Miracle-dz/UniFAR-FTUs huggingface dataset:
Broad Adaptive Immune Responses to M. tuberculosis Antigens Precede TST Conversion in Tuberculosis Exposed Household Contacts in a TB-Endemic Setting

Background The identification of Mycobacterium-tuberculosis (Mtb) infected individuals remains a challenge due to an insufficient understanding of immune responses detected with the current diagnostic tests for latent tuberculosis i.e. the tuberculin skin test (TST) or IFN–γ release assays (IGRAs) and an inability to distinguish infection stages with current immunologic assays. Further classification based on markers other than IFN–γ may help to define markers of early Mtb infection. Methods We assessed the TST status of Mtb-exposed household contacts at baseline and at 6 months. Contacts were classified into those with initial positive TST (TST+); those with baseline negative TST but TST conversion at 6 months (TST converters, TSTC) and those with persistently negative TST (PTST−). We assessed their short- and long-term immune responses to PPD and ESAT–6/CFP–10 (EC) via IFN–γ ELISPOT and a multiplex cytokine array in relation to TST status and compared them to those of TB cases to identify immune profiles associated with a spectrum of infection stages. Results After 1 and 6 days stimulation with EC, 12 cytokines (IFN–γ, IL–2, IP–10, TNF–α, IL–13, IL–17, IL–10, GMCSF, MIP–1β, MCP–3, IL–2RA and IL–1A) were not different in TSTC compared to TST+ suggesting that robust adaptive Mtb-specific immune responses precede TST conversion. Stratifying contacts by baseline IFN–γ ELISPOT to EC in combination with TST results revealed that IP–10 and IL–17 were highest in the group of TST converters with positive baseline ELISPOT, suggesting they might be markers for recent infection. Conclusion We describe a detailed analysis of Mtb-specific biomarker profiles in exposed household contacts in a TB endemic area that provides insights into the dynamic immune responses to Mtb infection and may help to identify biomarkers for ‘at-risk’ populations beyond TST and IGRA.
Prey fish escape by sensing the bow wave of a predator

Prey fish possess a remarkable ability to sense and evade an attack from a larger fish. Despite the importance of these events to the biology of fishes, it remains unclear how sensory cues stimulate an effective evasive maneuver. Here, we show that larval zebrafish (Danio rerio) evade predators using an escape response that is stimulated by the water flow generated by an approaching predator. Measurements of the high-speed responses of larvae in the dark to a robotic predator suggest that larvae respond to the subtle flows in front of the predator using the lateral line system. This flow, known as the bow wave, was visualized and modeled with computational fluid dynamics. According to the predictions of the model, larvae direct their escape away from the side of their body exposed to more rapid flow. This suggests that prey fish use a flow reflex that enables predator evasion by generating a directed maneuver at high speed. These findings demonstrate a sensory-motor mechanism that underlies a behavior that is crucial to the ecology and evolution of fishes.
Rebuttal of Miskolczi’s alternative greenhouse theory

In 2010 Miskolczi wrote a scientific paper suggesting that carbon dioxide increases in the Earth’s atmosphere do not lead to more heat being trapped within the Earth system and therefore cannot be a cause of global warming. We show his theory to be incorrect in its application of the physics of radiation theory. Further, previously published studies of both satellite measurements of the radiation escaping the Earth and of the observed water vapour trends in the atmosphere through time do not support his conclusions.
Embedding spatial context information into inverted filefor large-scale image retrieval

One most popular approach for large-scale content-based image retrieval is based on the Bag-of-Visual-Words model. Since the spatial context among local features is very important for visual content identification, many approaches index local features' geometric clues, such as location, scale and orientation for post-verification. To obtain consistent accuracy performance, the amount of top ranked images that post-verification approach needs to process is proportional to the image database size. When the database is very large, the verified images will be too many to be processed in real-time response. To address this issue, in this paper, we explore two approaches to embed spatial context information into the inverted file. The first one is to build a spatial relationship dictionary embedded with spatial context among local features, which we call one-one spatial relationship method. The second one is to generate a spatial context binary signature for each feature, which we call one-multiple spatial relationship method. Then we build an inverted file with spatial information between local features. The geometric verification is implicitly achieved while traversing the inverted file. Experimental results on benchmark Holidays dataset demonstrate the efficiency of the proposed algorithm.
An optimal channel assignment strategy for WLANs using distributed optimization

