Securitising participation in the Philippines: KALAHI and community-driven participation

Participatory approaches to development have been implemented increasingly. One form is the World Bank’s community-driven development (CDD) programme. Participation has, also, become increasingly securitised since 2001. One instance of these trends was the Kapit-Bisig Laban sa Kahirapan (KALAHI) project in the Philippines. This paper examines the implementation of CDD and the problems of its securitisation, using the Philippines as a case study. A composite conceptual framework is advanced that draws upon the international analyses of development. Adapting the concepts of securitisation and de-politicisation, it argues that a new hegemonic-development framework has appeared: the Securitised-Washington consensus. The analysis assesses these trends through the examples of KALAHI and Philippine politics and economics. It suggests that securitised CDD projects result in token efforts at political reform and poverty alleviation that often are contradicted by counter-trends towards development decline and militarisation. Unless these deep-rooted problems are confronted, localised participation is likely to remain ineffectual

The motivic Donaldson-Thomas invariants of (-2) curves

In this paper we calculate the motivic Donaldson-Thomas invariants for (-2)-curves arising from 3-fold flopping contractions in the minimal model programme. We translate this geometric situation into the machinery developed by Kontsevich and Soibelman, and using the results and framework developed previously by the authors we describe the monodromy on these invariants. In particular, in contrast to all existing known Donaldson-Thomas invariants for small resolutions of Gorenstein singularities these monodromy actions are nontrivial.Comment: 30 pages, 3 figure

Deep Learning Features at Scale for Visual Place Recognition

The success of deep learning techniques in the computer vision domain has triggered a range of initial investigations into their utility for visual place recognition, all using generic features from networks that were trained for other types of recognition tasks. In this paper, we train, at large scale, two CNN architectures for the specific place recognition task and employ a multi-scale feature encoding method to generate condition- and viewpoint-invariant features. To enable this training to occur, we have developed a massive Specific PlacEs Dataset (SPED) with hundreds of examples of place appearance change at thousands of different places, as opposed to the semantic place type datasets currently available. This new dataset enables us to set up a training regime that interprets place recognition as a classification problem. We comprehensively evaluate our trained networks on several challenging benchmark place recognition datasets and demonstrate that they achieve an average 10% increase in performance over other place recognition algorithms and pre-trained CNNs. By analyzing the network responses and their differences from pre-trained networks, we provide insights into what a network learns when training for place recognition, and what these results signify for future research in this area.Comment: 8 pages, 10 figures. Accepted by International Conference on Robotics and Automation (ICRA) 2017. This is the submitted version. The final published version may be slightly differen

Physics-Guided Adversarial Machine Learning for Aircraft Systems Simulation

In the context of aircraft system performance assessment, deep learning technologies allow to quickly infer models from experimental measurements, with less detailed system knowledge than usually required by physics-based modeling. However, this inexpensive model development also comes with new challenges regarding model trustworthiness. This work presents a novel approach, physics-guided adversarial machine learning (ML), that improves the confidence over the physics consistency of the model. The approach performs, first, a physics-guided adversarial testing phase to search for test inputs revealing behavioral system inconsistencies, while still falling within the range of foreseeable operational conditions. Then, it proceeds with physics-informed adversarial training to teach the model the system-related physics domain foreknowledge through iteratively reducing the unwanted output deviations on the previously-uncovered counterexamples. Empirical evaluation on two aircraft system performance models shows the effectiveness of our adversarial ML approach in exposing physical inconsistencies of both models and in improving their propensity to be consistent with physics domain knowledge

