DEEP-CARVING : Discovering Visual Attributes by Carving Deep Neural Nets

Most of the approaches for discovering visual attributes in images demand significant supervision, which is cumbersome to obtain. In this paper, we aim to discover visual attributes in a weakly supervised setting that is commonly encountered with contemporary image search engines. For instance, given a noun (say forest) and its associated attributes (say dense, sunlit, autumn), search engines can now generate many valid images for any attribute-noun pair (dense forests, autumn forests, etc). However, images for an attribute-noun pair do not contain any information about other attributes (like which forests in the autumn are dense too). Thus, a weakly supervised scenario occurs: each of the M attributes corresponds to a class such that a training image in class m ∈ {1, . . . , M} contains a single label that indicates the presence of the mᵗʰ attribute only. The task is to discover all the attributes present in a test image. Deep Convolutional Neural Networks (CNNs) [20] have enjoyed remarkable success in vision applications recently. However, in a weakly supervised scenario, widely used CNN training procedures do not learn a robust model for predicting multiple attribute labels simultaneously. The primary reason is that the attributes highly co-occur within the training data, and unlike objects, do not generally exist as well-defined spatial boundaries within the image. To ameliorate this limitation, we propose Deep-Carving, a novel training procedure with CNNs, that helps the net efficiently carve itself for the task of multiple attribute prediction. During training, the responses of the feature maps are exploited in an ingenious way to provide the net with multiple pseudo-labels (for training images) for subsequent iterations. The process is repeated periodically after a fixed number of iterations, and enables the net carve itself iteratively for efficiently disentangling features. Additionally, we contribute a noun-adjective pairing inspired Natural Scenes Attributes Dataset to the research community, CAMIT-NSAD, containing a number of co-occurring attributes within a noun category. We describe, in detail, salient aspects of this dataset. Our experiments on CAMIT-NSAD and the SUN Attributes Dataset [29], with weak supervision, clearly demonstrate that the Deep-Carved CNNs consistently achieve considerable improvement in the precision of attribute prediction over popular baseline methods.Cambridge Commonwealth Trust

Spatial and temporal variation of hydraulic conductivity and vegetation growth in green infrastructures using infiltrometer and visual technique

Hydraulic conductivity of a vegetated soil (i.e., mixed grass cover) is an important parameter governing the hydrological performance of green infrastructure (GI). This paper focuses on GI with mixed grass cover in the presence of trees. Due to shading effects (interception of radiant energy) of tree canopy, mixed grass cover in the vicinity of trees may not receive direct photosynthetically active radiation (PAR). This can hinder the growth rates resulting in the low grass cover (i.e., in density). The hydraulic conductivity and the performance of GI can be further affected. Several field studies were conducted to investigate hydraulic conductivity in different types of vegetated covers. However, any variation in growth and hydraulic conductivity of mixed grass cover in the vicinity of trees was rarely investigated. The objective of this study is to quantify spatial and temporal variation of vegetation growth and hydraulic conductivity in a mixed grass cover in the vicinity of a tree. Field monitoring of a mixed grass cover in the vicinity of a tree in a GI was conducted for about six months. Hydraulic conductivity tests were carried out using mini disk infiltrometer (MDI) at 149 locations in a selected site once every month. Vegetation density was quantified using image analysis and the images were captured by a DJI Phantom drone. The growth of mixed grass cover around tree vicinity (within 5 m radial distance) was found to be more uniform during months characterized by high rainfall depth. Spatial heterogeneity in both vegetation density and hydraulic conductivity is found to be more significant during a dry period than wet period. Variation of hydraulic conductivity with respect to the change in vegetation density is found to be significant in a wet period than dry period. It is also found that hydraulic conductivity is higher at the portions where shredded leaves are present. The obtained dynamic spatio-temporal relationship of soil, vegetation and atmospheric parameters can support the design of green infrastructures and contribute to a better understanding of the maintenance practices

Space Efficient Breadth-First and Level Traversals of Consistent Global States of Parallel Programs

Enumerating consistent global states of a computation is a fundamental problem in parallel computing with applications to debug- ging, testing and runtime verification of parallel programs. Breadth-first search (BFS) enumeration is especially useful for these applications as it finds an erroneous consistent global state with the least number of events possible. The total number of executed events in a global state is called its rank. BFS also allows enumeration of all global states of a given rank or within a range of ranks. If a computation on n processes has m events per process on average, then the traditional BFS (Cooper-Marzullo and its variants) requires $\mathcal{O}(\frac{m^{n-1}}{n})$ space in the worst case, whereas ou r algorithm performs the BFS requires $\mathcal{O}(m^2n^2)$ space. Thus, we reduce the space complexity for BFS enumeration of consistent global states exponentially. and give the first polynomial space algorithm for this task. In our experimental evaluation of seven benchmarks, traditional BFS fails in many cases by exhausting the 2 GB heap space allowed to the JVM. In contrast, our implementation uses less than 60 MB memory and is also faster in many cases