An important design goal for wireless local area networks (WLANs) is the performance. It is well known that the major issue challenge to the performance in WLANs is the wireless interference. This leads to an ambience that depends on the channel assignments among interfering access points (AP). Due to the limited number of non-overlapping channels, severe interference scenarios may arise if an unappropriate spectrum planning is considered. In our study we considered WLANs scenarios where APs belong to different administrative domains, a common situation in dense urban deployments. In such environment the use of centralized algorithms is not appropriate and the already proposed distributed methods do no guarantee optimal channel assignment. In this paper, we formalize the channel allocation as a distributed constraint optimization problem (DCOP) and propose a new cooperative channel allocation strategy using the distributed pseudo tree-optimization procedure (DPOP). The adjacent channel interference is analytically formulated for DPOP. The simulation results show that the proposed strategy always achieves the optimal solution and is scalable in terms of the number of exchanged messages.
Parallaxes and proper motions for 20 open clusters as based on the new Hipparcos catalogue

Context. A new reduction of the astrometric data as produced by the Hipparcos mission has been published, claiming that the accuracies for nearly all stars brighter than magnitude Hp = 8 are improved, by up to a factor 4, compared to the original catalogue. As correlations between the underlying abscissa residuals have also been reduced by more than an order of magnitude to an insignificant level, our ability to determine reliable parallaxes and proper motions for open clusters should be improved. Aims. The new Hipparcos astrometric catalogue is used to derive mean parallax and proper motion estimates for 20 open clusters. The HR-diagrams of the nearest clusters are compared and combined to provide future input to sets of observational isochrones. Methods. Three different methods are applied, according to the proximity of the cluster, to compensate, where needed, for projection effects, spread in position along the line of sight, and the internal velocity dispersion of the cluster stars. Results. The new parallaxes have accuracies between 2 and 2.5 times higher than what had been derived from the original Hipparcos catalogue. At least two to three groups of clusters, mostly of similar ages, are observed to each occupy their own specific space in the HR diagram. A significant discrepancy in distance moduli from those obtained with isochrone-based main-sequence fitting remains, in particular for one of these groups, containing the Pleiades, NGC 2516, and Blanco 1. The difference in absolute magnitudes between this group and one containing the Hyades and Praesepe clusters appears to be correlated with systematic differences in the Stromgren Δc0 index between those groups. The same dependency has been known to exist for a long time, and is again confirmed by the Hipparcos data, in variations in absolute magnitudes for field stars of the same effective temperature. Conclusions. The positions of the cluster HR diagrams are consistent within different groups of clusters shown for example by the near-perfect alignment of the sequences for the Hyades and Praesepe, for Coma Ber and UMa, and for the Pleiades, NGC 2516, and Blanco 1. The groups are mutually consistent when systematic differences in Δc0 are taken into account, where the effect of these differences on the absolute magnitudes has been calibrated using field-star observations.
Provably faithful evaluation of polynomials

We provide sufficient conditions that formally guarantee that the floating-point computation of a polynomial evaluation is faithful. To this end, we develop a formalization of floating-point numbers and rounding modes in the Program Verification System (PVS). Our work is based on a well-known formalization of floating-point arithmetic in the proof assistant Coq, where polynomial evaluation has been already studied. However, thanks to the powerful proof automation provided by PVS, the sufficient conditions proposed in our work are more general than the original ones.
Evaluation of StarCraft Artificial Intelligence Competition Bots by Experienced Human Players

StarCraft is one of the most successful real-time strategy (RTS) games and is also actively being researched by artificial intelligence (AI) communities. Since 2010, game AI researchers have hosted annual AI competition events to develop human-level RTS AIs using StarCraft. It ranks the AI bots by their winning ratio from thousands of AI vs. AI matches without human involvement. It is questionable whether successful AI bots are also competitive and preferable to human players. In this study, we invited 20 experienced players with varying expertise to evaluate skill levels, overall performance and human likeness of AI bots. Results show that human's ranking of AI bots are not identical to the current one from AI competitions. It suggests the need for developing new AI competitions that consider human factors ("human-likeness" or "adaptation"). Also, it revealed that the expertise levels of human players have high impact on overall performance and human-likeness evaluations of AI bots. It supports the concept of dynamically adjusting AI bots to satisfy different levels of human players. The outcomes of this study will also be useful to incorporate human factors in other active video AI competitions (e.g., Angry Birds, Fighting Game, and General Game Playing).
Radiative transfer model inversion based on multi-temporal CHRIS / PROBA data for LAI estimation

