Miracle-dz/UniFAR-FTUs huggingface dataset:
Toward Scene Recognition by Discovering Semantic Structures and Parts

Scene recognition is a fundamental and open problem in computer vision. It is an essential component of a variety of real-world applications, including image search, robotics, social media analysis, and many others. The key to success in scene recognition is to well understand the rich semantics embedded in scenes. For example, it is intuitive to label airport for a scene of sky, airplane, road, and building. In this thesis, we identify two directions for exploiting scene semantics. On one hand, we advocate for the discovery of scene parts that correspond to various semantic components in scenes, like objects and surfaces. On the other hand, we promote the discovery of scene structures that capture the spatial relations among scene parts, like sky-above-airplane. By leveraging scene parts and structures in scene recognition, we are able to build strong recognition systems. Our contributions are two-fold. First, we propose two clustering algorithms for the data-driven discovery of semantics in visual data. In detail, we develop latent maximum-margin clustering to model semantics as latent variables, and hierarchical maximum-margin clustering to discover treestructured semantic hierarchies. Our second contribution is the development of two scene recognition methods that leverage scene structure discovery and part discovery. The first method recognizes scene by considering a scene image as a structured collage of objects. The second method discovers scene parts that are both discriminative and representative for scene recognition.
Negative Dividends : Internality Losses can Outweigh Externality Gains

The Energy Star certification program highlights energy efficient goods with a label. Given that it could either focus attention on the energy consumption of alternatives or provide a license to ignore the attribute, it follows that recent research highlights heterogeneity in consumers’ responses to the label. This paper answers two questions implied by this heterogeneity. First, do individuals with particular characteristics respond to Energy Star certification in systematically different ways? Second, what do these differences imply about the value of the Energy Star to consumers? We present results from a stated choice experiment intended to answer these questions. Assuming an underlying random utility model and applying a mixed logit approach, we estimate utility parameters conditional on an individual’s sequence of choices. Our results show that differences in consumers’ responses to the Energy Star label are associated with individual characteristics. Moreover, the heterogeneity in responses implies that, though the Energy Star program lowers expected external costs associated with energy consumption, it could impose much greater internality costs on consumers. The Energy Star label may not be an appropriate non-pecuniary measure to increase consumers’ attention to the energy consumption attribute of alternative goods, at least in the context of light bulb choice considered here.
Type-preserving compilation of end-to-end verification of security enforcement

A number of programming languages use rich type systems to verify security properties of code. Some of these languages are meant for source programming, but programs written in these languages are compiled without explicit security proofs, limiting their utility in settings where proofs are necessary, e.g., proof-carrying authorization. Others languages do include explicit proofs, but these are generally lambda calculi not intended for source programming, that must be further compiled to an executable form. A language suitable for source programming backed by a compiler that enables end-to-end verification is missing. In this paper, we present a type-preserving compiler that translates programs written in FINE, a source-level functional language with dependent refinements and affine types, to DCIL, a new extension of the . NET Common Intermediate Language. FINE is type checked using an external SMT solver to reduce the proof burden on source programmers. We extract explicit LCF-style proof terms from the solver and carry these proof terms in the compilation to DCIL, thereby removing the solver from the trusted computing base. Explicit proofs enable DCIL to be used in a number of important scenarios, including the verification of mobile code, proof-carrying authorization, and evidence-based auditing. We report on our experience using FINE to build reference monitors for several applications, ranging from a plugin-based email client to a conference management server.
Generalization of an Inequality by Talagrand and Links with the Logarithmic Sobolev Inequality

Abstract We show that transport inequalities, similar to the one derived by M. Talagrand (1996, Geom. Funct. Anal. 6 , 587–600) for the Gaussian measure, are implied by logarithmic Sobolev inequalities. Conversely, Talagrand's inequality implies a logarithmic Sobolev inequality if the density of the measure is approximately log-concave, in a precise sense. All constants are independent of the dimension and optimal in certain cases. The proofs are based on partial differential equations and an interpolation inequality involving the Wasserstein distance, the entropy functional, and the Fisher information.
Mid-Pliocene equatorial Pacific sea surface temperature reconstruction: a multi-proxy perspective

The Mid-Pliocene is the most recent interval of sustained global warmth, which can be used to examine conditions predicted for the near future. An accurate spatial representation of the low-latitude Mid-Pliocene Pacific surface ocean is necessary to understand past climate change in the light of forecasts of future change. Mid-Pliocene sea surface temperature (SST) anomalies show a strong contrast between the western equatorial Pacific (WEP) and eastern equatorial Pacific (EEP) regardless of proxy (faunal, alkenone and Mg/Ca). All WEP sites show small differences from modern mean annual temperature, but all EEP sites show significant positive deviation from present-day temperatures by as much as 4.4°C. Our reconstruction reflects SSTs similar to modern in the WEP, warmer than modern in the EEP and eastward extension of the WEP warm pool. The east–west equatorial Pacific SST gradient is decreased, but the pole to equator gradient does not change appreciably. We find it improbable that increased greenhouse gases (GHG) alone would cause such a heterogeneous warming and more likely that the cause of Mid-Pliocene warmth is a combination of several forcings including both increased meridional heat transport and increased GHG.
Scene Parsing by Data Driven Cluster Sampling

