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Though the range of invariance in recognition of novel objects is a basic aspect of human vision, its characterization has remained surprisingly elusive. Here we report tolerance to scale and position changes in one-shot learning by measuring recognition accuracy of Korean letters presented in a flash to non-Korean subjects who had no previous experience with Korean letters. We found that humans have significant scale-invariance after only a single exposure to a novel object. The range of translation-invariance is limited, depending on the size and position of presented objects. To understand the underlying brain computation associated with the invariance properties, we compared experimental data with computational modeling results. Our results suggest that to explain invariant recognition of objects by humans, neural network models should explicitly incorporate built-in scale-invariance, by encoding different scale channels as well as eccentricity-dependent representations captured by neurons’ receptive field sizes and sampling density that change with eccentricity. Our psychophysical experiments and related simulations strongly suggest that the human visual system uses a computational strategy that differs in some key aspects from current deep learning architectures, being more data efficient and relying more critically on eye-movements.
In pathology, tissue images are evaluated using a light microscope, relying on the expertise and experience of pathologists. There is a great need for computational methods to quantify and standardize histological observations. Computational quantification methods become more and more essential to evaluate tissue images. In particular, the distribution of tumor cells and their microenvironment are of special interest. Here, we systematically investigated tumor cell properties and their spatial neighborhood relations by a new application of statistical analysis to whole slide images of Hodgkin lymphoma, a tumor arising in lymph nodes, and inflammation of lymph nodes called lymphadenitis. We considered properties of more than 400, 000 immunohistochemically stained, CD30-positive cells in 35 whole slide images of tissue sections from subtypes of the classical Hodgkin lymphoma, nodular sclerosis and mixed cellularity, as well as from lymphadenitis. We found that cells of specific morphology exhibited significant favored and unfavored spatial neighborhood relations of cells in dependence of their morphology. This information is important to evaluate differences between Hodgkin lymph nodes infiltrated by tumor cells (Hodgkin lymphoma) and inflamed lymph nodes, concerning the neighborhood relations of cells and the sizes of cells. The quantification of neighborhood relations revealed new insights of relations of CD30-positive cells in different diagnosis cases. The approach is general and can easily be applied to whole slide image analysis of other tumor types.
The morphology of presynaptic specializations can vary greatly ranging from classical single-release-site boutons in the central nervous system to boutons of various sizes harboring multiple vesicle release sites. Multi-release-site boutons can be found in several neural contexts, for example at the neuromuscular junction (NMJ) of body wall muscles of Drosophila larvae. These NMJs are built by two motor neurons forming two types of glutamatergic multi-release-site boutons with two typical diameters. However, it is unknown why these distinct nerve terminal configurations are used on the same postsynaptic muscle fiber. To systematically dissect the biophysical properties of these boutons we developed a full three-dimensional model of such boutons, their release sites and transmitter-harboring vesicles and analyzed the local vesicle dynamics of various configurations during stimulation. Here we show that the rate of transmission of a bouton is primarily limited by diffusion-based vesicle movements and that the probability of vesicle release and the size of a bouton affect bouton-performance in distinct temporal domains allowing for an optimal transmission of the neural signals at different time scales. A comparison of our in silico simulations with in vivo recordings of the natural motor pattern of both neurons revealed that the bouton properties resemble a well-tuned cooperation of the parameters release probability and bouton size, enabling a reliable transmission of the prevailing firing-pattern at diffusion-limited boutons. Our findings indicate that the prevailing firing-pattern of a neuron may determine the physiological and morphological parameters required for its synaptic terminals.
Relying on the theory of Saward (2010) and Disch (2015), we study political representation through the lens of representative claim-making. We identify a gap between the theoretical concept of claim-making and the empirical (quantitative) assessment of representative claims made in the real world’s representative contexts. Therefore, we develop a new approach to map and quantify representative claims in order to subsequently measure the reception and validation of the claims by the audience. To test our method, we analyse all the debates of the German parliament concerned with the introduction of the gender quota in German supervisory boards from 2013 to 2017 in a two-step process. At first, we assess which constituencies the MPs claim to represent and how they justify their stance. Drawing on multiple correspondence analysis, we identify different claim patterns. Second, making use of natural language processing techniques and logistic regression on social media data, we measure if and how the asserted claims in the parliamentary debates are received and validated by the respective audience. We come to the conclusion that the constituency as ultimate judge of legitimacy has not been comprehensively conceptualized yet.
