dataesr/scientific-paragraphs-categorization huggingface dataset:
The ImmUniverse project will bring IMID clinical management to a new level through novel, validated Schematic diagram of ImmUniverse data integration strategy. Ulcerative colitis and atopic dermatitis will be analyzed separately according to the same principle: First, supervised multi-omics analysis will be performed to identify candidate prognostic and predictive signatures from multiomics analysis of single tissue data and evaluate their performance in cross-validation (CV) or through publicly available data (as available). Signatures will be then correlated between sample matrices to identify 'proxy'signatures in matrices suitable for clinical exploitation. Features from prediction and correlation tasks will be pooled, mapped to pathways and ontologies and prioritized on the basis of their importance for prediction, across-tissue correlation and pathway annotation in a collaborative effort between project partners (clinicians, biologists and bioinformaticians).
Funding This ImmUniverse (www. immuniverse. eu)has received funding from the  Innovative Medicines Initiative 2 Joint Undertaking (JU)  under grant agreement No. 853995. The JU receives support from the  European Union 's  Horizon 2020 research and innovation program  and  EFPIA. The content provided in this publication reflects only the author's view and neither the  IMI JU  nor the European Commission are responsible for any use that may be made of the information it contains.
With continuously improving signal-to-noise ratios, a statistically sound analysis of quasi-elastic neutron scattering (QENS) spectra requires to fit increasingly complex models which poses several challenges. Simultaneous fits of the spectra for all recorded values of the momentum transfer become a standard approach. Spectrometers at spallation sources can have a complicated non-Gaussian resolution function which has to be described most accurately. At the same time, to speed up the fitting, an analytical convolution with this resolution function is of interest. Here, we discuss basic concepts to efficient approaches for fits of QENS spectra based on standard MATLAB and Python fit algorithms. We illustrate the fits with example data from IN16B, BASIS, and BATS.
It does not, however, address Bayesian inference, for instance regarding the number of Lorentzians present in a data set. This topic has been dealt with previously by other work  [16, 17]. Moreover, it is noted that several standard procedures for fitting are available inside standard software such as Mantid  [18]. Subsequent to a brief outline of script concepts in MATLAB and Python, this article illustrates the analytical convolution with the spectrometer resolution function employing IN16B, BASIS, and BATS example data on protein solutions. The aim of this work is to show the general applicability of the analysis framework to different instruments but not the comparison of instruments. Therefore, different spectra of similar, but not identical samples are analyzed.
"Wrapping" in Python for flexible models To keep the readability of the analysis code as good as possible as well as generally applicable, it is important to be able to easily change the model for the resolution function to apply the code to different spectrometers. This flexibility implies an automated convolution of the model function for the samples with the resolution function. Additionally, a change between different models can easily be performed in this way. Without going into any further detail of coding, such a flexible fit function call can for instance be written as follows using python and scipy. curve_fit: popt, pcov = curve_fit( lambda x, *p: \ wrapper_function( x, q, n, r, len( f0 ), \ p, **keyword_parameters ), \ x, y, p0=f0, bounds=(l,u), sigma=dy ) Therein, the concept of the lambda function allows to point to external functions, and the wrapper_function contains the fit function that can be modified by a choice from a library of functions or a building-block syntax for functions.
Fits in MATLAB In MATLAB, simultaneous fits for all q at once to the data y at the points in energy x can be performed using the lsqnonlin command from the optimization toolbox: lsqnonlin(model,f0,l,u,options,x,y); Therein, as in the python version, f 0 is the initial guess, and l and u are the lower and upper bounds, respectively. Importantly, x and y can be matrices. model is a function handle pointing to the function EvaluatedModel which returns a function of the model evaluated for the parameters f p. This "function of the model" has to take care of the weighting by the errors dy on the data, since lsqnonlin does not provide an option for explicit error weighting: model=@(fp, xdata, ydata)EvaluatedModel(. dy, q, fp, xdata, ydata, N, fixedp); This syntax establishes that EvaluatedModel can take more parameters -namely all parameters in the second pair of round brackets -than the function handle model is allowed to accept -namely only the parameters in the first pair of round brackets -and therefore allows to hand over the number of q-independent fit parameters N as well as the fixed parameters and resolution parameters fixedp, and the q values, to calculate the scattering function depending on q and ℏω.
Conveniently, the Voigt function V , as the convolution of Gaussian with Lorentzian function, is available in python via the Faddeeva function scipy. special. wofz [33] , f (z) = exp(-z * * 2) * erfc(-i * z): import numpy as np from scipy. special import wofz def V( x, sigma, gamma ): return np. real( wofz( (x + 1j*gamma) \ / sigma / np. sqrt(2. ) ) ) \ / sigma / np. sqrt( 2. *np. pi ) The voigt function V also exists as scipy. special. voigt_profile since scipy 1. 9. 1. In MATLAB, the Voigt function can be implemented as C++ code -for instance via the Faddeeva package available from mathworks. com -and linked as compiled mex file. In Figure  2  A direct comparison between the instruments is therefore not possible. In contrast, the present and subsequent figure illustrate the generality of the analysis approach.