Monitoring Partially Synchronous Distributed Systems using SMT Solvers

In this paper, we discuss the feasibility of monitoring partially synchronous distributed systems to detect latent bugs, i.e., errors caused by concurrency and race conditions among concurrent processes. We present a monitoring framework where we model both system constraints and latent bugs as Satisfiability Modulo Theories (SMT) formulas, and we detect the presence of latent bugs using an SMT solver. We demonstrate the feasibility of our framework using both synthetic applications where latent bugs occur at any time with random probability and an application involving exclusive access to a shared resource with a subtle timing bug. We illustrate how the time required for verification is affected by parameters such as communication frequency, latency, and clock skew. Our results show that our framework can be used for real-life applications, and because our framework uses SMT solvers, the range of appropriate applications will increase as these solvers become more efficient over time.Comment: Technical Report corresponding to the paper accepted at Runtime Verification (RV) 201

Fisheye Consistency: Keeping Data in Synch in a Georeplicated World

Over the last thirty years, numerous consistency conditions for replicated data have been proposed and implemented. Popular examples of such conditions include linearizability (or atomicity), sequential consistency, causal consistency, and eventual consistency. These consistency conditions are usually defined independently from the computing entities (nodes) that manipulate the replicated data; i.e., they do not take into account how computing entities might be linked to one another, or geographically distributed. To address this lack, as a first contribution, this paper introduces the notion of proximity graph between computing nodes. If two nodes are connected in this graph, their operations must satisfy a strong consistency condition, while the operations invoked by other nodes are allowed to satisfy a weaker condition. The second contribution is the use of such a graph to provide a generic approach to the hybridization of data consistency conditions into the same system. We illustrate this approach on sequential consistency and causal consistency, and present a model in which all data operations are causally consistent, while operations by neighboring processes in the proximity graph are sequentially consistent. The third contribution of the paper is the design and the proof of a distributed algorithm based on this proximity graph, which combines sequential consistency and causal consistency (the resulting condition is called fisheye consistency). In doing so the paper not only extends the domain of consistency conditions, but provides a generic provably correct solution of direct relevance to modern georeplicated systems

Pattern Matching in Link Streams: a Token-based Approach

International audienceLink streams model the dynamics of interactions in complex distributed systems as sequences of links (interactions) occurring at a given time. Detecting patterns in such sequences is crucial for many applications but it raises several challenges. In particular, there is no generic approach for the specification and detection of link stream patterns in a way similar to regular expressions and automata for text patterns. To address this, we propose a novel automata framework integrating both timed constraints and finite memory together with a recognition algorithm. The algorithm uses structures similar to tokens in high-level Petri nets and includes non-determinism and concurrency. We illustrate the use of our framework in real-world cases and evaluate its practical performances

A systematic review of current knowledge of HIV epidemiology and of sexual behaviour in Nepal

OBJECTIVE: To systematically review information on HIV epidemiology and on sexual behaviour in Nepal with a view to identifying gaps in current knowledge. METHODS: Systematic review covering electronic databases, web-based information, personal contact with experts and hand searching of key journals. RESULTS: HIV-1 seroprevalence has been rising rapidly in association with high-risk behaviours, with current levels of 40% amongst the nation's injecting drug users and approaching 20% amongst Kathmandu's female commercial sex workers (FCSWs). HIV seroprevalence remains low in the general population (0.29% of 15–49 year olds). There are significant methodological limitations in many of the seroprevalence studies identified, and these estimates need to be treated with caution. There are extensive migration patterns both within the country and internationally which provide the potential for considerable sexual networking. However, studies of sexual behaviour have focused on FCSWs and the extent of sexual networks within the general population is largely unknown. CONCLUSIONS: Whilst some of the ingredients are present for an explosive HIV epidemic in Nepal, crucial knowledge on sexual behaviour in the general population is missing. Research on sexual networking is urgently required to guide HIV control in Nepal. There is also a need for further good-quality epidemiological studies of HIV seroprevalence

Condition Monitoring of Rotating Elements Using Acoustic Emission: Indian Scenario

The first experimental study in the world on the phenomenon of generation of Acoustic Emission dates back to the 1950’s when Josef Kaiser heard these emissions during metal failure. But unfortunately, the work could not get impetus because of the limited means of sensing high frequency waves and the lack of knowledge dissemination. In the 1970’s, the electronic instrumentation improved resulting in the increased activity centers of AE. At that time, Rao initiated work in this area at the Indian Institute of Science, Banglore for the first time in India. With the pioneering efforts of his group at IISc, new vistas were opened in this field. As a consequence of it, the first paper from India on this phenomenon was published in 1977 by Eshwar, et al. [1], The paper dealt with the study of the form of AE observed on the cracking/breaking of plywood with prepared defects

Assurance of Distributed Algorithms and Systems: Runtime Checking of Safety and Liveness

This paper presents a general framework and methods for complete programming and checking of distributed algorithms at a high-level, as in pseudocode languages, but precisely specified and directly executable, as in formal specification languages and practical programming languages, respectively. The checking framework, as well as the writing of distributed algorithms and specification of their safety and liveness properties, use DistAlgo, a high-level language for distributed algorithms. We give a complete executable specification of the checking framework, with a complete example algorithm and example safety and liveness properties.Comment: Small fixes to improve property specifications, including improvements not in the RV 2020 final versio