Miracle-dz/UniFAR-FTUs huggingface dataset:
Probleme der Fehlererkennung in Masken für integrierte Halbleiterschaltungen bei kohärent-optischer Filterung

Übersicht: Bei der Herstellung von integrierten Halbleiterschaltkreisen wird die Ausbeute durch Fehler in den Maskenstrukturen stark herabgesetzt. Die vollständige Maskenkontrolle mit herkömmlichen Methoden führt zu untragbarem Zeitund Kostenaufwand. Bei Verwendung eines sog. Raumfrequenzfliters läßt sich die Bildintensität von Maskenfehlem gegenüber der Intensität der gewünschten Maskenstruktur anheben und außerdem die Auswertezeit reduzieren. Das Prinzip und die Grenzen des Verfahrens, Dimensionierungsprobleme der Prüfapparatur und des Filtere, unterschiedliche Einflüsse von Amplitudenund Phasenfehlern sowie Möglichkeiten der Fehlerauswertung werden erläutert.
Qualitätsvariation in der Schweiz: Analyse der Mamma-Resektionen bei Brustkrebs anhand des Qualitätsindikators der Brusterhaltung

Zusammenfassung Ziel der Studie Die Studie geht der Frage nach, ob in der Schweiz regionale Unterschiede bei den Behandlungsergebnissen von elektiven Mamma-Resektionen bei Brustkrebs bestehen und falls ja, ob eine mögliche Ursache in der Krankenhausplanung seitens der Kantone zu Grunde liegen könnte. Methodik Anhand der Inpatient Quality Indicators (CH-IQI) wird die Ergebnisqualität auf Ebene der Schweizer Kantone analysiert und mit den kantonalen Anforderungen, um diese Behandlung durchführen zu können, verglichen. Ergebnisse Kantonale Unterschiede wurden sowohl bei der Ergebnisqualität anhand des Qualitätsindikators der Brusterhaltung als auch bei dem Detaillierungsgrad der Anforderungen an die Struktur- und Prozessqualität deutlich. Schlussfolgerung Die Behandlungsqualität ist in der Schweiz kaum transparent vergleichbar; eine Interpretation der verfügbaren Qualitätsinformationen ist anspruchsvoll und für Patienten kaum möglich. Um die aufgezeigten Qualitätsunterschiede zu reduzieren, sollte die Krankenhausplanung interkantonal erfolgen, wie dies in der hochspezialisierten Medizin umgesetzt wird.
Study on the application of improved morphological component analysis method in the analysis of vehicle half-axle load

The measured torque signal of the vehicle half-axle is a periodic signal with rotational frequency and harmonics as the main components and interspersed with burr interference. Therefore, it is necessary to remove burrs from the measured signals for noise reduction before transforming them into bench load spectra based on the measured road signals. In this paper, we propose a method based on the improved morphological component and apply it to the burr removal of the measured signals of rotating components to achieve the separation of the actual half-axis load signals from the burr signals. The morphological component analysis based on fast adaptive step iterative shrinkage and P-index threshold noise reduction is proposed to address the drawback of slow convergence and poor noise reduction of the MCA algorithm based on iterative soft threshold shrinkage method. The simulation and the calculation results of a pure electric vehicle half-axle measured torque signal show that the improved morphological component analysis is significantly better than the traditional morphological component analysis in terms of convergence speed, and can effectively separate the burr components in the half-axle load signal.
Editorial: The African food environments

COPYRIGHT © 2023 Laar, Baye, Zotor, Asiki and Lartey. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: The African food environments
Case report: Injected corticosteroids for treating leprosy isolated neuritis

