Bridging the Gap between Programming Languages and Hardware Weak Memory Models

We develop a new intermediate weak memory model, IMM, as a way of modularizing the proofs of correctness of compilation from concurrent programming languages with weak memory consistency semantics to mainstream multi-core architectures, such as POWER and ARM. We use IMM to prove the correctness of compilation from the promising semantics of Kang et al. to POWER (thereby correcting and improving their result) and ARMv7, as well as to the recently revised ARMv8 model. Our results are mechanized in Coq, and to the best of our knowledge, these are the first machine-verified compilation correctness results for models that are weaker than x86-TSO

An InGrid based Low Energy X-ray Detector

An X-ray detector based on the combination of an integrated Micromegas stage with a pixel chip has been built in order to be installed at the CERN Axion Solar Telescope. Due to its high granularity and spatial resolution this detector allows for a topological background suppression along with a detection threshold below $1\,\text{keV}$. Tests at the CAST Detector Lab show the detector's ability to detect X-ray photons down to an energy as low as $277\,\text{eV}$. The first background data taken after the installation at the CAST experiment underline the detector's performance with an average background rate of $5\times10^{-5}\,/\text{keV}/\text{cm}^2/\text{s}$ between 2 and $10\,\text{keV}$ when using a lead shielding.Comment: 4 pages, 5 figures, Contributed to the 10th Patras Workshop on Axions, WIMPs and WISPs, CERN, June 29 to July 4, 201

Machine Learning for Human Activity Detection in Smart Homes

Recognizing human activities in domestic environments from audio and active power consumption sensors is a challenging task since on the one hand, environmental sound signals are multi-source, heterogeneous, and varying in time and on the other hand, the active power consumption varies significantly for similar type electrical appliances. Many systems have been proposed to process environmental sound signals for event detection in ambient assisted living applications. Typically, these systems use feature extraction, selection, and classification. However, despite major advances, several important questions remain unanswered, especially in real-world settings. A part of this thesis contributes to the body of knowledge in the field by addressing the following problems for ambient sounds recorded in various real-world kitchen environments: 1) which features, and which classifiers are most suitable in the presence of background noise? 2) what is the effect of signal duration on recognition accuracy? 3) how do the SNR and the distance between the microphone and the audio source affect the recognition accuracy in an environment in which the system was not trained? We show that for systems that use traditional classifiers, it is beneficial to combine gammatone frequency cepstral coefficients and discrete wavelet transform coefficients and to use a gradient boosting classifier. For systems based on deep learning, we consider 1D and 2D CNN using mel-spectrogram energies and mel-spectrograms images, as inputs, respectively and show that the 2D CNN outperforms the 1D CNN. We obtained competitive classification results for two such systems and validated the performance of our algorithms on public datasets (Google Brain/TensorFlow Speech Recognition Challenge and the 2017 Detection and Classification of Acoustic Scenes and Events Challenge). Regarding the problem of the energy-based human activity recognition in a household environment, machine learning techniques to infer the state of household appliances from their energy consumption data are applied and rule-based scenarios that exploit these states to detect human activity are used. Since most activities within a house are related with the operation of an electrical appliance, this unimodal approach has a significant advantage using inexpensive smart plugs and smart meters for each appliance. This part of the thesis proposes the use of unobtrusive and easy-install tools (smart plugs) for data collection and a decision engine that combines energy signal classification using dominant classifiers (compared in advanced with grid search) and a probabilistic measure for appliance usage. It helps preserving the privacy of the resident, since all the activities are stored in a local database. DNNs received great research interest in the field of computer vision. In this thesis we adapted different architectures for the problem of human activity recognition. We analyze the quality of the extracted features, and more specifically how model architectures and parameters affect the ability of the automatically extracted features from DNNs to separate activity classes in the final feature space. Additionally, the architectures that we applied for our main problem were also applied to text classification in which we consider the input text as an image and apply 2D CNNs to learn the local and global semantics of the sentences from the variations of the visual patterns of words. This work helps as a first step of creating a dialogue agent that would not require any natural language preprocessing. Finally, since in many domestic environments human speech is present with other environmental sounds, we developed a Convolutional Recurrent Neural Network, to separate the sound sources and applied novel post-processing filters, in order to have an end-to-end noise robust system. Our algorithm ranked first in the Apollo-11 Fearless Steps Challenge.Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No. 676157, project ACROSSIN