Global optimization The complexity of the models used to fit the experimental data implies that the fit function is not necessarily strictly convex, but multiple local minima can be found. Hence, the least-square algorithm is sensitive to the initial values for the parameters given by the user. To circumvent this problem, common sense and experience can help discarding solutions for which the fitted parameters are aberrant. However, multiple solutions can make sense in regard to the system studied and more robust methods are needed to find the different minima and identify the global minimum. To this end, global optimization methods can be used, such as the genetic algorithm  [36]  or basin-hopping  [37]. The basin-hopping algorithm consists in running  ) BSA protein solutions recorded on different spectrometers (symbols) and associated fits (solid red lines superimposed on the symbols).
Conclusions We have presented conceptual notes on MATLAB and Python implementations allowing for efficient scripting and fast fitting of neutron backscattering data, due to an analytical convolution. We have illustrated this fit procedure on example protein solution spectra. Spectrometer-design specific resolution functions can be routinely handled even in the case of strongly non-Gaussian resolution functions. Due to the computing time-saving analytical convolution, rather complex fits, including simultaneous fits of the spectra at all momentum transfers at once, can be carried out rapidly.
Code and Data Availability Matlab analysis code performing multidimensional fits for individual and several spectra are available from the corresponding author on reasonable request. python code is available from https://github. com/seydel/QENS_utilitiesas well as from https://github. com/kpounot/nPDyn. The experimental data collected at IN16b are curated by the  ILL  and can be accessed via the experiments  9-13-952 [39 ] and 1-20-69 [40]. The BASIS data were collected during the experiment IPTS 18578. 1. C. B. , K. P, I. M. , F. R. -R. and T. S. thank the organizers of the QENS/WINS 2022 conference for an excellent meeting in San Sebastian. We thank  M. Hennig  and  M. Grimaldo  for earlier versions of the employed MATLAB code. We thank the  DFG, ANR  ( ANR-21-CE06-0047 ),  BMBF  (ErUM-pro  05K19VTB  and  05K22VTA ), and  InnovaXN  ( EU MSCA COFUND 847439 ) for funding.
Many digital systems need to provide cryptographic 1 capabilities. A large part of these devices is easily accessible 2 by the malicious user, and may be vulnerable to side channel 3 attacks such as power or electromagnetic analysis. From one 4 side, the designer has to protect the architecture with proven 5 countermeasures; on the other, the actual implementation must 6 be validated in order to prove the absence of undesired leakages. 7 In this paper, we present an implementation of two optimized 8 and proven masking schemes of order 3 and 7 for an embedded 9 software AES, and prove its robustness by showing the absence 10 of significant leakage in the nonlinear layer. 11.
The current trend is to use masking schemes of order (i. e. , the degrees of random variability) larger than one, as simple masking can still be easily broken. Our goal is therefore to improve the efficiency of masked implementations (in our case, of the Advanced Encryption Standard -AES), especially at higher orders, in the hope that it will help moving from ad hoc and specialized countermeasures against side-channel attacks toward formally secure and generic ones. In this paper, we present an implementation of two optimized and proven masking schemes of order 3 and 7 (in order to compare with similar state of the art) for an embedded software AES, and prove its robustness by showing the absence of significant leakage in the nonlinear layer. 1 The paper is organised as follows. In the next section, some  In the 2000's, many attempts have been made to design ad hoc masked implementations and "provably secure" ones.
B. Masking the AES S-Box Every step other than SubBytes in the AES being linear and easily masked, the biggest differences between masked implementations are often found in the computation of the S-Box. There are mainly four popular approaches. Table lookups is a popular way to implement the AES S-1 box, even in implementations that are not masked. This method 2 consists in pre-computing tables to be used during the actual 3 computation. During the actual computation of the S-Box, the implementation just has to fetch the value at the offset 5 corresponding to the byte for which we want to compute the 6 S-Box. However, it may lead to cache-timing attacks when the 7 implementation is running on a processor where a data cache 8 is available  [19]. Thus, the precomputed tables must be built 9 depending on the value of the first d -1 masks of the masked 10 byte.
A. Randomness Generation Each gadget for the masked AND gate at order 3 (respectively 7) requires the generation of 5 (respectively 20) random masks. In our implementation, the masked functions for text encryption and key scheduler take as parameter a function pointer to rng_fill, a function that is used to fill a buffer of arbitrary size with random values. For our specific environment, rng_fill is implemented using the Random Number Generator (RNG) embedded in the STM32L432. This RNG is based on ring oscillators and allows to generate 32bit words. A specific control register in memory is updated by the RNG module to tell whether or not the random value is ready to be read for the data register in memory. It takes approximately 64 cycles to generate each 32-bit word. The performance impact of the RNG on the overall implementation is discussed in Section IV-B.
