dataesr/scientific-paragraphs-categorization huggingface dataset:
Exploring common CKKS pitfalls Implementing applications in FHE can be challenging for novice users, e. g. ,due to failing to properly manage scaling factors throughout a CKKS-based computation. While higher-level tools and compilers like EVA  [15]  increasingly provide automated solutions for these challenges, exploring them in a teaching setting continues to have value, since it requires transferring theoretical information about the scheme to practical application. We show how to use Pyfhel to explore several pitfalls in working with CKKS, using the computation (( + ) * ( * 5)) + 10, where ,  and  are (secret) inputs. First, we set up context and keys: Next, we explicitly encode the constant 10 the same as we encoded the inputs ,  and. This will lead to an error since the scale of ctxtProd has increased to 2 60 after the first multiplication, and multiplying it with ctxtSum which is still at scale 2 30 (addition does not change scale) causes ctxt_t to have scale 2 90.
Since, in fixed-point arithmetic, addition can only performed over numbers represented at the same scale, the addition will fail. ptxt_ten = HE. encode ( 10 , scale =2 ** 30 ) ctxt_result = ctxt_t + ptxt_ten # error : mismatched scales Of course, this can be resolved by encoding 10 at the correct scale, i. e. , setting ptxt_ten=HE. encode(10, scale=2**90). Alternatively, we can use ctxt_result=ctxt_t+10 and Pyfhel will automatically deduce the correct scale. However, if instead of a constant we have an input  that the user encrypted at the same scale as all other inputs, this solution no longer applies. Instead, we must use rescaling to homomorphically decrease the scale of ctxt_t to the initial scale. In CKKS, each rescaling reduces the scale down by one "step" (here Δ = 2 30 ), so we need to perform two consecutive rescaling operations.
DISCUSSION & FUTURE WORK We have presented Pyfhel, explored its design and usage, including how it can be used as a teaching tool. By providing a python-native abstraction layer on top of existing FHE implementations, Pyfhel makes working with FHE accessible to a significantly wider audience. However, even experts can benefit from the convenience offered by Pyfhel, eliminating potential error sources and reducing time to solution. In the future, we hope to expand the set of supported libraries and to continue our work of creating easy-to-use high-level APIs for low-level features.
integer = 45 int_ptxt = HE. encode ( integer ) # PyPtxt [45 , 45 ,. ] int_ctxt = HE. encrypt ( int_ptxt ) # PyCtxt import numpy as np np_array = np. array ([6 , 5 , 4 , 3 , 2 , 1] , dtype = np. int64 ) array_ptxt = HE. encode ( np_array ) # Accepts list too array_ctxt = HE. encrypt ( array_ptxt ) # PyCtxt # Decrypt and Decode ptxt_dec = HE. decrypt ( int_ctxt ) # PyPtxt integer_dec = HE. decode ( ptxt_dec ) # 45 # One -step encryption / decryption array_ctxt = HE. encrypt ( np_array ) 1 https://github. com/ibarrond/Pyfhel/tree/master/examples array_dec = HE. decrypt ( array_ctxt , decode = True ).
from Pyfhel import PyCtxt , Pyfhel , PyPtxt HE = Pyfhel () HE. contextGen ( scheme = ' CKKS ' , n= 16384 , qs =[ 30 ,30 ,30 , 30 , 30 ]) HE. keyGen () Now we can perform the ciphertext-ciphertext addition  +  and the ciphertext-plaintext multiplication  * 5 before performing the ciphertext-ciphertext multiplication between those two results. ctxt_x = HE. encrypt (3. 1 , scale =2 ** 30 ) # implicit encode ctxt_y = HE. encrypt (4. 1 , scale =2 ** 30 ) ctxt_z = HE. encrypt (5. 9 , scale =2 ** 30 ) ctxtSum = ctxt_x + ctxt_y ctxtProd = ctxt_z * 5 ctxt_t = ctxtSum * ctxtProd.
Triglyceride Quantification The Enzychrom TM triglyceride quantification kit (BioAssay Systems, Hayward, CA, USA) was used to measure total intracellular triglycerides. Following the MeHg treatment, 200,000 worms were fed the test diets for 48 h and were homogenized in triglyceride assay buffer. Extracts were incubated for 30 min at room temperature with the triglyceride assay reagent mix, and absorbency (optical density: 570 nm) was read. Data are expressed as mmol triglycerides/µg protein.
