Miracle-dz/UniFAR-FTUs huggingface dataset:
Batching and Matching for Food Delivery in Dynamic Road Networks

Given a stream of food orders and available delivery vehicles, how should orders be assigned to vehicles so that the delivery time is minimized? For a successful assignment strategy, two key decisions need to be made: (1) assignment of orders to vehicles, (2) grouping orders into batches to cope with limited vehicle availability. We show that the minimization problem is not only NP-hard but inapproximable in polynomial time. To mitigate this computational bottleneck, we develop an algorithm called FOODMATCH, which maps the vehicle assignment problem to that of minimum weight perfect matching on a bipartite graph. The solution quality is further enhanced by reducing batching to a graph clustering problem. Extensive experiments on food-delivery data from large metropolitan cities establish that FOODMATCH is substantially better than baseline strategies on a number of metrics.
Perla: A Conversational Agent for Depression Screening in Digital Ecosystems. Design, Implementation and Validation

Most depression assessment tools are based on self-report questionnaires, such as the Patient Health Questionnaire (PHQ-9). These psychometric instruments can be easily adapted to an online setting by means of electronic forms. However, this approach lacks the interacting and engaging features of modern digital environments. With the aim of making depression screening more available, attractive and effective, we developed Perla, a conversational agent able to perform an interview based on the PHQ-9. We also conducted a validation study in which we compared the results obtained by the traditional self-report questionnaire with Perla's automated interview. Analyzing the results from this study we draw two significant conclusions: firstly, Perla is much preferred by Internet users, achieving more than 2.5 times more reach than a traditional form-based questionnaire; secondly, her psychometric properties (Cronbach's {\alpha} of 0.81, sensitivity of 96% and specificity of 90%) are excellent and comparable to the traditional well-established depression screening questionnaires.
The behavior of stock market prices throughout the episodes of capital inflows

This study aims to investigate the behavior of stock prices throughout the episodes of foreign capital flows using data of daily stock prices and quarterly foreign capital flows from 14 EMEs. To this end, the episodes of capital flows are identified using the threshold and the k-means clustering approaches. Next, the stock index changepoints are detected using the Pruned Exact Linear Time (PELT) method. Finally, we combine the results by distributing the detected changepoints over the identified capital flows. The results reveal that the stock indices have been rarely pushed further during the entire surge episodes identified by both approaches, and thus surges of capital flows do not necessarily lead to further appreciation of stock prices. In the meantime, a significant appreciation of stock prices is observed during the normal state of capital flows. On the other hand, it is noticed that the stock prices have not often depreciated during the episodes of foreign capital outflows in all the selected EMEs, which means that stock prices have been less vulnerable to reversals of foreign capital flows
Trace elements in bovine milk from different regions in Norway

Abstract Dairy milk samples from 37 milk tours in 15 areas of Norway were collected during indoor feeding or on pasture in 2008 and analyzed with respect to 6 major elements (Ca, P, K, S, Na, Mg) and 28 trace elements (As, Ba, Be, Bi, Cd, Ce, Co, Cr, Cs, Cu, Dy, Fe, I, La, Li, Mn, Mo, Nd, Pb, Rb, Sb, Se, Sm, Sr, V, Y, Yb and Zn). Surprisingly, the least-square means of major and trace elements in milk did not vary much from one region to another. The concentration levels of iodine, copper, manganese and lithium in milk from the northern region were higher compared with milk from other regions. Milk from the south was enriched with respect to selenium, yttrium, cerium and bismuth, while milk from the western region was enriched with respect to rubidium.
Stress correlations in frictional granular media

This paper investigates whether in frictional granular packings, like in Hamiltonian amorphous elastic solids, the stress autocorrelation matrix presents long range anisotropic contributions just as elastic Green's functions. We find that in a standard model of frictional granular packing this is not the case. We prove quite generally that mechanical balance and material isotropy constrain the stress auto-correlation matrix to be fully determined by two spatially isotropic functions: the pressure and torque auto-correlations. The pressure and torque fluctuations being respectively normal and hyperuniform force the stress autocorrelation to decay as the elastic Green's function. Since we find the torque fluctuations to be hyper-uniform, the culprit is the pressure whose fluctuations decay slower than normally as a function of the system's size. Investigating the reason for these abnormal pressure fluctuations we discover that anomalous correlations build up already during the compression of the dilute system before jamming. Once jammed these correlations remain frozen. Whether this is true for frictional matter in general or is it the consequence of the model properties is a question that must await experimental scrutiny and possible alternative models.
Corruption and Audit in Strategic Argumentation

