Miracle-dz/UniFAR-FTUs huggingface dataset:
A Conjoint Approach to Multipart Pricing

Multipart pricing is commonly used by service providers such as car rentals, prescription drug plans, health maintenance organizations, and wireless telephony. The general structure of these pricing schemes is a fixed access fee, which sometimes entitles users to a certain level of product use; a variable fee for additional use; and still another fee for add-on features that are priced individually and/or as bundles. The authors propose a method using conjoint analysis for multipart pricing. The method reflects the two-way dependence between prices and consumption and incorporates consumers' uncertainty about their use of a service. The proposed method estimates both choice probabilities and usage levels for each consumer as functions of the product features and the different price components. The authors then use these estimates to evaluate the expected revenues and profits of alternative plans and pricing schemes. They illustrate this method using data from a conjoint study of cell phone services. They compare the results with those obtained from using several competing models. Finally, they use the proposed procedure to identify the optimal set of features in a base plan and the pricing of optional features for a provider of cell phone services.
Corticocortical connections of visual, sensorimotor, and multimodal processing areas in the parietal lobe of the macaque monkey

We studied the corticocortical connections of architectonically defined areas of parietal and temporoparietal cortex, with emphasis on areas in the intraparietal sulcus (IPS) that are implicated in visual and somatosensory integration. Retrograde tracers were injected into selected areas of the IPS, superior temporal sulcus, and parietal lobule. The distribution of labeled cells was charted in relation to architectonically defined borders throughout the hemisphere and displayed on computer‐generated three‐dimensional reconstructions and on cortical flat maps. Injections centered in the ventral intraparietal area (VIP) revealed a complex pattern of inputs from numerous visual, somatosensory, motor, and polysensory areas, and from presumed vestibular‐ and auditory‐related areas. Sensorimotor projections were predominantly from the upper body representations of at least six somatotopically organized areas. In contrast, injections centered in the neighboring ventral lateral intraparietal area (LIPv) revealed inputs mainly from extrastriate visual areas, consistent with previous studies. The pattern of inputs to LIPv largely overlapped those to zone MSTdp, a newly described subdivision of the medial superior temporal area. These results, in conjunction with those from injections into other parietal areas (7a, 7b, and anterior intraparietal area), support the fine‐grained architectonic partitioning of cortical areas described in the preceding study. They also support and extend previous evidence for multiple distributed networks that are implicated in multimodal integration, especially with regard to area VIP. J. Comp. Neurol. 428:112–137, 2000. © 2000 Wiley‐Liss, Inc.
A server-to-server view of the internet

While the performance characteristics of access networks and end-user-to-server paths are well-studied, measuring the performance of the Internet's core remains, largely, an uncharted territory. With more content being moved closer to the end-user, server-to-server paths have increased in length and have a significant role in dictating the quality of services offered by content and service providers. In this paper, we present a large-scale study of the effects of routing changes and congestion on the end-to-end latencies of server-to-server paths in the core of the Internet. We exploit the distributed platform of a large content delivery network, composed of thousands of servers around the globe, to assess the performance characteristics of the Internet's core. We conduct measurement campaigns between thousands of server pairs, in both forward and reverse directions, and analyze the performance characteristics of server-to-server paths over both long durations (months) and short durations (hours). Our analyses show that there is a large variation in the frequency of routing changes. While routing changes typically have marginal or no impact on the end-to-end round-trip times (RTTs), 20% of them impact IPv4 (IPv6) paths by at least 26 ms (31 ms). We highlight how dual-stack servers can be utilized to reduce server-to-server latencies by up to 50 ms. Our results indicate that significant daily oscillations in end-to-end RTTs of server-to-server paths is not the norm, but does occur, and, in most cases, contributes about a 20 ms increase in server-to-server path latencies.
Generative Adversarial Imitation Learning

Consider learning a policy from example expert behavior, without interaction with the expert or access to reinforcement signal. One approach is to recover the expert's cost function with inverse reinforcement learning, then extract a policy from that cost function with reinforcement learning. This approach is indirect and can be slow. We propose a new general framework for directly extracting a policy from data, as if it were obtained by reinforcement learning following inverse reinforcement learning. We show that a certain instantiation of our framework draws an analogy between imitation learning and generative adversarial networks, from which we derive a model-free imitation learning algorithm that obtains significant performance gains over existing model-free methods in imitating complex behaviors in large, high-dimensional environments.
Density Maximization for Improving Graph Matching With Its Applications

