dataesr/scientific-paragraphs-categorization huggingface dataset:
We demonstrate the numerical robustness of our implementation using real datasets from MAVIS. We achieve performance improvement up to two orders of magnitude for TLR-MVM compared to the regular dense MVM, while maintaining an acceptable image quality. This further enables to meet the real-time challenges of capturing the turbulence evolution, which can potentially increase the AO performance by either reducing the overall latency of the AO system response to incoming perturbations or offering more room for additional fine grain processing in the overall HRTC pipeline. Finally, we highlight the importance of time reproducibility and predictability, which both ensure stable operations of this closed loop control system, as well as the need to rely on standardized libraries and programming models to build a hardware-software ecosystem when targeting complex multi-billion-Euro telescope campaigns sustainable over long hardware lifetimes.
Our main contributions can be summarized as follows. For the first time, we identify the data sparsity of the command matrix in AO real-time controllers that has an impact on many instruments currently deployed or yet to be deployed on-sky, beyond the herein studied MAVIS instrument. We envision this study will help advance the research in computational astronomy for groundbased telescopes. We provide a portable software solution for the TLR approximation applied to MVM that optimizes data locality by stacking the compressed bases. We study its accuracy impact on real datasets using HPC systems from various vendors. We perform an extensive performance benchmarking campaign and assess our implementation by looking at sustained bandwidth on each system via roofline performance models. To our knowledge, this is the first TLR approximations are used to accelerate HRTC workloads in the context of computational astronomy.
In this paper, we identify and demonstrate for the first time the feasibility of deploying TLR matrix approximation in the Hard-RTC (HRTC) pipeline. HRTC is mostly driven by the dense Matrix-Vector Multiplication (MVM) that operates on the command matrix coming from the SRTC computational phase. In fact, these matrices are not sparse but data-sparse. In other words, their original data structure is dense but they can be compressed using tile low-rank approximation. The resulting compressed tile data structure expressed with tall (left) or squat (right) bases. Though the block they represent is data-sparse, these objects are dense and the standard SpMV data structures (e. g. , CSR, COO, ELL, SELL-C, etc. ) do not apply. Instead, we have to stack the bases for contiguous memory access purposes and reformulate the original dense MVM using an algebraic formulation by means of batch dense MVMs with variable sizes.
AO INSTRUMENTS: CHALLENGES AND OPPORTUNITIES MAVIS  [43]  is a general-purpose instrument for exploiting the highest possible angular resolution of any single optical telescope available in the next decade with sensitivity comparable to or better than larger aperture facilities. The principal innovation of MAVIS is to deliver a large AO-corrected FoV with an image quality close to the diffraction limit at optical wavelengths, across the vast majority of the sky. By probing the frontier of angular resolution and sensitivity across a large portion of the observable sky, MAVIS will enable progress on an array of scientific topics, from our own planetary system to those around other stars, and from the physics of star formation in the Milky Way to the first star clusters in the Universe. The MAVIS MCAO module will provide a beam, cleaned of atmospheric turbulence, to an imager and an Integral Field Unit spectrograph.
Relying on Standard Programming Models Our TLR-MVM implementation is written in C and uses the MPI + OpenMP programming model. Algorithm 1 shows the OpenMPonly pseudo-code of TLR-MVM with the three computational phases. The three phases are parallelized using standard OpenMP for loop pragma. We link our code against the corresponding sequential BLAS library provided by the vendor optimized numerical software. Nested parallelism in this context has not shown performance superiority. Algorithm 1 OpenMP pseudo-code of TLR-MVM. Algorithm 2 highlights the MPI+OpenMP version of the TLR-MVM implementation. We use a 1D cyclic block data distribution similar to ScaLAPACK  [10]  to mitigate the load imbalance that may appear with variable ranks. We split the  and  bases vertically among the MPI processes.
Environment Settings The experiments are carried on several x86 and accelerator-based systems from the major vendors, i. e. , Intel Cascade Lake (codenamed CSL), AMD Epyc Rome (codenamed Rome), AMD Instinct GPU MI100 (codename MI100), Fujitsu A64FX (codenamed A64FX), NVIDIA A100 GPU (codenamed A100), and NEC SX-Aurora Tsubasa (codenamed Aurora). A detailed hardware and software description is illustrated in Table  1. While most of our experiments are done on shared-memory systems, we also report performance scalability on a small number of Fujitsu A64FX nodes linked by the TOFU interconnect and multiple NEC Vector Engines connected via Infiniband. All computations are performed in single precision arithmetic and we report performance jitter out of 5000 runs.
