Something More Real than Art: Homelessness and Alternative Tactics of Public Address, New York, 1989

Senior Project submitted to The Division of Arts of Bard College

Transverse Gravitational Theories

Transverse Gravity General Relativity PPN fofmalism Diffeomorphism Metri

Identifying the relevant dependencies of the neural network response on characteristics of the input space

The relation between the input and output spaces of neural networks (NNs) is investigated to identify those characteristics of the input space that have a large influence on the output for a given task. For this purpose, the NN function is decomposed into a Taylor expansion in each element of the input space. The Taylor coefficients contain information about the sensitivity of the NN response to the inputs. A metric is introduced that allows for the identification of the characteristics that mostly determine the performance of the NN in solving a given task. Finally, the capability of this metric to analyze the performance of the NN is evaluated based on a task common to data analyses in high-energy particle physics experiments

Mask then classify: multi-instance segmentation for surgical instruments.

PURPOSE The detection and segmentation of surgical instruments has been a vital step for many applications in minimally invasive surgical robotics. Previously, the problem was tackled from a semantic segmentation perspective, yet these methods fail to provide good segmentation maps of instrument types and do not contain any information on the instance affiliation of each pixel. We propose to overcome this limitation by using a novel instance segmentation method which first masks instruments and then classifies them into their respective type. METHODS We introduce a novel method for instance segmentation where a pixel-wise mask of each instance is found prior to classification. An encoder-decoder network is used to extract instrument instances, which are then separately classified using the features of the previous stages. Furthermore, we present a method to incorporate instrument priors from surgical robots. RESULTS Experiments are performed on the robotic instrument segmentation dataset of the 2017 endoscopic vision challenge. We perform a fourfold cross-validation and show an improvement of over 18% to the previous state-of-the-art. Furthermore, we perform an ablation study which highlights the importance of certain design choices and observe an increase of 10% over semantic segmentation methods. CONCLUSIONS We have presented a novel instance segmentation method for surgical instruments which outperforms previous semantic segmentation-based methods. Our method further provides a more informative output of instance level information, while retaining a precise segmentation mask. Finally, we have shown that robotic instrument priors can be used to further increase the performance

Full or Weak annotations? An adaptive strategy for budget-constrained annotation campaigns

Annotating new datasets for machine learning tasks is tedious, time-consuming, and costly. For segmentation applications, the burden is particularly high as manual delineations of relevant image content are often extremely expensive or can only be done by experts with domain-specific knowledge. Thanks to developments in transfer learning and training with weak supervision, segmentation models can now also greatly benefit from annotations of different kinds. However, for any new domain application looking to use weak supervision, the dataset builder still needs to define a strategy to distribute full segmentation and other weak annotations. Doing so is challenging, however, as it is a priori unknown how to distribute an annotation budget for a given new dataset. To this end, we propose a novel approach to determine annotation strategies for segmentation datasets, whereby estimating what proportion of segmentation and classification annotations should be collected given a fixed budget. To do so, our method sequentially determines proportions of segmentation and classification annotations to collect for budget-fractions by modeling the expected improvement of the final segmentation model. We show in our experiments that our approach yields annotations that perform very close to the optimal for a number of different annotation budgets and datasets

CataNet: Predicting remaining cataract surgery duration

Cataract surgery is a sight saving surgery that is performed over 10 million times each year around the world. With such a large demand, the ability to organize surgical wards and operating rooms efficiently is critical to delivery this therapy in routine clinical care. In this context, estimating the remaining surgical duration (RSD) during procedures is one way to help streamline patient throughput and workflows. To this end, we propose CataNet, a method for cataract surgeries that predicts in real time the RSD jointly with two influential elements: the surgeon's experience, and the current phase of the surgery. We compare CataNet to state-of-the-art RSD estimation methods, showing that it outperforms them even when phase and experience are not considered. We investigate this improvement and show that a significant contributor is the way we integrate the elapsed time into CataNet's feature extractor.Comment: Accepted at MICCAI 202

A 2-pyridone-amide inhibitor targets the glucose metabolism pathway of Chlamydia trachomatis.

