Miracle-dz/UniFAR-FTUs huggingface dataset:
A case study on the vertical distribution and characteristics of aerosols using ground-based raman lidar, satellite and model over Western India

ABSTRACT Ahmedabad is an urban site in western region of India. Dust loading and its variability are very high over this semi-arid location. A state-of-the-art ground-based Raman lidar has been used for the study of day-to-day variability in vertical distribution of aerosol loading over Ahmedabad (23.02° N, 72.57° E) from 8 to 11 May 2018. We have done a comprehensive study about vertical profiles of aerosols across two wavelengths recorded simultaneously, namely, 355 nm and 532 nm, over this location. An interesting hump feature has been noted on 9 May at around 18 h (Local Time), with an additional peak at 19 h on 10 May. The loading over this region has more fine-mode aerosols than coarse-mode particles. Further, findings from ground-based lidar have been compared with MACC-II model simulations and Cloud-Aerosol Lidar with Orthogonal Polarization on-board Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observational. Back-trajectory analysis showed majority of the influx came from middle-east, carrying mineral dust and traces of marine aerosols from Arabian Sea, except for 11 May, where low-level dust carrying trajectories and also coarser dust particles were not seen. This type of manifestation has also been observed while classifying aerosols over Ahmedabad using Lidar Depolarization Ratio and Lidar Ratio obtained from Raman Lidar. Fine mode aerosols have been characterized as industrial, vehicular and marine, while coarser as mineral dust and biomass burning. This study focuses on local variation and classification of aerosols based on size and type, which will further help in better estimation of radiative budget and other impacts of aerosols.
Decadal gross level assessment of green and blue consumptive water use over Indian agro-ecosystems

ABSTRACT Assessment of consumptive water use (CWU) and water productivity at the regional scale is important to diagnose vulnerable zones to improve water use efficiencies in cropland to achieve the sustainable development goal 6.0 prescribed by the United Nations. The present study was carried out to segregate and quantify CWU into agricultural green (CWUg) and blue (CWUb) water use and water productivity (AWP) on seasonal, annual, and decadal (2009–2018) scales over the Indian region using satellite remote-sensing data from geostationary and polar-orbiting platforms. A logical algorithm was used to determine partitioned water use from a combination of satellite-based estimates of key variables such as agricultural water demand (AWD), actual evapotranspiration (ETa), and effective rainfall (ER). Satellite-based estimates of CWU were evaluated with respect to ground reference that showed underestimation of the order of 15–32% but with a strong Pearson’s correlation coefficient (r = 0.80–0.99) and coefficient of determination (R 2 = 0.64–0.98). The reasons for this difference and uncertainties in satellite-based inputs have been explained. The decadal mean of CWU at annual scale showed wide spatial variation over India with 84% and 16% share of CWUg and CWUb, respectively, in kharif season and 27% and 73%, respectively, in rabi season. A non-significant increasing trend in CWUg,kharif (0.57%) and a decreasing trend in CWUb,kharif (–5.18%), CWUg,rabi (–2.81%), and CWUb,rabi (–1.77%) were observed over 10 years. A decreasing trend in green AWP (AWPg) (–0.18%) in kharif season reveals a lack of sustainable adoption of green water management practices while a significantly increasing trend in AWPb (2.65%) in rabi season reveals sustainable adoption of efficient irrigation management practices over 10 years. These long-term estimates would help in smoothing out trade-offs of water use versus water productivity, reducing the vulnerability and aiding in decision-making for water savings, controlled water allocation, strategic policy formulations for water, and food security especially through sustainable management practices in rainfed agriculture.
XFUSE: An Infrastructure for Running Filesystem Services in User Space

