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We study involuntary micro-movements of both eyes, in addition to saccadic macro-movements, as biometric characteristic. We develop a deep convolutional neural network that processes binocular eye-tracking signals and verifies the viewer’s identity. In order to detect presentation attacks, we develop a model in which the movements are a response to a controlled stimulus. The model detects replay attacks by processing both the controlled but randomized stimulus and the ocular response to this stimulus. We acquire eye movement data from 150 participants, with 4 sessions per participant and conduct experiments on this new and legacy data sets with varying tracker precision and sampling rate. We observe that the model detects replay attacks reliably. For identification and identity verification, the model attains substantially lower error rates than prior work. We explore the relationships between training population size, training data volume, types of visual stimuli, number of training and enrollment sessions, interval between enrollment and probe sessions on one hand and the model performance on the other hand.

Computational drug sensitivity models have the potential to improve therapeutic outcomes by identifying targeted drug components that are likely to achieve the highest efficacy for a cancer cell line at hand at a therapeutic dose. State of the art drug sensitivity models use regression techniques to predict the inhibitory concentration of a drug for a tumor cell line. This regression objective is not directly aligned with either of these principal goals of drug sensitivity models: We argue that drug sensitivity modeling should be seen as a ranking problem with an optimization criterion that quantifies a drug’s inhibitory capacity for the cancer cell line at hand relative to its toxicity for healthy cells. We derive an extension to the well-established drug sensitivity regression model PaccMann that employs a ranking loss and focuses on the ratio of inhibitory concentration and therapeutic dosage range. We find that the ranking extension significantly enhances the model’s capability to identify the most effective anticancer drugs for unseen tumor cell profiles based in on in-vitro data.

International audience; We present results of the classification contest organized for the EMSLIBS 2019 conference. For this publication, we chose only the five best approaches and discussed their algorithm in detail. The main focus of the contest reflected both recent and long-term challenges of Laser-Induced Breakdown Spectroscopy (LIBS) data processing. The contest was designed with a purpose to raise a challenge in handling and processing a very large dataset, containing high-dimensional elemental spectra. For the contest, 138 samples were measured using a lab-based LIBS system. In total, the data set consisted of 70,000 spectra, separated into 12 classes according to their elemental composition. Due to its extensivity and complexity, the data set is unique within the LIBS community. The central idea was to simulate the so-called “out-of-sample” classification (i.e. according to similar elemental composition), implying various real-world applications. Even more, it reflects the current level of expertise in the LIBS community and the capability of the LIBS method itself.

The temporal prediction of groundwater levels plays an important role in groundwater management, such as the estimation of anthropogenic impacts as well as consequences of climatic changes. Therefore, the modeling of groundwater levels using physics-based approaches is an integral part of hydrogeology. However, data-driven approaches have only recently been used, in particular for the prediction of groundwater levels using machine learning techniques (e.g., Random Forest and Neural Networks). For this purpose, one model per observation well or time series is always set up. In order to further develop this, an approach is presented which uses a single model for the prediction of groundwater levels at several observation wells (n > 200). The model is a three-dimensional Convolutional Neural Net (CNN).In addition to the time series of groundwater levels meteorological data on precipitation (P) and temperature (T) serves as additional input channels. The CNN "sees" not only the P- or T-value of the grid cell in which the observation well lies, but also surrounding values. This has the advantage that even influences of meteorological patterns in the spatial vicinity of the observation well on the groundwater level can be learned. The forecasts are calculated for periods up to six months. In addition to the comparison with the measured values, a comparison of the error averaged over all observation wells compared to a baseline model is used for the validation. To further improve predictability, the hyperparameters are optimized and other area data (e.g., geology, soil properties, land use) used as input. This model should form the basis for a regionalized forecast of groundwater levels.

Detection of malware-infected computers and detection of malicious web domains based on their encrypted HTTPS traffic are challenging problems, because only addresses, timestamps, and data volumes are observable. The detection problems are coupled, because infected clients tend to interact with malicious domains. Traffic data can be collected at a large scale, and antivirus tools can be used to identify infected clients in retrospect. Domains, by contrast, have to be labeled individually after forensic analysis. We explore transfer learning based on sluice networks; this allows the detection models to bootstrap each other. In a large-scale experimental study, we find that the model outperforms known reference models and detects previously unknown malware, previously unknown malware families, and previously unknown malicious domains., Comment: Mach Learn (2019)

In order to evade network-traffic analysis, an increasing proportion of malware uses the encrypted HTTPS protocol. We study the problem of detecting malware on client computers based on HTTPS traffic analysis. Here, malware has to be detected based on the host address, timestamps, and data volume information of aggregated packets that are sent and received by all the applications on the client. We develop a scalable protocol that allows us to collect network flows of known malicious and benign applications as training data and derive a malware-detection method based on a neural language model and a long short-term memory (LSTM) network. We study the method's ability to detect new malware in a large-scale empirical study.