Synthetic Datasets We first assess our TLR-MVM implementation on randomly generated  and  with constant rank. Although this may not be realistic to have exact same ranks everywhere for computational astronomy (but can be useful if minimum padding is an option), it still provides some insights on how each hardware architecture responds to a memory-bound workload with batch executions supported via the OpenMP for loop pragma for all systems except the NVIDIA A100 GPUs. For the latter, we replace the OpenMP loop with a single call of batch cuBLAS GEMM kernel, since the batch cuBLAS GEMV kernel is not supported. Of course, we set the number of columns of the resulting matrix to 1 to fall back to a batch cuBLAS GEMV kernel. Figure  7  highlights the impact of the tile sizes on the sustained bandwidth, as calculated in Section 5.
MAVIS Datasets In this section, we now run TLR-MVM against the MAVIS AO system. In the presented simulations, it has 19078 measurements and 4092 actuators, resulting in a matrix reconstructor of dimensions  = 4092,  = 19078 (i. e. , the dimensions of the matrix which undergoes TLR compression). For the full description of the MAVIS system, we refer to  [43]. For the end-to-end AO simulations presented in Section 6, the MAVIS system is assessed due to its moderate dimensionality, tight error budget, capacity for predictive control, and latency demand. The performance of a predictive controller in AO depends on the wind-velocity profile and the turbulence strength profile (both as a  function of height). To verify the stability of the TLR-MVM compression method for a variety of observing conditions, the simulations were performed with varying atmospheric parameters.
Performance Results Figure  11  shows the sustained bandwidth achieved with the dimension and dataset from MAVIS AO system. We can see that NEC Aurora and AMD Rome achieves almost similar bandwidth with different memory technologies. The tiny GEMV kernels in phase 1 and phase 3 of TLR-MVM are able to fit in LLC and greatly benefit from higher cache memory bandwidth. Figure  12  pictures time to solution for TLR-MVM using MAVIS system. AMD Rome and NEC Aurora are below 200 microseconds for a single TLR-MVM call, which open new opportunities moving forward. On real datasets, our TLR-MVM achieves up to 8. 2X/15. 5X/2. 2X performance speedups compared to vendor optimized multithreaded dense SGEMV kernel on Intel CSL / A64FX / NEC SX-Aurora, respectively. On AMD Epyc/Rome, we obtain up to 76. 2X performance speedup against the vendor supported BLIS library.
These actual speedup factors are much higher than the theoretical speedup factors shown in Figure  5. Our TLR-MVM implementation is capable of better utilizing the underlying hardware resources, thanks to data locality considerations and optimized memory accesses. Figure  13  highlights the performance jitter of TLR-MVM, which is critical to maintain it low for HRTC. We can see that NEC Aurora reproduces the same time to solution for most of the iteration runs. However, Intel CSL and Fujitsu A64FX suffer the most. Figure  14  reports bandwidth jitter for MAVIS, which represents the same trend in Figure  13 , with Intel CSL and Fujitsu A64FX showing a large pyramid base, as opposed to NEC Aurora. Figure  15  reports time to solution for all various MAVIS profiles, as described in Table  2. Fujitsu A64FX and NEC Aurora are oblivious  show performance scalability on multiple A64FX nodes and NEC Aurora cards, respectively.
As we increase the number of processing units, the workload per node/cards decreases and may not saturate the bandwidth anymore, as seen when running on single processing units. We consider additionally larger matrix sizes that are representative of other instruments under consideration for the European Extremely Large Telescope. We synthetically generate their rank distributions and show our software capabilities when increasing the number of nodes/cards. For the EPICS instrument, we can saturate the bandwidth and achieves a decent performance scalability on both systems. Figures  18  and 19  show the roofline performance models of our TLR-MVM implementation on AMD Epyc Rome and Fujitsu A64FX systems on Mavis dataset. We can see that the sustained bandwith on the AMD Epyc Rome system is decoupled from main memory and is bound by LLC bandwidth.
