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Mass spectrometry (MS) is a fundamental technology of analytical chemistry for measuring the structure of molecules, with many application fields such as clinical biomarker analysis or pharmacokinetics. In the context of proteomic analysis with MS, the superposition of the isotopic patterns of different proteins, in various charge-states produces MS spectra difficult to decipher. The complexity of the pattern models and the large size of the data again increase the difficulty of the analysis step.

In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function. Unlike TE2E, the GE2E loss function updates the network in a way that emphasizes examples that are difficult to verify at each step of the training process. Additionally, the GE2E loss does not require an initial stage of example selection.

It is known that the calculation of a matrix–vector product can be accelerated if this matrix can be recast (or approximated) by the Kronecker product of two smaller matrices. In array signal processing, the manifold matrix can be described as the Kronecker product of two other matrices if the sensor array displays a separable geometry. This forms the basis of the Kronecker Array Transform (KAT), which was previously introduced to speed up the calculations of acoustic images with microphone arrays.

In this paper, we describe a method we have employed at the University of Bristol to improve the undergraduate project experience. We describe the methodology we employ, which consists of a pre-built environment, close supervision by a researcher, and compiled reference material. We then compare the methodology with its absence using the frequency of undergraduates publishing as our metric. We observe a noticeable increase in the number of undergraduate publications under the new methodology, as well as a number of unexpected benefits for the students.

Being affected by mental stress during conversations might have a direct or indirect effect on our speech acoustics as well as on our physiological responses. This paper presents a study on finding the relationship between these two modalities, speech acoustics and physiology, during stressful conversations between humans. Heart rate and respiratory sinus arrhythmia have been considered as physiological variables in the present study. Two datasets, one from stress induction sessions and the other one from in-lab discussions of relationship conflicts between couples, have been analyzed.

In this study, we propose advancing all-neural speech recognition by directly incorporating attention modeling within the Connectionist Temporal Classification (CTC) framework. In particular, we derive new context vectors using time convolution features to model attention as part of the CTC network. To further improve attention modeling, we utilize content information extracted from a network representing an implicit language model. Finally, we introduce vector based attention weights that are applied on context vectors across both time and their individual components.

The emerging compressed sensing (CS) technique enables new reduced-complexity designs of sensor nodes and helps to save overall transmission power in wireless sensor network. Because of the linearity of its encoding process, CS is vulnerable to Ciphertext-Only Attack (COA) and Known-Plaintext Attack (KPA). The prior works use multiple sensing matrices as the shared secret key, however, the complexity overhead of front-end sensor and synchronization issue arising from multiple keys should be well considered.

Acoustic source localization (ASL) is an important problem. Despite much attention over the past few decades, rapid and

Motivated by privacy issues in the Internet of Things systems, we generalize a proposed privacy-preserving packet obfuscation scheme to guarantee differential privacy. We propose a locally differentially private packet obfuscation mechanism as a defense against packet-size side-channel attacks in IoT networks. We formulate the problem as an optimization over a conditional probability distribution (channel) between the original and obfuscated packet sizes and show that the optimal set of obfuscated packet sizes is a strict subset of the set of original packet sizes.