Miracle-dz/UniFAR-FTUs huggingface dataset:
On-demand high-capacity ride-sharing via dynamic trip-vehicle assignment

Significance Ride-sharing services can provide not only a very personalized mobility experience but also ensure efficiency and sustainability via large-scale ride pooling. Large-scale ride-sharing requires mathematical models and algorithms that can match large groups of riders to a fleet of shared vehicles in real time, a task not fully addressed by current solutions. We present a highly scalable anytime optimal algorithm and experimentally validate its performance using New York City taxi data and a shared vehicle fleet with passenger capacities of up to ten. Our results show that 2,000 vehicles (15% of the taxi fleet) of capacity 10 or 3,000 of capacity 4 can serve 98% of the demand within a mean waiting time of 2.8 min and mean trip delay of 3.5 min. Ride-sharing services are transforming urban mobility by providing timely and convenient transportation to anybody, anywhere, and anytime. These services present enormous potential for positive societal impacts with respect to pollution, energy consumption, congestion, etc. Current mathematical models, however, do not fully address the potential of ride-sharing. Recently, a large-scale study highlighted some of the benefits of car pooling but was limited to static routes with two riders per vehicle (optimally) or three (with heuristics). We present a more general mathematical model for real-time high-capacity ride-sharing that (i) scales to large numbers of passengers and trips and (ii) dynamically generates optimal routes with respect to online demand and vehicle locations. The algorithm starts from a greedy assignment and improves it through a constrained optimization, quickly returning solutions of good quality and converging to the optimal assignment over time. We quantify experimentally the tradeoff between fleet size, capacity, waiting time, travel delay, and operational costs for low- to medium-capacity vehicles, such as taxis and van shuttles. The algorithm is validated with ∼3 million rides extracted from the New York City taxicab public dataset. Our experimental study considers ride-sharing with rider capacity of up to 10 simultaneous passengers per vehicle. The algorithm applies to fleets of autonomous vehicles and also incorporates rebalancing of idling vehicles to areas of high demand. This framework is general and can be used for many real-time multivehicle, multitask assignment problems.
Texture mapping of flat-like 3D models

Recently, thanks to the diffusion of scanning devices and the availability of powerful 3D modeling software, as well as to the improvements in the automation of the image-based modeling approach, it is foreseen that 3D models will become more and more ubiquitous in several research fields. However, 3D models obtained by these technologies often are lacking for a suitable photorealistic appearance, due to the low quality generated texture, or to the complete absence of it. A post-processing texture mapping is often used to obtain highly realistic models, through an accurate manual alignment of the model and the related texture. In this work, we propose an automatic approach for mapping the texture on flat-like 3D models, in which the depth component is smaller compared to the object's height and width, such as in bass-relieves, coins, paintings or facades. The method relies on the evaluation from the model geometry of a depth map, used to align the texture image. The results show the effectiveness of the proposed method.
Source-Interference Recovery Over Broadcast Channels: Asymptotic Bounds and Analog Codes

We consider the problem of joint recovery of a bivariate Gaussian source and of interference over the two-user Gaussian degraded broadcast channel in the presence of common interference. The interference, that is available non-causally at the encoder, is assumed to be Gaussian and correlated to the sources. The tradeoff between the distortion of the sources and the interference estimation error is studied; information-theoretic outer and inner bounds based on ideas from rate-distortion theory and hybrid coding are derived, respectively. More precisely, the outer bound is found by assuming additional knowledge at each user; the inner bound, however, is obtained by analyzing the distortion of a layered hybrid scheme based on proper power splitting, Costa and Wyner-Ziv coding. Low delay and complexity coding schemes based on analog mapping are next proposed. More specifically, parametric mappings based on linear and sawtooth curves are studied and optimized by minimizing an upper bound on the system's distortion; nonparametric mappings based on joint optimization between the encoder and the decoder using an iterative algorithm are designed. Numerical results show that for the special cases that are previously considered by Abou Saleh et al. (with no fading), the derived outer bound is tighter and the proposed hybrid scheme has a lower complex structure with no loss in performance. In addition, the proposed low delay nonlinear schemes outperform the linear scheme and perform relatively close to the inner bound under certain system settings.
High-Dimensional Visual Analytics: Interactive Exploration Guided by Pairwise Views of Point Distributions

