp, and seed of mature berries and shows steady accumulation over the course of whole berry development. In addition, the VvGGT3 transcript in whole berries is upregulated upon Botrytis cinerea infection as well as mechanical damage to leaf tissue. VvGGT3-GFP fusion proteins transiently over-expressed in onion cells were used to study subcellular localization. To confirm VvGGT3 activity and localization in vivo, the fluorescent γ-glutamyl-7-amido-4-methylcoumarin substrate was added to Nicotiana benthamiana leaves transiently over-expressing VvGGT3. In combination, these results suggest that the functional VvGGT3 is associated with membrane-like structures. This is not consistent with its closely related functionally characterized GGTs from Arabidopsis, radish and garlic.
Dynamic Sensor Renting in RF-powered Crowdsensing Service Market with Blockchain

Embedding sensors on wireless devices for collaborative environment sensing has been envisioned as a cost-effective solution for IoT applications. However, existing IoT platforms faces challenges, e.g., unsustainablility due to the limited on-device battery and tremendous cost of deploying middlewares for centralized task dispatching. In this paper, we employ wireless energy transfer and permissionless blockchains to construct a sustainable and decentralized IoT crowdsensing platform. Therein, IoT sensing cloud composed of multiple co-located sensors is wirelessly powered by RF-energy beacons for data sensing and transmission. The data is then forwarded to the blockchain for distributed data/transaction verification and trading. The data users access the crowdsensing service by renting sensors from the sensing clouds. Both the sensing clouds and data users are self-interested and aim to maximize their individual profits. The sensing clouds handle the interference of concurrent wireless transmissions and the on-chain transaction cost. Meanwhile, each user distributes its limited budget over the sensing clouds to optimize the service quality. We formulate a Stackelberg differential game to analyze the interaction among the sensing clouds and data users. Then, we investigate the Stackelberg equilibrium by capitalizing on Pontryagin's maximum principle. Furthermore, we provide a series of insightful numerical results about the Stackelberg equilibrium.
Service-Aware Virtual Network Function placement in Software Defined Networks

Current mobile units have multiple network interfaces and can connect to multiple access points simultaneously. This represents new opportunities. Choosing the right network, and the optimal route through that network, requires knowledge about the state of the network. With Software Defined Networking where control plane and data plane are separated, SDN controllers can have a good overview over a wide variety of network parameters. Thus, traffic from a service with specific service parameter requirements can be routed so that these requirements are likely to be met. In this paper we have investigated how the optimal placement of a network function in SDN varies based on service parameters such as latency, price, packet loss and throughput. We present a method to choose the best placement based on a service profile according to the current network state. The results are validated by implementing the methods in an emulated SDN network.
Simultaneous Multi-Source Integrated Energy Harvesting System for IoE Applications

This paper presents an integrated modular multi-source energy harvesting system based on the Dickson charge pump and hill-climbing algorithm. The proposed maximum output power extraction is optimizing the system’s end-to-end efficiency. It collects the power simultaneously from multiple energy sources and boosts their DC voltages to a regulated output voltage that can be used for Internet of Everything (IoE) applications. The proposed implementation utilizes the whole capacitance on-chip regardless of the number of stages in use. A demonstration system using the proposed techniques is implemented for two sources in 0.18 μm CMOS technology and utilizes a total of 900 pF MIM capacitance. The simulation results shows that the proposed system achieves a peak MPPT efficiency of 90%, charge pump efficiency of 70% and end-to-end efficiency of 55% at a regulated output voltage of 1.5 V.
Deep Reinforcement Learning for Cooperative Edge Caching in Future Mobile Networks

To satisfy rapidly increasing multimedia service requests from mobile users, content caching at the network edges (e.g., base stations) has been regarded as a promising technique in future mobile networks. In this paper, by virtue of Deep Reinforcement Learning (DRL) with respect to solving complicated control problems, we propose a framework on Double Deep Q-Network for cooperative edge caching in mobile networks. Particularly, we aim at minimizing the long-term average content fetching delay of mobile users without requiring any priori knowledge of content popularity distribution. Trace-driven simulation results show that our proposed framework outperforms some existing caching algorithms, including Least Recently Used (LRU), Least Frequently Used (LFU) and First-In First-Out (FIFO) caching strategies by 7%, 11% and 9% improvements, respectively. Besides, our proposed work is further shown that only average 4% performance loss exists compared to an omniscient oracle algorithm.
Towards a standard gamma-ray burst: tight correlations between the prompt and the afterglow plateau phase emission

