dataesr/scientific-paragraphs-categorization huggingface dataset:
A hybrid solution was also proposed in  [16] , using an FPGA to host a reconfigurable low-latency event engine. It is capable of recognizing spatio-temporal spike patterns and delivering sub-millisecond feedback. Further processing is performed in real-time on computer software, with higher processing latencies. To our knowledge, existing full hardware platforms are dedicated to spike-based  [17]  or LFP  [18]  processing and lack versatility to handle wide varieties of activity patterns. With Multimed, we intend to overcome the limitations of software solutions (lack of integrability and high processing latency) while maintaining the modularity that they offer. It utilizes VHDL (the standardized Hardware Description Language for digital circuits) modules, optimized for resource cost and speed, to assemble full-hardware processing architectures with high performance.
System Architecture and FPGA Design Within the context being described in Methods, we designed Multimed, a module-based, multi-application platform dedicated to the acquisition and processing of biological signals. It meets configurability and real-time constraints, and features filtering, detection, measurement, and decision-making capabilities. This section describes the resulting system in more detail. Specifically, it gives an overview of Multimed's architecture, as well as a list of all the available processing modules. Then, processing performance figures are given. Finally, examples of experiments are given to highlight Multimed's ability to conduct multichannel recording.
Commercial multichannel recording and stimulation systems  [19]  usually contain multiple buffering levels that impede real-time control and feedback capacity. On the contrary, Multimed is fully pipelined or parallelized and provides a constant, well-characterized processing latency. Furthermore, all of the intermediate processing stages are accessible in higher-level layers of the system for both display and recording. All of the processing and display modules are configurable via a computer before and during experiments. Each Multimed digital processing stage is an application-specific arrangement of processing units from a dedicated library, as detailed in Processing units and Function combinations. The processing architecture may be remodeled, as illustrated in the examples in Figure  3b ,c, depicting, respectively, an open-loop architecture dedicated to signal analysis and monitoring  [20]  and its counterpart for open-loop stimulation driven by a Spiking Neural Network (SNN).
Processing Units As previously stated, Multimed is a multi-application platform with extensive configurability features. For that purpose, and to support interchangeable data flows, a module-based approach has been used, in which communication and processing units are implemented on a FPGA. A library of functional blocks has thus been developed in VHDL, with standardized connectivity rules for easy, straightforward design of real-time, multichannel processing chains. Processing modules are designed to be fully configurable, with two categories of parameters: dynamic parameters, which are modifiable by the user, while the system is in operation, and static parameters (generally architecture-related, such as the number of channels and data width) set during the architecture design phase. Dynamic parameter implementation has been designed to keep data consistency while updates are performed.
To illustrate stimulation generation performance in Multimed, Figure  7  presents the computation steps in a simple case: in a 64-channel acquisition chain, a single AP is detected by IIR filtering and adaptive thresholding, and triggers a stimulus on an MCS STG 4004 (STG 4004, MCS, Multi Channel Systems, Reutlingen, Germany). All of the processing steps between the raw signal and the actual electrical stimulus (filtering, threshold computation, and routing) are represented, including the external stimulator time lag, as specified by the manufacturer. In this case, our stimulation control module output a TTL trigger and computed the "routing" step in 2. 56 µs, giving a total of 103. 62 µs between the AP and the stimulus. The stimulation control is associated with a blanking function that drops any event detected within a time window after the stimulation has been triggered.
Simple modules are fully pipelined and implemented at very little resource and time cost, while more complex modules are not fully pipelined: data is buffered in a local memory. Although this communication scheme may sometimes induce duplicated storage in the system, it improves the processing latency (quantified in Table  2 ). Multimed features are summarized in Table  3. With the described environment, it is relatively straightforward to build a fully real-time system where maximum processing latency remains well below the sampling period. Furthermore, the user can freely upscale full data processing chains, even if the overall processing latency is longer than the sampling period, as all of the computation modules obey the real-time processing constraint ("real-time processing constraint" meaning that computation time is bounded by design).
Biological Protocols In neuron preparations, cell activity was measured using 60-electrode MEAs from the Qwane Biosciences Company [Qwane, Lausanne, Switzerland]. These biochips provide 60 platinum electrodes with 40 µm-diameter tips and 200 µm spacing, a larger area for the grounded electrode, and a 6 mm-high glass cylinder, used as a culture chamber. Primary neuron cell cultures were obtained from the cortex of embryonic (E18) Sprague-Dawley rats, following a standard protocol (see  [26]  for a full protocol description). The MCS pre-amplifier (MEA1060-Inv, MCS, Multi Channel Systems, Reutlingen, Germany) had a gain of 1200 and raw data were sampled at 10 kHz/channel. Neuron culture age was 48 DIV (Days in Vitro) in the monitoring application and 36 DIV in the stimulation application; both when neuronal activity mainly consisted of bursts  [27, 28].