The basal slowing response assay was performed as previously described  [31]. The number of forward-Toxics 2021, 9, 287 5 of 19 directed body-bends was scored for worms placed either on NGM or 2x cholesterol NGM plates seeded or unseeded with OP50, HB101, or HT115 E. coli. For all behavioral assays, 2x cholesterol plates were only used for experiments where worms were fed OP50 grown on 2x cholesterol plates. These data are presented as the change in body-bends, calculated by subtracting the number of body-bends of worms plated on E. coli-seeded plates from the number of body-bends of worms plated on unseeded plates.
Protein Oxidation Quantification In this study, 2,4-dinitrophenylhydrazine (DNPH) labeling was used to measure protein carbonylation, a type of protein oxidation, as previously described. Using Yasuda et al. 's method  [34] , 50,000 treated and test-diet-fed worms were sonicated in 5 mM EDTA with protease inhibitors. Protein was precipitated out of solution using 20% trichloroacetic acid and then incubated with 10 mM DNPH for 1 h. After excess DNPH was washed off, samples were suspended in 6 M guanidine hydrochloride, loaded onto a 96-well plate, and absorbance was read at 380 nm. Concentration of oxidized protein was calculated using Beer-Lambert's law (molar absorptivity coefficient of DNPH is 21 mM -1 cm -1 ). Data were normalized to total protein concentration.
Statistics Statistical analyses were performed using Prism 8 software (Graphpad, San Diego, CA, USA). Statistical analysis of significance was carried out either by Student's t-test of LD 50 (Figure  1 ) or two-way analysis of variance (ANOVA) followed by Tukey's multiple comparisons test. Values of p < 0. 05 were considered statistically significant. LD50 (Figure  1 ) or two-way analysis of variance (ANOVA) followed by Tukey's multiple comparisons test. Values of p < 0. 05 were considered statistically significant.
B cell and T cell phenotyping Phenotyping was assessed on freshly isolated PBMCs stained with a combination of anti-human antibodies (Table  S6 ). Intracellular staining was performed on fixed and permeabilized cells (using the FOXP3 Transcription Factor Staining Buffer kit, eBioscience). Cells were acquired on a BD FACSCanto II Flow cytometer (BD biosciences) and analyzed with FlowJo v10 software (FlowJo, LLC).
Neutralization Assay U2OS-ACE2 GFP1-10 and GFP11 cells, also referred to as S-Fuse cells  (21) , were mixed (1:1 ratio) and plated at 8x10 3 cells per well in a 96-well flat-bottom plate (μClear, #655090) 24 hours before infection. Cells were then infected with SARS-CoV-2 (strain BetaCoV/France/IDF0372/2020) at an MOI of 0. 1. At 18 hours post-infection, cells were fixed in 4% PFA for 30 min at room temperature, washed, and resuspended in PBS containing Hoechst 33342 (1:10000). Images were acquired on an Opera Phenix High Content Screening System (PerkinElmer) and analyzed on Harmony High-Content Imaging and Analysis Software.
Statistical analysis Variables were expressed as the median. Several nonparametric tests were used including, the Mann-Whitney U test to compare two independent groups, the Wilcoxon test to compare paired values, and the chi-square test to compare antibody positive rates. The spearman correlation test was used to measure the correlation between two variables. Statistics were corrected for multiple comparisons with Dunn's test. Significant P-values are indicated as described: * p < 0. 05; **p < 0. 01; ***p < 0. 001; ****p < 0. 0001. Statistical analysis was performed using GraphPad Prism software, V6 (GraphPad, San Diego).
Acknowledgments: The authors wish to thank the patients that agreed to participate in this study, as well as the doctors and nurses from the endocrinology, metabolism, nutrition and diabetology departments of Institut  E3M  ( Assistance Publique Hôpitaux de Paris (AP-HP), Hôpital Pitié-Salpêtrière, 75013 Paris, France ) who participated in this study, all members from the  Immunology Department, (Assistance Publique Hôpitaux de Paris (AP-HP), Hôpital Pitié-Salpêtrière, 75013 Paris, France ), who volunteered to process blood and BAL samples, and administrative staff at the  Research and Innovation office of Pitié-Salpêtrière Hospital  for their support,  Laura Wakselman  from the  Clinical Research Unit (URC) of Pitié-Salpêtrière Hospital  for help with regulatory and ethical compliance,  Claire Fieschi  for the recruitment of SARS-CoV-2-exposed health care workers, and  Avidan U.
