Reversible Architectures for Arbitrarily Deep Residual Neural Networks

Recently, deep residual networks have been successfully applied in many computer vision and natural language processing tasks, pushing the state-of-the-art performance with deeper and wider architectures. In this work, we interpret deep residual networks as ordinary differential equations (ODEs), which have long been studied in mathematics and physics with rich theoretical and empirical success. From this interpretation, we develop a theoretical framework on stability and reversibility of deep neural networks, and derive three reversible neural network architectures that can go arbitrarily deep in theory. The reversibility property allows a memory-efficient implementation, which does not need to store the activations for most hidden layers. Together with the stability of our architectures, this enables training deeper networks using only modest computational resources. We provide both theoretical analyses and empirical results. Experimental results demonstrate the efficacy of our architectures against several strong baselines on CIFAR-10, CIFAR-100 and STL-10 with superior or on-par state-of-the-art performance. Furthermore, we show our architectures yield superior results when trained using fewer training data.Comment: Accepted at AAAI 201

Assessing the rock glacier kinematics on three different timescales: a case study from the southern Swiss Alps

Surface temperature increases since the 1990s have often been associated with an increase in the speed of rock glaciers. Evidence of similar links on the centennial to millennial scale are, however, still lacking due to less focus to date on the medium- and long-term kinematics of these landforms. In order to assess (palaeo)climatic variations in rock glacier kinematics, we analysed the movements of the Stabbio di Largario rock glacier in the southern Swiss Alps using three different timescales. The Schmidt hammer exposure-age dating (SHD) was applied to study long-term kinematics in order to extrapolate the minimal age of the formation of the rock glacier, which may have started its development after the Mid-Holocene climate optimum, and to detect possible accelerations of the horizontal surface velocity during the Medieval Warm Period. Georeferentiation and orthorectification of six historical photographs of the rock glacier taken between ad 1910 and today were analysed using monoplotting to detect the rock glacier displacement on the decennial scale from the end of the Little Ice Age. Finally, differential global positioning system (dGPS) monitoring data available since ad 2009 were used to assess annual and seasonal creep rates of the rock glacier at present. Our results show a link between the periods of increase in mean air temperature on different timescales and variations in rock glacier kinematics and provide important new insights into rock glacier development and evolution on the long-term scale

Peer influence on disruptive classroom behavior depends on teachers' instructional practice

This study investigated whether early adolescents’ disruptive classroom behavior is predicted by descriptive classroom norms for such behavior (i.e., mean level of classmates’ disruptive behaviors). We further examined whether classmates’ influence on a student’s disruptive behavior varies based on teacher’s instructional practice. Participants were 701 adolescents (M = 13.12 years; 48.8% girls) who were followed across six measurement points from Grades 7 through 9. Multilevel analyses showed that subsequent individual disruptive behavior was predicted by earlier levels of disruptive behavior in the classroom. Peer influence on disruptive behavior was lower when students perceived that their teacher’s instruction was more supportive and interesting. When students reported that their teacher used more ability differentiation (e.g., ability grouping), peer influence on disruptive behavior was higher

Towards implementing climate services in Peru – The project CLIMANDES

CLIMANDES is a pilot twinning project between the National Weather Services of Peru and Switzerland (SENAMHI and MeteoSwiss), developed within the Global Framework for Climate Services of the World Meteorological Organization (WMO). Split in two modules, CLIMANDES aims at improving education in meteorology and climatology in support of the WMO Regional Training Center in Peru, and introducing user-tailored climate services in two pilot regions in the Peruvian Andes. Four areas were prioritized in the first phase of CLIMANDES lasting from 2012 to 2015 to introduce climate services in Peru. A demand study identified the user needs of climate services and showed that climate information must be reliable, of high-quality, and precise. The information should be accessible and timely, understandable and applicable for the users’ specific needs. Second, the quality of climate data was enhanced through the establishment of quality control and homogenization procedures at SENAMHI. Specific training and application of the implemented methods at stations in the pilot regions was promoted to ensure the sustainability of the work. Third, the specific work on climate data enabled the creation of a webpage to disseminate climate indicators among users. The forth priority of the project enhanced the broad communication strategy of SENAMHI through creation of a specialized network of journalists, diverse climate forums, and the establishment of a user database. The efforts accomplished within CLIMANDES improved the quality of the climate services provided by SENAMHI. The project hence contributed successfully to higher awareness and higher confidence in the climate information by SENAMHI.Por pare

Ongoing toxin-positive diphtheria outbreaks in a federal asylum centre in Switzerland, analysis July to September 2022.

Two diphtheria outbreaks occurred in a Swiss asylum center from July to October 2022, one is still ongoing. Outbreaks mainly involved minors and included six symptomatic respiratory diphtheria cases requiring antitoxin. Phylogenomic analyses showed evidence of imported and local transmissions of toxigenic strains in respiratory and skin lesion samples. Given the number of cases (n = 20) and the large genetic diversity accumulating in one centre, increased awareness and changes in public health measures are required to prevent and control diphtheria outbreaks

Below Average Midsummer to Early Autumn Precipitation Evolved Into the Main Driver of Sudden Scots Pine Vitality Decline in the Swiss Rhône Valley

The vitality of Scots pine (Pinus sylvestris L.) is declining since the 1990s in many European regions. This was mostly attributed to the occurrence of hotter droughts, other climatic changes and secondary biotic stressors. However, it is still not well understood which specific atmospheric trends and extremes caused the observed spatio-temporal dieback patterns. In the Swiss Rhône valley, we identified negative precipitation anomalies between midsummer and early autumn as the main driver of sudden vitality decline and dieback events. Whereas climate change from 1981 to 2018 did not lead to a reduced water input within this time of the year, the potential evapotranspiration strongly increased in spring and summer. This prolonged and intensified the period of low soil moisture between midsummer and autumn, making Scots pines critically dependent on substantial precipitation events which temporarily reduce the increased water stress. Thus, local climate characteristics (namely midsummer to early autumn precipitation minima) are decisive for the spatial occurrence of vitality decline events, as the lowest minima outline the most affected regions within the Swiss Rhône valley. Mortality events will most likely spread to larger areas and accelerate the decline of Scots pines at lower elevations, whereas higher altitudes may remain suitable Scots pine habitats. The results from our regional study are relevant on larger geographic scales because the same processes seem to play a key role in other European regions increasingly affected by Scots pine dieback events

The influence of station density on climate data homogenization

Relative homogenization methods assume that measurements of nearby stations experience similar climate signals and rely therefore on dense station networks with high-temporal correlations. In developing countries such as Peru, however, networks often suffer from low-station density. The aim of this study is to quantify the influence of network density on homogenization. To this end, the homogenization method HOMER was applied to an artificially thinned Swiss network. Four homogenization experiments, reflecting different homogenization approaches, were examined. Such approaches include diverse levels of interaction of the homogenization operators with HOMER, and different application of metadata. To evaluate the performance of HOMER in the sparse networks, a reference series was built by applying HOMER under the best possible conditions. Applied in completely automatic mode, HOMER decreases the reliability of temperature records. Therefore, automatic use of HOMER is not recommended. If HOMER is applied in interactive mode, the reliability of temperature and precipitation data may be increased in sparse networks. However, breakpoints must be inserted conservatively. Information from metadata should be used only to determine the exact timing of statistically detected breaks. Insertion of additional breakpoints based solely on metadata may lead to harmful corrections due to the high noise in sparse networks