A Unified View of Piecewise Linear Neural Network Verification

The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. Despite the reputation of learned NN models to behave as black boxes and the theoretical hardness of proving their properties, researchers have been successful in verifying some classes of models by exploiting their piecewise linear structure and taking insights from formal methods such as Satisifiability Modulo Theory. These methods are however still far from scaling to realistic neural networks. To facilitate progress on this crucial area, we make two key contributions. First, we present a unified framework that encompasses previous methods. This analysis results in the identification of new methods that combine the strengths of multiple existing approaches, accomplishing a speedup of two orders of magnitude compared to the previous state of the art. Second, we propose a new data set of benchmarks which includes a collection of previously released testcases. We use the benchmark to provide the first experimental comparison of existing algorithms and identify the factors impacting the hardness of verification problems.Comment: Updated version of "Piecewise Linear Neural Network verification: A comparative study

Adaptive Neural Compilation

This paper proposes an adaptive neural-compilation framework to address the problem of efficient program learning. Traditional code optimisation strategies used in compilers are based on applying pre-specified set of transformations that make the code faster to execute without changing its semantics. In contrast, our work involves adapting programs to make them more efficient while considering correctness only on a target input distribution. Our approach is inspired by the recent works on differentiable representations of programs. We show that it is possible to compile programs written in a low-level language to a differentiable representation. We also show how programs in this representation can be optimised to make them efficient on a target distribution of inputs. Experimental results demonstrate that our approach enables learning specifically-tuned algorithms for given data distributions with a high success rate.Comment: Submitted to NIPS 2016, code and supplementary materials will be available on author's pag

P3 & beyond: move making algorithms for solving higher order functions

In this paper, we extend the class of energy functions for which the optimal \alpha-expansion and \alpha \beta-swap moves can be computed in polynomial time. Specifically, we introduce a novel family of higher order clique potentials, and show that the expansion and swap moves for any energy function composed of these potentials can be found by minimizing a submodular function. We also show that for a subset of these potentials, the optimal move can be found by solving an st-mincut problem. We refer to this subset as the {\cal P}^n Potts model. Our results enable the use of powerful \alpha-expansion and \alpha \beta-swap move making algorithms for minimization of energy functions involving higher order cliques. Such functions have the capability of modeling the rich statistics of natural scenes and can be used for many applications in Computer Vision. We demonstrate their use in one such application, i.e., the texture-based image or video-segmentation problem

Efficient Relaxations for Dense CRFs with Sparse Higher Order Potentials

Dense conditional random fields (CRFs) have become a popular framework for modelling several problems in computer vision such as stereo correspondence and multi-class semantic segmentation. By modelling long-range interactions, dense CRFs provide a labelling that captures finer detail than their sparse counterparts. Currently, the state-of-the-art algorithm performs mean-field inference using a filter-based method but fails to provide a strong theoretical guarantee on the quality of the solution. A question naturally arises as to whether it is possible to obtain a maximum a posteriori (MAP) estimate of a dense CRF using a principled method. Within this paper, we show that this is indeed possible. We will show that, by using a filter-based method, continuous relaxations of the MAP problem can be optimised efficiently using state-of-the-art algorithms. Specifically, we will solve a quadratic programming (QP) relaxation using the Frank-Wolfe algorithm and a linear programming (LP) relaxation by developing a proximal minimisation framework. By exploiting labelling consistency in the higher-order potentials and utilising the filter-based method, we are able to formulate the above algorithms such that each iteration has a complexity linear in the number of classes and random variables. The presented algorithms can be applied to any labelling problem using a dense CRF with sparse higher-order potentials. In this paper, we use semantic segmentation as an example application as it demonstrates the ability of the algorithm to scale to dense CRFs with large dimensions. We perform experiments on the Pascal dataset to indicate that the presented algorithms are able to attain lower energies than the mean-field inference method

Sociodemographic Profile and Treatment-Seeking Behavior of HIV Infected Children Accessing Care at Pediatric ART Clinic of a Tertiary Care Hospital in Delhi

Background: Acquired immunodeficiency syndrome (AIDS) has emerged as one of the most serious public health problems in India. The parents of HIV-infected children are more likely to die and thus render the children orphan. The sociodemographic characteristics of children with HIV infection are different than the other children of the same age group. With the diverse range of manifestations, the symptoms of HIV/AIDS can appear in children at any time during the course of infection. After appearance of various signs/symptoms, the parents wander to various health agencies for relief and thus lose their vital time.Aims: The present study was conducted (1) to study the sociodemographic profile of children living with HIV/AIDS and (2) to know the treatment-seeking behavior of HIV/AIDS patients before coming to the tertiary hospital.Materials and Methods: The study was a hospital-based cross-sectional study where 216 children registered at the pediatric ART clinic of a tertiary care hospital in Delhi and their caregivers were included in the study. Semi-structured, pretested interview schedule was used for data collection through face-to-face interviews.Results: Out of the 216 children, males outnumbered females in the ratio of 2.48:1. Most of the children were in the age group of 10–14 years (48.1%) and the majority belonged to urban areas (63.4%). Most of the children were going to school. Majority of children (46.3%) belonged to social class-4. Hospitals (62.5%) were consulted first followed by private practitioners (33.3%) by these patients after appearance of earliest symptoms. Presenting symptoms of HIV in children were not specific and the most common symptom was fever (79.1%) followed by not gaining weight (69.4%), recurrent diarrhea (65.3%), cough (41.7%) and vomiting (30.6%). The average number of consultations sought by these patients before coming to this hospital was 2.34 per patients. Hospitals (39.8%) and prior experience at same hospital (33.3%) were the most common source of information about the ART center.

