Algorithmic iteration for computational intelligence

Machine awareness is a disputed research topic, in some circles considered a crucial step in realising Artificial General Intelligence. Understanding what that is, under which conditions such feature could arise and how it can be controlled is still a matter of speculation. A more concrete object of theoretical analysis is algorithmic iteration for computational intelligence, intended as the theoretical and practical ability of algorithms to design other algorithms for actions aimed at solving well-specified tasks. We know this ability is already shown by current AIs, and understanding its limits is an essential step in qualifying claims about machine awareness and Super-AI. We propose a formal translation of algorithmic iteration in a fragment of modal logic, formulate principles of transparency and faithfulness across human and machine intelligence, and consider the relevance to theoretical research on (Super)-AI as well as the practical import of our results

Predictors of suicidal behaviour in 36,304 individuals sickness absent due to stress-related mental disorders - a Swedish register linkage cohort study

BACKGROUND: Stress-related mental disorders (SRMD), which correspond to the diagnostic code F43 in the International Classification of Diseases, version 10, rank among the leading causes of sickness absence in several European countries. Despite the size of this health problem, research on risk factors for severe medical outcomes, like suicidal behavior, is lacking to date. The aim of this study was to investigate predictors of suicide attempt and suicide among sickness absentees with SRMD. METHODS: A cohort of 36,304 non-retired individuals, aged 16–64 years on 31.12.2004, with at least one sickness absence spell due to SRMD, initiated in 2005, was followed up with regard to suicide attempt (2006–2009) and suicide (2006–2008). Univariate and multivariate hazard ratios (HR) with 95% confidence intervals (CI) were estimated for a number of predictors. RESULTS: During the follow-up period, 266 individuals attempted suicide and 34 committed suicide. In the multivariate analyses, the following factors increased the risk of suicide attempt: =< 25 years of age, low educational level, lone parenthood, > 1 sickness absence spell, long duration of the first spell of sickness absence due to SRMD (> 180 days), > 4 and > 8 days of inpatient care due to somatic or mental diagnoses (2000–2005), and > 4 and > 1 outpatient visits due to somatic or mental diagnoses (2001–2005), respectively. Hazard ratios ranged from 1.4 to 4.2. Health care due to mental diagnoses and > 1 spell of sickness absence regardless of diagnosis were predictive of suicide. CONCLUSIONS: Several predictors related to socio-demographics, sickness absence and health-care consumption were identified as risk factors for suicidal behavior. Consideration of these risk factors is of both clinical and public health importance

Research and Design of a Routing Protocol in Large-Scale Wireless Sensor Networks

无线传感器网络，作为全球未来十大技术之一，集成了传感器技术、嵌入式计算技术、分布式信息处理和自组织网技术，可实时感知、采集、处理、传输网络分布区域内的各种信息数据，在军事国防、生物医疗、环境监测、抢险救灾、防恐反恐、危险区域远程控制等领域具有十分广阔的应用前景。 本文研究分析了无线传感器网络的已有路由协议，并针对大规模的无线传感器网络设计了一种树状路由协议，它根据节点地址信息来形成路由，从而简化了复杂繁冗的路由表查找和维护，节省了不必要的开销，提高了路由效率，实现了快速有效的数据传输。 为支持此路由协议本文提出了一种自适应动态地址分配算——ADAR（AdaptiveDynamicAddre...As one of the ten high technologies in the future, wireless sensor network, which is the integration of micro-sensors, embedded computing, modern network and Ad Hoc technologies, can apperceive, collect, process and transmit various information data within the region. It can be used in military defense, biomedical, environmental monitoring, disaster relief, counter-terrorism, remote control of haz...学位：工学硕士院系专业：信息科学与技术学院通信工程系_通信与信息系统学号：2332007115216

Gated Recurrent Units Viewed Through the Lens of Continuous Time Dynamical Systems

Gated recurrent units (GRUs) are specialized memory elements for building recurrent neural networks. Despite their incredible success on various tasks, including extracting dynamics underlying neural data, little is understood about the specific dynamics representable in a GRU network. As a result, it is both difficult to know a priori how successful a GRU network will perform on a given task, and also their capacity to mimic the underlying behavior of their biological counterparts. Using a continuous time analysis, we gain intuition on the inner workings of GRU networks. We restrict our presentation to low dimensions, allowing for a comprehensive visualization. We found a surprisingly rich repertoire of dynamical features that includes stable limit cycles (nonlinear oscillations), multi-stable dynamics with various topologies, and homoclinic bifurcations. At the same time we were unable to train GRU networks to produce continuous attractors, which are hypothesized to exist in biological neural networks. We contextualize the usefulness of different kinds of observed dynamics and support our claims experimentally.</jats:p

Development of a tool for predicting HNF1B mutations in children and young adults with congenital anomalies of the kidneys and urinary tract

Background We aimed to develop a tool for predicting HNF1B mutations in children with congenital abnormalities of the kidneys and urinary tract (CAKUT). Methods The clinical and laboratory data from 234 children and young adults with known HNF1B mutation status were collected and analyzed retrospectively. All subjects were randomly divided into a training (70%) and a validation set (30%). A random forest model was constructed to predict HNF1B mutations. The recursive feature elimination algorithm was used for feature selection for the model, and receiver operating characteristic curve statistics was used to verify its predictive effect. Results A total of 213 patients were analyzed, including HNF1B-positive (mut + , n = 109) and HNF1B-negative (mut − , n = 104) subjects. The majority of patients had mild chronic kidney disease. Kidney phenotype was similar between groups, but bilateral kidney anomalies were more frequent in the mut + group. Hypomagnesemia and hypermagnesuria were the most common abnormalities in mut + patients and were highly selective of HNF1B. Hypomagnesemia based on age-appropriate norms had a better discriminatory value than the age-independent cutoff of 0.7 mmol/l. Pancreatic anomalies were almost exclusively found in mut + patients. No subjects had hypokalemia; the mean serum potassium level was lower in the HNF1B cohort. The abovementioned, discriminative parameters were selected for the model, which showed a good performance (area under the curve: 0.85; sensitivity of 93.67%, specificity of 73.57%). A corresponding calculator was developed for use and validation. Conclusions This study developed a simple tool for predicting HNF1B mutations in children and young adults with CAKUT