Graph matching has been widely used in both image processing and computer vision domain due to its powerful performance for structural pattern representation. However, it poses three challenges to image sparse feature matching: the combinatorial nature limits the size of the possible matches; it is sensitive to outliers because its objective function prefers more matches; and it works poorly when handling many-to-many object correspondences, due to its assumption of one single cluster of true matches. In this paper, we address these challenges with a unified framework called density maximization (DM), which maximizes the values of a proposed graph density estimator both locally and globally. DM leads to the integration of feature matching, outlier elimination, and cluster detection. Experimental evaluation demonstrates that it significantly boosts the true matches and enables graph matching to handle both outliers and many-to-many object correspondences. We also extend it to dense correspondence estimation and obtain large improvement over the state-of-the-art methods. We further demonstrate the usefulness of our methods using three applications: instance-level image retrieval; mask transfer; and image enhancement.
Small-time behavior of beta coalescents

For a finite measureon (0,1), the �-coalescent is a coalescent process such that, whenever there are b clusters, each k-tuple of clusters merges into one at rate R 1 0 x k 2 (1 x) b k �(dx). It has recently been shown that if 1 < � < 2, the �-coalescent in whichis the Beta(2 �,�) distribution can be used to describe the genealogy of a continuous-state branching process (CSBP) with an �-stable branching mechanism. Here we use facts about CSBPs to establish new results about the small-time asymptotics of beta coalescents. We prove an a.s. limit theorem for the number of blocks at small times, and we establish results about the sizes of the blocks. We also calculate the Hausdorff and packing dimensions of a metric space associated with the beta coalescents, and we find the sum of the lengths of the branches in the coalescent tree, both of which are determined by the behavior of coalescents at small times. We extend most of these results to other �-coalescents for whichhas the same asymptotic behavior near zero as the Beta(2 �,�) distribution. This work complements recent work of Bertoin and Le Gall, who also used CSBPs to study small-time properties of �-coalescents.
Occipital Nerve Block in the Management of Headache and Cervical Pain

The origins of chronic headache and the role of the greater occipital nerve in headache syndromes are reviewed. The anatomical pathways and physiological basis of these headaches are discussed with a view to synthesizing some current concepts of headache generation. Studies of occipital nerve blockade for treatment of headaches of various types are assessed and a retrospective analysis of our own experience is presented.
Do Patents Lead to Market Concentration and Excess Profits?

Market concentration in technology intensive industries has been a subject of interest to both scholars and policy analysts. This paper provides a first empirical assessment on how the patenting system contributes to market concentration and the generation of economic rents in three key sectors – pharmaceuticals, chemicals and ICTs. Using data for US multinationals and their foreign affiliates on the one hand, and locally registered private and public companies in Brazil, India and China, we conclude that the concentration of patent ownership is found significantly to relate to market concentration in the USA. In developing countries such as Brazil, India, and China, a strengthening of patent rights has contributed to greater returns for affiliates of U.S. companies but has not stimulated their R&D intensity. The affiliates of U.S. multinationals have enjoyed greater profitability relative to their local competitors in Brazil, India, and China. The paper draws implications for the setting of intellectual property policy and offers suggestions on the role of competition policy in curbing market concentration and related effects on inequality and access. The statistical analysis of firm level data on U.S. multinational companies and their foreign affiliates, and U.S. affiliates of foreign multinational companies was conducted at the Bureau of Economic Analysis, United States (U.S.) Department of Commerce, under arrangements that maintain legal confidentiality requirements. Views expressed in this paper are those of the authors and do not necessarily reflect official positions of the U.S. Department of Commerce. * Padmashree Gehl Sampath is currently a Fellow at the Berkman Klein Center, Harvard University. She is also senior advisor with the Global Access for Action Program at the Center, and an adjunct professor at the University of Aalborg, Denmark. Walter Park is a professor of economics at the American University. GDAE Working Paper No. 19-02: Do Patents Lead to Market Concentration and Excess Profits?
Linguistic Analysis Methods for Studying Small Groups