Strategic argumentation provides a simple model of disputation and negotiation among agents. Although agents might be expected to act in our best interests, there is little that enforces such behaviour. (Maher, 2016) introduced a model of corruption and resistance to corruption within strategic argumentation. In this paper we identify corrupt behaviours that are not detected in that formulation. We strengthen the model to detect such behaviours, and show that, under the strengthened model, all the strategic aims in (Maher, 2016) are resistant to corruption.
Multi-Node Multi-GPU Diffeomorphic Image Registration for Large-Scale Imaging Problems

We present a Gauss-Newton-Krylov solver for large deformation diffeomorphic image registration. We extend the publicly available CLAIRE library to multi-node multi-graphics processing unit (GPUs) systems and introduce novel algorithmic modifications that significantly improve performance. Our contributions comprise (i) a new preconditioner for the reduced-space Gauss-Newton Hessian system, (ii) a highly-optimized multi-node multi-GPU implementation exploiting device direct communication for the main computational kernels (interpolation, high-order finite difference operators and Fast-Fourier-Transform), and (iii) a comparison with state-of-the-art CPU and GPU implementations. We solve a 2563-resolution image registration problem in five seconds on a single NVIDIA Tesla V100, with a performance speedup of 70% compared to the state-of-the-art. In our largest run, we register 20483 resolution images (25B unknowns; approximately 152$\times$ larger than the largest problem solved in state-of-the-art GPU implementations) on 64 nodes with 256 GPUs on TACC’s Longhorn system.
Self-Supervised Video Representation Learning by Uncovering Spatio-Temporal Statistics

This paper proposes a novel pretext task to address the self-supervised video representation learning problem. Specifically, given an unlabeled video clip, we compute a series of spatio-temporal statistical summaries, such as the spatial location and dominant direction of the largest motion, the spatial location and dominant color of the largest color diversity along the temporal axis, etc. Then a neural network is built and trained to yield the statistical summaries given the video frames as inputs. In order to alleviate the learning difficulty, we employ several spatial partitioning patterns to encode rough spatial locations instead of exact spatial Cartesian coordinates. Our approach is inspired by the observation that human visual system is sensitive to rapidly changing contents in the visual field, and only needs impressions about rough spatial locations to understand the visual contents. To validate the effectiveness of the proposed approach, we conduct extensive experiments with four 3D backbone networks, i.e., C3D, 3D-ResNet, R(2+1)D and S3D-G. The results show that our approach outperforms the existing approaches across these backbone networks on four downstream video analysis tasks including action recognition, video retrieval, dynamic scene recognition, and action similarity labeling. The source code is publicly available at: https://github.com/laura-wang/video_repres_sts.
The Food Habits of Stoneflies (Plecoptera) in the Upper Gunnison River, Colorado

Gut contents of 1,463 stonefly nymphs, comprising 10 species, from the Gunnison River, Colorado, were analyzed from Dec., 1974–Oct., 1975, in relation to food availability. Pteronarcella badia fed primarily on detritus and moss. Perlidae and Perlodidae mature nymphs were all carnivorous, but showed considerable seasonal-developmental shifting in diets and preference during earlier stages. Early instar Isoperla fulva nymphs were herbivore-detritivores, then gradually shifted through an omnivorous to carnivorous diet as development proceeded. Claassenia sabulosa and Hesperoperla pacifica remained carnivorous throughout development. Dominant prey groups were chironomids, mayflies and caddisflies. Horn's Coefficient of Dietary Overlap showed significance among all predator species for major food categories, but subtle mechanisms such as prey species-and size-selectivity and temporal succession provided sufficient partitioning of the abundant food resources to allow for coexistence. Large Claassenia sabulosa nymphs in August selected more mayflies after dark than in the afternoon. No behavioral selection by predacious stoneflies was indicated for the chironomids Ablabesmyia sp. , Cricotopus sp. , Prodiamesa sp. , and Rheotanytarsus sp.
Shannon Meets Turing: Non-Computability and Non-Approximability of the Finite State Channel Capacity

The capacity of finite state channels (FSCs) has been established as the limit of a sequence of multi-letter expressions only and, despite tremendous effort, a corresponding finite-letter characterization remains unknown to date. This paper analyzes the capacity of FSCs from a fundamental, algorithmic point of view by studying whether or not the corresponding achievability and converse bounds on the capacity can be computed algorithmically. For this purpose, the concept of Turing machines is used which provide the fundamental performance limits of digital computers. To this end, computable continuous functions are studied and properties of computable sequences of such functions are identified. It is shown that the capacity of FSCs is not Banach-Mazur computable which is the weakest form of computability. This implies that there is no algorithm (or Turing machine) that can compute the capacity of a given FSC. As a consequence, it is then shown that either the achievability or converse must yield a bound that is not Banach-Mazur computable. This also means that there exist FSCs for which computable lower and upper bounds can never be tight. To this end, it is further shown that the capacity of FSCs is not approximable, which is an even stricter requirement than non-computability. This implies that it is impossible to find a finite-letter entropic characterization of the capacity of general FSCs. All results hold even for finite input and output alphabets and finite state set. Finally, connections to the theory of effective analysis are discussed. Here, results are only allowed to be proved in a constructive way, while existence results, e.g., proved based on the axiom of choice, are forbidden.
The Relative Role of Bars and Galaxy Environments in AGN Triggering of SDSS Spirals