This paper presents a data-driven cluster sampling framework for parsing scene images into generic regions (such as the sky, mountain and water) and objects (such as cows, horses and cars). We adopt generative models for both generic regions and objects, thus their likelihood probabilities are comparable and are learned under a common information projection principle. The inference algorithm follows the data-driven Markov Chain Monte Carlo (DDMCMC) paradigm where the object and generic region models cooperate and compete for an optimal interpretation of the scene in a Bayesian framework. The algorithm has two phases: (i) Bottom-up computation for generating data-driven proposals. There are two types of proposals: proposals for regular-shape objects using the active basis models and proposals for both generic regions and irregularshape objects (such as crouching cows) by training a set of discriminative models on the appearance. A candidacy graph is constructed to summarize all the bottomup information by treating proposals as nodes and cooperative/competitive contextual relations among proposals as +/edges. (ii) Top-down computation by cluster sampling for seeking the optimal solution that maximizes the Bayesian posterior probability. The cluster sampling algorithm consists of reversible jumps to explore the solution space effectively. At each step, it samples the +/edge probabilities on the candidacy graph and divides the candidacy graph into a set of composQ. Zhou†,∗, T.F. Wu‡,∗, W. -Y. Liu† and S. -C. Zhu‡,? ,∗ Department of †Electronic and Information Engineering, Hua Zhong University of Science and Technology, Wuhan, China Department of ‡Statistics and ? Computer Science, University of California, Los Angeles, USA ∗Lotus Hill Research Institute (LHI), Ezhou, China E-mail: qzhou.lhi@gmail.com, {tfwu, sczhu}@stat.ucla. edu, liuwy@mail.hust. edu.cn ite connected-components (CCCP’s) on which the reversible jumps are carried out. In experiments, our algorithm outperforms the state-of-the-art methods on the LHI 15-class dataset and obtains comparable results on the MSRC 21-class dataset (the LHI 15-class dataset has more accurate annotations and we release it with this publication).
Incorporating microbial dormancy dynamics into soil decomposition models to improve quantification of soil carbon dynamics of northern temperate forests

Soil carbon dynamics of terrestrial ecosystems play a significant role in the global carbon cycle. Microbial‐based decomposition models have seen much growth recently for quantifying this role, yet dormancy as a common strategy used by microorganisms has not usually been represented and tested in these models against field observations. Here we developed an explicit microbial‐enzyme decomposition model and examined model performance with and without representation of microbial dormancy at six temperate forest sites of different forest types. We then extrapolated the model to global temperate forest ecosystems to investigate biogeochemical controls on soil heterotrophic respiration and microbial dormancy dynamics at different temporal‐spatial scales. The dormancy model consistently produced better match with field‐observed heterotrophic soil CO2 efflux (RH) than the no dormancy model. Our regional modeling results further indicated that models with dormancy were able to produce more realistic magnitude of microbial biomass (<2% of soil organic carbon) and soil RH (7.5 ± 2.4 Pg C yr−1). Spatial correlation analysis showed that soil organic carbon content was the dominating factor (correlation coefficient = 0.4–0.6) in the simulated spatial pattern of soil RH with both models. In contrast to strong temporal and local controls of soil temperature and moisture on microbial dormancy, our modeling results showed that soil carbon‐to‐nitrogen ratio (C:N) was a major regulating factor at regional scales (correlation coefficient = −0.43 to −0.58), indicating scale‐dependent biogeochemical controls on microbial dynamics. Our findings suggest that incorporating microbial dormancy could improve the realism of microbial‐based decomposition models and enhance the integration of soil experiments and mechanistically based modeling.
Energy bugs in mobile devices: A survey

Portable electronic devices, such as smartphones and tables, have become the most popular computing devices for end users. The portability, flexibility, and powerful hardware support make it easy to execute complex computation on a smartphone or process video applications on a tablet in a very efficient manner. However, users of these mobile devices are now facing problems with poor battery life. In this paper, we conduct a survey to explore the possible reasons behind battery drains in portable electronic devices. We also analyze energy bugs based on various criteria. This paper provides a comprehensive survey of energy bugs in mobile devices, which is an important step to help hardware designers and application developers eliminate energy bugs in hardware platforms and mobile applications.
Weak Convergence of the Regularization Path in Penalized M‐Estimation