To find out the astrophysical processes responsible for gamma-ray burst (GRB), it is crucial to discover and understand the relations between their observational properties. This work was performed in the GRB rest frames using a sample of 62 long Swift GRBs with known redshifts. Following the earlier analysis of the correlation between afterglow luminosity (L*a) and break time (T*a), we extend it to correlations between the afterglow and the prompt emission GRB physical parameters. We find a tight physical scaling between the mentioned afterglow luminosity L*a and the prompt emission mean luminosity 〈L*p〉45≡Eiso/T*45. The distribution, with the Spearman correlation coefficient reaching 0.95 for the most accurately fitted subsample, scales approximately as L*a∝〈L*p〉0.745. We have also analysed correlations of L*a with several prompt emission parameters, including the isotropic energy Eiso and the peak energy in the νFν spectrum, Epeak. As a result, we obtain significant correlations also between these quantities, discovering that the highest correlated GRB subsample in the afterglow analysis leads also to the highest prompt–afterglow correlations. Such events can be considered to form a sample of standard GRBs for astrophysics and cosmology.
Deep Sequence-to-Sequence Entity Matching for Heterogeneous Entity Resolution

Entity Resolution (ER) identifies records from different data sources that refer to the same real-world entity. Conventional ER approaches usually employ a structure matching mechanism, where attributes are aligned, compared and aggregated for ER decision. The structure matching approaches, unfortunately, often suffer from heterogeneous and dirty ER problems. That is, entities from different data sources are described using different schemas, and attribute values may be misplaced, missing, or noisy. In this paper, we propose a deep sequence-to-sequence entity matching model, denoted Seq2SeqMatcher, which can effectively solve the heterogeneous and dirty problems by modeling ER as a token-level sequence-to-sequence matching task. Specifically, we propose an align-compare-aggregate neural network for Seq2Seq entity matching, which can learn the representations of tokens, capture the semantic relevance between tokens, and aggregate matching evidence for accurate ER decisions in an end-to-end manner. Experimental results show that, by comparing entity records in token level and learning all components in an end-to-end manner, our Seq2Seq entity matching model can achieve remarkable performance improvements on 9 standard entity resolution benchmarks.
Maximizing Throughput with Minimum Channel Assignment for Cellular-VANET Het-Nets

In this paper, we study the channel assignment problem in cellular-VANET heterogeneous wireless networks. The D2D communication technology can be applied to VANET. Vehicular device-to-device (D2D) network as an underlying network to the cellular network can share the uplink channel resources of the cellular network. Interference as a critical element has an impact on the utilization in channel assignment. To minimize the interference when allocating channels, we present a novel channel assignment algorithm based on reuse distance. Essentially we have limited spectrum resources that can be shared by vehicular transmitters and cellular users in an area, to assign the minimal number of channels to vehicles in a prescribed area is our first concern. Since the interference between co-channel devices is related to their distance, we divide the area to small hexagon regions then use Region-based Channel Assignment Algorithm to assign different channel sets to each region. In this case, three sets of resources can fulfill the channel assignment requirements to all vehicles. We also prove the theoretical guarantee as approximation factor of 3 for the minimal channel assignment problem. To improve the system throughput with limited channel resources in the HetNets, we propose a Local Search Throughput Maximization algorithm to find the vehicular transmitters and cellular users combinations. We prove the optimal approximation factor is (1-ε) and the complexity of our algorithm in each small region. We show the effectiveness and efficiency of proposed algorithm in experiments.
Impact of Temporally Correlated Nakagami- $m$ Interferers in D2D Cache-Aided Networks

In this paper, we exploit tools from stochastic geometry to characterize the average probability of successful content delivery in a cache-enabled device-to-device (D2D) network under Nakagami- $m$ fading. Specifically, we focus on the impact of temporal interference correlation due to erroneous packets retransmissions. The aggregate network interference is characterized under a slotted Aloha scheme in a homogeneous Poisson field of static interferers. In addition, the effect of different system parameters, such as the content popularity, intensity of devices, and D2D communication range, on the optimal activity pattern of devices is investigated. Theoretical findings of this work have been validated via Monte-Carlo simulations. Finally, we compare the system performance under temporal correlation to the approximate independent interference assumption. Furthermore, this work draws conclusions about several network design insights.
When FPGA-Accelerator Meets Stream Data Processing in the Edge