Branch and Bound for Piecewise Linear Neural Network Verification

The success of Deep Learning and its potential use in many safety-critical applications has motivated research on formal verification of Neural Network (NN) models. In this context, verification involves proving or disproving that an NN model satisfies certain input-output properties. Despite the reputation of learned NN models as black boxes, and the theoretical hardness of proving useful properties about them, researchers have been successful in verifying some classes of models by exploiting their piecewise linear structure and taking insights from formal methods such as Satisifiability Modulo Theory. However, these methods are still far from scaling to realistic neural networks. To facilitate progress on this crucial area, we exploit the Mixed Integer Linear Programming (MIP) formulation of verification to propose a family of algorithms based on Branch-and-Bound (BaB). We show that our family contains previous verification methods as special cases. With the help of the BaB framework, we make three key contributions. Firstly, we identify new methods that combine the strengths of multiple existing approaches, accomplishing significant performance improvements over previous state of the art. Secondly, we introduce an effective branching strategy on ReLU non-linearities. This branching strategy allows us to efficiently and successfully deal with high input dimensional problems with convolutional network architecture, on which previous methods fail frequently. Finally, we propose comprehensive test data sets and benchmarks which includes a collection of previously released testcases. We use the data sets to conduct a thorough experimental comparison of existing and new algorithms and to provide an inclusive analysis of the factors impacting the hardness of verification problems

Impact of land-use changes on soil properties and carbon pools in India: A meta-analysis

Not AvailableLand-use changes (LUC), primarily due to deforestation and soil disturbance, are one of the major causes of soil quality degradation and greenhouse gas emissions. Effects of LUC on soil physicochemical properties and changes in soil quality and land use management strategies that can effectively restore soil carbon and microbial biomass levels have been reported from all over the world, but the impact analysis of such practices in the Indian context is limited. In this study, over 1,786 paired datasets (for meta-analysis) on land uses (LUs) were collected from Indian literature (1990–2019) to determine the magnitude of the influence of LUC on soil carbon, microbial biomass, and other physical and chemical properties at three soil depths. Meta-analysis results showed that grasslands (36.1%) lost the most soil organic carbon (SOC) compared to native forest lands, followed by plantation lands (35.5%), cultivated lands (31.1%), barren lands (27.3%), and horticulture lands (11.5%). Our findings also revealed that, when compared to forest land, the microbial quotient was lower in other LUs. Due to the depletion of SOC stock, carbon dioxide equivalent (CO2 eq) emissions were significantly higher in all LUs than in forest land. Results also showed that due to the conversion of forest land to cultivated land, total carbon, labile carbon, non-labile carbon, microbial biomass carbon, and SOC stocks were lost by 21%, 25%, 32%, 26%, and 41.2%, respectively. Changes in soil carbon pools and properties were more pronounced in surface (0–15 cm) soils than in subsurface soils (15–30 cm and 30–45 cm). Restoration of the SOC stocks from different LUs ranged from a minimum of 2% (grasslands) to a maximum of 48% (plantation lands). Overall, this study showed that soil carbon pools decreased as LUC transitioned from native forestland to other LUs, and it is suggested that adopting crop-production systems that can reduce CO2 emissions from the intensive LUs such as the ones evaluated here could contribute to improvements in soil quality and mitigation of climate change impacts, particularly under Indian agro-climatic conditions.Not Availabl

Hepatotoxicity induced by horse ATG and reversed by rabbit ATG: a case report

<p>Abstract</p> <p>Background</p> <p>The use of antilymphocyte agents has improved patient and graft survival in hematopoietic stem cell and solid organ transplantation but has been associated with the development of short-term toxicities as well as long-term complications.</p> <p>Case presentation</p> <p>We report a young female with Fanconi anemia who received antithymocyte globulin as part of the conditioning regimen prior to her planned allogeneic hematopoietic stem cell transplant at King Faisal Specialist Hospital and Research Centre in Riyadh. She developed sudden and severe hepatotoxicity after receiving the first dose of horse antithymocyte globulin, manifested by marked elevation of serum transaminases and mild elevation of serum bilirubin level. Immediately after withdrawal of the offending agent and shifting to the rabbit form of antithymocyte globulin, the gross liver dysfunction started to subside and the hepatic profile results returned to the pre-transplant levels few weeks later. The patient had her allogeneic hematopoietic stem cell transplant as planned without any further hepatic complications. After having a successful allograft, she was discharged from the stem cell transplant unit. During her follow up at the outpatient clinic, the patient remained very well and no major complication was encountered.</p> <p>Conclusion</p> <p>Hepatotoxicity related to the utilization of antithymocyte globulin varies considerably in severity and may be transient or long standing. There may be individual or population based susceptibilities to the development of side effects and these adverse reactions may also vary with the choice of the agent used. Encountering adverse effects with one type of antithymocyte agents should not discourage clinicians from shifting to another type in situations where continuation of the drug is vital.</p

Measurement of the t-channel single top quark production cross section

The D0 collaboration reports direct evidence for electroweak production of single top quarks through the t-channel exchange of a virtual W boson. This is the first analysis to isolate an individual single top quark production channel. We select events containing an isolated electron or muon, missing transverse energy, and two, three or four jets from 2.3 fb^-1 of ppbar collisions at the Fermilab Tevatron Collider. One or two of the jets are identified as containing a b hadron. We combine three multivariate techniques optimized for the t-channel process to measure the t- and s-channel cross sections simultaneously. We measure cross sections of 3.14 +0.94 -0.80 pb for the t-channel and 1.05 +-0.81 pb for the s-channel. The measured t-channel result is found to have a significance of 4.8 standard deviations and is consistent with the standard model prediction.Comment: 7 pages, 6 figure