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This paper investigates empirical predictions of a connectionist model of word learning. The model predicts that, although the mapping between word form and meaning is arbitrary (thus rendering words as being symbols in the semiotic sense), novel pseudowords will be able to prime the concepts corresponding to word forms that are orthographically similar. If, however, pseudowords acquire meaning through an arbitrary mapping, this priming should be reduced. Two experiments support this hypothesis. Pseudowords, derived from and thus orthographically similar to English words, primed a categorization task involving those similar words. After a subsequent learning phase, in which subjects are asked to learn meanings for the pseudowords, this priming disappears. This interplay between iconic and symbolic use of words is proposed to emerge from connectionist learning procedures.

In this position paper we would like to lay out our view on the importance of grounding and situatedness for cognitive science. Furthermore we would like to suggest that both aspects become relevant almost automatically if one consequently pursues the original ideas from connectionism. Finally we discuss the relevance of grounding for theories of meaning and the possible contribution of symbol grounding for autonomous agents.

The aim of this paper is to introduce a simple connectionist model for the acquisition of word meaning, and to demonstrate how this model can be enhanced based on empirical observations about language learning in children. The main sources are observations by Markman (1989, 1990) about constraints children place on word meaning, and Nelson (1988), as well as Benelli (1988), about the role of language in the acquisition of concept taxonomies. The model enhancements based on these observations, and those authors' conclusions, are mainly built on well-known neural mechanisms such as resonance, reset and recruitment, as first introduced in the adaptive resonance theory (ART) models by Grossberg (1976). This way the strength of connectionist models in plausibly modeling detailed aspects of natural language is underlined.

Background: Liver metastases are common in patients with breast cancer, and determining the factors associated with such metastases may improve both their early detection and treatment. Given that liver function protein level changes in these patients have not been determined, the aim of our study was to investigate liver function protein level changes over time, spanning 6 months before the detection of liver metastasis to 12 months after. Methods: We retrospectively studied 104 patients with hepatic metastasis from breast cancer who were treated at the Departments of Internal Medicine I and the Department of Obstetrics and Gynecology at the Medical University of Vienna between 1980 and 2019. Data were extracted from patient records. Results: Aspartate aminotransferase, alanine aminotransferase, gamma-glutamyltransferase, lactate dehydrogenase and alkaline phosphatase were significantly elevated when compared to normal range 6 months before the detection of liver metastases (p<0.001) Albumin was decreased (p<0.001). The values of aspartate aminotransferase, gamma-glutamyltransferase, and lactate dehydrogenase were significantly increased at the time of diagnosis compared to 6 months prior (p<0.001). Patient- and tumor-specific parameters had no influence on these liver function indicators. Elevated aspartate aminotransferase (p = 0.002) and reduced albumin (p = 0.002) levels at the time of diagnosis were associated with shorter overall survival. Conclusion: Liver function protein levels should be considered as potential indicators when screening for liver metastasis in patients with breast cancer. With the new treatment options available, it could lead to prolonged life. [ABSTRACT FROM AUTHOR]

Evidence theory by Dempster-Shafer for determination of hormone receptor status in breast cancer samples was introduced in our previous paper. One major topic pointed out here is the link between pieces of evidence found from different origins. In this paper the challenge of selecting appropriate ways of fusing evidence, depending on the type and quality of data involved is addressed. A parameterized family of evidence combination rules, covering the full range of potential needs, from emphasizing discrepancies in the measurements to aspiring accordance, is covered. The consequences for real patient samples are shown by modeling different decision strategies. [ABSTRACT FROM AUTHOR]

Digitalisation is changing all areas of our daily life. This changing environment requires new competences from physicians in all specialities. This study systematically surveyed the knowledge, attitude, and interests of medical students. These results will help further develop the medical curriculum, as well as increase our understanding of future physicians by other healthcare market players. A web-based survey consisting of four sections was developed: Section one queried demographic data, section two assessed the current digital health knowledge of medical students, section three queried their attitudes about the future impact of digital health in medicine and section four assessed the recommendations medical students have for the medical curriculum in terms of digital health. This survey was distributed to all (11,978) student at all public Austrian medical schools. A total of 8.4% of the medical student population started the survey. At the knowledge self-assessment section, the medical students reached mean of 11.74 points (SD 4.42) out of a possible maximum of 32 (female mean 10.66/ SD 3.87, male mean 13.34/SD 4.50). The attitude section showed that students see digitalisation as a threat, especially with respect to the patient–physician relationship. The curriculum recommendation section showed a high interest for topics related to AI, a per study year increasing interest in impact of digital health in communication, as well as a decreasing interest in robotic related topics. The attitude towards digital health can be described as sceptical. To ensure that future physicians keep pace with this development and fulfil their responsibility towards the society, medical schools need to be more proactive to foster the understanding of medical students that digital health will persistently alter the medical practice. [ABSTRACT FROM AUTHOR]