: Leaf area index (LAI) is a key variable for the understanding and modelling of several eco-physiological processes within a vegetation canopy. The LAI could thus provide vital information for the management of the environment and agricultural practices when estimated continuously over time and space thanks to remote sensing sensors such as CHRIS/PROBA. The spaceborne ESA-mission CHRIS/PROBA provides multi-temporal observations of the land surface in the spectral and directional information dimensions. This system represents a rich source of information for Earth observation purposes specifically adapted for monitoring the high dynamic of agricultural crops. For this purpose a radiative transfer model (RTM) is coupled to a canopy structure dynamic model (CSDM). The coupled models are used to exploit the complementary content of the spectral and temporal information dimensions for LAI estimation over a maize canopy. The resulting estimation of the temporal and spatial variation of LAI is improved by integrating multi-temporal CHRIS/PROBA data and ground meteorological observations. Further, the presented method provides the continuous LAI time course over the season, which is required by crop growth and land surface process models. ABSTRACT: Leaf area index (LAI) is a key variable for the understanding and modelling of several eco-physiological processes within a vegetation canopy. The LAI could thus provide vital information for the management of the environment and agricultural practices when estimated continuously over time and space thanks to remote sensing sensors such as CHRIS/PROBA. The spaceborne ESA-mission CHRIS/PROBA provides multi-temporal observations of the land surface in the spectral and directional information dimensions. This system represents a rich source of information for Earth observation purposes specifically adapted for monitoring the high dynamic of agricultural crops. For this purpose a radiative transfer model (RTM) is coupled to a canopy structure dynamic model (CSDM). The coupled models are used to exploit the complementary content of the spectral and temporal information dimensions for LAI estimation over a maize canopy. The resulting estimation of the temporal and spatial variation of LAI is improved by integrating multi-temporal CHRIS/PROBA data and ground meteorological observations. Further, the presented method provides the continuous LAI time course over the season, which is required by crop growth and land surface process models.
Fault-tolerant control of uncertain nonlinear systems with nonlinearly parameterized fuzzy systems

The design of fault-tolerant control (FTC) for a class of uncertain nonlinear systems is studied in this paper. The considered system cannot be feedback linearized, and the nonlinear system functions are all unknown. Besides, some unknown actuator faults and external disturbances enter into the system during operation. A novel fault-tolerant controller is proposed by incorporating adaptive fuzzy approximation, backstepping design and robust control into FTC technique. The fuzzy systems which are used to be approximators are nonlinearly parameterized because the fuzzy membership functions are uncertain to designer. Both approximation errors and external disturbances are attenuated by additional control. The designed controller guarantees the closed-loop system stable and the output tracking to the reference signal. A simulation example is given to show the effectiveness of the proposed control approach.
Enhancing First-Pass Attachment Prediction

This paper explores the convergence between cognitive modeling and engineering solutions to the parsing problem in NLP. Natural language presents many sources of ambiguity, and several theories of human parsing claim that ambiguity is resolved by using past (linguistic) experience. In this paper we analyze and refine a connectionist paradigm (Recursive Neural Networks) capable of processing acyclic graphs to perform supervised learning on syntactic trees extracted from a large corpus of parsed sentences. Following a widely accepted hypothesis in psycholinguistics, we assume an incremental parsing process (one word at a time) that keeps a connected partial parse tree at all times. By implementing a parsing simulation procedure, we collect a large amount of data that shows the viability of the RNN as informant of a disambiguation process. We analyze what kind of information is exploited by the connectionist system in order to resolve different sources of ambiguity, and we see how the generalization performance of the system is affected by the tree complexity and the frequency of specific subtrees. We finally propose some enhancements to the architecture in order to achieve a better prediction accuracy.
Performance of Biological-Ecological Process for Rural Domestic Sewage Treatment in Taihu Lake Region

Rural domestic sewage is one of the main pollution sources of Taihu Lake. To seek suitable sewage treatment technology, investigation and pilot research was conducted in rural places in Taihu Lake region. Field investigation was done by questionnaire and sewage sampling analysis. Demonstration project was built with a biological-ecological process consisting of anaerobic hydrolysis, waterfall aeration contact oxidation and constructed wetland in a village named Linzhuang. The influent and effluent samples of this system were monitored for 6 months. Field investigation data showed sewage indexes in rural villages fluctuated greatly. Operation results of the pilot system indicated this combined process was efficient in pollutants removal. Chemical oxygen demand (COD), total phosphorus (TP) and total nitrogen (TN) in the effluent from this process was stably below 50 mg/l, 0.5 mg/l and 12.9 mg/l respectively. COD was mainly removed by anaerobic hydrolysis and contact oxidation, while nitrogen was mostly distripped by nitrification in contact oxidation unit and denitrification in hydrolysis unit. Most of TP removal was accomplished in the constructed wetland by filter interception, plant adsorption and microbial transformation. Owing to mutually complementary interaction between biological and ecological methods, the performance of this system was not obviously affected by influent and temperature fluctuation. This system was efficient in pollutants removal and cost effective in building and operating.
Single Outdoor Image Depth Map Generation Based on Scene Classification and Object Detection