Lymphoma Study Association patients Samples were collected prospectively as a substudy of the multicentric, randomized phase 2 BREACH trial (ClinicalTrials. gov; NCT02292979), evaluating brentuximab vedotin 1 doxorubicin, vinblastine, dacarbazine (AVD) (BV-AVD) vs ABVD in patients with cHL. PET/CT scans were available at diagnosis, after 2 cycles, and at end of treatment.
GISTIC Regions of recurring genomic gains and losses were identified using Genomic Identification of Significant Targets in Cancer (GISTIC 2. 0). 38 GISTIC focuses on focal aberrations and reports broad events separately. For each recurrent CNA, GISTIC determines the minimally common region and its frequency. In order to correct for possible errors because of adjacent passenger CNAs, GISTIC also defines a probabilistically established wider region that is more robust to contain the actual target genes. The analysis was run with default log2 thresholds and a significance q value threshold set to 0. 15 at 90% confidence. Relevant genes located within the latter wider GISTIC regions were filtered using CancerMine,  39  and Gene Ontology (GO) datasets of biological processes pivotal in cHL.
ctDNA correlates with MTVs and with HRS cell burden We hypothesized that, at diagnosis, the plasma concentration of ctDNA correlates with MTVs, as measured by PET/CT. This hypothesis was addressed in a subset of the Leuven cohort. The MTVs were computed from PET/CT using MIM. Data in 21 cases revealed a strong correlation between MTVs and ctDNA concentration (r 0. 73, P ,. 01) (data not shown). In cHL biopsies, the bulk of MTVs is contributed mainly by the inflammatory tissue component rather than by the minor fraction of HRS cells. In order to examine the correlation between ctDNA and the HRS cell burden at diagnosis, we assessed the density of HRS cells by CD30 immunostaining of 12 formalin-fixed paraffin-embedded biopsies. Based on this 2-dimensional density, we corrected the MTVs for HRS content and found a stronger correlation between the HRS density-corrected MTVs and ctDNA concentration (r 0.
Acknowledgments The authors are grateful to  L. Lenaerts  and  N. Brison  for providing control samples and  N. Brison , in particular, for practical help, and  Laura Rengifo  for assistance with FISH; and to  Alexandra Traverse-Glehen  for providing the EBV status of cases. This study was supported by grants of the  Research Foundation Flanders (FWO)   G0A1116N  (I. W. and P. V. ) and the  Foundation Against Cancer (STK)   FAF-C/2016/836  (P. V. ).
Unauthorized resource access represents a typical security threat in the Internet of things (IoT), while distributed ledger technologies (e. g. , blockchain and IOTA) hold great promise to address this threat. Although blockchain-based IoT access control schemes have been the most popular ones, they suffer from several significant limitations, such as high monetary cost and low throughput of processing access requests. To overcome these limitations, this paper proposes a novel IoT access control scheme by combining the fee-less IOTA technology and the Ciphertext-Policy Attribute-Based Encryption (CP-ABE) technology. To control the access to a resource, a token, which records access permissions to this resource, is encrypted by the CP-ABE technology and uploaded to the IOTA Tangle (i. e. , the underlying database of IOTA).
This work was supported in part by the Japan Society for the Promotion of Science (JSPS) KAKENHI (A) under Grant 19H01103 and the Support Center for Advanced Telecommunications (SCAT) Technology Research Foundation. The corresponding author is Y. Zhang (yy90zhang@ieee. org). R. Nakanishi, Y. Zhang, M. Sasabe and S. Kasahara are with the Graduate School of Science and Technology, Nara Institute of Science and Technology, 8916-5 Takayama, Ikoma, Nara, 630-0192, Japan. Email: nakanishi. ruka. nm0@is. naist. jp, {yy90zhang, m-sasabe, kasahara}@ieee. org. threatening our safety and privacy. Therefore, protecting the devices by enforcing appropriate access control is essential in the IoT era. Access control is the process of defining the authority of users (i. e. , subjects) and making sure that only authorized subjects can access the objects (e.