The formulation of the Partial Information Decomposition (PID) framework by Williams and Beer in 2010 attracted a significant amount of attention to the problem of defining redundant (or shared), unique and synergistic (or complementary) components of mutual information that a set of source variables provides about a target. This attention resulted in a number of measures proposed to capture these concepts, theoretical investigations into such measures, and applications to empirical data (in particular to datasets from neuroscience). In this Special Issue on “Information Decomposition of Target Effects from Multi-Source Interactions” at Entropy, we have gathered current work on such information decomposition approaches from many of the leading research groups in the field. We begin our editorial by providing the reader with a review of previous information decomposition research, including an overview of the variety of measures proposed, how they have been interpreted and applied to empirical investigations. We then introduce the articles included in the special issue one by one, providing a similar categorisation of these articles into: i. proposals of new measures; ii. theoretical investigations into properties and interpretations of such approaches, and iii. applications of these measures in empirical studies. We finish by providing an outlook on the future of the field.
This paper describes work on the morphological and syntactic annotation of Sumerian cuneiform as a model for low resource languages in general. Cuneiform texts are invaluable sources for the study of history, languages, economy, and cultures of Ancient Mesopotamia and its surrounding regions. Assyriology, the discipline dedicated to their study, has vast research potential, but lacks the modern means for computational processing and analysis. Our project, Machine Translation and Automated Analysis of Cuneiform Languages, aims to fill this gap by bringing together corpus data, lexical data, linguistic annotations and object metadata. The project’s main goal is to build a pipeline for machine translation and annotation of Sumerian Ur III administrative texts. The rich and structured data is then to be made accessible in the form of (Linguistic) Linked Open Data (LLOD), which should open them to a larger research community. Our contribution is two-fold: in terms of language technology, our work represents the first attempt to develop an integrative infrastructure for the annotation of morphology and syntax on the basis of RDF technologies and LLOD resources. With respect to Assyriology, we work towards producing the first syntactically annotated corpus of Sumerian.
Anaplastic large cell lymphoma (ALCL) and classical Hodgkin lymphoma (cHL) are lymphomas that contain CD30-expressing tumor cells and have numerous pathological similarities. Whereas ALCL is usually diagnosed at an advanced stage, cHL more frequently presents with localized disease. The aim of the present study was to elucidate the mechanisms underlying the different clinical presentation of ALCL and cHL. Chemokine and chemokine receptor expression were similar in primary ALCL and cHL cases apart from the known overexpression of the chemokines CCL17 and CCL22 in the Hodgkin and Reed-Sternberg (HRS) cells of cHL. Consistent with the overexpression of these chemokines, primary cHL cases encountered a significantly denser T cell microenvironment than ALCL. Additionally to differences in the interaction with their microenvironment, cHL cell lines presented a lower and less efficient intrinsic cell motility than ALCL cell lines, as assessed by time-lapse microscopy in a collagen gel and transwell migration assays. We thus propose that the combination of impaired basal cell motility and differences in the interaction with the microenvironment hamper the dissemination of HRS cells in cHL when compared with the tumor cells of ALCL.
Our recently developed LRSX Tool implements a technique to automatically prove the correctness of program transformations in higher-order program calculi which may permit recursive let-bindings as they occur in functional programming languages. A program transformation is correct if it preserves the observational semantics of programs- In our tool the so-called diagram method is automated by combining unification, matching, and reasoning on alpha-renamings on the higher-order metalanguage, and automating induction proofs via an encoding into termination problems of term rewrite systems. We explain the techniques, we illustrate the usage of the tool, and we report on experiments.
The focus of this paper are space-improvements of programs, which are transformations that do not worsen the space requirement during evaluations. A realistic theoretical treatment must take garbage collection method into account. We investigate space improvements under the assumption of an optimal garbage collector. Such a garbage collector is not implementable, but there is an advantage: The investigations are independent of potential changes in an implementable garbage collector and our results show that the evaluation and other similar transformations are space-improvements.
Human readers have the ability to infer knowledge from text, even if that particular information is not explicitly stated. In this thesis, we address the phenomena of text-level implicit information and outline novel automated methods for its recovery.
The main focus of this work is on two types of unexpressed content that arises between sentences (implicit discourse relations) and within sentences (implicit semantic roles).