Implementing the ﬁlesystem in user space reduces development complexity [28, 30] and decreases dependency on the underlying OS platform. Implementing the ﬁlesystem at the user level as opposed to inside the OS kernel, however, has traditionally meant lower performance [11,17,22]. This performance overhead is increasingly limiting with high performance storage devices based on new persistent memory technology (e.g. 3D XPoint [13]) and advanced networking techniques (e.g. RDMA [14]). User space ﬁle systems have also been associated with poor reliability, availability and ser-viceability (RAS) characteristics [26]. As a result, there is a tendency to consider user space ﬁlesystems as prototypes and proof-of-concepts. In this paper, we systematically analyze the concerns with deploying user space ﬁlesystem to provide production ﬁle storage services. We present XFUSE, a ﬁlesys-tem in user space framework that addresses the performance and RAS concerns, and that enables ﬁle storage services to be effectively deployed at the user level. Our performance analysis indicates that XFUSE enables ﬁlesystem requests made through standard kernel interfaces to be processed at the user level with latency in the 4 microseconds range, and offers throughput exceeding 8 GB/s.
Fair Scheduling for AVX2 and AVX-512 Workloads

CPU schedulers such as the Linux Completely Fair Sched-uler try to allocate equal shares of the CPU performance to tasks of equal priority by allocating equal CPU time as a technique to improve quality of service for individual tasks. Recently, CPUs have, however, become power-limited to the point where different subsets of the instruction set allow for different operating frequencies depending on the complexity of the instructions. In particular, Intel CPUs with support for AVX2 and AVX-512 instructions often reduce their frequency when these 256-bit and 512-bit SIMD instructions are used in order to prevent excessive power consumption. This frequency reduction often impacts other less power-intensive processes, in which case equal allocation of CPU time results in unequal performance and a substantial lack of performance isolation. We describe a modiﬁcation to existing schedulers to restore fairness for workloads involving tasks which execute complex power-intensive instructions. In particular, we present a technique to identify AVX2/AVX-512 tasks responsible for frequency reduction, and we modify CPU time accounting to increase the priority of other tasks slowed down by these AVX2/AVX-512 tasks. Whereas previously non-AVX applications running in parallel to AVX-512 applications were slowed down by 24.9% on average, our prototype reduces the performance difference between non-AVX tasks and AVX-512 tasks in such scenarios to 5.4% on average, with a similar improvement for workloads involving AVX2 applications.
Zico: Efficient GPU Memory Sharing for Concurrent DNN Training

GPUs are the workhorse in modern server infrastructure fueling advances in a number of compute-intensive workloads such as deep neural network (DNN) training. Several recent works propose solutions on sharing GPU resources across multiple concurrent DNN training jobs, but none of them address rapidly increasing memory footprint introduced by such job co-locations, which greatly limit the effectiveness of sharing GPU resources. In this paper, we present Zico, the first DNN system that aims at reducing the system-wide memory consumption for concurrent training. Zico keeps track of the memory usage pattern of individual training job by monitoring its progress on GPU computations and makes memory reclaimed from the job globally sharable. Based on this memory management scheme, Zico automatically decides a strategy to share memory among concurrent jobs with minimum delay on training while not exceeding a given memory budget such as GPU memory capacity. Our evaluation shows that Zico outperforms existing GPU sharing approaches and delivers benefits over a variety of job co-location scenarios.
Jump-Starting Multivariate Time Series Anomaly Detection for Online Service Systems

With the booming of online service systems, anomaly detection on multivariate time series, such as a combination of CPU utilization, average response time, and requests per second, is important for system reliability. Although a collection of learning-based approaches have been designed for this purpose, our empirical study shows that these approaches suffer from long initialization time for sufficient training data. In this paper, we introduce the Compressed Sensing technique to multivariate time series anomaly detection for rapid initialization. To build a jump-starting anomaly detector, we propose an approach named JumpStarter. Based on domainspecific insights, we design a shape-based clustering algorithm as well as an outlier-resistant sampling algorithm for JumpStarter. With real-world multivariate time series datasets collected from two Internet companies, our results show that JumpStarter achieves an average F1 score of 94.12%, significantly outperforming the state-of-the-art anomaly detection algorithms, with a much shorter initialization time of twenty minutes. We have applied JumpStarter in online service systems and gained useful lessons in real-world scenarios.
Modernizing File System through In-Storage Indexing