Introduction Linguistic analysis of collaboration is a research area with stakeholders from multiple fields, including learning sciences, organizational behavior, sociolinguistics and other areas of linguistics, language technologies and machine learning, social psychology, and sociology. Each field brings with it its own valuable store of wisdom as well as its own sets of research questions and methodologies that bring out different subsets of facets. While we view collaboration from a multidisciplinary perspective, we adopt a technical definition of what linguistic analysis is, specifically an analysis that employs constructs from the field of linguistics that are designed to study language as a primary focus of inquiry. While a wide variety of valuable frameworks for studying the language of collaboration have been developed within the learning sciences community, instead of considering these linguistic analyses of collaboration per se, we consider them applications of theoretical constructs from the learning sciences to language interactions. The focus is on learning processes viewed through language rather than the language processes themselves. The advantage of a linguistic framework is that it allows us to avoid making a commitment to one specific theoretical perspective within the learning sciences. Within the broad field of linguistics, we focus on a specific subcommunity referred to as Systemic Functional Linguistics. The field of systemic functional linguistics is a largely descriptive linguistic tradition that provides a firm foundation in analyses of genres of writing or text-based interaction (Martin & Rose, 2007; Martin & White, 2005), as well as face-to-face interaction (Veel, 1999), characterized in terms of the choices authors and speakers make about how to present themselves through language (Halliday, 1994). The choices that authors and 2 speakers make are mapped out in decision tree representations referred to as systems, which is where it gets its name. The constructs coming from the field of systemic functional linguistics provide us with a common foundation for exploring stylistic norms of conversational behavior across genres of dialogic interactions. What makes it an ideal choice for our efforts is the historical fact that it grew up side-by-side with one of the few sociological theories that was informed by linguistic data. As such, in contrast to other existing linguistic theories, it was fashioned specifically for the purpose of explaining sociological processes at multiple levels and within a wide range of contexts. Beyond these social phenomena, mechanisms such as grammatical metaphor (Halliday, 1994) endow it with the ability to represent …
Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments

Abstract Through observing system simulation experiments, this two-part study exploits the potential of using the ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation. Part I focuses on the performance of the EnKF under the perfect model assumption in which the truth simulation is produced with the same model and same initial uncertainties as those of the ensemble, while Part II explores the impacts of model error and ensemble initiation on the filter performance. In this first part, the EnKF is implemented in a nonhydrostatic mesoscale model [the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)] to assimilate simulated sounding and surface observations derived from simulations of the “surprise” snowstorm of January 2000. This is an explosive East Coast cyclogenesis event with strong error growth at all scales as a result of interactions between convective-, meso-, and subsynoptic-scale dynamics. It is found that the EnKF is very effective in keeping th.
FOR THE CONTROL OF FUNGAL PATHOGENS IN PLANTS

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Discovery of a Wide Substellar Companion to a Nearby Low-Mass Star

We report the discovery of a wide (135 ± 25 AU), unusually blue L5 companion, 2MASS J17114559+4028578, to the nearby M4.5 dwarf G203-50 as a result of a targeted search for common proper motion pairs in the Sloan Digital Sky Survey and the Two Micron All Sky Survey. Adaptive optics imaging with Subaru indicates that neither component is a nearly equal-mass binary with separation >0.18″ and places limits on the existence of additional faint companions. An examination of TiO and CaH features in the primary’s spectrum is consistent with solar metallicity and provides no evidence that G203-50 is metal-poor. We estimate an age for the primary of 1-5 Gyr based on activity. Assuming coevality of the companion, its age, gravity, and metallicity can be constrained from properties of the primary, making it a suitable benchmark object for the calibration of evolutionary models and for determining the atmospheric properties of peculiar blue L dwarfs. The low total mass (Mtot = 0.21 ± 0.03 M☉), intermediate mass ratio (q = 0.45 ± 0.14), and wide separation of this system demonstrate that the star formation process is capable of forming wide, weakly bound binary systems with low-mass and brown dwarf components. Based on the sensitivity of our search we find that no more than 2.2% of the early-to-mid-M dwarfs (9.0 < MV < 13.0) have wide substellar companions with m≳ 0.06 M☉.
Alignment of RNA base pairing probability matrices

MOTIVATION Many classes of functional RNA molecules are characterized by highly conserved secondary structures but little detectable sequence similarity. Reliable multiple alignments can therefore be constructed only when the shared structural features are taken into account. Since multiple alignments are used as input for many subsequent methods of data analysis, structure-based alignments are an indispensable necessity in RNA bioinformatics. RESULTS We present here a method to compute pairwise and progressive multiple alignments from the direct comparison of base pairing probability matrices. Instead of attempting to solve the folding and the alignment problem simultaneously as in the classical Sankoff's algorithm, we use McCaskill's approach to compute base pairing probability matrices which effectively incorporate the information on the energetics of each sequences. A novel, simplified variant of Sankoff's algorithms can then be employed to extract the maximum-weight common secondary structure and an associated alignment. AVAILABILITY The programs pmcomp and pmmulti described in this contribution are implemented in Perl and can be downloaded together with the example datasets from http://www.tbi. univie.ac. at/RNA/PMcomp/. A web server is available at http://rna.tbi. univie.ac. at/cgi-bin/pmcgi.pl
Graphite Foam Heat Exchanger for