Traditional approaches mostly rely on costly rich linguistic features, e.g., sentiment or frame-based lexicons, and require heuristics or manual feature engineering.
As an improvement, we propose a collection of generic resource-lean methods, implemented in the form of statistical background knowledge or by means of neural architectures.
Our models are largely language-independent and produce state-of-the-art performance, e.g., in the classification of Chinese implicit discourse relations, or the detection of locally covert predicative arguments in free texts.
In novel experiments, we quantitatively demonstrate that both types of implicit information are mutually dependent insofar as, for instance, some implicit roles directly correlate with implicit discourse relations of similar properties.
We show that implicit information processing further benefits downstream applications and demonstrate its applicability to the higher-level task of narrative story understanding.
In the conclusion of the dissertation, we argue for the need of implicit information processing in order to realize the goal of true natural language understanding.
The development of multimodal sensor-based applications designed to support learners with the improvement of their skills is expensive since most of these applications are tailor-made and built from scratch. In this paper, we show how the Presentation Trainer (PT), a multimodal sensor-based application designed to support the development of public speaking skills, can be modularly extended with a Virtual Reality real-time feedback module (VR module), which makes usage of the PT more immersive and comprehensive. The described study consists of a formative evaluation and has two main objectives. Firstly, a technical objective is concerned with the feasibility of extending the PT with an immersive VR Module. Secondly, a user experience objective focuses on the level of satisfaction of interacting with the VR extended PT. To study these objectives, we conducted user tests with 20 participants. Results from our test show the feasibility of modularly extending existing multimodal sensor-based applications, and in terms of learning and user experience, results indicate a positive attitude of the participants towards using the application (PT+VR module).
Programmable hardware in the form of FPGAs found its place in various high energy physics experiments over the past few decades. These devices provide highly parallel and fully configurable data transport, data formatting, and data processing capabilities with custom interfaces, even in rigid or constrained environments. Additionally, FPGA functionalities and the number of their logic resources have grown exponentially in the last few years, making FPGAs more and more suitable for complex data processing tasks. ALICE is one of the four main experiments at the LHC and specialized in the study of heavy-ion collisions. The readout chain of the ALICE detectors makes use of FPGAs at various places. The Read-Out Receiver Cards (RORCs) are one example of FPGA-based readout hardware, building the interface between the custom detector electronics and the commercial server nodes in the data processing clusters of the Data Acquisition (DAQ) system as well as the High Level Trigger (HLT). These boards are implemented as server plug-in cards with serial optical links towards the detectors. Experimental data is received via more than 500 optical links, already partly pre-processed in the FPGAs, and pushed towards the host machines. Computer clusters consisting of a few hundred nodes collect, aggregate, compress, reconstruct, and prepare the experimental data for permanent storage and later analysis. With the end of the first LHC run period in 2012 and the start of Run 2 in 2015, the DAQ and HLT systems were renewed and several detector components were upgraded for higher data rates and event rates. Increased detector link rates and obsolete host interfaces rendered it impossible to reuse the previous RORCs in Run 2.
This thesis describes the development, integration, and maintenance of the next generation of RORCs for ALICE in Run 2. A custom hardware platform, initially developed as a joint effort between the ALICE DAQ and HLT groups in the course of this work, found its place in the Run 2 readout systems of the ALICE and ATLAS experiments. The hardware fulfills all experiment requirements, matches its target performance, and has been running stable in the production systems since the start of Run 2. Firmware and software developments for the hardware evaluation, the design of the board, the mass production hardware tests, as well as the operation of the final board in the HLT, were carried out as part of this work. 74 boards were integrated into the HLT hardware and software infrastructure, with various firmware and software developments, to provide the main experimental data input and output interface of the HLT for Run 2. The hardware cluster finder, an FPGA-based data pre-processing core from the previous generation of RORCs, was ported to the new hardware. It has been improved and extended to meet the experimental requirements throughout Run 2. The throughput of this firmware component could be doubled and the algorithm extended, providing an improved noise rejection and an increased overall mean data compression ratio compared to its previous implementation. The hardware cluster finder forms a crucial component in the HLT data reconstruction and compression scheme with a processing performance of one board equivalent to around ten server nodes for comparable processing steps in software.