We argue that a key-value interface between a file system and an SSD is superior to the legacy block interface by presenting KEVIN. KEVIN combines a fast, lightweight, and POSIXcompliant file system with a key-value storage device that performs in-storage indexing. We implement a variant of a log-structured merge tree in the storage device that not only indexes file objects, but also supports transactions and manages physical storage space. As a result, the design of a file system with respect to space management and crash consistency is simplified, requiring only 10.8K LOC for full functionality. We demonstrate that KEVIN reduces the amount of I/O traffic between the host and the device, and remains particularly robust as the system ages and the data become fragmented. Our approach outperforms existing file systems on a block SSD by a wide margin – 6.2× on average – for metadataintensive benchmarks. For realistic workloads, KEVIN improves throughput by 68% on average.
Corpus Paralelo de Español, Inglés y Chino y Análisis contrastivo del tiempo pasado del español a partir de corpus

El presente estudio se dedica al desarrollo de un corpus paralelo trilingüe denominado CPEIC (Corpus Paralelo de Español, Inglés y Chino) cuyo fin es el de aportar conocimientos a las investigaciones sobre la traducción, el análisis contrastivo, el aprendizaje y la enseñanza de una lengua extranjera. Dicho CPEIC abarca las tres lenguas más habladas del mundo (español, inglés y chino) y contiene aproximadamente 4 millones de palabras. Basándose en el corpus paralelo desarrollado, se realizó un análisis contrastivo del tiempo pasado, el cual se expresa de manera diferente en las tres lenguas mencionadas. Los resultados obtenidos (a) avalan estudios previos sobre la relación entre el pretérito del español con el marcador aspectual chino "le", así como también la relación entre el imperfecto del español con "would" y "was/were+Ving" del inglés, (b) contradicen las presunciones con respecto a la conexión entre el imperfecto del español y el marcador aspectual chino "zhe", y (c) proporcionan una nueva perspectiva sobre la relación entre el pretérito del español y la voz pasiva en los tres idiomas.
This paper is included in the Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation

After request completion, an I/O device must decide either to minimize latency by immediately ﬁring an interrupt or to optimize for throughput by delaying the interrupt, anticipating that more requests will complete soon and help amortize the interrupt cost. Devices employ adaptive interrupt coalescing heuristics that try to balance between these opposing goals. Unfortunately, because devices lack the semantic information about which I/O requests are latency-sensitive, these heuristics can sometimes lead to disastrous results. Instead, we propose addressing the root cause of the heuristics problem by allowing software to explicitly specify to the device if submitted requests are latency-sensitive. The device then “calibrates” its interrupts to completions of latency-sensitive requests. We focus on NVMe storage devices and show that it is natural to express these semantics in the kernel and the application and only requires a modest two-bit change to the device interface. Calibrated interrupts increase through-put by up to 35%, reduce CPU consumption by as much as 30%, and achieve up to 37% lower latency when interrupts are coalesced.
Decentralized Cochlear Implant Programming Network Improves Access, Maintains Quality, and Engenders High Patient Satisfaction