) are available to investigate the effects of new drugs in the treatment of diabetes, while our system provides direct access to the physiological response of the islets  [4]. Screening measurements were conducted to determine the behavior of murine pancreatic islets in response to changes in the main physiological stimulus (Glucose) and can be easily extended to other nutrients and hormones. The Multimed configuration (see Figure  11 ) for this application was able to record raw data, as well as detecting APs, AP bursts, and Slow Potentials (SPs). It also included measurement modules to compute AP and SP frequency and FOPP, as well as the spatial average of those parameters. Figure  12  shows the changes in both signal amplitude and SP frequency in response to changes in the perfused glucose solutions.
Real-time processing emphasizes the dynamics of islet response, particularly when the glucose concentration increased (from G3 to G7). It is also possible to perform statistical analyses, and more specific, costly calculations on events. We hypothesized that SPs were better biomarkers of islet function and insulin demand than APs  [4]. In  [4] , we validated the AP-SP correlation by detecting APs and SPs off-line and evaluating their respective angular position. We reproduced that result using Multimed's online and real-time detection. Figure  13  shows an example of a statistical analysis that was performed on a set of online-recorded data on mouse islets (MCS 60-channel MEA). 2608 APs and 329 SPs were detected in real-time and the computation of their relative positions, as performed in MATLAB (MathWorks, Cambridge, UK), revealed that the majority of APs fired during the downward phase of SPs.
Multimed's performance in decision-making and its architectural possibilities were designed to make it suitable for closed-loop experiments involving pulsed stimulation. It would act as a feedback controller for an external stimulator that loops back into the biological medium. The main advantage when compared to previously presented architectures is the Output processing module, which configures different signals, combinations of signals, and network dynamics as the source of one of the stimulation trigger outputs (see Figure  14 ). Figure  15  presents the results of a demonstration experiment on a cortical neuron network, in which a stimulation trigger was sent whenever the system detected network synchronization between six user-defined channels. In this experiment, no stimulation artifact was observed as no stimulation was actually performed on the culture.
It was finally hinted that routing in Multimed had been designed to permit closed-loop experiments, and that single events, as well as network bursting, could be utilized as stimulation triggers. These results demonstrated the viability of a system such as Multimed, as it could successfully adapt to the various materials and paradigms proposed. As a sidenote, we remind that the performance of each submodule remained constant, even with the increasingly complex architectures presented. This again underlines the advantage in utilizing distributed logic, rather than software processing.
Discussion Few recording systems described in the literature perform multi-application, multichannel, real-time processing with closed-loop and versatility constraints. The Neurorighter  [36]  open-source platform meets those constraints and is a great example of a versatile system: all processing is performed in computer software, which makes it extremely flexible. This, however, is to the detriment of global latency and integrability. Software solutions generally suffer from these drawbacks, though latency can greatly be reduced with dedicated RTOS (real-time operating systems), like is the case with RTXI and its 60 µs closed-loop latency  [15]. A more comparable system is that depicted in  [16] , involving a closed-loop neural network consisting of cortical neuron clusters interconnected with electronic synapses.
Synapse-based stimulation is computed on a reconfigurable event processor: in order to minimize processing latency and to achieve acausal synaptic stimulation and decision making within the STDP window, critical processing is done in an FPGA. The system's processing architecture in itself is not configurable, but the decision-making event engine is extremely flexible. Although Multimed features a similar module, the one that is depicted in  [16]  is more complex and capable of creating entire event-processing sub-architectures. Comparable hardware is produced by Intan (Intan Technologies, Los Angeles, CA, USA) with its RHD2000 interface board product line: an acquisition board is mounted with an Opal Kelly FPGA board and has SPI interfaces to connect various Intan headstages. This hardware, with its open-source control and real-time processing software, is the product of choice for recent open-source electrophysiology projects.
While the community recognizes the potential of Intan hardware to perform sub-millisecond feedback thanks to its FPGA, it is not a native feature and no dedicated modules have been released yet. Finally, Open Ephys proposes a very popular system that is targeted at researchers that uses implantable devices in non-human subjects. It is an open-source system that can be built at a low price (less than $100 per channel). Although its electronics utilize an FPGA, signal processing is performed in software, resulting in fairly high processing latencies: the closed-loop latency (typically between 10 and 20 ms) is attributed to software buffers and USB communication delays  [11]. Again, this project trades a great deal of flexibility for performance, as all data is available for online display in software.