Background: Diagnosis of cardiac amyloidosis (CA) requires advanced imaging techniques. Typical surface ECG patterns have been described, but their diagnostic abilities are limited.Objective: The aim was to perform a thorough electrophysiological characterisation of patients with CA and derive an easy-to-use tool for diagnosis.Methods: We applied electrocardiographic imaging (ECGI) to acquire electroanatomical maps in patients with CA and controls. A machine learning approach was then used to decipher the complex data sets obtained and generate a surface ECG-based diagnostic tool.Findings: Areas of low voltage were localised in the basal inferior regions of both ventricles and the remaining right ventricular segments in CA. The earliest epicardial breakthrough of myocardial activation was visualised on the right ventricle. Potential maps revealed an accelerated and diffuse propagation pattern. We correlated the results from ECGI with 12-lead ECG recordings. Ventricular activation correlated best with R-peak timing in leads V1-V3. Epicardial voltage showed a strong positive correlation with R-peak amplitude in the inferior leads II, III and aVF. Respective surface ECG leads showed two characteristic patterns. Ten blinded cardiologists were asked to identify patients with CA by analysing 12-lead ECGs before and after training on the defined ECG patterns. Training led to significant improvements in the detection rate of CA, with an area under the curve of 0.69 before and 0.97 after training.Interpretation: Using a machine learning approach, an ECG-based tool was developed from detailed electroanatomical mapping of patients with CA. The ECG algorithm is simple and has proven helpful to suspect CA without the aid of advanced imaging modalities. [ABSTRACT FROM AUTHOR]

Objectives. The retinoblastoma (RB) pathway is crucial in the development and progression of many cancers. To better understand the biology of progressive breast cancer (BC), we examined protein expression of the RB pathway in primary BCs and matched axillary lymph node metastases (LM). Methods. Immunohistochemistry was used to evaluate cyclin D1, CDK4/6, RB, phosphorylated RB (pRB), and E2F1 expression in tissue arrays containing cores of 50 primary BCs and matched LM. The number of positive tumor cells and staining intensity were scored. Results. The proteins were localized in the nucleus, while CDK6 was detected in the cytoplasm and CDK4 was found in both. pRB and E2F1 showed higher expression in matched LM than in primary tumors. Expression of these proteins differed significantly by the percentage of positive tumor cells, while proteins in the proximal portion of the RB pathway showed no significant differences. The main path of alteration consisted of high pRB in primary BC, remaining pRB high in the majority of LM, variations occurring in fewer cases. All matched LM of the few primary tumors that had unaltered RB and pRB expression showed changes in RB or pRB expression. Conclusion. Expression of pRB and E2F1 was significantly higher in LM than in primary BC. A majority of cancers with LM showed altered RB or pRB expression, suggesting that proteins downstream in the RB pathway play a critical role in metastatic BC and disease progression. So looking at the RB pathway could be an option for chemotherapy decisions in patients with only few LM. [ABSTRACT FROM AUTHOR]

Purpose: State‐of‐the‐art whole‐brain MRSI with spatial‐spectral encoding and multichannel acquisition generates huge amounts of data, which must be efficiently processed to stay within reasonable reconstruction times. Although coil combination significantly reduces the amount of data, currently it is performed in image space at the end of the reconstruction. This prolongs reconstruction times and increases RAM requirements. We propose an alternative k‐space‐based coil combination that uses geometric deep learning to combine MRSI data already in native non‐Cartesian k‐space. Methods: Twelve volunteers were scanned at a 3T MR scanner with a 20‐channel head coil at 10 different positions with water‐unsuppressed MRSI. At the eleventh position, water‐suppressed MRSI data were acquired. Data of 7 volunteers were used to estimate sensitivity maps and form a base for simulating training data. A neural network was designed and trained to remove the effect of sensitivity profiles of the coil elements from the MRSI data. The water‐suppressed MRSI data of the remaining volunteers were used to evaluate the performance of the new k‐space‐based coil combination relative to that of a conventional image‐based alternative. Results: For both approaches, the resulting metabolic ratio maps were similar. The SNR of the k‐space‐based approach was comparable to the conventional approach in low SNR regions, but underperformed for high SNR. The Cramér‐Rao lower bounds show the same trend. The analysis of the FWHM showed no difference between the two methods. Conclusion: k‐Space‐based coil combination of MRSI data is feasible and reduces the amount of raw data immediately after their sampling. [ABSTRACT FROM AUTHOR]

This paper compares conventional symbolic rule systems with distributed network models, considerably arguing for the latter. NETZSPRECH - a network that transcribes German texts similar to NetTalk is first introduced for this purpose and serves as an example for the arguments.