Blockchain is the underlying technology of cryptocurrencies such as Bitcoin  [28]  and Ethereum  [29] , which is a distributed database that is kept cooperatively in Peer-to-Peer (P2P) networks. Peers sync, verify and reach consensus on the data to record to the blockchain, making the database tamper resistant. In addition, the data are distributed since every peer keeps its own copy of the entire blockchain. Owing to these attractive properties, blockchains are considered suitable for storing access rights. Another promising feature in the blockchain technology are smart contracts, which are executable codes that are embedded in the blockchain. Implemented in some blockchains including Ethereum  [30] , smart contracts transform the blockchain system from a mere database to a distributed and trustworthy computing platform.
Although new blocks are added to the blockchain at regular intervals, each block can contain only a limited amount of data, lowering the throughput when there is an increased amount of incoming data  [32]. These two shortcomings have to be addressed in order to adapt to large-scale IoT systems. To address the limitations in blockchain-based schemes, there has been an attempt to apply the IOTA technology  [33]  to access control recently. IOTA is a next-generation distributed ledger technology designed for the IoT. It aims to solve the limitations that blockchains face, i. e. , low throughput and high transaction fee, which are addressed by adopting a different data structure for the ledger and changing the way consensus is reached, respectively. These features will be introduced in greater detail in Section III.
B. Blockchain and Smart Contract Blockchain is the underlying technology of cryptocurrencies such as Bitcoin [28] and Ethereum  [29]. It is a distributed and tamper-resistant database that is managed over P2P networks. In blockchain, pieces of data representing remittance between peers, called transactions, are collected and encapsulated as blocks and appended to the blockchain at regular intervals. One important property of blockchains is their tamper resistance, which is supported by two main features. One is that every block includes the hash value of the previous block, which makes the blockchain tamper evident  [31]. When a peer tampers with the content of a block, the hash value of this block changes, which leads to inconsistency in the hash values of all succeeding blocks. This means that all succeeding blocks have to be tampered with as well, in order to maintain consistency.
III. PRELIMINARIES A. IOTA IOTA is a next-generation distributed ledger technology designed for the IoT. Unlike blockchain-based cryptocurrencies like Bitcoin and Ethereum, the distributed ledger of IOTA, called the Tangle, forms a Directed Acyclic Graph (DAG) as shown in Fig. 1. Transactions (Txs) are linked together directly using one-way hash functions, instead of being encapsulated in blocks. Since the limit of block size is removed, newly incoming data can be recorded with high throughput. Another significant characteristic of IOTA is the removal of mining. In IOTA, the consistency of the Tangle is maintained by requiring every peer to verify and approve two existing transactions to issue a new transaction. This not only eliminates the need of transaction fees but also accelerates the speed at which new transactions are approved, because the increase of the number of incoming transactions leads to more existing transactions being approved.
B. Masked Authenticated Messaging (MAM) Although smart contracts have not been implemented in IOTA, a novel data communication protocol called Masked Authenticated Messaging (MAM)  [46]  is available, which is a promising solution for access control. Using MAM, peers can record data to and retrieve data from the Tangle in a tamper-resistant fashion. Peers can record data to the Tangle by masking (i. e. , encrypting) them and issuing them as special transactions (MAM transactions). Each MAM transaction is associated with an address with which any peer can refer to the transaction. MAM transactions issued by the same peer are linked together chronologically, forming a channel (i. e. , a chain of transactions). MAM channels are useful to record and retrieve sequential data, like periodic recording of temperature data from a smart sensor device, as illustrated in Fig.
2. In addition, a signature of the issuer is attached to every MAM transaction, which enables subscribers to verify the authenticity of the issuer (authenticated messaging). A master password that every peer in the IOTA network keeps, called seed, is used to generate the addresses and signatures. Only the owner of the seed can publish messages to his/her channel. With MAM, peers can safely exchange data via the Tangle by subscribing to each other's channel. This enables us to use the Tangle as a database for storing access rights, in terms of access control.
Policies state the conditions of attributes that need to be satisfied to decrypt the ciphertext. The decryption succeeds if and only if the set of attributes associated with the private key satisfies the policy. Fig. 3  shows an example. Given a ciphertext encrypted using the policy "Division: IS AND Role: Staff," users with the private key corresponding to the set of attributes {Division: IS, Role: Staff} can decrypt it, while users with the private key corresponding to the set of attributes {Division: IS, Role: Student} cannot. As seen above, CP-ABE enables fine-grained ABAC to data by restricting the successful decryption to a specific group of users using policies. In our scheme, we encrypt tokens using CP-ABE and store them on the Tangle to achieve flexible access right authorization.