Three dimensional television (3DTV) has attracted more and more attention in the area of TV broadcasting. However, the applications are constrained due to the content shortage. It is an economical way by converting monoscopic 2D video to 3D (2D-3D) so as to reuse the existed huge amount of 2D videos materials by using Depth-Image-Based-Rendering (DIBR). In this paper, an efficient framework for extracting depth information from the single image is proposed, which is based on scene classification and object detection. In the proposed scheme, by applying that real similar 3D scenes may have a similar depth map, we construct an image set including many kinds of images (their corresponding depth maps are given) with different scene structures first. The image set is classified into some categories manually. For a certain input image, k-Nearest Neighbor (KNN) algorithm is employed to judge that whether the input image corresponds to outdoor scene or not. Then, the initial depth map is obtained by fusing the depth maps of those images in the category which the input image belongs to, After that, we incorporate the image segmentation results to detect the sky region and the ground region by using color information. Finally, the depth map is obtained by refining the initial depth map using the sky and ground region. Experimental results demonstrate that the proposed scheme can generate smooth and reliable depth maps with satisfied performance.
Decision support system using ANT colony optimization

Oxford defines Research as work directed towards the `Innovation' `Introduction' and `Improvement'. The three `I' integrates well when critically evaluated. Evaluation is an important process. Every work is sent to 2-3 experts based on some criteria set. The decision is subjective as per as our study through literature is concerned. Generally majority voting is followed but their overall performance index for acceptance and rejection does not match criteria set. Hence this approach lacks the affective factor related to optimal results. To address that problem we are proposing here a new scheme that will be based on criteria set and as it will give unbiased/ unprejudiced decision on the acceptance and rejection of papers. The Ant Colony Algorithm well defines the solution to this problem. We are providing a phenomenal approach in research evaluation which will help in conflict resolving in different experts opinion.
An Assessment of Reactive Power / Voltage Control Devices In Distribution Networks

This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Carnegie Mellon University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to pubs-permissions@ieee.org. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. Abstract—This paper provides an assessment of candidate voltage/reactive power control devices for distribution systems. The recent trend toward Distributed Energy Resources (DERs), and Distributed Generation (DG), in particular, is often based on the rationale to support voltage and compensate for reactive power closer to the end users. This situation calls for a systematic approach to assessing alternatives for voltage control, both old and new. In this paper we illustrate on a simplified illustrate how their dynamic characteristics differ. Moreover, we show that enhancing control logic of the existing controllers is often as good as adding the latest hardware. In order for such enhanced performance to be achieved a more involved system-wide coordination is often necessary. The ultimate decision is based on several criteria and it reflects trade-offs between the complexity of devices, their cost and communications requirements.
The Security of Organizations and Individuals in Online Social Networks

The serious privacy and security problems related to online social networks (OSNs) are what fueled two complementary studies as part of this thesis. In the first study, we developed a general algorithm for the mining of data of targeted organizations by using Facebook (currently the most popular OSN) and socialbots. By friending employees in a targeted organization, our active socialbots were able to find new employees and informal organizational links that we could not find by crawling with passive socialbots. We evaluated our method on the Facebook OSN and were able to reconstruct the social networks of employees in three distinct, actual organizations. Furthermore, in the crawling process with our active socialbots we discovered up to 13.55% more employees and 22.27% more informal organizational links in contrast to the crawling process that was performed by passive socialbots with no company associations as friends. In our second study, we developed a general algorithm for reaching specific OSN users who declared themselves to be employees of targeted organizations, using the topologies of organizational social networks and utilizing socialbots. We evaluated the proposed method on targeted users from three actual organizations on Facebook, and two actual organizations on the Xing OSN (another popular OSN platform). Eventually, our socialbots were able to reach specific users with a success rate of up to 70% on Facebook, and up to 60% on Xing.
A simulation and modeling environment for the analysis and design of charge redistribution DACs used in SAR ADCs

The optimal design of SAR ADCs requires the accurate estimate of nonlinearity and parasitic effects in the feedback charge-redistribution DAC. Since the effects of both mismatch and stray capacitances depend on the specific array topology, complex calculations, custom modeling and heavy simulations in common circuit design environments are often required. This paper presents a novel MATLAB-based numerical tool to assist the design of classic, split and with attenuation capacitor binary weighted capacitive array topologies with an even number of bits from 6 to 14. The tool allows to perform both parametric and statistical simulations taking into account capacitive mismatch and parasitic capacitances in order to compute both differential- (DNL) and integral nonlinearity (INL). SNDR and ENoB degradation due to static non-linear effects is also estimated. An excellent agreement with the results obtained by the available circuit simulators (e.g. Cadence Spectre) is shown but featuring up to 104 shorter simulation time.