One of the main manifestations of leprosy is peripheral nerve impairment. Early diagnosis and treatment are important to reduce the impact of neurological impairment, which can cause deformities and physical disabilities. Leprosy neuropathy can be acute or chronic, and neural involvement can occur before, during, or after multidrug therapy, and especially during reactional episodes when neuritis occurs. Neuritis causes loss of function in the nerves and can be irreversible if left untreated. The recommended treatment is corticosteroids, usually through an oral regimen at an immunosuppressive dose. However, patients with clinical conditions that restrict corticosteroid use or that have focal neural involvement may benefit from the use of ultrasound-guided perineural injectable corticosteroids. In this study, we report two cases that demonstrate how the treatment and follow-up of patients with neuritis secondary to leprosy, using new techniques, can be provided in a more individualized way. Nerve conduction studies in association with neuromuscular ultrasound were used to monitor the response to treatment with injected steroids, focusing on neural inflammation. This study provides new perspectives and options for this profile of patients.
The impact of conflict avoidance strategies of internal and external container trucks on the traffic scheduling in automated container terminals

The current automated container terminal frequently completely isolates the external and internal container trucks in space for the safety of the vehicles. Physical isolation needs more space for roads and will reduce yard space. Furthermore, when a traditional container terminal is upgraded to an automated container terminal with physical isolation, the plan layout needs to be changed dramatically with substantial investment. With the development of technology, unmanned container trucks are gradually enabled in automated container terminals. It is necessary to ensure traffic safety in the absence of physical isolation in the port. To address path conflicts that arise when internal unmanned and external container trucks are combined in traffic, this article focuses on the impact of internal unmanned and external container truck avoidance strategies on traffic scheduling in automated container terminals. A mathematical model is built in this paper and solved by a genetic algorithm to study the influence of two avoidance methods: Internal Unmanned Container Truck First and External Container Truck First. The study's findings indicate a correlation between avoidance strategy benefits and drawbacks and the proportion of internal unmanned trucks to external container trucks.
Text data-based casual analysis of initial delay of high-speed trains

As the railway network structure becomes more complex and capacity resources become tighter, risk factors such as equipment disruption, bad weather and human interference pose greater challenges to the on-time operation of high-speed trains. The study of the correlation between primary delay and risk factors plays an important role in improving the punctuality of trains and real-time dispatching command. Firstly, the disruptions were divided into 13 categories; secondly, a Bayesian network structure was constructed based on expert experience, and the structure was modified using a greedy thick thinning algorithm, and the expectation maximization algorithm was used for parameter learning. Finally, the TF-IDF algorithm was used to complete the keyword extraction of the text data of the emergencies that triggered delayed trains from 2016-2019, and the structured and processed 0-1 matrix data were fed into a Bayesian network for inference studies using the joint tree algorithm. The results show that the more likely types of disruptions when trains have a primary delay are, respectively, foreign object strikes or foreign invasion, contact network fault, track circuit fault, shaking or noise. The results can provide useful and valuable information for the optimization of inspection and maintenance schemes for key disruptions.
Research on the construction of smart city information security evaluation index system based on Delphi-DEMATEL

In order to build an information security evaluation index system for smart cities, this paper collects 19 risk factors that affect the information security of smart cities from the four dimensions of environment, data, users and management based on literature analysis. Then, the study uses Delphi and DEMATEL to discriminate and improve the above risk factors, to build an information security evaluation index system for the current situation of smart cities in China. This study can provide certain reference suggestions for the development of smart city construction.
TEA: Time-Proportional Event Analysis