Linearizability with Ownership Transfer

Linearizability is a commonly accepted notion of correctness for libraries of concurrent algorithms. Unfortunately, it assumes a complete isolation between a library and its client, with interactions limited to passing values of a given data type. This is inappropriate for common programming languages, where libraries and their clients can communicate via the heap, transferring the ownership of data structures, and can even run in a shared address space without any memory protection. In this paper, we present the first definition of linearizability that lifts this limitation and establish an Abstraction Theorem: while proving a property of a client of a concurrent library, we can soundly replace the library by its abstract implementation related to the original one by our generalisation of linearizability. This allows abstracting from the details of the library implementation while reasoning about the client. We also prove that linearizability with ownership transfer can be derived from the classical one if the library does not access some of data structures transferred to it by the client

Signatures for Solar Axions/WISPs

Standard solar physics cannot account for the X-ray emission and other puzzles, the most striking example being the solar corona mystery. The corona temperature rise above the non-flaring magnetized sunspots, while the photosphere just underneath becomes cooler, makes this mystery more intriguing. The paradoxical Sun is suggestive of some sort of exotic solution, axions being the (only?) choice for the missing ingredient. We present atypical axion signatures, which depict solar axions with a rest mass max ~17 meV/c2. Then, the Sun has been for decades the overlooked harbinger of new particle physics.Comment: To appear in the proceedings of the 6th Patras Workshop, Zurich 5-9 July 201

Aerial Networking: Creating a Resilient Wireless Network for Multiple Unmanned Aerial Vehicles

The goal of this report is to design the groundwork of a wireless communications system between several Unmanned Aerial Vehicles (UAVs) that will help conduct Search and Rescue (SAR) missions. UAVs could help with these missions because they can provide aerial reconnaissance at low cost and risk. To maximize efficiency, the architecture of our ad hoc network includes several UAVs with cameras (drones) relaying their data through a central UAV called a mothership. Our specific objectives, which we successfully met, were to demonstrate the feasibility of such a network in the laboratory and to lay the groundwork for the physical implementation of the system, including the assembly of a motherboard and Wi-Fi transmitters that will perform the communication between the user and UAVs

Two-Dimensional Convolutional Recurrent Neural Networks for Speech Activity Detection

Speech Activity Detection (SAD) plays an important role in mobile communications and automatic speech recognition (ASR). Developing efficient SAD systems for real-world applications is a challenging task due to the presence of noise. We propose a new approach to SAD where we treat it as a two-dimensional multilabel image classification problem. To classify the audio segments, we compute their Short-time Fourier Transform spectrograms and classify them with a Convolutional Recurrent Neural Network (CRNN), traditionally used in image recognition. Our CRNN uses a sigmoid activation function, max-pooling in the frequency domain, and a convolutional operation as a moving average filter to remove misclassified spikes. On the development set of Task 1 of the 2019 Fearless Steps Challenge, our system achieved a decision cost function (DCF) of 2.89%, a 66.4% improvement over the baseline. Moreover, it achieved a DCF score of 3.318% on the evaluation dataset of the challenge, ranking first among all submissions

Acute: high-level programming language design for distributed computation

Existing languages provide good support for typeful programming of standalone programs. In a distributed system, however, there may be interaction between multiple instances of many distinct programs, sharing some (but not necessarily all) of their module structure, and with some instances rebuilt with new versions of certain modules as time goes on. In this paper we discuss programming language support for such systems, focussing on their typing and naming issues. We describe an experimental language, Acute, which extends an ML core to support distributed development, deployment, and execution, allowing type-safe interaction between separately-built programs. The main features are: (1) type-safe marshalling of arbitrary values; (2) type names that are generated (freshly and by hashing) to ensure that type equality tests suffice to protect the invariants of abstract types, across the entire distributed system; (3) expression-level names generated to ensure that name equality tests suffice for type-safety of associated values, e.g. values carried on named channels; (4) controlled dynamic rebinding of marshalled values to local resources; and (5) thunkification of threads and mutexes to support computation mobility. These features are a large part of what is needed for typeful distributed programming. They are a relatively lightweight extension of ML, should be efficiently implementable, and are expressive enough to enable a wide variety of distributed infrastructure layers to be written as simple library code above the byte-string network and persistent store APIs. This disentangles the language runtime from communication intricacies. This paper highlights the main design choices in Acute. It is supported by a full language definition (of typing, compilation, and operational semantics), by a prototype implementation, and by example distribution libraries