Today, stream data applications represent the killer applications for Edge computing: placing computation close to the data source facilitates real-time analysis. Previous efforts have focused on introducing light-weight distributed stream processing (DSP) systems and dividing the computation between Edge servers and the clouds. Unfortunately, given the limited computation power of Edge servers, current efforts may fail in practice to achieve the desired latency of stream data applications. In this vision paper, we argue that by introducing FPGAs in Edge servers and integrating them into DSP systems, we might be able to realize stream data processing in Edge infrastructures. We demonstrate that through the design, implementation, and evaluation of F-Storm, an FPGA-accelerated and general-purpose distributed stream processing system on Edge servers. F-Storm integrates PCIe-based FPGAs into Edge-based stream processing systems and provides accelerators as a service for stream data applications. We evaluate F-Storm using different representative stream data applications. Our experiments show that compared to Storm, F-Storm reduces the latency by 36% and 75% for matrix multiplication and grep application. It also obtains 1.4x and 2.1x improvement for these two applications, respectively. We expect this work to accelerate progress in this domain.
Assessment of motor coordination and dexterity of six years old children: A psychometric analysis

La coordinacion motora en desarrollo del ninos de seis anos fue examinada por la Evaluacion de la coordinacion y destreza motora (Avaliacao da Coordenacao e Destreza Motora - ACOORDEM) con objetivo de evaluar la confiabilidad prueba-reprueba e investigar si el desempeno motor es influenciado por el sexo, tipo de escuela y sitio de la morada. Fueron evaluados 85 ninos y sus padres y profesores respondieron cuestionarios. Para confiabilidad prueba- reprueba, la ACOORDEM fue aplicada de nuevo en 10 ninos. Pruebas de Mann-Whitney y Chi-cuadrado identificaron significativa influencia del sexo, tipo de escuela y sitio de morada solo en algunos items. La confiabilidad prueba-reprueba fue moderada para los items de desempeno y de buena a excelente para la mayoria de los items del los cuestionarios. Se concluye que algunos items deben ser revisados y tablas normativas de desempeno para la identificacion del atraso motor pueden ser creadas considerando solo la variable edad. Estudios futuros deben dar continuad al proceso de creacion del instrumento con evaluacion de ninos mas jovenes.
TAB-VCR: Tags and Attributes based VCR Baselines

Reasoning is an important ability that we learn from a very early age. Yet, reasoning is extremely hard for algorithms. Despite impressive recent progress that has been reported on tasks that necessitate reasoning, such as visual question answering and visual dialog, models often exploit biases in datasets. To develop models with better reasoning abilities, recently, the new visual commonsense reasoning(VCR) task has been introduced. Not only do models have to answer questions, but also do they have to provide a reason for the given answer. The proposed baseline achieved compelling results, leveraging a meticulously designed model composed of LSTM modules and attention nets. Here we show that a much simpler model obtained by ablating and pruning the existing intricate baseline can perform better with half the number of trainable parameters. By associating visual features with attribute information and better text to image grounding, we obtain further improvements for our simpler & effective baseline, TAB-VCR. We show that this approach results in a 5.3%, 4.4% and 6.5% absolute improvement over the previous state-of-the-art on question answering, answer justification and holistic VCR.
Comparative Morpho-Biochemical Responses of Sunflower Lines Sensitive and Tolerant to Water Stress

Water stress is likely the most important factor that adversely affects plant growth and development. In our study two inbred lines of sunflower (Helianthus annuus L.) with contrasting behavior to water deficit, the inbred lines B59, sensitive, and B71, tolerant, were subjected to water stress and compared in terms of growth parameters, relative water content, photosynthetic pigments and osmo-compatibles contents. Mannitol treatment generated a moderate water stress which resulted in a relative water content decline in both lines. In aerial part of B59 and B71 stressed-seedlings, a dramatic reduction of osmotic potential was observed, although in B71 the osmotic potential was 1.8-fold lower than in B59. The B71 line recovered the osmotic potential to control values after 8 d of re-watering. Several morphological parameters were altered by the water stress. Shoot and root relative fresh weight decreased in both lines under water stress, although B71 showed a minor drop. However, the root was less affected than the shoot in stress condition and recovery period. Water stress was affected in a greater proportion shoot dry weight (DW) than root DW in B59 and B71 lines. Nevertheless, a significant increase in root DW/shoot DW ratio was observed in stressed seedlings. A higher reduction in the individual leaf area was observed in B71 line under stress. The basal chlorophyll content was higher in the tolerant line B71 than in the sensitive B59, and carotenoids showed a high content in B71 control and stressed seedlings. Under water stress, the aerial and root proline content increased in the tolerant B71 line. Glycinebetaine decreased significantly in the aerial part of both lines in response to water stress indicating that this compound is not the main osmo-regulator. In conclusion, B71 has suitable mechanisms to enable it to respond more effectively to moderate water stress.
A Hedonic Pricing Analysis of Hotel Room Rates in Bangkok