39 This can be seen as a necessary condition to potential attacks against the implementation, but the number of total traces analysed is crucial in experimental evaluation. It is not uncommon to start observing significant differences between the two datasets only after several hundred thousands traces measured if the environmental measurement noise is significant or when some counter-measures are implemented. The underlying statistical test commonly used is a Welch's t-test done for each point in time. The common approach to aggregate every trace inside each dataset is to compute their mean and to try to distinguish this mean from the mean of the other dataset. The Welch's t-test is producing a statistic t: t = µ 1 -µ 2 s1 2 n1 + s2 2 n2 , with µ i the mean of the i-th dataset, s i its variance and n i its size.
The greater the absolute value of t, the more statistically significant the difference. In the original proposal of the TVLA, the two datasets are compared using a common threshold directly on the value of t. This threshold is fixed to ±4. 5 for every point in time. This means that the device under test is said to successfully pass the test (i. e. , no leakage is found) if the maximum absolute value of t over all points in time is lower than 4. 5. In  [26] , the authors proposed to try to distinguish these datasets by using moments of higher order (variance, skewness, kurtosis,. ) instead of the variances. Replacing the Welch's t-test by Pearson's χ 2 -test has been proposed in 2015 as a complementary approach  [27]. As for the t-test, this test aims at trying to distinguish the two datasets.
(d) Random masks at order 7 (2 × 1 000 000 traces). al. [27]. In order to limit the time and memory complexity of a 1 bivariate analysis, we keep only the points in time that actually 2 carry information. We do so by filtering out the ones that do  The diagonal thus corresponds to the univariate analysis. 12 Since our bivariate analysis relies on a symmetric combination 13 function of each point, the resulting graph is also symmetric. dataset are sufficient to detect clear differences between the 18 two datasets whereas after 1 000 000 traces for each dataset, no bivariate leakage has been found for the implementation at both order 3 and 7 when the embedded RNG is used to generate the masks.
Test Vector Leakage Assessment (TVLA) is a proce-28 dure that consists in trying to detect statistically meaningful 29 differences between two datasets containing traces measured 30 during the computation on the device under test and differ-31 ing with respect to a specific parameter: for instance, one 32 containing the traces measured during the encryption of a 33 constant plaintext with a constant key, whereas the second 34 dataset contains the traces measured during the encryption of a 35 random plaintext with the same key. Being able to statistically 36 distinguish the two sets of traces would mean that what is 37 leaked during the execution depends on the plaintext on which 38 the encryption is performed.
26 B. Leakage Assessment 27 The 2627 Test Vector Leakage Assessment (TVLA) is a proce-28 dure that consists in trying to detect statistically meaningful 29 differences between two datasets containing traces measured 30 during the computation on the device under test and differ-31 ing with respect to a specific parameter: for instance, one 32 containing the traces measured during the encryption of a 33 constant plaintext with a constant key, whereas the second 34 dataset contains the traces measured during the encryption of a 35 random plaintext with the same key. Being able to statistically 36 distinguish the two sets of traces would mean that what is 37 leaked during the execution depends on the plaintext on which 38 the encryption is performed.
This is especially relevant when cross validation methods are used to assess performance on a single modestly-sized dataset. In this context, a key consideration is how to identify, characterise, and when possible, mitigate the influence of key sources of variability (SoVs) introduced by the measurement process and to understand their influence against changes to symptom severity and disease progression. This challenge is further intensified by the heterogeneous nature of PD expression leading to high intra-and inter-study variability. 9 For example, to address a specific hypothesis, selection of study subjects is often biased (e. g. , early disease only) and therefore typical variability associated with disease heterogeneity is reduced within a specific study. Ideally, to address this issue multiple data sources would be needed.
However, the availability of data sets using DHTs is limited and analyses on limited data can artificially increase the degree of explained variability, leading to bias in insights and predictions. Variability introduced by the measurement process, such as differences in the placement of a wearable, lack of control of the home environment, device software upgrades in the course of a study, or the accuracy of the specific model of sensor used, must be set into the context of normal variability in the subject and how this is impacted by PD. This paper provides a conceptual framework to assist clinical research teams to identify, characterise and mitigate the influence of key SoVs introduced by the measurement process and contrast their effect against changes due to PD severity and progression.