We introduce a method for organizing multivariate displays and for guiding interactive exploration through high-dimensional data. The method is based on nine characterizations of the 2D distributions of orthogonal pairwise projections on a set of points in multidimensional Euclidean space. These characterizations include such measures as density, skewness, shape, outliers, and texture. Statistical analysis of these measures leads to ways for 1) organizing 2D scatterplots of points for coherent viewing, 2) locating unusual (outlying) marginal 2D distributions of points for anomaly detection and 3) sorting multivariate displays based on high-dimensional data, such as trees, parallel coordinates, and glyphs
Nitrogen Source and External Medium pH Interaction Differentially Affects Root and Shoot Metabolism in Arabidopsis

Ammonium nutrition often represents an important growth-limiting stress in plants. Some of the symptoms that plants present under ammonium nutrition have been associated with pH deregulation, in fact external medium pH control is known to improve plants ammonium tolerance. However, the way plant cell metabolism adjusts to these changes is not completely understood. Thus, in this work we focused on how Arabidopsis thaliana shoot and root respond to different nutritional regimes by varying the nitrogen source (NO3- and NH4+), concentration (2 and 10 mM) and pH of the external medium (5.7 and 6.7) to gain a deeper understanding of cell metabolic adaptation upon altering these environmental factors. The results obtained evidence changes in the response of ammonium assimilation machinery and of the anaplerotic enzymes associated to Tricarboxylic Acids (TCA) cycle in function of the plant organ, the nitrogen source and the degree of ammonium stress. A greater stress severity at pH 5.7 was related to NH4+ accumulation; this could not be circumvented in spite of the stimulation of glutamine synthetase, glutamate dehydrogenase, and TCA cycle anaplerotic enzymes. Moreover, this study suggests specific functions for different gln and gdh isoforms based on the nutritional regime. Overall, NH4+ accumulation triggering ammonium stress appears to bear no relation to nitrogen assimilation impairment.
Vaccination to Conserved Influenza Antigens in Mice Using a Novel Simian Adenovirus Vector, PanAd3, Derived from the Bonobo Pan paniscus

Among approximately 1000 adenoviruses from chimpanzees and bonobos studied recently, the Pan Adenovirus type 3 (PanAd3, isolated from a bonobo, Pan paniscus) has one of the best profiles for a vaccine vector, combining potent transgene immunogenicity with minimal pre-existing immunity in the human population. In this study, we inserted into a replication defective PanAd3 a transgene expressing a fusion protein of conserved influenza antigens nucleoprotein (NP) and matrix 1 (M1). We then studied antibody and T cell responses as well as protection from challenge infection in a mouse model. A single intranasal administration of PanAd3-NPM1 vaccine induced strong antibody and T cell responses, and protected against high dose lethal influenza virus challenge. Thus PanAd3 is a promising candidate vector for vaccines, including universal influenza vaccines.
A tale of two helicopters

This paper discusses similarities and differences in autonomous helicopters developed at USC and CSIRO. The most significant differences are in the accuracy and sample rate of the sensor systems used for control. The USC vehicle, like a number of others, makes use of a sensor suite that costs an order of magnitude more than the vehicle. The CSIRO system, by contrast, utilizes low-cost inertial, magnetic, vision and GPS to achieve the same ends. We describe the architecture of both autonomous helicopters, discuss the design issues and present comparative results.
Influence of applied voltage on droplet size distribution in electrospraying of thermoplastic polyurethane

Textiles substrate coated with electrosprayed polymer droplets have a large specific surface area and sub micron range coating compared to commercial textiles, making them excellent candidates for use in medical and filtration applications. While the process of electrospraying is known for over half a century, current understanding of the process and the parameters that influence the properties of the polymer droplets produced from it is very limited. In this work, we have evaluated the influence of applied voltage on polymer droplet diameter. We find that applied voltage strongly affects the diameter of polymer droplet, and droplet diameter decreases with an increase in applied voltage.
Laser ablation atmospheric pressure photoionization mass spectrometry imaging of phytochemicals from sage leaves.