With Multimed, we hope to bring out the best of both worlds, with the performance of full-hardware processing and easily reconfigurable modules and architectures. Still, Multimed's most immediate limitation is its current analog front-end that limits its usage to 60 electrodes. Sampling frequency is less constrained: the 10 kHz sampling frequency that we used in experiments is set to keep coherency with the 3 kHz bandpass of our pre-amplifiers; that sampling frequency can be increased to 15 kHz for all of the channels without any modification to Multimed. The individual channel sampling rate can also be increased simply by reducing the number of recorded channels to maintain the global sample rate to its current value. The Multimed computing architecture would not be immediately impacted by higher input figures (e.
g. , number of electrodes or frequency), as processing modules are parameterized and scalable. Still, implementing the modules library to process more inputs may at some point require a FPGA upgrade to provide sufficient hardware computation resources: Multimed is now equipped with a lowcost Spartan 6 FPGA (Xilinx xc6slx150), which is used at 50% of its computation capacity for the whole system. We performed a theoretical study that showed that same FPGA can handle in Multimed more than 1024 individual channels, the limiting resource being embedded memory. Upscaling input data (number of electrodes or sampling frequency) will also multiply the amount of data to save in the same proportion. To be able to keep up with this extra amount of data, the system would need extra storage capability.
Final storage is not the primary issue, since SD-XC cards can now handle eight times more data that our reference 32 GB SD-HC card, which corresponds to 14-h recording of 60 electrodes sampled at 40 kHz. The main issue comes from the SD-XC pre-record data buffer: its required size has not yet been fully characterized by the authors, when embedded in the Multimed control architecture. It is also worth mentioning that using highly-configurable systems, such as Multimed, requires appropriate training for end-users: biologists or clinicians. Although Multimed modules are designed to be easily interconnected through a standardized interface, building a customized architecture requires experimenters that are trained in digital system design. Overcoming this constraint would require an advanced, high-level, human-machine interface, which is suitable for use in constrained environments.
This interface has not yet been developed, since it was not a functional priority. The Multimed system can be considered either as an electrophysiology platform or as a prototype for electroceutic devices, such as cortical stimulators, which now bundle many separate functions into a single implantable package  [37, 38]. Specifically, the synthesized digital architecture provides a ready-to-use netlist for on-chip physical implementations. Multimed settles a proof of concept, but more development is required to produce an embeddable device. A VLSI implementation would greatly improve package size and power consumption, but the resulting IC would be highly application-dependent. Multimed's architecture brings massive multi-electrode computing and multi-source fusion within reach, but few experiment paradigms to date have required such performance levels.
Conclusions Our architecture demonstrated its accuracy in real-time, long-term  [4, 21, [33] [34] [35] 39]  experiments. Furthermore, current FPGA use is below its maximum processing potential, as only a few electrodes and simple feedback rules have been exploited. The system that is presented here represents technological advancement in the field of open-and closed-loop experiments for fundamental research in bio-mechanism exploration and functional rehabilitation. It consists of a highly-configurable, generic hardware, which is associated with a library of interoperable modules. The resulting platform makes it easy to build application-specific, open-or closed-loop setups, with high computing efficiency and a large number of channels. Targeting the use of living cells as sensing elements, Multimed provides long term, continuous monitoring, and/or processing of signals recorded by bio-sensors.
As recording and display are only used for characterization and evaluation, removing these modules produces an autonomous, embeddable system. This device is then able to perform multi-parametric, multi-channel monitoring and analysis, and provide condensed reports, consultable from time to time (e. g. , once or twice a day), via a low-bandwidth connection. Multimed setups have been installed in multiple sites and are being used by partner laboratories in collaborative projects, always aiming for closed-loop experiments (BRAINBOW (EU project 284772, ICT-FET FP7/2007-2013, FET Young Explorers)  [33] , CENAVEX (ANR grant 2013-NEUC-0001-01 and NIH grant 5 R01 NS086088-02)  [34, 35, 40] , ISLET CHIP (ANR grant 2013-PRTS-0017)  [25, 41] , HYRENE (ANR grant 2010-BLANC-0316-01)  [21] ). New needs in terms of processing capabilities arise from existing collaborations and can also emerge from new collaborations, leading to regular updates of the digital library.
The answer to the questionnaire is anonymous, as the purpose of the questionnaire is not to evaluate individual adherence, but rather to allow health centers to periodically obtain the rates of adherence for all patients or groups of patients. The WebAd-Q is designed to provide self-answers on a computer, accessible via the Internet d or offline, containing multimedia resources and automatically generating a database of answers of restricted access. It is formatted as an interactive cartoon, which asks three questions about the use of medications in the last seven days. The texts were audio recorded so that the questionnaire could be both read and heard by the respondents. Based on ours instructions, a web designer produced three alternate designs for the images and screens, from which we chose two: one with a female image and one with a male image.