Low-impact SoVs are those that are well-understood and mitigation strategies are readily available, often already incorporated in devices or as a standard feature of data processing software. Medium impact SoVs are well understood and effective means for their control and mitigation are widely available and in common use, for example, through the application of appropriate algorithms or user experience design approaches. Compared to low-impact SoVs, they require more attention, and their mitigation should be specifically addressed in study design but appropriate mitigation measures but do not require extensive further investigation. Finally, high impact SoVs are those that present a significant risk to influence the performance of the digital outcome measure of interest, their characteristics are not adequately documented and quantified, thus mitigation approaches are not readily available or require further validation.
20  tested a variety of devices and found negligible variability in the calculation of common acoustic voice measures using a commercial software tool including many of those widely used in PD  21. However, they discovered considerable overall differences when alternative algorithms were used to calculate the same measure, suggesting that software artifacts present a higher impact SoV. In this case, algorithm implementation would be considered a medium-impact SoV as it was still possible to mitigate software variability by adjusting for the observed trend across calculated measures, which followed similar patterns.
When specific SoVs are identified as potentially high-impact a full followup investigation would be required for example by modelling their impact in terms of erroneous diagnosis. Case study 1: Device type, number of sensors, and sampling rate Using data collected during exploratory in-clinic piloting of WATCH-PD (cf. Methods section below and Adams et al. 28  , we investigate variability across device types including a popular consumer platform, and differences due to their placement, sampling rate and sampling locations. To compare consumer-(Apple Watch and iPhone) against research-grade (APMD Opal) devices, we analysed data recorded during a one-minute-walk task, where both devices were simultaneously employed (APMD Opal sensors were placed under the Apple device). Figure  1  shows angular velocity calculations obtained from gyroscope data from several subjects, comparing Opal against Apple Watch and iPhone.
Opal data were recorded at 128 Hz, down-sampled and timeshifted to align with Apple Watch measurements (bottom) and separately with iPhone (top). Figure  1  suggests that both consumer-grade devices reproduce high, low, and intermediate frequencies at comparable quality to the Opal reference (with correlation of 0. 984 and 0. 976 correspondingly). Further, we compared gait features obtained from Opal measurements using the Mobility Lab software provided by APDM (cf. https://apdm. com/mobility/) now part of Clario, against iPhone data processed using software developed inhouse (by co-author TRH). The latter, employs the El-Gohary et al. 29  algorithm to identify gait bouts after turns, and subsequently extract gait features using GaitPy  30  following the approach suggested by ref. 31. In-house developed software (also by TRH) was used to compute rotational velocity at the wrist during arm swings per gait cycle.
Case study 2: Environmental Factors Variability due to environmental factors, that is, factors relating to the setting within which the measurement process is performed, rather than the process per se, can also affect outcome measures. For example, Perraudin et al. 33  identified the height of the chair used to perform sit-to-stand transition Fig. 4  Comparison against Consumer-grade Devices. Comparing features estimated using Opal versus consumer-grade devices using the software developed in-house. All features use iPhone measurements except arm swing that employs data sampled using the Apple Watch. time tests as a key environmental SoV this context. Using data provided the DOMVar project obtained from an actigraphy bracelet incorporating gyroscope and accelerometer (cf. Methods Section below), Fig.
6  illustrates the effect of three chairs of different heights (39. 5 cm, 51. 5 cm, and 59. 5 cm) on average across 12 transitions for each of three subjects. Note the significantly higher variability when data are aggregated across chair heights. Hence, passive monitoring at a patient's home, where typically different chair types would be present, is likely to result into less consistent outcome measure estimation. Further, walking speed can be strongly influenced by room size and the arrangement of furniture within in. Figure  7  illustrates stride time variability for two healthy subjects. Data is collected passively using the actigraphy bracelet in four different settings, namely: (i) large empty room, (ii) large room containing furniture obstacles, (iii) small empty room, and (iv) small room containing furniture obstacles.
The above observation does not preclude adopting a hybrid approach if necessary. In this case, the presented framework and case studies still offers a useful guide to determine the potential influence of SoVs on studyspecific Concepts of Interest. Further, a key motivation in initiating this work was the need to clearly contrast variability due to the measurement process against variability caused by the disease. To this end, we believe that a core requirement towards the further development of mitigation techniques for a wider range of SoVs is to place greater emphasis on normative data sets reflecting performance by healthy subjects. This information is critical to establish ground truths of expected variability. Finally, an inherent characteristic of DHTs is the rapid rate of advance in sensor technologies and the ability of modern software tools, such as machine learning and artificial intelligence, to improve their quantitative performance.
Such rapid innovation can exacerbate the impact of SoVs, for example hardware used in a prospective clinical study might become outdated by the time the study is finished; or, algorithm performance might be enhanced by updating the software mid-study based on additional training data. Clearly, SoVs introduced by the availability of improved tools must also be managed in adopting a similar approach to the suggested SOV framework presented here. Alternatively, requiring new prospective studies for every major hardware, firmware, or model upgrade would represent a major barrier to innovation.