Objective: Cochlear implants have become a powerful tool in managing patients with moderate to profound sensorineural hearing loss and lack of benefit from hearing aids. Currently, less than 10% of people who would benefit from a CI receive the treatment. This lack of access is multi-factorial yet largely due in part to an inadequate referral system. The purpose of the current study was to evaluate speech outcomes and patient satisfaction for adult cochlear implant (CI) recipients in a single-surgeon, high volume CI center that uses a decentralized network (DCN) of private practice programming audiologists. Study Design: Retrospective case series. Setting: Tertiary otology/neurotology practice. Patients: Adult CI recipients implanted between April 2017 and August 2019. Intervention(s): Surgical/rehabilitative. Main Outcome Measure(s): Speech outcomes (AzBio quiet) were evaluated 6-months post implantation. Patients were surveyed to determine satisfaction with programming services, surgical services, and CI benefit. Results: The senior author (A.J.) performed 223 CI operations: 136 (61%) were programmed at the CI center and 84 (38%) at outside audiology clinics through a DCN. The 6-month speech outcomes (AzBio, quiet) revealed similar scores regardless of where programming services were received; CI center: 25% pre-op versus 55% post-op; DCN: 24% pre-op versus 59% post-op. Additionally, patients reported high-levels of satisfaction with programming services, surgical services, and CI benefit. Conclusion: A decentralized network of well-trained private practice audiologists who identify CI candidates and program their devices after surgery 1) improves patient access to cochlear implantation, 2) maintains excellent speech outcomes, and 3) engenders high levels of patient satisfaction.
A Virtualization Platform Designed for Irregular Multi-Process Applications

Many High-Performance Computing applications involve multi-process programming. While multiple processes running in parallel can greatly reduce the completion time of applications, there are many irregular applications where the workloads on different processes may vary significantly and thus are not balanced. An irregular application is unbalanced in the sense that processes with smaller workloads have to be suspended to wait for processes with larger workloads when each process is allocated a sole CPU. Since such unbalanced workloads can lead to a tremendous waste of computational resources, we propose the RRP-Xen virtualization platform for these irregular applications. Specifically, RRP-Xen can cluster multiple processes with small workloads from one or more irregular applications onto the same CPU. Compared to other HPC cloud solutions, which also involve virtualization, RRP-Xen can offer real-timeperformanceguaranteesto each process. Consequently, the number of CPUs needed for deploying multiple irregular applications can be reduced while the completion of each irregular application experiences only minor delays.
Do corporate tax cuts boost economic growth?∗

The empirical literature on the impact of corporate taxes on economic growth reaches ambiguous conclusions: corporate tax cuts increase, reduce, or do not significantly affect growth. We apply meta-regression methods to a novel dataset with 441 estimates from 42 primary studies. There is evidence for publication selectivity in favour of reporting growth-enhancing effects of corporate tax cuts. Correcting for this bias, we cannot reject the hypothesis of a zero effect of corporate taxes on growth. Several factors influence reported estimates, including researcher choices concerning the measurement of growth and corporate taxes, and controlling for other budgetary components. ————————— 1 Macroeconomic Policy Institute, Düsseldorf, FMM Fellow, Bamberg University, sebastian-gechert@boeckler.de 2 Corresponding Author. Vienna Institute for International Economic Studies (wiiw), heimberger@wiiw.ac. at Do corporate tax cuts boost economic growth? ∗ Sebastian Gechert† Philipp Heimberger‡ May 17, 2021 Abstract. The empirical literature on the impact of corporate taxes on economic growth reaches ambiguous conclusions: corporate tax cuts increase, reduce, or do not significantly affect growth. We apply meta-regression methods to a novel dataset with 441 estimates from 42 primary studies. There is evidence for publication selectivity in favour of reporting growth-enhancing effects of corporate tax cuts. Correcting for this bias, we cannot reject the hypothesis of a zero effect of corporate taxes on growth. Several factors influence reported estimates, including researcher choices concerning the measurement of growth and corporate taxes, and controlling for other budgetary components. The empirical literature on the impact of corporate taxes on economic growth reaches ambiguous conclusions: corporate tax cuts increase, reduce, or do not significantly affect growth. We apply meta-regression methods to a novel dataset with 441 estimates from 42 primary studies. There is evidence for publication selectivity in favour of reporting growth-enhancing effects of corporate tax cuts. Correcting for this bias, we cannot reject the hypothesis of a zero effect of corporate taxes on growth. Several factors influence reported estimates, including researcher choices concerning the measurement of growth and corporate taxes, and controlling for other budgetary components.
An Analysis of Development Countermeasures for Promoting Reform and Optimization of Transportation Structures Based on Highway-Railway Combined Transport in China