RATIONALE Despite fast advances in ambient mass spectrometry imaging (MSI), the study of neutral and nonpolar compounds directly from biological matrices remains challenging. In this contribution, we explore the feasibility of laser ablation atmospheric pressure photoionization (LAAPPI) for MSI of phytochemicals in sage (Salvia officinalis) leaves. METHODS Sage leaves were studied by LAAPPI-time-of-flight (TOF)-MSI without any sample preparation. Leaf mass spectra were also recorded with laser ablation electrospray ionization (LAESI) mass spectrometry and the spectra were compared with those obtained by LAAPPI. RESULTS Direct probing of the plant tissue by LAAPPI efficiently produced ions from plant metabolites, including neutral and nonpolar terpenes that do not have polar functional groups, as well as oxygenated terpene derivatives. Monoterpenes and monoterpenoids could also be studied from sage by LAESI, but only LAAPPI was able to detect larger nonpolar compounds, such as sesquiterpenes and triterpenoid derivatives, from the leaf matrix. Alternative MSI methods for nonpolar compounds, such as desorption atmospheric pressure photoionization (DAPPI), do not achieve as good spatial resolution as LAAPPI (<400 µm). CONCLUSIONS We show that MSI with LAAPPI is a useful tool for concurrently studying the distribution of polar and nonpolar compounds, such as phytochemicals, directly from complex biological samples, and it can provide information that is not available by other, established methods.
Call for Discussion: Building a New Standard Dataset for Relation Extraction Tasks

This paper is an attempt to raise pertinent questions and act as platform to generate fruitful discussions within the AKBC community about the need for a large scale dataset for relation extraction. For proper training and evaluation of relation extraction tasks, the weaknesses of datasets used so far need to be tackled: mainly the size (too small) and the amount of data that is actually labelled (unlabelled data leading to recall problems). We have the vision of building a new large and fully labelled dataset for entity pairs connected via binary relations from both Freebase as well as other datasets, such as Clueweb. Concerning the process of building, we present pioneering work on a roadmap which will serve as the foundation for the intended discussion within the community. Points to discuss arise within the following steps: first, the source data has to be preprocessed in order to ensure that the set of relations consists of valid relations only; second, we suggest a method to find the most relevant relations for an entity pair; and third, we outline approaches on how to actually label the data. It is necessary to discuss several key issues in the process of generating this dataset. This will enable us to thoroughly create a dataset that will have the potential to serve as a standard to the community.
On the Perceptual Organization of Image Databases Using Cognitive Discriminative Biplots

A human-centered approach to image database organization is presented in this study. The management of a generic image database is pursued using a standard psychophysical experimental procedure followed by a well-suited data analysis methodology that is based on simple geometrical concepts. The end result is a cognitive discriminative biplot, which is a visualization of the intrinsic organization of the image database best reflecting the user's perception. The discriminating power of the introduced cognitive biplot constitutes an appealing tool for image retrieval and a flexible interface for visual data mining tasks. These ideas were evaluated in two ways. First, the separability of semantically distinct image classes was measured according to their reduced representations on the biplot. Then, a nearest-neighbor retrieval scheme was run on the emerged low-dimensional terrain to measure the suitability of the biplot for performing content-based image retrieval (CBIR). The achieved organization performance when compared with the performance of a contemporary system was found superior. This promoted the further discussion of packing these ideas into a realizable algorithmic procedure for an efficient and effective personalized CBIR system.
Learning Disjunctions: Near-Optimal Trade-off between Mistakes and "I Don't Know's"

@ mistakes while answering "I don't know" O(n2 log log n) times. On the lower bound side, we show that any algorithm making o(n/log n) mistakes must answer "I don't know" a superpolynomial number of times. By contrast, no previous lower bounds were known, and the best previous algorithms (by Sayedi et al. who introduced the model) either make at most 1/3n mistakes while answering "I don't know" O(n) times with linear running time per answer, or make O(n/log n) mistakes while answering "I don't know" O(n2) times with exponential running time per answer. Our lower bound establishes optimality of the latter mistake bound, assuming a polynomial number of "I don't know"s. The running time of our algorithms (per answer) are
In situ monitoring of dispersion dynamics of carbon nanotubes during sonication using electrical conductivity measurements

The main challenge in the fabrication of carbon nanotube- (CNT-) based composite materials is the optimization of the sonication time in order to obtain homogenous and uniformdispersion of CNTs. Past studiesmostly relied on postprocessing characterization techniques to address this issue. In the present, however, in situ monitoring of dispersion dynamics of CNTs in distilled water is carried out using instantaneous conductivity measurements. Using a computer controlled data acquisition system, the time evolution of the solution conductivity was carefully recorded. The data were then used to evaluate the intensity of turbulent fluctuations, which clearly highlighted the existence of three distinct sonication phases. During the first phase, the conductivity fluctuations initially increased attaining ultimately a maximum, thus indicating the occurrence of large agglomerates of CNTs. During the second phase of sonication, the solution conductivity showed a rather steep increase while fluctuations steadily declined. This phenomenon can be attributed to the breakdown of large CNT agglomerates, resulting in greater dispersion homogeneity of CNTs. During the third phase, after almost 650 kJ/L of sonication energy, the conductivity increase was almost negligible. The fluctuation intensity also remained constant during this phase signifying that the further sonication was no longer required.
Evaluation of Stability of k-Means Cluster Ensembles with Respect to Random Initialization