Despite the ongoing global financial slowdown and a series of natural disasters that have taken place in Thailand, Bangkok still ranks high on the list of top tourist destinations in terms of the number of international visitors. This dramatic increase in the number of international tourist arriving in Bangkok benefitted the hotel industry in Bangkok and brought about huge revenue earnings for the country. This study analyzed the status of Bangkok hotel room rates, differentiated by hotel characteristics. Using a hedonic pricing method, this study based its analysis on the data obtained from online hotel reservations for 141 different Bangkok hotels. The findings showed that the general room rates charged by hotel chains were approximately 32% higher than independent hotels. During high seasons, the weekend room prices charged by hotel chains were higher if the room had larger space and if the hotel was located closer to the city center. Regardless of seasonality, any new hotel chain located near the Bangkok city center that also had its own bar and restaurant facility, charged weekend room rates that were higher than older chain hotels, located further from the city center, and without bar and restaurant facilities. This study also noted in particular how accessibility to the Bangkok Mass Transit System (BTS Sky Train) and proximity to the city center affected hotel pricing, coming to the conclusion that any hotel in Bangkok located either near a BTS station or the city center charged room rates much higher than hotels located further away.
Conditional Denoising of Remote Sensing Imagery Using Cycle-Consistent Deep Generative Models

The potential of using remote sensing imagery for environmental modelling and for providing real time support to humanitarian operations such as hurricane relief efforts is well established. These applications are substantially affected by missing data due to non-structural noise such as clouds, shadows and other atmospheric effects. In this work we probe the potential of applying a cycle-consistent latent variable deep generative model (DGM) for denoising cloudy Sentinel-2 observations conditioned on the information in cloud penetrating bands. We adapt the recently proposed Frechet Distance metric to remote sensing images for evaluating performance of the generator, demonstrate the potential of DGMs for conditional denoising, and discuss future directions as well as the limitations of DGMs in Earth science and humanitarian applications.
Choropleth Maps on High Resolution CRTs: The Effects of Number of Classes and Hue on Communication.

The research reported here was designed to determine how quickly and accurately map readers viewing choropleth maps on a high-resolution computer monitor are able to identify to which class an areal unit on the map belongs, when the map has between four and eight classes and is produced in shades of either gray, green or magenta. As expected, accuracy rates decreased and reaction times increased as the number of classes on the map increased, Accuracy rates ranged from 91.9% for four-class maps to 68.2% for eight-class maps (averaged for all three colors used in the study). Hue also affected accuracy rates and reaction times, the best results being obtained with achromatic (gray-shaded) maps: 84.5% correct, averaged over all numbers of classes. Maps shaded with magenta proved to be the least satisfactory with an accuracy rate of 72.8%. The study provides cartographers with empirical guidelines regarding what level of map-reading accuracy might be expected for choropleth maps designed with a given number of map classes, in a specific hue, and displayed on a high-resolution graphics monitor.
Extending Lehnert’s Revised Quantum Electrodynamics to Fractal Media and Cantor Sets: Towards Physics Beyond Standard Model

Extending Lehnert’s Revised Quantum Electrodynamics to Fractal Media and Cantor Sets: Towards Physics beyond Standard Model Victor Christianto Abstract In a series of papers, Bo Lehnert proposed a novel and revised version of Quantum Electrodynamics (RQED) based on Proca equations. However, as far as I know there is no paper yet for extending his RQED to fractal media and Cantor Sets. Drawing similarity between Proca and Maxwell equations, in the present paper I extend RQED further based on a recent paper published at Advances in High Energy Physics (AHEP) journal, where Yang Zhao et al. derived Maxwell equations on Cantor sets from the local fractional vector calculus. It can be shown that Maxwell equations on Cantor sets in a fractal bounded domain give efficiency and accuracy for describing the fractal electric and magnetic fields. I also extend RQED to anisotropic fractal media based on the work of Martin Ostoja-Starzewski. It is hoped that this paper may stimulate further investigations and experiments in particular for finding physics beyond Standard Model in fractal media. It may be expected to have some impact to fractal cosmology modeling too.