Abstract. We consider a function defined as the pointwise minimization of a doubly index random process. We are interested in the weak convergence of the minimizer in the space of bounded functions. Such convergence results can be applied in the context of penalized M‐estimation, that is, when the random process to minimize is expressed as a goodness‐of‐fit term plus a penalty term multiplied by a penalty weight. This weight is called the regularization parameter and the minimizing function the regularization path. The regularization path can be seen as a collection of estimators indexed by the regularization parameter. We obtain a consistency result and a central limit theorem for the regularization path in a functional sense. Various examples are provided, including the ℓ1‐regularization path for general linear models, the ℓ1‐ or ℓ2‐regularization path of the least absolute deviation regression and the Akaike information criterion.
Smart Instrument for Automotive Battery Application

The battery management system (BMS) for charge and health monitoring has always been a challenge for automotive industry. This paper presents the design and development of an intelligent online battery monitoring instrument. The instrument is designed using non-linear behavior modeling of the battery and is implemented using PIC micro controller coupled with battery parameter sensors. The instrument takes battery terminal voltage, current drawn, internal resistance, temperature and time as input parameters. Algorithms define uses neuro-fuzzy approach to indicate state of charge (SoC) of the battery. The prototype of the instrument has been tested successfully on 12V Lead-Acid battery, Exide make MF40sv used for automotive application. The average error (RSS) of the instrument is 8.7921% and the total response time of the system is 4.8 seconds.
Towards cooperative planning of data mining workflows

A major challenge for third generation data mining and knowledge discovery systems is the integration of different data mining tools and services for data understanding, data integration, data preprocessing, data mining, evaluation and deployment, which are distributed across the network of computer systems. In this paper we outline how an intelligent assistant that is intended to support end-users in the difficult and time consuming task of designing KDD-Workﬂows out of these distributed services can be built. The assistant should support the user in checking the correctness of workﬂows, understanding the goals behind given workﬂows, enumeration of AI planner generated workﬂow completions, storage, retrieval, adaptation and repair of previous workﬂows. It should also be an open easy extendable system. This is reached by basing the system on a data mining ontology (DMO) in which all the services (operators) together with their in-/output, pre-/postconditions are described. This description is compatible with OWL-S and new operators can be added importing their OWL-S speciﬁcation and classifying it into the operator ontology.
Local Fog Detection Based on Saturation and RGB-Correlation

Fog is a natural and meteorological phenomenon that seems to be very dangerous for road driving. In its presence, the driver has a high perturbation in his field of view and must redouble vigilance. Therefore, it's primary to detect its presence to be able to adapt any advanced driver assistance system according to the density of fog. In this paper, we present a new local approach for detecting daytime fog based on the saturation and the correlation between the components of the RGB color space computed for a region around the picture vanishing point. Results show good fog detection for synthetic pictures and fast time-computation.
A Quantitative Method for Pulse Strength Classification Based on Decision Tree

Pulse diagnosis is one of the most important examinations in Traditional Chinese Medicine (TCM). In response to the subjectivity and fuzziness of pulse diagnosis in TCM, quantitative systems or methods are needed to modernize pulse diagnosis. In pulse diagnosis, strength is one of the most difficult factors to recognize. To explore the quantitative recognition of pulse strength, a novel method based on decision tree (DT) is presented. The proposed method is testified by applying it to classify four hundreds pulse signal samples collected from clinic. The results are mostly accord with the expertise, which indicate that the method we proposed is feasible and effective and can identify pulse signals accurately, which can be expected to facilitate the modernization of pulse diagnosis.
Effective Melanin Depigmentation of Human and Murine Ocular Tissues: An Improved Method for Paraffin and Frozen Sections

Purpose The removal of excessive melanin pigments that obscure ocular tissue morphology is important to address scientific questions and for differential diagnosis of ocular tumours based on histology. Thus, the goal of the present study was to establish an effective and fast melanin bleaching method for paraffin and frozen mouse and human ocular tissues. Methods Paraffin-embedded and frozen ocular specimens from mice and human donors were subjected to bleaching employing two methods. The first employed potassium permanganate (KMnO4) with oxalic acid, and the second 10% hydrogen peroxide (H2O2). To determine optimal bleaching conditions, depigmentation was carried out at various incubation times. The effect of diluents used for 10% H2O2 was assessed using phosphate-buffered saline (PBS), and deionized water. Three different slide types and two fixatives, which were ice-cold acetone with 80% methanol, and 4% paraformaldehyde (PFA) were used to determine the optimal conditions for better tissue adherence during bleaching. All tissues were stained in hematoxylin and eosin for histological evaluation. Results Optimal bleaching was achieved using warm 10% H2O2 diluted in PBS at 65°C for 120 minutes. Chromium-gelatin-coated slides prevented tissue detachment. Adherence of cryosections was also improved with post-fixation using 4% PFA and overnight air-drying at RT after cryosectioning. Tissue morphology was preserved under these conditions. Conversely, tissues bleached in KMnO4/oxalic acid demonstrated poor depigmentation with extensive tissue damage. Conclusions Warm dilute H2O2 at 65°C for 120 minutes rapidly and effectively bleached both cryo- and paraffin sections of murine and human ocular tissues.
Blending the Logic Paradigm into C + +

