Miracle-dz/UniFAR-FTUs huggingface dataset:
Validation of the Portuguese Version of the Risk Instrument for Screening in the Community (RISC) Among Older Patients in Primary Care in Northern Portugal

Background: Aging is associated with an increase in adverse health outcomes for older people. Short screening instruments that easily and quickly identify those at highest risk can enable decision-makers to anticipate future needs, allocate scarce resources and act to minimize risk. The Risk Instrument for Screening in the Community (RISC) is a brief (2–5 min) Likert scale that scores one-year risk of institutionalization, hospitalization and death from low (1/5) to severe (5/5). Objectives: To externally validate the RISC, scored by general practitioners (GP's), in primary care in Northern Portugal. Methods: The RISC was translated and culturally adapted to Portuguese. A cohort of 457 older adults (aged ≥65) under active follow-up with their GP's were screened. Outcomes at one-year were recorded. Accuracy was determined from the area under the curve (AUC) of receiver operating curve analysis. Results: The mean age of participants was 75.2 years; 57% were female. The proportion identified as being at maximum risk (RISC scores of 3-5/5) of institutionalization, hospitalization and death, were 14.9, 52.4 and 38.4%, respectively. At follow-up 2% (10/431) were institutionalized, 18.6% (84/451) were hospitalized and 3% (14/456) died. Those who were institutionalized (p = 0.021), hospitalized (p = 0.012) or dead (p < 0.001) at one-year were significantly older. Those living alone were more likely to be institutionalized (p = 0.007). The RISC showed fair accuracy in predicting hospitalization (AUC of 0.62 [95% CI: 0.55–0.69]) and good accuracy for Institutionalization (AUC of 0.79 [95% CI: 0.62–0.96]) and death (AUC of 0.77 [95% CI: 0.65–0.88]). Conclusions: The Portuguese version of the RISC accurately predicted institutionalization and death at one-year but like most short screens was less able to predict hospitalization. Given its brevity, the RISC is useful for quickly identifying and stratifying those at increased risk in primary care.
TIPS: Making Volatile Index Structures Persistent with DRAM-NVMM Tiering

We propose T IPS – a framework to systematically make volatile indexes persistent. T IPS neither places restrictions on the concurrency model nor requires in-depth knowledge of the volatile index. T IPS relies on novel DRAM-NVMM tiering to support an index-agnostic conversion, durable linearizability and its concurrency model called the tiered concurrency to achieve a good performance, scalability. T IPS proposes a hybrid low overhead logging technique called the UNO logging to guarantee crash consistency and to handle persistent memory leaks across crashes. We converted seven volatile indexes with different concurrency models and the Redis key-value store application using T IPS and evaluated them using YCSB. Our evaluations show that T IPS -enabled indexes outperform the state-of-the-art index conversion techniques PRONTO, NVTraverse, RECIPE, and the NVMM-optimized B+Tree indexes (BzTree, FastFair), Hash indexes (CCEH and Level Hash) and Trie (WOART) indexes by 3-10 × while supporting strong consistency and index-agnostic conversion.
Food Waste and Upcycled Foods: Can a Logo Increase Acceptance of Upcycled Foods?

ABSTRACT Upcycled foods, made from ingredients that are generally discarded, are a promising new category of foods that could help reduce food waste. However, the commercial success of upcycled foods hinges on consumer acceptance of these foods. This research finds that consumers see many benefits of upcycled foods but also have concerns about the quality of these foods. Based on these findings, we posit that appropriate marketing communications that address the concerns and highlight the benefits could improve acceptability of upcycled foods. We propose that an appropriate upcycled logo is a necessary and key element in marketing communications about these foods. Using a theoretical lens, we predict and empirically demonstrate that an appropriately designed upcycled logo increases perceived quality and, in turn, consumers’ willingness to buy these foods. Findings from a series of studies suggest a marketing communication design strategy that will help increase consumer acceptance of upcycled foods.
Journalism Beyond the Command Post: Local Journalists as Strategic Citizen Stakeholders in Natural Disaster Recovery

ABSTRACT On Memorial Day weekend 2015, the Blanco River crested at 42 feet, drowning the town of Wimberley, Texas (USA), resulting in the death of 11 people and damaging more than 300 homes and businesses. Journalists flocked to Wimberley to report the destruction, but as the hype from national news organizations died down, only a few local journalists remained to tell the story of the town’s struggle for recovery. Using case study methods and narrative theory, this study examined local news stories, interviews, and observations of local journalists, and conversations with community members to evaluate how local journalists consider their role as one that contributes to long-term recovery and resilience. Conversations with local journalists revealed a reporting pressure created by geographic proximity to change the focus of stories. An evaluation of the narratives expressed by journalists and how those transfer into newswork provided a deeper understanding of the tensions created when a journalist is also a citizen stakeholder. These implications contribute to the development of the journalist as citizen (JAC) model, and addresses the way local journalists are strategic in the narratives they adopt in the first six months after a natural disaster.
The 2020 Nobel Memorial Prize in economics: the Canterbury connection

The 2020 Nobel Memorial Prize in Economics (officially known as The Sveriges Riksbank Prize in Economic Sciences in Memory of Alfred Nobel), was awarded to two professors from Stanford University, Paul Milgrom and Robert Wilson, ‘for improvements to auction theory and inventions of new auction formats’. What is probably less well-known is the notable impact made by two New Zealand economists, both economics graduates from the University of Canterbury, in shaping the theory of auctions for which the prize was awarded. This paper looks at the ‘web of influence’ among the two New Zealand economists and the two new Nobel prize winners in as far as the development of auction theory is concerned.
Lightning flash rate nowcasting based on polarimetric radar data and machine learning