A distributed camera system for multi-resolution surveillance

We describe an architecture for a multi-camera, multi-resolution surveillance system. The aim is to support a set of distributed static and pan-tilt-zoom (PTZ) cameras and visual tracking algorithms, together with a central supervisor unit. Each camera (and possibly pan-tilt device) has a dedicated process and processor. Asynchronous interprocess communications and archiving of data are achieved in a simple and effective way via a central repository, implemented using an SQL database. Visual tracking data from static views are stored dynamically into tables in the database via client calls to the SQL server. A supervisor process running on the SQL server determines if active zoom cameras should be dispatched to observe a particular target, and this message is effected via writing demands into another database table. We show results from a real implementation of the system comprising one static camera overviewing the environment under consideration and a PTZ camera operating under closed-loop velocity control, which uses a fast and robust level-set-based region tracker. Experiments demonstrate the effectiveness of our approach and its feasibility to multi-camera systems for intelligent surveillance

DEVELOPMENT OF EXPERIMENTS FOR THE HARVESTING AND STORING OF VIDALIA SWEET ONIONS

Sweet onions brought in nearly $70 million in 1998. Sweet onions have a soft bulb which makes them susceptible to injury if handled too roughly. Once injured, invasion by pathogens tends to render the bulbs unusable except for quick sale. They were harvested by hand until about ten years ago when efforts were begun to develop a mechanical harvester. Also, at that time, work was underway to develop a way of storing part of the crop so as to extend the market window beyond the traditional 6-8 weeks. Low temperature and low oxygen atmosphere conditions proved to be the most suitable. In 1998 and 1999, sweet onions were hand and machine harvested. They were stored for 30 weeks to determine the practicality of extending the market window. Storability of onions in bulk from the two harvest methods were compared using a mixed model analysis. A mixed model analysis was done on all the research data collected between 1992 and 1998 on curing and storing onions

UPPER LIMB KINEMATICS DURING THE TOPSPIN DOUBLE-HANDED BACKHAND STROKE IN TENNIS

The purpose of this study was to compare non-dominant wrist kinematics during tennis double-handed backhand strokes in players using either an eastern or continental grip position. Trajectory data for two grips (eastern & continental) and depths (deep & short) were captured for sixteen sub-elite right-handed tennis players using a 12-camera Vicon motion capture system (250 Hz). The eastern grip demonstrated significantly faster horizontal racket head velocities compared to the continental grip. However, no differences were observed in accuracy or spin rate between grips (p \u3e 0.05). In the non-dominant upper limb for the continental condition, elbow flexion was smaller while wrist extension was larger throughout the swing. Collectively, these data suggest that the continental grip may place the wrist in a position that is more vulnerable to overuse injury

SmOOD: Smoothness-based Out-of-Distribution Detection Approach for Surrogate Neural Networks in Aircraft Design

Aircraft industry is constantly striving for more efficient design optimization methods in terms of human efforts, computation time, and resource consumption. Hybrid surrogate optimization maintains high results quality while providing rapid design assessments when both the surrogate model and the switch mechanism for eventually transitioning to the HF model are calibrated properly. Feedforward neural networks (FNNs) can capture highly nonlinear input-output mappings, yielding efficient surrogates for aircraft performance factors. However, FNNs often fail to generalize over the out-of-distribution (OOD) samples, which hinders their adoption in critical aircraft design optimization. Through SmOOD, our smoothness-based out-of-distribution detection approach, we propose to codesign a model-dependent OOD indicator with the optimized FNN surrogate, to produce a trustworthy surrogate model with selective but credible predictions. Unlike conventional uncertainty-grounded methods, SmOOD exploits inherent smoothness properties of the HF simulations to effectively expose OODs through revealing their suspicious sensitivities, thereby avoiding over-confident uncertainty estimates on OOD samples. By using SmOOD, only high-risk OOD inputs are forwarded to the HF model for re-evaluation, leading to more accurate results at a low overhead cost. Three aircraft performance models are investigated. Results show that FNN-based surrogates outperform their Gaussian Process counterparts in terms of predictive performance. Moreover, SmOOD does cover averagely 85% of actual OODs on all the study cases. When SmOOD plus FNN surrogates are deployed in hybrid surrogate optimization settings, they result in a decrease error rate of 34.65% and a computational speed up rate of 58.36 times, respectively