We quantify the relative role of galaxy environment and bar presence on AGN triggering in face-on spiral galaxies using a volume-limited sample with 0.02 < z < 0.055, Mr < 19.5, and σ > 70 selected from Sloan Digital Sky Survey (SDSS) Data Release 7. To separate their possible entangled effects, we divide the sample into bar and non-bar samples, and each sample is further divided into three environment cases of isolated galaxies, interacting galaxies with a pair, and cluster galaxies. The isolated case is used as a control sample. For these six cases, we measure AGN fractions at a fixed central star formation rate and central velocity dispersion, σ. We demonstrate that the internal process of the bar-induced gas inflow is more efficient in AGN triggering than the external mechanism of the galaxy interactions in groups and cluster outskirts. The significant effects of bar instability and galaxy environments are found in galaxies with a relatively less massive bulge. We conclude that from the perspective of AGN–galaxy coevolution, a massive black hole is one of the key drivers of spiral galaxy evolution. If it is not met, a bar instability helps the evolution, and in the absence of bars, galaxy interactions/mergers become important. In other words, in the presence of a massive central engine, the role of the two gas inflow mechanisms is reduced or almost disappears. We also find that bars in massive galaxies are very decisive in increasing AGN fractions when the host galaxies are inside clusters.
Composição bromatológica e concentrações de nutrientes do capim braquiária adubado com nitrogênio e fósforo

The experiment was conducted to evaluate the chemical composition of the leaf blade and stem + sheath of Brachiaria decumbens Stapf. , fertilized with nitrogen and phosphorus. The completely randomized design was used, with three repetitions, in a 4 x 3 factorial scheme, with four doses of nitrogen (0; 100; 200 and 300kg/ha/year), and three doses of phosphorus (0; 50 and 100kg/ha/year). The potassium was applied 40kg/ha/year. The dry matter content decreased as nitrogen levels increased. The nitrogen content increased with increasing doses of nitrogen. The neutral detergent fiber (NDF) and acid detergent fiber (ADF) percentages of leaf and stem + sheath decreased with increasing of nitrogen doses. The phosphorus concentration in the leaf blade and stem + sheath decreased as increased nitrogen levels, with linear and quadratic effect. The calcium concentration in the leaf and stem + sheath showed a quadratic effect and the concentration of magnesium in the leaf blade increased with the nitrogen. The levels of nitrogen fertilization alters the chemical composition of Brachiaria decumbens grass, and the phosphorus did not influence the chemical composition. Increasing doses of N is a major factor in reducing the NDF and ADF levels in leaf blade and stem + sheath.
Quantum error correction and large $N$

In recent years quantum error correction (QEC) has become an important part of AdS/CFT. Unfortunately, there are no field-theoretic arguments about why QEC holds in known holographic systems. The purpose of this paper is to fill this gap by studying the error correcting properties of the fermionic sector of various large NN theories. Specifically we examine SU(N)SU(N) matrix quantum mechanics and 3-rank tensor O(N)^3O(N)3 theories. Both of these theories contain large gauge groups. We argue that gauge singlet states indeed form a quantum error correcting code. Our considerations are based purely on large NN analysis and do not appeal to a particular form of Hamiltonian or holography.
Milky Way Accelerometry via Millisecond Pulsar Timing.

The temporal stability of millisecond pulsars is remarkable, rivaling even some terrestrial atomic clocks at long timescales. Using this property, we show that millisecond pulsars distributed in the galactic neighborhood form an ensemble of accelerometers from which we can directly extract the local galactic acceleration. From pulsar spin period measurements, we demonstrate acceleration sensitivity with about 1σ precision using 117 pulsars. We also present a complementary analysis using orbital periods of 13 binary pulsar systems that eliminates the systematics associated with pulsar braking and results in a local acceleration of (1.7±0.5)×10^{-10} m/s^{2} in good agreement with expectations. This work is a first step toward dynamically measuring acceleration gradients that will eventually inform us about the dark matter density distribution in the Milky Way galaxy.
Low-Power Wide-Area Network Design