Many clustering algorithms, including cluster ensembles, rely on a random component. Stability of the results across different runs is considered to be an asset of the algorithm. The cluster ensembles considered here are based on k-means clusterers. Each clusterer is assigned a random target number of clusters, k and is started from a random initialization. Here, we use 10 artificial and 10 real data sets to study ensemble stability with respect to random k, and random initialization. The data sets were chosen to have a small number of clusters (two to seven) and a moderate number of data points (up to a few hundred). Pairwise stability is defined as the adjusted Rand index between pairs of clusterers in the ensemble, averaged across all pairs. Nonpairwise stability is defined as the entropy of the consensus matrix of the ensemble. An experimental comparison with the stability of the standard k-means algorithm was carried out for k from 2 to 20. The results revealed that ensembles are generally more stable, markedly so for larger k. To establish whether stability can serve as a cluster validity index, we first looked at the relationship between stability and accuracy with respect to the number of clusters, k. We found that such a relationship strongly depends on the data set, varying from almost perfect positive correlation (0.97, for the glass data) to almost perfect negative correlation (-0.93, for the crabs data). We propose a new combined stability index to be the sum of the pairwise individual and ensemble stabilities. This index was found to correlate better with the ensemble accuracy. Following the hypothesis that a point of stability of a clustering algorithm corresponds to a structure found in the data, we used the stability measures to pick the number of clusters. The combined stability index gave best results
Observer-based controller synthesis for discrete systems under bounded peak disturbances: Convergence within ellipsoids

This paper investigates the problems of observer-based feedback controller synthesis for discrete-time linear systems under bounded peak disturbances. The control objective is to make the system state of the closed-loop system under bounded peak disturbances converges to an ellipsoid that is as small as possible or converges to a pre-specified ellipsoid. In order to determine the observer gain and feedback gain in the observer-based controller, the matrix decoupling technique is adopted to derive the single-step matrix inequality conditions. A numerical example is given to show the validity of the theoretical findings.
Performance Analysis of the Tec h nology C h ess Program

Many Artificial Intelligence programs exhibit behavior that can be meaningfully compared against the performance of another system, e.g. against a human. A satisfactory understanding of such a program must include an analysis of the behavior of the program and the reasons for that behavior in terms of the program's structure. The primary goal of this thesis is to analyze carefully the performance of the Technology Chess Program as a paradigm for analysis of other complex AI performance programs. The analysis uses empirical, analytical, and statistical methods. The empirical explorations included entering TECH in U. S. Chess Federation tournaments, earning an official USCF rating of 1243 (Class D). This rating is used to predict the performance of faster TECH-Iike programs. Indirect rating methods are considered. TECH's performance is compared with the moves made in the Spassky/Fischer 1972 World Championship match. TECH's tree-searching efficiency is compared with a theoretical model of the alpha-beta algorithm. The search is found to be much closer to perfect ordering than to random ordering, so that little improvement can be expected by improving the order of moves considered in the tree. An analysis of variance (ANOVA) is performed to determine the relative importance of the tactical heuristics in limiting a tree search. Sorting captures in the tree is found to be very important; the killer heuristic is found to be ineffective in this environment. The major contribution of the thesis is its paradigm for performance analysis of AI programs. Important individual contributions include the use of ANOVA to assign credit to program mechanisms, direct and indirect methods for determining playing ability, the use of an analytical model to show how close the search is to perfect ordering, and a new interpretation of the Samuel coefficient.
CILIA FORMATION IN THE ADULT CAT BRAIN AFTER PARGYLINE TREATMENT

The brains of four adult cats treated with pargyline (a nonhydrazide monoaminoxidase inhibitor) were examined at both the light and electron microscopic levels. Formation of typical mature cilia with the 9 + 2 pattern was observed in neural cells in the following areas: habenula nuclei, interpeduncular nuclei, hippocampus, mammillary bodies, thalamus, and caudate nucleus. The most marked ciliation occurs in the habenula nuclei. In general, glial cells greatly predominate in the formation of cilia. It is not clear whether ciliation in the central nervous system is the direct result of pargyline or if it occurs indirectly as a result of inhibition of monoaminoxidase. These findings are compared with the serotonin effect on ciliation in the embryogenesis of lower forms. It is suggested that pharmacological stimulation of centriolar reproduction without subsequent mitosis may lead to ciliary formation.