As computer architectures become increasingly complex and heterogeneous, it becomes progressively more difficult to write applications that make good use of hardware resources. Performance analysis tools are hence critically important as they are the only way through which developers can gain insight into the reasons why their application performs as it does. State-of-the-art performance analysis tools capture a plethora of performance events and are practically non-intrusive, but performance optimization is still extremely challenging. We believe that the fundamental reason is that current state-of-the-art tools in general cannot explain why executing the application's performance-critical instructions take time. We hence propose Time-Proportional Event Analysis (TEA) which explains why the architecture spends time executing the application's performance-critical instructions by creating time-proportional Per-Instruction Cycle Stacks (PICS). PICS unify performance profiling and performance event analysis, and thereby (i) report the contribution of each static instruction to overall execution time, and (ii) break down per-instruction execution time across the (combinations of) performance events that a static instruction was subjected to across its dynamic executions. Creating time-proportional PICS requires tracking performance events across all in-flight instructions, but TEA only increases per-core power consumption by ~3.2 mW (~0.1%) because we carefully select events to balance insight and overhead. TEA leverages statistical sampling to keep performance overhead at 1.1% on average while incurring an average error of 2.1% compared to a non-sampling golden reference; a significant improvement upon the 55.6%, 55.5%, and 56.0% average error for AMD IBS, Arm SPE, and IBM RIS. We demonstrate that TEA's accuracy matters by using TEA to identify performance issues in the SPEC CPU2017 benchmarks lbm and nab that, once addressed, yield speedups of 1.28× and 2.45×, respectively.
NN-Stretch: Automatic Neural Network Branching for Parallel Inference on Heterogeneous Multi-Processors

Mobile devices are increasingly equipped with heterogeneous multiprocessors, e.g., CPU + GPU + DSP. Yet existing Neural Network (NN) inference fails to fully utilize the computing power of the heterogeneous multi-processors due to the sequential structures of NN models. Towards this end, this paper proposes NN-Stretch, a new model adaption strategy, as well as the supporting system. It automatically branches a given model according to the processor architecture characteristics. Compared to other popular model adaption techniques such as model pruning that often sacrifices accuracy, NN-Stretch accelerates inference while preserving accuracy. The key idea of NN-Stretch is to horizontally stretch a model structure, from a long and narrow model to a short and wide one with multiple branches. We formulate the model branching into an optimization problem. NN-Stretch attempts to narrow down the design space by taking into account the hard latency constraints through varying where the branches converge and how each branch is scaled to fit heterogeneous processors, as well as the soft accuracy constraints through maintaining the model skeleton and expressiveness of each branch. According to the constraints, NN-Stretch can efficiently generate accurate and efficient multi-branch models. To facilitate easy deployment, this paper also devises a subgraph-based spatial scheduler for existing inference frameworks to parallelly execute the multi-branch models. Our experimental results are very promising, with up to 3.85× speedup compared to single CPU/GPU/DSP execution and up to 0.8% accuracy improvement.
RowPress: Amplifying Read Disturbance in Modern DRAM Chips

Memory isolation is critical for system reliability, security, and safety. Unfortunately, read disturbance can break memory isolation in modern DRAM chips. For example, RowHammer is awell-studied read-disturb phenomenon where repeatedly opening and closing (i.e., hammering) a DRAM row many times causes bitflips in physically nearby rows. This paper experimentally demonstrates and analyzes another widespread read-disturb phenomenon, RowPress, in real DDR4 DRAM chips. RowPress breaks memory isolation by keeping a DRAM row open for a long period of time, which disturbs physically nearby rows enough to cause bitflips. We show that RowPress amplifies DRAM's vulnerability to read-disturb attacks by significantly reducing the number of row activations needed to induce a bitflip by one to two orders of magnitude under realistic conditions. In extreme cases, RowPress induces bitflips in a DRAM row when an adjacent row is activated only once. Our detailed characterization of 164 real DDR4 DRAM chips shows that RowPress 1) affects chips from all three major DRAM manufacturers, 2) gets worse as DRAM technology scales down to smaller node sizes, and 3) affects a different set of DRAM cells from RowHammer and behaves differently from RowHammer as temperature and access pattern changes. We also show that cells vulnerable to RowPress are very different from cells vulnerable to retention failures. We demonstrate in a real DDR4-based system with RowHammer protection that 1) a user-level program induces bitflips by leveraging RowPress while conventional RowHammer cannot do so, and 2) a memory controller that adaptively keeps the DRAM row open for a longer period of time based on access pattern can facilitate RowPress-based attacks. To prevent bitflips due to RowPress, we describe and analyze four potential mitigation techniques, including a new methodology that adapts existing RowHammer mitigation techniques to also mitigate RowPress with low additional performance overhead. We evaluate this methodology and demonstrate that it is effective on a variety of workloads. We open source all our code and data to facilitate future research on RowPress.
Shogun: A Task Scheduling Framework for Graph Mining Accelerators

