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On 21 April 2021, the European Commission presented its long-awaited proposal for a Regulation “laying down harmonized rules on Artificial Intelligence”, the so-called “Artificial Intelligence Act” (AIA). This article takes a critical look at the proposed regulation. After an introduction (1), the paper analyzes the unclear preemptive effect of the AIA and EU competences (2), the scope of application (3), the prohibited uses of Artificial Intelligence (AI) (4), the provisions on high-risk AI systems (5), the obligations of providers and users (6), the requirements for AI systems with limited risks (7), the enforcement system (8), the relationship of the AIA with the existing legal framework (9), and the regulatory gaps (10). The last section draws some final conclusions (11).
The bulk built-in current sensor (BBICS) is a cost-effective solution for detection of energetic particle strikes in integrated circuits.
With an appropriate number of BBICSs distributed across the chip, the soft error locations can be identified, and the dynamic fault-tolerant mechanisms can be activated locally to correct the soft errors in the affected logic.
In this work, we introduce a pulse stretching BBICS (PS-BBICS) constructed by connecting a standard BBICS and a custom-designed pulse stretching cell.
The aim of PS-BBICS is to enable the on-chip measurement of the single event transient (SET) pulse width, allowing to detect the linear energy transfer (LET) of incident particles, and thus assess more accurately the radiation conditions.
Based on Spectre simula-tions, we have shown that for the LET from 1 to 100 MeV cm2 mg -1, the SET pulse width detected by PS-BBICS varies by 620-800 ps. The threshold LET of PS-BBICS increases linearly with the number of monitored inverters, and it is around 1.7 MeV cm2 mg- 1 for ten monitored inverters.
On the other hand, the SET pulse width is in-dependent of the number of monitored inverters for LET > 4 MeV cm2 mg -1. It was shown that supply voltage, temperature and process variations have strong impact on the response of PS-BBICS.
Patient monitoring technology has been used to guide therapy and alert staff when a vital sign leaves a predefined range in the intensive care unit (ICU) for decades. However, large amounts of technically false or clinically irrelevant alarms provoke alarm fatigue in staff leading to desensitisation towards critical alarms.
With this systematic review, we are following the Preferred Reporting Items for Systematic Reviews (PRISMA) checklist in order to summarise scientific efforts that aimed to develop IT systems to reduce alarm fatigue in ICUs. 69 peer-reviewed publications were included. The majority of publications targeted the avoidance of technically false alarms, while the remainder focused on prediction of patient deterioration or alarm presentation.
The investigated alarm types were mostly associated with heart rate or arrhythmia, followed by arterial blood pressure, oxygen saturation, and respiratory rate.
Most publications focused on the development of software solutions, some on wearables, smartphones, or headmounted displays for delivering alarms to staff.
The most commonly used statistical models were tree-based. In conclusion, we found strong evidence that alarm fatigue can be alleviated by IT-based solutions.
However, future efforts should focus more on the avoidance of clinically non-actionable alarms which could be accelerated by improving the data availability.
One of the most challenging difficulties for incumbent organisations, especially small- and medium-sized enterprises (SMEs), is to manage digital transformation driven by technological change. Incumbent organisations' responses to digital transformation have been extensively studied in the current literature.
However, most research neglects digital transformation in SMEs. There are hardly any valid developed measures for the maturity of digital transformation. We present a holistic digital transformation maturity model based on an extensive literature review, qualitative computer-assisted data analysis, and empirical findings.
The digital transformation maturity model focuses on small- and medium-sized enterprises' unique features and characteristics.
We proved the practical applicability and relevance of the digital transformation maturity model in an extensive study involving various organisations, particularly German SMEs (n = 310).
Organisations can use this model to assess themselves initially and, through this process, gain a comprehensive understanding of the multiple forms of digital transformation.
Analysis of single event transient effects in standard delay cells based on decoupling capacitors
(2022)
Single Event Transients (SETs), i.e., voltage glitches induced in combinational logic as a result of the passage of energetic particles, represent an increasingly critical reliability threat for modern complementary metal oxide semiconductor (CMOS) integrated circuits (ICs) employed in space missions.
In rad-hard ICs implemented with standard digital cells, special design techniques should be applied to reduce the Soft Error Rate (SER) due to SETs.
To this end, it is essential to consider the SET robustness of individual standard cells. Among the wide range of logic cells available in standard cell libraries, the standard delay cells (SDCs) implemented with the skew-sized inverters are exceptionally vulnerable to SETs. Namely, the SET pulses induced in these cells may be hundreds of picoseconds longer than those in other standard cells.