Neumann  for discussions and ideas. We thank  Nathalie Aulner  and the  UtechS Photonic BioImaging (UPBI) core facility (Institut Pasteur) , a member of the  France BioImaging network , for image acquisition and analysis. Funding: D. S. was supported for this work by a  Pasteur/APHP interface fellowship. The study was supported by  Fondation de France ,  « Tous  unis contre le virus » framework  Alliance  ( Fondation de France ,  AP-HP, Institut Pasteur)  in collaboration with  Agence Nationale de la Recherche  ( ANR Flash COVID19 program ), and by the  SARS-CoV-2 Program  of the  Faculty of Medicine from Sorbonne University ICOViD  programs, PI: G. G. ). Work in UPBI is funded by grant  ANR-10-INSB-04-01  and  Reǵion Ile-de-France  program DIM1-Health. Author contributions:  A. Ma. , J. F.
For each RTI file, 42 photographs were taken using a ∼30 cm diameter, automated light dome driving a Canon EOS 5D Mark III digital camera coupled to a Canon MP-E 65 mm macrolens. Original photographs were optimized using Adobe Photoshop CS6 prior to RTI-processing, itself achieved using the RTIbuilder software (Cultural Heritage Imaging). See the online Dryad dataset for the generated RTI files. Items composing Figures 4. 6 and 10. 3 were extracted using the 'specular enhancement' mode. Items composing Figure  10. 2 and 10. 4 were extracted using the 'normals visualisation' mode, which assigns a color code to each pixel according to the orientation of the vector perpendicular to the tangent plane of the object at the corresponding point. These extracts were optimized for contrast. All drawings were finalized using Adobe Illustrator CS6 (Adobe Systems, San Jose, California, USA).
Acknowledgments We are grateful to two anonymous reviewers and  S. Storozhenko  for useful comments on a previous version of the current account, and to the editorial board of  Journal of Paleontology  for managing the submission process and for relevant comments. We thank  R. Werneburg  and  A. Amelang  ( Naturhistorisches Museum Schloss Bertholdsburg ) for organizing excavations and providing specimens from the Sperbersbach and Cabarz excavation sites. We are grateful to  C. Brauckmann  and  G. Heumann  ( Bonn University ) for providing data and photographs of various specimens. We are grateful to the  Museum of Comparative Zoology and Harvard University  for allowing the reproduction of copyrighted material. We are grateful to the administration of the Cabarz quarry for allowing fossil sampling.
The first author thanks the  Ernst Mayr Travel Grant  for financial support during her research visit to the F. M. Carpenter collection (February-March 2012) at the  Museum of Comparative Zoology , and the  German Academic Exchange Service (DAAD)  for financial support during her stay in Germany, April 2013 to April 2014, during which time the core data for this research was collected and prepared. JWS thanks the  German Research Foundation (DFG)  for support by the grant  DFG-Schn 408/22  and  Kazan Federal University, Russia , for support in the framework of the state assignment no. 5. 2192. 2017/4. 6. The senior author thanks the  Alexander von Humboldt Foundation  for financial support for a visit at Freiberg in 2013. This research was supported by the  National Natural Science Foundation of China  (No.
While experimental techniques such as the two-hybrid system have provided a partial view of ST interactome (see Glossary) maps  [38] [39] [40] [41] [42] [43] [44] [45] [46] [47] , understanding the ST interactome has a great potential to provide new insights into plant development, plant physiology, plant interactions with their abiotic and biotic environments. To address these questions, we screened the membrane-based Interaction Network Database (MIND) for arabidopsis (Arabidopsis thaliana) protein interactions  [48]  to identify candidate ST-interactors potentially involved in the regulation of carbon allocation in a wide range of conditions including abiotic or biotic stress. These candidates are putative interactors, as the MIND is based on a heterologous system and requires in planta validation.