Graph mining is an emerging application of great importance to big data analytic. Graph mining algorithms are bottle-necked by both computation complexity and memory access, hence necessitating specialized hardware accelerators to improve the processing efficiency. Current accelerators have extensively exploited task-level and fine-grained parallelism in these algorithms. However, their task scheduling still has room for optimization. They use either breadth-first search, depth-first search or a combination of both, leading to either poor intermediate data locality, low parallelism or inter-depth barriers. In this paper, two key insights on graph mining scheduling are gained, inspired by which a novel task scheduling framework named Shogun is proposed. First, task execution can be out-of-order to eliminate unnecessary barriers and improve PE utilization rate. Second, sacrificing intermediate data locality causes little harm to performance, when a locality monitoring mechanism is adopted to avoid severe locality loss. Hence, Shogun enables adaptive locality-aware out-of-order task scheduling by deploying a task tree to decouple the task generation and execution pipeline stages. Moreover, based on the flexible scheduling design, Shogun further develops accelerator optimizations including task tree splitting for load balance, and search tree merging to explore multiple search trees in parallel on one PE. Experimental results show that Shogun improves performance of a state-of-the-art accelerator by 63% on average with an area overhead of approximately 4%.
Bitrate and Adaptive Streaming: What are We Measuring and Why?

Currently, a significant increase in bandwidth requirements is expected in the next couple of years, particularly due to an increase in the device resolution [1]: a typical bit-rate for the 4K video is between 15 to 18 Mbps, while it is considered to be more than twice the High-Definition (HD) video bit-rate and a factor of nine the Standard-Definition (SD) video bit-rate. As a result, there is currently a strong demand to decrease video transmission bit-rate, substantially without reducing the visual presentation quality [1]. Bitrate is one of the most well-known and intuitively understood concepts in video transmission. With that said, there are several different definitions of how bitrate is measured, and different methods of measurement are applicable for different situations. Video compression standards, such as H.265/MPEG-HEVC [2], provide a normative definition of bitrate which applies to all Network Abstraction Layer (NAL) units or their subset, and it is followed by video encoders in a corresponding manner. However, we are not transmitting "bare" NAL units, and not necessarily doing so in a significantly constrained pipe. In addition, a ubiquitously used definition of bitrate in the MPEG-2 TS is provided within the measurement technical report for Digital Video Broadcasting (DVB) systems - ETSI 101 290 [3]. Furthermore, the situation is different in the adaptive video streaming: for example, a 1-sec sliding window over a stream is less relevant when the unit of transmission is a segment. MPEG DASH [4] and Apple HLS [5] have their own definitions of bitrate, based on maxima of segment bitrates, and extended signaling for content-adaptive encoding. The concept of a constant-rate "pipe" is also often irrelevant - video traffic is de-facto multiplexed with all other traffic within: e.g., an LTE cell or a service group in a DOCSIS broadband network and CDN storage, and egress become the limiting per-stream factor. With that said, segment-level measurement is important for streaming clients reasoning the sustainability of a given variant. Lastly, ISPs and CDNs often use the so-called 95/5 burstable model, where 95th percentile of a 5-min average is used in lieu of second or segment-based calculation. In this work, we first review various bitrate measurement models and illustrate the difference between them by using different measurement methods, which are applied to a set of constant-rate and content-adaptive streams, being generated by a variety of commercial encoders. Then, we show benefits from using the segment rate, rather than a sliding window, as the target rate. The latter approach showed statistically significant improvements in compression efficiency by using the open source x265 encoder [6],[7], which is a popular open-source encoder that generates bitstreams compliant with the HEVC video coding standard [2].
Adapting Datacenter Capacity for Greener Datacenters and Grid