LPWAN is an enabling technology for long-range, low-power, and low-cost IoT/CPS applications. Recently, multiple LPWAN technologies have been developed that operate in the licensed (e.g., 5G) and ISM (e.g., LoRa) bands. To avoid the crowd in the ISM band and the cost of the licensed band, we propose a novel LPWAN called Sensor Network Over White Spaces (SNOW) by utilizing the TV white spaces. Specifically, we design, develop, and experiment SNOW, which is highly scalable, energy-efficient, and has a long communication range. SNOW achieves scalability and energy efficiency by enabling concurrent packets reception at a BS using a single radio from numerous sensors and concurrent packets transmission to numerous sensors from the BS using a single radio, simultaneously, which we achieve by proposing a distributed implementation of OFDM. To enable the low-cost and scalable SNOW deployment in practical applications, we implement SNOW using the low-cost and small form-factored COTS devices, where we address multiple practical challenges including the high peak-to-average power ratio, channel state estimation, and carrier offset estimation. Also, we propose an adaptive transmission power protocol to handle the near-far power problem. To enable connecting tens of thousands of nodes over hundreds of kilometers, we further propose a network architecture called SNOW-tree through a seamless integration of multiple SNOWs where they form a tree structure and are under the same management/control. We address the intra- and inter-SNOW interferences by formulating a constrained optimization problem called the scalability optimization problem (SOP) whose objective is to maximize scalability by managing the spectrum sharing across the SNOWs. By proving the NP-hardness of SOP, we then propose two polynomial-time methods to solve it: a greedy heuristic algorithm and a 1/2-approximation algorithm.
DCN-M: Improved Deep & Cross Network for Feature Cross Learning in Web-scale Learning to Rank Systems

Learning effective feature crosses is the key behind building recommender systems. However, the sparse and large feature space requires exhaustive search to identify effective crosses. Deep & Cross Network (DCN) was proposed to automatically and efficiently learn bounded-degree predictive feature interactions. Unfortunately, in models that serve web-scale traffic with billions of training examples, DCN showed limited expressiveness in its cross network at learning more predictive feature interactions. Despite significant research progress made, many deep learning models in production still rely on traditional feed-forward neural networks to learn feature crosses inefficiently. In light of the pros/cons of DCN and existing feature interaction learning approaches, we propose an improved framework DCN-M to make DCN more practical in large-scale industrial settings. In a comprehensive experimental study with extensive hyper-parameter search and model tuning, we observed that DCN-M approaches outperform all the state-of-the-art algorithms on popular benchmark datasets. The improved DCN-M is more expressive yet remains cost efficient at feature interaction learning, especially when coupled with a mixture of low-rank architecture. DCN-M is simple, can be easily adopted as building blocks, and has delivered significant offline accuracy and online business metrics gains across many web-scale learning to rank systems.
A Cost-Effective Person-Following System for Assistive Unmanned Vehicles with Deep Learning at the Edge

The vital statistics of the last century highlight a sharp increment of the average age of the world population with a consequent growth of the number of older people. Service robotics applications have the potentiality to provide systems and tools to support the autonomous and self-sufficient older adults in their houses in everyday life, thereby avoiding the task of monitoring them with third parties. In this context, we propose a cost-effective modular solution to detect and follow a person in an indoor, domestic environment. We exploited the latest advancements in deep learning optimization techniques, and we compared different neural network accelerators to provide a robust and flexible person-following system at the edge. Our proposed cost-effective and power-efficient solution is fully-integrable with pre-existing navigation stacks and creates the foundations for the development of fully-autonomous and self-contained service robotics applications.
Magnetic Field Turbulence in the Solar Wind at Sub‐ion Scales: In Situ Observations and Numerical Simulations

We investigate the transition of the solar wind turbulent cascade from MHD to sub‐ion range by means of a detailed comparison between in situ observations and hybrid numerical simulations. In particular, we focus on the properties of the magnetic field and its component anisotropy in Cluster measurements and hybrid 2D simulations. First, we address the angular distribution of wave vector in the kinetic range between ion and electron scales by studying the variance anisotropy of the magnetic field components. When taking into account a single-direction sampling, like that performed by spacecraft in the solar wind, the main properties of the fluctuations observed in situ are also recovered in our numerical description. This result confirms that solar wind turbulence in the sub‐ion range is characterized by a quasi-2D gyrotropic distribution of k-vectors around the mean field. We then consider the magnetic compressibility associated with the turbulent cascade and its evolution from large-MHD to sub‐ion scales. The ratio of field aligned to perpendicular fluctuations, typically low in the MHD inertial range, increases significantly when crossing ion scales and its value in the sub‐ion range is a function of the total plasma beta only, as expected from theoretical predictions, with higher magnetic compressibility for higher beta. Moreover, we observe that this increase has a gradual trend from low to high beta values in the in situ data; this behavior is well captured by the numerical simulations. The level of magnetic field compressibility that is observed in situ and in the simulations is in fairly good agreement with theoretical predictions, especially at high beta, suggesting that, in the kinetic range explored, the turbulence is supported by low-frequency and highly oblique fluctuations in pressure balance, like kinetic Alfvén waves or other slowly evolving coherent structures. The resulting scaling properties as a function of the plasma beta and the main differences between numerical and theoretical expectations and in situ observations are also discussed.