The Membrane-Based Interaction Network Database (MIND) Membrane proteins mediate fundamental roles in many biological processes. Membrane proteins allow for transport of ions and metabolites, and protein trafficking across subcellular membranes. Some of the transporters (called transceptors) detect environmental stimuli and transduce signals into the cells; some catalyze chemical reactions  [49]. The regulation of transport activity and the transduction of environmental signals depend to a substantial extent on interactions of membrane proteins with themselves (homodimerization), with other membrane proteins, and/or with soluble proteins  [50]. The mating-based split-ubiquitin system (mbSUS) paved the way for major advances in the identification of membrane protein interactions  [51]. mbSUS identified homo-, hetero-, and oligomeric interactions in arabidopsis, for example, among K + channels  [52] , between the Acyl-CoA-binding protein ACBP6 and the plasmodesmata-located protein PDLP8  [53] , among subunits of glutamate-like receptors (GLRs)  [54] , among aquaporins and kinase receptors  [55].
Using mbSUS in yeast, 12. 102 high-confidence membrane/signaling protein interactions were identified and recorded in MIND (https://associomics. dpb. carnegiescience. edu/ Associomics/Home. html) [50, 56]. More than 99% of the putative PPIs identified were previously unknown  [56]. MIND data were partially validated in orthogonal in planta split-green fluorescent protein interaction assays at a rate of 32%, similar as the confirmation rate obtained for published interactions (38%)  [56]. MIND also predicted PPIs within the membrane proteome of arabidopsis roots that were confirmed by size exclusion chromatography-mass spectrometry  [57]. Split GFP, antibody-pulldown assays, and Förster resonance energy transfer for PPI studies are orthologous assays that can be used to validate candidates present in the MIND database.
Identification of Genes Coexpressed with Sugar Transporters and Coding for ST Interactors Coexpression network analysis (ATTED-II; http://atted. jp) [67]  allowed to capture patterns of transcriptome organization whereby gene clusters and coexpression across diverse conditions are identified. Coexpression can indicate that genes are controlled by the same transcriptional regulatory pathway, may be functionally related, or be members of the same pathway or protein complex  [68]. Here, within the set of proteins interacting with STs defined with MIND, we identified a small subset of genes encoding proteins that are coexpressed with a ST using the ATTED-II database (Table  1 ). Out of the 34 genes of interacting STs, two did not show co-expression. Eight genes (Table  1 ) were significantly coexpressed with at least one other gene represented in ATTED-II, and the encoded proteins interact as well (MIND).
Functions of proteins encoded by genes with a significant correlation with an ST expression indicated possible crosslinks to ammonium transport, cell trafficking and signaling, and hormone regulation related to sugar transport. S2  in the supplemental material online), the protein sequences encoding nine AtSUTs, 17 AtSWEETs, and 53 AtMSTs (see Table  S1  in the supplemental material online) were used to interrogate the MIND Database (https://associomics. dpb. carnegiescience. edu/Associomics/Home. html). Building the sugar transporters-protein interaction network. The sugar transporters-membrane proteins (interactant, INTPROT) interaction network was built from a list of interaction tuples (ST i , INTPROT i , F i ). The interaction network is an unoriented graph that includes nodes and edges with attributes computed using the NetworkX Python package of Python 3  [89].
The attribute definitions have been summarized in the Glossary and examples of attributes of a network of interactions between sugar transporters and interacting proteins are presented in Figure  1. Visualization of the interaction network was carried out via the Gephi software  [90]  using the Fruchterman Reingold algorithm. The node sizes are proportional to their regular degree, and the color of the edges depends on their interaction confidence. Calculations and presentations. All calculations were performed with Jupyter Notebooks  [91]  and rendered with the Bokeh Python library i. SUT, SWEET, and MST (Figure legend continued at the bottom of the next page. ).
Although a large proportion of these proteins has no known biological function to date, several are involved either in transport or in cellular processes as trafficking or signaling. Since MIND represents only a subset of all possible interactions, the network must be substantially larger. The MIND database is based on a library of 3233 membrane proteins and soluble signaling proteins, whereas at least several thousands of other membrane proteins exist and that many membrane proteins will interact with soluble proteins. It thus seems pivotal to generate both a complete interactome of membrane proteins, with membrane proteins and soluble proteins, and to link it with soluble protein interactome as developed by Trig and collaborators  [88]. Such studies could then be further expanded by large-scale interactomes integrating responses to multiple conditions, and in diverse arabidopsis genetic backgrounds to gain a deeper knowledge in functional relationships and potential network differences in arabidopsis, and by extent or comparative phylogenomics, in crops (see Outstanding Questions).