The work on the firmware development, especially on the hardware cluster finder, once more demonstrated that developing and maintaining data processing algorithms with the common low-level hardware description methods is tedious and time-consuming. Therefore, a high-level synthesis (HLS) hardware description method applying dataflow computing at an algorithmic level to FPGAs was evaluated in this context. The hardware cluster finder served as an example of a typical data processing algorithm in a high energy physics readout application. The existing and highly optimized low-level implementation provided a reference for comparisons in terms of throughput and resource usage. The cluster finder algorithm could be implemented in the dataflow description with comparably little effort, providing fast development cycles, compact code and at, the same time, simplified extension and maintenance options. The performance results in terms of throughput and resource usage are comparable to the manual implementation. The dataflow environment proved to be highly valuable for design space explorations. An integration of the dataflow description into the HLT firmware and software infrastructure could be demonstrated as a proof of concept. A high-level hardware description could ease both the design space exploration, the initial development, the maintenance, and the extension of hardware algorithms for high energy physics readout applications.
Background: Microarray analysis represents a powerful way to test scientific hypotheses on the functionality of cells. The measurements consider the whole genome, and the large number of generated data requires sophisticated analysis. To date, no gold-standard for the analysis of microarray images has been established. Due to the lack of a standard approach there is a strong need to identify new processing algorithms.
Methods: We propose a novel approach based on hyperbolic partial differential equations (PDEs) for unsupervised spot segmentation. Prior to segmentation, morphological operations were applied for the identification of co-localized groups of spots. A grid alignment was performed to determine the borderlines between rows and columns of spots. PDEs were applied to detect the inflection points within each column and row; vertical and horizontal luminance profiles were evolved respectively. The inflection points of the profiles determined borderlines that confined a spot within adapted rectangular areas. A subsequent k-means clustering determined the pixels of each individual spot and its local background.
Results: We evaluated the approach for a data set of microarray images taken from the Stanford Microarray Database (SMD). The data set is based on two studies on global gene expression profiles of Arabidopsis Thaliana. We computed values for spot intensity, regression ratio, and coefficient of determination. For spots with irregular contours and inner holes, we found intensity values that were significantly different from those determined by the GenePix Pro microarray analysis software. We determined the set of differentially expressed genes from our intensities and identified more activated genes than were predicted by the GenePix software.
Conclusions: Our method represents a worthwhile alternative and complement to standard approaches used in industry and academy. We highlight the importance of our spot segmentation approach, which identified supplementary important genes, to better explains the molecular mechanisms that are activated in a defense responses to virus and pathogen infection.
The production of K∗(892)0 and ϕ(1020) mesons has been measured in p–Pb collisions at √sNN = 5.02 TeV. K∗0 and ϕ are reconstructed via their decay into charged hadrons with the ALICE detector in the rapidity range - 0.5 < y < 0. The transverse momentum spectra, measured as a function of the multiplicity, have a pT range from 0 to 15 GeV/c for K∗0 and from 0.3 to 21 GeV/c for ϕ. Integrated yields, mean transverse momenta and particle ratios are reported and compared with results in pp collisions at √s = 7 TeV and Pb–Pb collisions at √sNN = 2.76 TeV. In Pb–Pb and p–Pb collisions, K∗0 and ϕ probe the hadronic phase of the system and contribute to the study of particle formation mechanisms by comparison with other identified hadrons. For this purpose, the mean transverse momenta and the differential proton-to-ϕ ratio are discussed as a function of the multiplicity of the event. The short-lived K∗0 is measured to investigate re-scattering effects, believed to be related to the size of the system and to the lifetime of the hadronic phase.
Heterologously expressed genes require adaptation to the host organism to ensure adequate levels of protein synthesis, which is typically approached by replacing codons by the target organism’s preferred codons. In view of frequently encountered suboptimal outcomes we introduce the codon-specific elongation model (COSEM) as an alternative concept. COSEM simulates ribosome dynamics during mRNA translation and informs about protein synthesis rates per mRNA in an organism- and context-dependent way. Protein synthesis rates from COSEM are integrated with further relevant covariates such as translation accuracy into a protein expression score that we use for codon optimization. The scoring algorithm further enables fine-tuning of protein expression including deoptimization and is implemented in the software OCTOPOS. The protein expression score produces competitive predictions on proteomic data from prokaryotic, eukaryotic, and human expression systems. In addition, we optimized and tested heterologous expression of manA and ova genes in Salmonella enterica serovar Typhimurium. Superiority over standard methodology was demonstrated by a threefold increase in protein yield compared to wildtype and commercially optimized sequences.