In this work, an alternative design of a SDC based on two inverters and two decoupling capacitors is introduced. Electrical simulations have shown that the propagation delay and SET robustness of the proposed delay cell are strongly influenced by the transistor sizes and supply voltage, while the impact of temperature is moderate. The proposed design is more tolerant to SETs than the SDCs with skew-sized inverters, and occupies less area compared to the hardening configurations based on partial and complete duplication.
Due to the low transistor count (only six transistors), the proposed delay cell could also be used as a SET filter.
Over the past years, NGS has become a crucial workhorse for open-view pathogen diagnostics.
Yet, long turnaround times result from using massively parallel high-throughput technologies as the analysis can only be performed after sequencing has finished. The interpretation of results can further be challenged by contaminations, clinically irrelevant sequences, and the sheer amount and complexity of the data.
We implemented PathoLive, a real-time diagnostics pipeline for the detection of pathogens from clinical samples hours before sequencing has finished.
Based on real-time alignment with HiLive2, mappings are scored with respect to common contaminations, low-entropy areas, and sequences of widespread, non-pathogenic organisms.
The results are visualized using an interactive taxonomic tree that provides an easily interpretable overview of the relevance of hits. For a human plasma sample that was spiked in vitro with six pathogenic viruses, all agents were clearly detected after only 40 of 200 sequencing cycles.
For a real-world sample from Sudan, the results correctly indicated the presence of Crimean-Congo hemorrhagic fever virus. In a second real-world dataset from the 2019 SARS-CoV-2 outbreak in Wuhan, we found the presence of a SARS coronavirus as the most relevant hit without the novel virus reference genome being included in the database.
For all samples, clinically irrelevant hits were correctly de-emphasized.
Our approach is valuable to obtain fast and accurate NGS-based pathogen identifications and correctly prioritize and visualize them based on their clinical significance: PathoLive is open source and available on GitLab and BioConda.
In this increasingly data-rich world, visual recordings of human behavior are often unable to be shared due to concerns about privacy.
Consequently, data sharing in fields such as behavioral science, multimodal communication, and human movement research is often limited.
In addition, in legal and other non-scientific contexts, privacy-related concerns may preclude the sharing of video recordings and thus remove the rich multimodal context that humans recruit to communicate.
Minimizing the risk of identity exposure while preserving critical behavioral information would maximize utility of public resources (e.g., research grants) and time invested in audio-visual research.
Here we present an open-source computer vision tool that masks the identities of humans while maintaining rich information about communicative body movements. Furthermore, this masking tool can be easily applied to many videos, leveraging computational tools to augment the reproducibility and accessibility of behavioral research.
The tool is designed for researchers and practitioners engaged in kinematic and affective research. Application areas include teaching/education, communication and human movement research, CCTV, and legal contexts.
Objective:
Hypertension has long been recognized as one of the most important predisposing factors for cardiovascular diseases and mortality.
In recent years, machine learning methods have shown potential in diagnostic and predictive approaches in chronic diseases.
Electronic health records (EHRs) have emerged as a reliable source of longitudinal data. The aim of this study is to predict the onset of hypertension using modern deep learning (DL) architectures, specifically long short-term memory (LSTM) networks, and longitudinal EHRs.
Materials and Methods:
We compare this approach to the best performing models reported from previous works, particularly XGboost, applied to aggregated features.
Our work is based on data from 233 895 adult patients from a large health system in the United States. We divided our population into 2 distinct longitudinal datasets based on the diagnosis date.
To ensure generalization to unseen data, we trained our models on the first dataset (dataset A "train and validation") using cross-validation, and then applied the models to a second dataset (dataset B "test") to assess their performance.
We also experimented with 2 different time-windows before the onset of hypertension and evaluated the impact on model performance.
Results:
With the LSTM network, we were able to achieve an area under the receiver operating characteristic curve value of 0.98 in the "train and validation" dataset A and 0.94 in the "test" dataset B for a prediction time window of 1 year. Lipid disorders, type 2 diabetes, and renal disorders are found to be associated with incident hypertension.
Conclusion:
These findings show that DL models based on temporal EHR data can improve the identification of patients at high risk of hypertension and corresponding driving factors. In the long term, this work may support identifying individuals who are at high risk for developing hypertension and facilitate earlier intervention to prevent the future development of hypertension.
This vision article outlines the main building blocks of what we term AI Compliance, an effort to bridge two complementary research areas: computer science and the law.
Such research has the goal to model, measure, and affect the quality of AI artifacts, such as data, models, and applications, to then facilitate adherence to legal standards.