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The first years of an infant’s life represent a sensitive period for neurodevelopment and see the emergence of nascent forms of executive function (EF), which are required to support complex cognition. Few tests exist for measuring EF during infancy, and the available tests require painstaking manual coding of infant behaviour. In modern clinical and research practice, human coders collect data on EF performance by manually labelling video recordings of infant behaviour during toy or social interaction. Besides being extremely time-consuming, video annotation is known to be rater-dependent and subjective. To address these issues, starting from existing cognitive flexibility research protocols, we developed instrumented toys as a new task instrumentation and data collection tool suitable for infant use. A commercially available device comprising a Barometer and Inertial Measurement Unit (IMU) embedded in a 3D-printed lattice structure was used to detect when and how the infant interacts with the toy. The data collected using the instrumented toys provides a rich dataset describing the sequence of toy interaction and individual toy interaction patterns, from which EF-relevant aspects of infant cognition may be inferred. Such a tool potentially provides an objective, reliable, and scalable method of collecting early developmental data in socially interactive contexts.

Most existing results in the analysis of quantum reservoir computing (QRC) systems with classical inputs have been obtained using the density matrix formalism. This paper shows that alternative representations can provide better insights when dealing with design and assessment questions. More explicitly, system isomorphisms are established that unify the density matrix approach to QRC with the representation in the space of observables using Bloch vectors associated with Gell-Mann bases. It is shown that these vector representations yield state-affine systems (SAS) previously introduced in the classical reservoir computing literature and for which numerous theoretical results have been established. This connection is used to show that various statements in relation to the fading memory (FMP) and the echo state (ESP) properties are independent of the representation, and also to shed some light on fundamental questions in QRC theory in finite dimensions. In particular, a necessary and sufficient condition for the ESP and FMP to hold is formulated using standard hypotheses, and contractive quantum channels that have exclusively trivial semi-infinite solutions are characterized in terms of the existence of input-independent fixed points., 19 pages, 4 figures

Serious complications after ultrasound-guided regional anaesthesia can be devastating for the patient. The pathogenesis of postoperative neurological complications (PONC) is multifactorial and includes mechanical, vascular and chemical factors besides the nerve puncture itself. The primary aim of this study was to assess the incidence of PONC after brachial plexus block (BPB). This is an observational retrospective single-centre study conducted at the regional anaesthesia unit of a teaching hospital. All BPBs performed from January 2011 to November 2019 were included. The outcomes analysed were the incidence, aetiology and diagnosis of PONCs and the incidence of other postoperative complications such as local anaesthetic systemic toxicity (LAST), pneumothorax, wrong-side block, etc. The performance of trainees and experienced anaesthesiologists was compared across all the outcomes. From a total of 5340 BPBs included, 15 cases developed PONC, yielding a rate of 2.81:1000 (95% CI 1.70–4.63). Thirteen patients underwent neurophysiological exams which confirmed nine neuropathies. The rate of PONCs for supervised trainees was 1.80:1000 (95% CI 0.701–4.62), not statistically different from that of experienced anaesthesiologists (p = 0.241). Three cases were considered to present with a PONC probably related to BPB [0.562:1,00 (95% CI 0.191–1.65)]. The incidence of long-term PONCs was 1.12:1000 (95% CI 0.515–2.45). Such complications proved irreversible in 2 cases. The incidences of LAST, pneumothorax and other complications observed were 0.749:1000 (95% CI 0.291–1.92), 0.187:1000 (95% CI 0.0331–1.06) and 4.31:1000 (95% CI 2.87–6.46), respectively. This survey suggests that complications after ultrasound-guided BPB, including blocks performed by trainees, are uncommon. Clinicaltrials.gov ID: NCT04451642.

Research in machine-learning classification of electroencephalography (EEG) data offers important perspectives for the diagnosis and prognosis of a wide variety of neurological and psychiatric conditions, but the clinical adoption of such systems remains low. We propose here that much of the difficulties translating EEG-machine learning research to the clinic result from consistent inaccuracies in their technical reporting, which severely impair the interpretability of their often-high claims of performance. Taking example from a major class of machine-learning algorithms used in EEG research, the support-vector machine (SVM), we highlight three important aspects of model development (normalization, hyperparameter optimization and cross-validation) and show that, while these 3 aspects can make or break the performance of the system, they are left entirely undocumented in a shockingly vast majority of the research literature. Providing a more systematic description of these aspects of model development constitute three simple steps to improve the interpretability of EEG-SVM research and, in fine, its clinical adoption.

Reservoir computing systems are constructed using a driven dynamical system in which external inputs can alter the evolving states of a system. These paradigms are used in information processing, machine learning, and computation. A fundamental question that needs to be addressed in this framework is the statistical relationship between the input and the system states. This paper provides conditions that guarantee the existence and uniqueness of asymptotically invariant measures for driven systems and shows that their dependence on the input process is continuous when the set of input and output processes are endowed with the Wasserstein distance. The main tool in these developments is the characterization of those invariant measures as fixed points of naturally defined Foias operators that appear in this context and which have been profusely studied in the paper. Those fixed points are obtained by imposing a newly introduced stochastic state contractivity on the driven system that is readily verifiable in examples. Stochastic state contractivity can be satisfied by systems that are not state-contractive, which is a need typically evoked to guarantee the echo state property in reservoir computing. As a result, it may actually be satisfied even if the echo state property is not present., 33 pages, 5 figures

We revisit the role played by sentiment extracted from news articles related to earnings announcements as a driver of firms’ return, volatility, and trade volume dynamics. To this end, we apply causal machine learning on the earnings announcements of a wide cross-section of U.S. companies. This approach allows us to investigate firms’ price and volume reactions to different types of post-earnings announcement sentiment (positive, negative, and mixed sentiments) under various underlying macroeconomic, financial, and aggregated investors’ moods in a properly defined causal framework. Our empirical results support the presence of (i) economically sizable differences in the effects among sentiment types that are mostly of a non-linear nature depending on the underlying economic and financial conditions; (ii) a leverage effect in sentiment where reactions are (on average) larger for negative sentiment; and (iii) investors’ underreaction to news. In particular, we show that the difference in the average causal effects of the sentiment’s types is larger and more relevant when the general macroeconomic conditions are worse, the investors are pessimist about the behavior of the market and/or its uncertainty is higher, and in market regimes characterized by high stocks’ liquidity.