As pollution due to the China’s transportation environment, and the sharpening of the contradiction between the supply of transportation and the demand, it has become the top priority for current freight transportation to increase the proportion of railway transportation and optimize the existing structures. It is of great significance to advance the reform of “ highway-to-railway” for freight transport to do well in the Blue Sky Protection Campaign and pollution prevention, improve comprehensive transport efficiency, reduce logistics costs, and promote economic and social development of China. This paper firstly reviews China’s policy orientation in promoting the optimization of transportation structure, and then analyzes the advantages and development prospects of highway-railway combined transport. At last, it puts forward countermeasures and suggestions on how to better develop the combined transport in terms of increasing subsidies for highway-railway combined transport, regulating market order, formulating standardized rules, encouraging the reform of business entities, and building a full-process smart logistics service platform.
Analysis of the Algorithm for the Classification and Recognition of the Unbalanced Data Fragments in Large Database

: In the traditional algorithm of data fragment recognition, the influence of fragment's own attributes is often ignored, which leads to the reduction of the accuracy of data fragment recognition. Therefore, we propose a large database based on lrfu strategy and association analysis to identify and classify the unbalanced data segments. Algorithm. Based on the resampling algorithm, the unbalanced data segments are upsampled; the zero filling filter coefficients based on the sampling results and the convolution calculation of the unbalanced data segments based on the filter banks, as well as the reconstruction segments used to obtain the segment feature sequences. The similar partition linear method is used to deal with the highly integrated and unevenly distributed data segments with sequences to achieve segment classification. Transform the objective function, and get the suitable function by combining the extended matrix. The matching function uses lrfu strategy to schedule, correlation analysis to determine the attribute value of the integrated unbalanced data segment, and realize the recognition of the unbalanced data segment. The experimental results show that if the improved method is used to classify and recognize the non-uniform data segments, its suitability and recognition accuracy are high, and it has specific advantages.
The Management of Down Syndrome

Down syndrome occurs when individuals experience abnormal cell division, resulting in a partial or complete copy of chromosome 21. Persons with Down syndrome have short necks, protruding tongues, poor muscle tones, flattened faces, and excessive flexibility. The genetic disorder affects approximately 400,000 adults and children in the United States. Unmanaged Down syndrome leads to serious health complications like ear infections, eye diseases, heart defects, hearing loss, seizures, gastrointestinal disorders, obesity, and obstructive sleep apnea. The literature review completes a comprehensive analysis of pharmacological and non-pharmacological methods for managing Down syndrome to protect individuals from adverse complications. Specifically, the report examines the benefits of medications, such as donepezil, rivastigmine, memantine, and galantamine, in managing Down syndrome associated with dementia. They review the impact of non-pharmacological methods, including speech therapy, occupational therapy, physical therapy, assistive technologies, and surgeries, on managing Down syndrome. The collection of data for the literature review is based on secondary research. Particularly, the researcher collected and applied data from peer-reviewed journals, e-books, or agencies reports published between 2011 and 2021. The literature review has specific gaps as the peer-reviewed articles did not provide information on particular surgeries for managing complications of Down syndrome, the accurate dosage of medications for adults and children, and the side effects of pharmacological methods. Although the literature has gaps, patients and clinicians should apply the discussed pharmacological and non-pharmacological strategies for managing Down syndrome. The clinical significance of the literature review is to: • Facilitate the early intervention and management of Down syndrome. • Reduce the adverse complications of Down syndrome in children and adults. • Enhance knowledge and awareness of Down syndrome and its evidence-based management approaches among clinicians, vulnerable persons, and community members. • Increase the safety and clinical outcomes of individuals with Down syndrome.