Cloud providers are adapting datacenter (DC) capacity to reduce carbon emissions. With hyperscale datacenters exceeding 100 MW individually, and in some grids exceeding 15% of power load, DC adaptation is large enough to harm power grid dynamics, increasing carbon emissions, power prices, or reduce grid reliability. To avoid harm, we explore coordination of DC capacity change varying scope in space and time. In space, coordination scope spans a single datacenter, a group of datacenters, and datacenters with the grid. In time, scope ranges from online to day-ahead. We also consider what DC and grid information is used (e.g. real-time and day-ahead average carbon, power price, and compute backlog). For example, in our proposed PlanShare scheme, each datacenter uses day-ahead information to create a capacity plan and shares it, allowing global grid optimization (over all loads, over entire day). We evaluate DC carbon emissions reduction. Results show that local coordination scope fails to reduce carbon emissions significantly (3.2%–5.4% reduction). Expanding coordination scope to a set of datacenters improves slightly (4.9%–7.3%). PlanShare, with grid-wide coordination and full-day capacity planning, performs the best. PlanShare reduces DC emissions by 11.6%–12.6%, 1.56x–1.26x better than the best local, online approach’s results. PlanShare also achieves lower cost. We expect these advantages to increase as renewable generation in power grids increases. Further, a known full-day DC capacity plan provides a stable target for DC resource management.
Efficient Bounded Timestamping from Standard Synchronization Primitives

Bounded timestamps [10, 20] allow a temporal ordering of events in executions of concurrent algorithms. They are a fundamental and well studied building block used in many shared memory algorithms. A concurrent timestamp system keeps track of m timestamps, which is usually greater or equal to the number of processes in the system, n. A process may, at any point, obtain a new timestamp, and later determine a total order of all process's most recent timestamps. Known timestamp algorithms do not scale well in the number of processes. Getting a new timestamp takes at least a linear number of steps, and a lower bound by Israeli and Li [20] implies that each timestamp needs to be represented by at least Ω(m) bits. We introduce a slightly different semantics for timestamping, where there is no fixed timestamp value associated with an event. A process can execute operation updateTS() to update its latest timestamp, associating it with the (linearization) point of that operation, and isEarlier(p,q) to determine the temporal order of the latest updateTS() operations executed by processes p and q. Since no static timestamp value is returned by updateTS(), the lower bound of Israeli and Li does not apply. We present efficient linearizable and wait-free implementations of these methods using a single bounded fetch-and-add object and O(n2) bounded compare-and-swap objects, which are available on standard hardware. The step complexity of each method call is constant, and base objects need only store O(log n) bits.
Teaching Blue Elephants the Maths for Machine Learning

Code-generation suits well for reverse mode automatic differentiation as it stores each partial derivative as a virtual register. Since the introduction of just-in-time compilation in PostgreSQL, an efficient operator for automatic differentiation seems feasible. Such an operator would allow for in-database machine learning and thus eliminate the need for data extraction. In this paper, we propose automatic differentiation in PostgreSQL: We extend our proposed SQL lambda functions to compute the derivatives even for matrix operations by traversing the expression tree. The evaluation proves that the compiled execution is up to six times faster than the interpreted runs for regression tasks.
Poster: Image Acquisition and Storage System for Battery-Free WiFi Camera

Generally, image acquisition and storage requires a considerable amount of energy. To address this issue, we present an innovative approach towards developing a battery-free WiFi camera. Our system uses FRAM and DMA to cache images quickly, select appropriate color modes, and make some optimizations to the code, allowing for the acquisition and storage of a grayscale image of 144 × 176 pixels using only 6mJ of energy in 96ms. Our proposed system provides a promising step towards the realization of a battery-free WiFi camera, enabling energy-efficient image acquisition and storage in applications such as environmental monitoring, surveillance, and IoT.