Because of the high thermal conductivity (1700 W/(m.K)) and the low density (0.2 to 0.6 g/cm), the graphite foam is a good option for heat exchangers in vehicles. However, there is a high pressure drop through the foam due to the open cells of the foam. In order to evaluate the performance of graphite foam heat exchanger, a simulation comparison between a convectional aluminum louver fin heat exchanger and a corrugated graphite foam heat exchanger is carried out in this paper. The aim of the comparison is to investigate the coefficient of performance (COP), power density (PD), and compactness factor (CF). Useful recommendations are highlighted to promote the development of graphite foam heat exchangers in vehicles. Nomenclature A area (m) Lf foam walls pitch (mm) Ab heated base area (m) Lp louver pitch (mm) cp specific heat (J.kg.K) Lw louver width (mm) CF Forchheimer coefficient m mass (kg) CF compactness factor (kW.m) Nu Nusselt number COP coefficient of performance PD power density (kW.kg) D length scale (m) Ppump the input power of pump (W) Dh hydraulic diameter (m) Qremoved the removed heat (W) FL fin length (mm) Tp tube pitch (mm) Fp fin pitch (mm) u velocity (m.s) h heat transfer coefficient (W.m.K) V the volume (m) k thermal conductivity (W.m.K) △T the temperature difference (K) Greek Symbols α permeability (m) ε porosity δ thickness of graphite foam (mm) μ dynamic viscosity (Pa·s) θ louver angle (degree) ρ density (kg.m) Subscripts eff effective in inlet f fluid s solid HEX heat exchanger
Social, Communication, and Cortical Structural Impairments in Epac2-Deficient Mice

Deficits in social and communication behaviors are common features of a number of neurodevelopmental disorders. However, the molecular and cellular substrates of these higher order brain functions are not well understood. Here we report that specific alterations in social and communication behaviors in mice occur as a result of loss of the EPAC2 gene, which encodes a protein kinase A-independent cAMP target. Epac2-deficient mice exhibited robust deficits in social interactions and ultrasonic vocalizations, but displayed normal olfaction, working and reference memory, motor abilities, anxiety, and repetitive behaviors. Epac2-deficient mice displayed abnormal columnar organization in the anterior cingulate cortex, a region implicated in social behavior in humans, but not in somatosensory cortex. In vivo two-photon imaging revealed reduced dendritic spine motility and density on cortical neurons in Epac2-deficient mice, indicating deficits at the synaptic level. Together, these findings provide novel insight into the molecular and cellular substrates of social and communication behavior.
Interannual Temperature Events and Shifts in Global Temperature: A ''Multiwavelet'' Correlation Approach

Abstract For the purpose of climate signal detection, we introduce a method for identifying significant episodes of large-scale oscillatory variability. The method is based on a multivariate wavelet algorithm that identifies coherent patterns of variation simultaneously within particular ranges of time and periodicity (or frequency) that may vary regionally in the timing and amplitude of the particular temperature oscillation. By using this methodology, an analysis is performed of the instrumental record of global temperatures spanning the past 140 years. The duration of an “episode” is chosen to correspond to 3–5 cycles at a specified oscillation period, which is useful for detecting signals associated with the global El Nino/Southern Oscillation (ENSO) phenomenon. To confirm the robustness of signals detected in the earliest, sparse data (only 111 5° longitude by 5° latitude grid points are available back to 1854), we performed multiple analyses overlapping in time, using increasingly dense subsets of t.
Deep Segments: Comparisons between Scenes and their Constituent Fragments using Deep Learning

We examine the problem of visual scene understanding and abstraction from first person video. This is an important prob-ion from first person video. This is an important problem and successful approaches would enable complex scene characterization tasks that go beyond classification, for example characterization of novel scenes in terms of previously encountered visual experiences. Our approach utilizes the final layer of a convolutional neural network as a high-level, scene specific, representation which is robust enough to noise to be used with wearable cameras. Researchers have demonstrated the use of convolutional neural networks for object recognition. Inspired by results from cognitive and neuroscience, we use output maps created by a convolutional neural network as a sparse, abstract representation of visual images. Our approach abstracts scenes into constituent segments that can be characterized by the spatial and temporal distribution of objects. We demonstrate the viability of the system on video taken from Google Glass. Experiments examining the ability of the system to determine scene similarity indicate a ρ (384) = +0.498 correlation to human evaluations and 90% accuracy on a category match problem. Finally, we demonstrate high-level scene prediction by showing that the system matches two scenes using only a few initial segments and predicts objects that will appear in subsequent segments.