Outstanding Questions Is the MIND database a good tool for identifying metabolic and signaling networks through an in silico approach? Is the sugar transporter interactome a good tool for defining sugar transporters as a strong node or a single shuffle for a better comprehension of plant sugar transport? Do different sugar transporter regulations reflect different strategies used by the plant when responding to environmental changes: either a specific and direct response by transcriptional regulation of sugar transporters or a more complex metabolic regulation that can occur at the protein level (via protein-protein interactions)? Trends in Plant Science.
The attribute definitions have been summarized in the Glossary and examples of attributes of a network of interactions between sugar transporters and interacting proteins are presented in Figure1. Visualization of the interaction network was carried out via the Gephi software [90]  using the Fruchterman Reingold algorithm. The node sizes are proportional to their regular degree, and the color of the edges depends on their interaction confidence. Calculations and presentations. All calculations were performed with Jupyter Notebooks [91]  and rendered with the Bokeh Python library i. SUT, SWEET, and MST.
Acknowledgments C. P. had a grant from the  French Ministry of Research and Higher Education. D. W. and P. E. C. thank the following institutions for financial support: the  division of Plant Health and Environment of the French National Institute for Agriculture, Food, and Environment (INRAE)  and the  Burgundy Franche-Comté Regional Council  ( PUMPER  Project). W. F. is supported by an  Alexander von Humboldt Professorship and Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ), under Germany´s Excellence Strategy -EXC-2048/1project ID 390686111.
A. Security Model In this work, we consider an embedded system with a singlecore processor, which comprises a first-level cache. We assume this system can run multiple tasks due to the presence of a small operating system (such as FreeRTOS, VxWorks, or any other equivalent). An important assumption we make is that all tasks share the same address space. In other words, we assume that there is no Memory Management Unit (MMU) and, instead, memory isolation between processes is done through a static partitioning of the address space. We assume that an attacker may gain privilege on the system by corrupting a task running on the operating system: either remotely by exploiting software vulnerabilities or through a physical attack (e. g. , debug port or a memory corruption). Being an embedded system, we also assume an attacker can measure the global execution time of applications through sidechannel measurements  [1].
Random data placement is very effective against cache attacks and has very low effect on performances. However, a challenging aspect of these architectures is the hardware implementation of the permutation. Especially considering first-level caches where the access latency should be as low as possible. The Scramble Cache proposed in this work, uses a very simple permutation that can be implemented with only few logic gates. The security of the Scramble Cache is ensured by changing the address layout very frequently. Our architecture allows to select the security level and include mechanisms to limit the overhead on performances.
We propose a history mechanism to move data from old locations, while preserving security properties. We describe the Scramble Cache starting from a set associative cache, with physical tags where S bits of the effective address (the set bits) are used to locate the cache sets. The cache contains 2 S sets, each made on N lines, for a total size of N × 2 S cache words. The Scramble Cache architecture is depicted on Figure  3. Compared to a baseline cache, the architecture adds three important elements:  (1)  The permutation of the input address, (2) a generation management mechanism, (3) a history table. Random seeds come from a cache-internal.
B. Address Permutation The Scramble Cache applies a permutation on the set index extracted from the cache input address. Any address transformation (bijective or not) can be applied, because the cache always checks the physical tags before supplying the data to the CPU. We represent the permutation as a function π r (s), where r is a randomization parameter and s is the input set address. A cheap way to implement a permutation in hardware is to use an "eXclusive-OR" (XOR) function, with π r (s) = s ⊕ r. However, a XOR only allows limited number of permutations. For example, when S = 8 (the cache has 256 sets), out of possible 256! permutations, only 256 can be reached with a XOR-based permutation. Therefore an attacker may successfully perform an exhaustive search of the permutation seed r.
D. History Mechanism The history mechanism is a core element of the Scramble Cache. It allows reducing the miss rate when changing the permutation by enabling older data to be moved directly to their new location. This is done by storing R previous seed values in a table (the History Table in Figure  3 ). To ensure strict process isolation, we associate a process identifier with each entry in the table. Upon a the cache yields control to an FSM, which takes care of the searching if the data is cached by using the previous R seeds, by implementing algorithm 1. This algorithm scans the history table in order, from the newest generation to the older ones, and checks if an older value of r leads to valid data. In case the data is valid, it is swapped with the current line to avoid looking in the table in the future.