A. System Configuration As shown in Fig. 8 , we used a Microsoft Surface Laptop 3 (1. 5 GHz Intel Core i7, 16GB RAM) as the object owner and a Google Pixel 3 XL (2. 5 GHz Qualcomm Snapdragon 845, 4GB RAM) as the subject. The owner manages three objects, "camera1" (IODATA TS-WRLP/E), "sensor1" (OMRON 2JCIE-BU01) and "led1" (TP-LINK KL130). The objects can be accessed through the IoT gateway (NEC EGW001: 1. 46 GHz Intel Atom, 2GB RAM) which performs access right verification. The official JavaScript API  [48]  was used to issue and fetch MAM transactions and implementation based on  [49]  was used to handle the CP-ABE encryption and decryption. The gateway service was implemented by an HTTP server which listens to requests from the subject. At the subject side, we developed a native Android application which can fetch MAM transactions from the Tangle, handle CP-ABE and fire access requests.
on this, the total execution time is calculated and compared in Fig. 25. We can see that our scheme can authorize the subjects in a significantly shorter time, as expected. This is because the number of times the owner has to attach data to the Tangle is much less in our scheme, which is equal to the number of policies. Since there are two policies in this scenario, the owner attaches encrypted tokens to the Tangle for a mere two times, while in DCACI original copies of tokens are attached to the Tangle for every subject, i. e. , 1200 times. Although fetching tokens from the Tangle has to be performed by every subject in our scheme, it takes much less time than attaching tokens to the Tangle, leading to the overall reduction in execution time. As can be seen here, our scheme provides faster authorization for a large number of subjects than DCACI.
The ViewpointS Web Application The ViewpointS Web Application consists of a Java kernel based on the Spring Framework ® model-view-controller, in synchronization with a PostgreSql database implementing the persistence. Figure  2  illustrates the GUI providing the Perspective ⊗ Search mechanism. The left part of the figure (in grey) illustrates a small extract of the KG itself (a possibly huge bipartite graph) that the guests DO NOT see. The rest of the figure shows a perspective "P1" chosen by the current guest as well as the KM resulting from a search around the target resource "Francesco Di Castri", according to this perspective. The KM is displayed through two complementary views: the tabular view and the graphical view. In Figure  1 , each line stands for a viewpoint. Line 2046: "adefa" has interconnected "Humour au travail" and "adefa" by a factual viewpoint meaning that s/he is the contact resource for the document.
The ViewpointS Web Application The ViewpointS Web Application consists of a Java kernel based on the Spring Framework ® model-view-controller, in synchronization with a PostgreSql database implementing the persistence. Figure  2  illustrates the GUI providing the Perspective ⊗ Search mechanism. The left part of the figure (in grey) illustrates a small extract of the KG itself (a possibly huge bipartite graph) that the guests DO NOT see. The rest of the figure shows a perspective "P 1 " chosen by the current guest as well as the KM resulting from a search around the target resource "Francesco Di Castri", according to this perspective. The KM is displayed through two complementary views: the tabular view and the graphical view. Choosing a perspective consists of: • Tuning the 5 sliders in order to attribute relative weights to the respective viewpoints' types; these weights will be computed when aggregating the viewpoints into the links labelled with distances that appear in the graphical view; • Selecting a temporal range, if needed;.
The Data Collection and Measures The ViewpointS Web Application (VWA) is equipped with two instruments enabling the KG analyses: Firstly, the time-stamped viewpoints of the knowledge graph can be extracted on demand via the "Scan viewpoints" menu item and exported to. csv (see an example in Figure  1 ). The exhaustive set of all the viewpoints emitted during the 78 days of the experiment have been extracted and compiled; they form the Garden-party knowledge graph. Secondly, each perspective-dependent KM resulting from a search can be exported under its tabular view to. csv. We have undertaken three series of searches under specific perspectives; each search results in a Garden-party knowledge map. The first series exploits all viewpoints of the 30 guests; we call them the complete maps. The second and third series exploit complementary sets of viewpoints and yield stereoscopic maps.