The two drawings, as well as the writing of the questions, were analyzed in two phases by volunteer patients from two health centers with interviews in the waiting room and focus groups. For this stage of development of the instrument, we defined an intentional sample of 38 patients, from both sexes, different social strata, education level, ethnic groups, and different therapeutic regimens, who are monitored at the STD/AIDS Reference and Training Center of the State of São Paulo (CRT-DST/AIDS-SP) and at the Clinic of Medical Specialties of São Bernardo do Campo (SAE-SBC). The operation of the answer system and the database was tested at CRT-DST/AIDS-SP. Based on the consultations made, the research team instructed the final design and supported the web designer in the design of the application.
d The questionnaire is available at: www2. fm. usp. br/webad-q. For access, the code "1999" must be used. Possible answers for each question are: "No" (adherence answer), "Yes" or "I don't know/I don't remember" (non-adherence answers). In addition to the measures for each individual question, we constructed a scale of non-adherence of four categories, taking into account all three questions: 1) no non-adherence answer, 2) one non-adherence answer, 3) two non-adherence answers, and 4) three non-adherence answers. For the validity study, we invited 90 adult patients on HAART for at least three months at the SAE-SBC, which is a health center that monitored approximately 800 persons on HAART at the time of data collection. The calculation of the sample size had sensitivity of 80%, precision of 10%, and prevalence of adherence of 70% as parameters.
The results of the viral load of each patient were collected routinely in the SAE-SBC. In this center, viral load tests are requested from all patients in the follow-up every four months. For each patient, we analyzed the result of the viral load test closest to the answer to the WebAd-Q, collected before or after answering the questionnaire. As a mathematical resource to calculate the viral load log, we used the unit to express individuals with undetectable viral load. For the analysis of the social, demographic, and clinical data of the participants, we calculated the absolute and relative frequencies and the respective 95% confidence intervals (95%CI) of the qualitative variables, and the medians, means, and respective standard deviations of the continuous variables. The WebAd-Q was answered by the patients at the end of the sixtieth day of research, in a specific room for this purpose, after receiving instructions from previously trained health professionals.
RESULTS Of the 90 invited patients, 74 (82. 2%) answered the WebAd-Q questionnaire. The average follow-up time in the study was 60. 7 days [standard deviation (SD) of six days]. Among those who did not answer the questionnaire, there were two (2. 2%) refusals and 14 (15. 6%) drop-outs. Table  1  presents the sociodemographic and clinical data of the patients who completed the study. We observed a higher proportion of male participants, mean age above 45 years, high proportion of nine years or more of education, long treatment time, reports of previous non-adherence and adverse reactions, and low proportion of persons who regularly use alcohol and drugs. Among the 74 participants in the study, four (5. 4%) reported difficulty in using MEMS, which resulted in the loss of information for MEMS dose and MEMS time in the last seven days.
This study showed that the groups of patients who provided non-adherence answers to the WebAd-Q were also considered less adherent by the other methods. The questionnaire ensures the anonymity of the respondents, minimizing the desirable response bias. Although the research environment requires informed consent, the high non-adherence results obtained suggest that patients were comfortable to admit that they did not take or that they changed the dose of their medication. The study showed the good pragmatic properties of the WebAd-Q: patients reported no difficulties in answering it; the average time to answer it was 5 min 47 sec, which is short considering that the questionnaire has an introduction and acknowledgments at the end; and the database is easily fed with answers, which dismisses the use of typists and can be consulted and shared online.
Internet of Things (IoT)   applications are becoming increasingly critical and require rigorous formal verification. In this paper we target Contiki, a widely used open-source OS for IoT, and present a verification case study of one of its most critical modules: that of linked lists. Its API and list representation differ from the classical linked list implementations, and are particularly challenging for deductive verification. The proposed verification technique relies on a parallel view of a list through a companion ghost array. This approach makes it possible to perform most proofs automatically using the Frama-C/WP tool, only a small number of auxiliary lemmas being proved interactively in the Coq proof assistant. We present an elegant segment-based reasoning over the companion array developed for the proof.
Introduction Connected devices and services, also referred to as Internet of Things (IoT), are gaining wider and wider adoption in many security critical domains. This raises important security challenges, which can be addressed using formal verification. This paper focuses on Contiki  [10] , a popular open-source operating system for IoT devices providing full low-power IPv6 connectivity, including 6TiSCH, 6LoWPAN, RPL, or CoAP standards. It is implemented in C with an emphasis on memory and power optimization, and contains a kernel linked to platform-specific drivers at compiletime. When Contiki was created in 2002, no particular attention was paid to security. Later, communication security was integrated, but formal verification of code was not performed until very recent case studies  [19, 21].