Germline variations in the BRCA‐1 and BRCA‐2 genes are associated with an increased risk of breast cancer. These variants are found in 5% of all breast cancer cases. Prophylactic mastectomy is the most effective risk‐reducing method and shows high rates of patient satisfaction and acceptance. We established a registry of Austrian BRCA‐1 and BRCA‐2 mutation carriers who had undergone mastectomy for oncologic or prophylactic reasons. Data were collected on the type of operation, complications, and type of reconstructive surgery for patients between 2014 and 2017. The complication rate in patients with nipple‐sparing mastectomy was significantly lower (23.1%) than in those with other types of mastectomies (60.7%; P =.005). In patients with implant‐based breast reconstruction, subpectoral placement was associated with a significantly higher rate of complications than prepectoral placement (P =.025). Median implant volume was 350 cc (range: 155‐650 cc), and a 100‐cc increase was associated with doubling of the odds of a complication (regression coefficient = 0.007); based on this finding, some surgeons may decide on using smaller implants. In summary, we identified significant associations between the risk of complications and surgical characteristics, and found host factors like diabetes, BMI, and smoking among Austrian patients with BRCA‐1 and BRCA‐2 variants. [ABSTRACT FROM AUTHOR]

Methods of suicide have received considerable attention in suicide research. The common approach to differentiate methods of suicide is the classification into "violent" versus "non-violent" method. Interestingly, since the proposition of this dichotomous differentiation, no further efforts have been made to question the validity of such a classification of suicides. This study aimed to challenge the traditional separation into "violent" and "non-violent" suicides by generating a cluster analysis with a data-driven, machine learning approach. In a retrospective analysis, data on all officially confirmed suicides (N = 77,894) in Austria between 1970 and 2016 were assessed. Based on a defined distance metric between distributions of suicides over age group and month of the year, a standard hierarchical clustering method was performed with the five most frequent suicide methods. In cluster analysis, poisoning emerged as distinct from all other methods – both in the entire sample as well as in the male subsample. Violent suicides could be further divided into sub-clusters: hanging, shooting, and drowning on the one hand and jumping on the other hand. In the female sample, two different clusters were revealed – hanging and drowning on the one hand and jumping, poisoning, and shooting on the other. Our data-driven results in this large epidemiological study confirmed the traditional dichotomization of suicide methods into "violent" and "non-violent" methods, but on closer inspection "violent methods" can be further divided into sub-clusters and a different cluster pattern could be identified for women, requiring further research to support these refined suicide phenotypes. [ABSTRACT FROM AUTHOR]

Research on machine learning approaches for upper-limb prosthesis control has shown impressive progress. However, translating these results from the lab to patient’s everyday lives remains a challenge because advanced control schemes tend to break down under everyday disturbances, such as electrode shifts. Recently, it has been suggested to apply adaptive transfer learning to counteract electrode shifts using as little newly recorded training data as possible. In this paper, we present a novel, simple version of transfer learning and provide the first user study demonstrating the effectiveness of transfer learning to counteract electrode shifts. For this purpose, we introduce the novel Box and Beans test to evaluate prosthesis proficiency and compare user performance with an initial simple pattern recognition system, the system under electrode shifts, and the system after transfer learning. Our results show that transfer learning could significantly alleviate the impact of electrode shifts on user performance in the Box and Beans test. [ABSTRACT FROM AUTHOR]

One of the central goals in finance is to find better models for pricing and hedging financial derivatives such as call and put options. We present a new semi-nonparametric approach to risk-neutral density extraction from option prices which is based on an extension of the concept of mixture density networks. The central idea is to model the shape of the risk-neutral density in a flexible, nonlinear way as a function of the time horizon. Thereby, stylized facts such as negative skewness and excess kurtosis are captured. The approach is applied to a very large set of intraday options data on the FTSE 100 recorded at LIFFE. It is shown to yield significantly better results in terms of out-of-sample pricing accuracy in comparison to the basic and an extended Black-Scholes model. It is also significantly better than a more elaborate GARCH option pricing model which includes a time-dependent volatility process. From the perspective of risk management, the extracted risk-neutral densities provide valuable information for Value-at-Risk estimations. Index Terms--Hedging, mixture density networks, options, pricing, risk-neutral densities.