UnlabelledIn a screen for compounds that inhibit infectivity of the obligate intracellular pathogen Chlamydia trachomatis, we identified the 2-pyridone amide KSK120. A fluorescent KSK120 analogue was synthesized and observed to be associated with the C. trachomatis surface, suggesting that its target is bacterial. We isolated KSK120-resistant strains and determined that several resistance mutations are in genes that affect the uptake and use of glucose-6-phosphate (G-6P). Consistent with an effect on G-6P metabolism, treatment with KSK120 blocked glycogen accumulation. Interestingly, KSK120 did not affect Escherichia coli or the host cell. Thus, 2-pyridone amides may represent a class of drugs that can specifically inhibit C. trachomatis infection.ImportanceChlamydia trachomatis is a bacterial pathogen of humans that causes a common sexually transmitted disease as well as eye infections. It grows only inside cells of its host organism, within a parasitophorous vacuole termed the inclusion. Little is known, however, about what bacterial components and processes are important for C. trachomatis cellular infectivity. Here, by using a visual screen for compounds that affect bacterial distribution within the chlamydial inclusion, we identified the inhibitor KSK120. As hypothesized, the altered bacterial distribution induced by KSK120 correlated with a block in C. trachomatis infectivity. Our data suggest that the compound targets the glucose-6-phosphate (G-6P) metabolism pathway of C. trachomatis, supporting previous indications that G-6P metabolism is critical for C. trachomatis infectivity. Thus, KSK120 may be a useful tool to study chlamydial glucose metabolism and has the potential to be used in the treatment of C. trachomatis infections

Deep Learning-Based Automated Detection of Retinal Breaks and Detachments on Fundus Photography.

PURPOSE The purpose of this study was to develop a deep learning algorithm, to detect retinal breaks and retinal detachments on ultra-widefield fundus (UWF) optos images using artificial intelligence (AI). METHODS Optomap UWF images of the database were annotated to four groups by two retina specialists: (1) retinal breaks without detachment, (2) retinal breaks with retinal detachment, (3) retinal detachment without visible retinal breaks, and (4) a combination of groups 1 to 3. The fundus image data set was split into a training set and an independent test set following an 80% to 20% ratio. Image preprocessing methods were applied. An EfficientNet classification model was trained with the training set and evaluated with the test set. RESULTS A total of 2489 UWF images were included into the dataset, resulting in a training set size of 2008 UWF images and a test set size of 481 images. The classification models achieved an area under the receiver operating characteristic curve (AUC) on the testing set of 0.975 regarding lesion detection, an AUC of 0.972 for retinal detachment and an AUC of 0.913 for retinal breaks. CONCLUSIONS A deep learning system to detect retinal breaks and retinal detachment using UWF images is feasible and has a good specificity. This is relevant for clinical routine as there can be a high rate of missed breaks in clinics. Future clinical studies will be necessary to evaluate the cost-effectiveness of applying such an algorithm as an automated auxiliary tool in a large practices or tertiary referral centers. TRANSLATIONAL RELEVANCE This study demonstrates the relevance of applying AI in diagnosing peripheral retinal breaks in clinical routine in UWF fundus images

Globalisation and Economic Insecurity

Summaries As global processes have deepened in recent decades there has been a concomitant and causally related increase in economic insecurity in many parts of the global economy. One transmission mechanism has been the heightened volatility in the global economy. The reduction in barriers to the inter?country flow of finance, banking deregulation and the abolition of capital controls, and the development and utilisation of new technologies, can be seen as primary causal drivers of volatility. Growing poverty (in both its absolute and relative senses) has been a second way in which insecurity has been globalised. Here, too, this form of insecurity can be related causally to developments in the global economy. Global competition, particularly in labour?intensive activities, has led to a bidding down of wage rates for unskilled work. This has not only undermined real living standards, but simultaneously widened the distributional gap, both within and between countries. Similar unequalising developments in the nineteenth?century bred a culture of insecurity which led to the erection of barriers to sustained international?isation. Recent protests against the WTO suggest that unless these insecurities ? both perceived and real ? in the twenty?first century are reduced, we may yet see a recurrence of opposition to sustained globalisation