The Logic paradigm (LP) provides a powerful programming model that has been underrepresented in mainstream programming as compared to the object-oriented, functional and imperative paradigms. Lot of work can be cited in the space of integrating logic into functional programming or the functional into imperative paradigm but relatively less has been written about integrating logic programming into popular object-oriented languages used by the majority of software developers. Consequently a vast unexplored territory exists in the space of multiparadigm programming techniques that leverage logic programming in the dominant universe of imperative and object–oriented programming. This paper introduces a technique for seamlessly integrating Logic programming techniques into C++. In this approach, support for LP is provided in the form of a few library primitives and does not require any extensions or modifications to the C++ language. The proposed technique is directly based on the imperative paradigm and can be implemented in about 400 lines of C++ code. The code presented here is based on the Castor library. More advanced facilities, built using the core parts described here, are provided in Castor but not covered in this paper.
A small-world DHT built on generalized network coordinates

Large-scale distributed hash tables (DHT) are typically implemented without respect to node location or characteristics, thus producing physically long routes and squandering network resources. Some systems have integrated round trip times through proximity-aware identifier selection (PIS), proximity-aware route selection (PRS), and proximity-aware neighbor selection (PNS). While PRS and PNS tend to optimize existing systems, PIS deterministically selects node identifiers based on physical node location, leading to a loss of scalability and robustness. The trade off between the scalability and robustness gained from a DHT's randomness and the better allocation of network resources that comes with a location-aware, deterministically structured DHT make it difficult to design a system that is both robust and scalable and resource conserving. We present initial ideas for the construction of a small-world DHT which mitigates this trade off by retaining scalability and robustness while effectively integrating round trip times and additional node quality with the help of Vivaldi network coordinates.
Curriculum and preventing dating violence in schools

This paper presents a discuss between the findings from a two-fold national study on the acceptance of violence and the prevalence of victimization in intimate relationships among Portuguese adolescents and the possibility for integrating these themes in school curriculum. The survey includes a questionnaire (4562 participants) and focus groups with girls and boys from 4 different cities. Findings presented in this paper are crosscut with the three dimensions found as pertinent and relevant in the focus groups: masculinities and femininities, romantic love and adolescence sexualization and jealousy and control. Based on these findings, this article argues for the urgency of introducing intimate partner violence in the formal curriculum as part of citizen education.
Design of digital thermometer based on PIC16F77A single chip microcontroller

This paper elucidates simple design method of a low cost digital thermometer based on PIC16F77A microcontroller and temperature sensor LM35. LM35 is an analog sensor which converts the surrounding temperature to a proportional analog voltage. The sensor's output is connected to one of the ADC (Analog to Digital Converter) channel inputs of the microcontroller to originate the equivalent temperature value in digital format. Within a short duration 10 digital values of temperature are stored and computed to have maximum, minimum and average temperature among these. Then, the computed formats of temperature are displayed in a 16×2 character LCD in °C scale correspondingly. This design process is more convenient than others as it is more economical, efficient and simple.
User involvement in ontology matching using an online active learning approach

We propose a semi-automatic ontology matching system using a hybrid active learning and online learning approach. Following the former paradigm, those mappings whose validation is estimated to lead to greater quality gain are selected for user validation, a process that occurs in each iteration, following the online learning paradigm. Experimental results demonstrate the effectiveness of our approach.
WA-Method-TLV improves reliability of GMPLS WDM networks against multiple link failures

Concurrent multi-link failures in GMPLS Wavelength Division Multiplexing (WDM) networks can disrupt a large number of active optical circuits. These circuits need to be quickly restored in order to avoid prolonged traffic disruptions at the higher layers. The distributed nature of the GMPLS control plane requires that independent restoration attempts are conducted for each disrupted circuit. These concurrent restoration attempts are likely to face race conditions, i.e., they race to reserve network resources (e.g., wavelengths) from a common pool. Race conditions are the cause of blocking (a restoration attempt cannot be completed) or at a minimum delayed completion of the restoration attempt. The study in this paper investigates how both blocking and completion time of circuit restoration attempts are affected by a recently proposed MIX Wavelength Assignment (WA) strategy based on a feature of the IETF RSVP-TE called WA-Method-TLV. Simulation results show that the proposed use of WA-Method-TLV can efficiently reduce blocking probability, average hop count and restoration time of the rerouted circuit.