ABSTRACT This study investigates the prediction of lightning flash rates using a ground-based, S-band polarimetric weather radar and storm cell-based lightning prediction. Both short-term cases during multiple years and long-term cases are used for training and testing by combining the radar and ground-based lightning detection network measurements. The identified lightning-generating storm cells are tracked during the data pre-processing to initiate our machine learning (ML) algorithm. The selection of features for ML algorithm implementation is discussed, and the performance of lightning rate prediction is evaluated using Pearson’s correlation coefficient (r) and mean percentage error (MPE) metrics. The results show that using polarimetric measurement can improve prediction accuracy with short-term data sets, while for long-term datasets, the improvement is less significant. In general, reasonable prediction accuracy is achieved for up to 30 minutes of lead time.
Palleon: A Runtime System for Efficient Video Processing toward Dynamic Class Skew

On par with the human classiﬁcation accuracy, convolutional neural networks (CNNs) have fueled the deployment of many video processing systems on cloud-backed mobile platforms ( e.g. , cell phones and robotics). Nevertheless, these video processing systems often face a tension between intensive energy consumption from CNNs and limited resources on mobile platforms. To address this tension, we propose to accelerate video processing with a widely-available, but not yet well-explored runtime input-level information, namely class skew . Through such runtime-proﬁled information, it strives to automatically optimize CNNs toward the time-varying video stream. Speciﬁcally, we build Palleon, a runtime system that dynamically adapts and selects a CNN model with the least energy consumption based on the automatically detected class skews, while still achieving the desired accuracy. Extensive evaluations on state-of-the-art CNNs and real-world videos demonstrate that Palleon enables efﬁcient video processing with up to 6 . 7 × energy saving and 7 . 9 × latency reduction.
First Responder: Persistent Memory Simultaneously as High Performance Buffer Cache and Storage

Persistent Memory (PM) is a new media with favorable characteristics that can vastly improve storage I/O performance. While new PM based file systems have been developed to exploit PM, most work have not been successful in fully integrating PM media with traditional storage media such as SSDs and HDDs. We present First Responder (FR), a means to exploit the beneficial features of PM, while making use of modern and mature file systems such as Ext4 developed for traditional storage devices. Conceptually, FR is much like a buffer cache, but much more is involved such as maintaining consistency under failure and providing featherweight management overhead. FR brings about multiple benefits. First, we retain the maturity of existing file systems allowing deployment of FR at settings where traditional file systems are deployed. Second, traditional storage devices supported by these file systems can be used allowing easy integration of PM with traditional storage. Finally, FR allows in-order file system semantics at close to PM device latency. With experimental evaluations with the Intel DC PMM, we show that FR, when used in cache form, can outperform Ext4 by more than 9×, while providing durable in-order file system semantics, whereas Ext4 cannot. We also show that when used as part of a typical file system, performance is comparable with NOVA and Ext4-DAX.
This paper is included in the Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation

Writing a correct operating system kernel is notoriously hard. Kernel code requires manual memory management and type-unsafe code and must efﬁciently handle complex, asynchronous events. In addition, increasing CPU core counts further complicate kernel development. Typically, monolithic kernels share state across cores and rely on one-off synchronization patterns that are specialized for each kernel structure or subsystem. Hence, kernel developers are constantly reﬁn-ing synchronization within OS kernels to improve scalability at the risk of introducing subtle bugs. We present NrOS, a new OS kernel with a safer approach to synchronization that runs many POSIX programs. NrOS is primarily constructed as a simple, sequential kernel with no concurrency, making it easier to develop and reason about its correctness. This kernel is scaled across NUMA nodes using node replication, a scheme inspired by state machine replication in distributed systems. NrOS replicates kernel state on each NUMA node and uses operation logs to maintain strong consistency between replicas. Cores can safely and concurrently read from their local kernel replica, eliminating remote NUMA accesses. Our evaluation shows that NrOS scales to 96 cores with performance that nearly always dominates Linux at scale, in some cases by orders of magnitude, while retaining much of the simplicity of a sequential kernel.
Max: A Multicore-Accelerated File System for Flash Storage

The bandwidth of ﬂash storage has been surging in recent years. Employing multicores to fully unleash its abundant bandwidth becomes a necessary step towards building high performance storage systems. This paper presents the de-sign and implementation of M ax , a multicore-friendly log-structured ﬁle system (LFS) for ﬂash storage. With three main techniques, M ax systematically improves the scalability of LFS while retaining the ﬂash-friendly design. First, we pro-pose a new reader-writer semaphore to scale the user I / Os with negligible impact on the internal operations of LFS. Second, we introduce ﬁle cell to scale the access to in-memory index and cache while delivering concurrency-and ﬂash-friendly on-disk layout. Third, to fully exploit the ﬂash parallelism, we advance the single log design with runtime-independent log partitions, and delay the ordering and consistency guarantees to crash recovery. We implement M ax based on the F2FS in the Linux kernel. Evaluations show that M ax signiﬁcantly improves scalability, and achieves an order of magnitude higher throughput than existing Linux ﬁle systems.
Retrofitting High Availability Mechanism to Tame Hybrid Transaction/Analytical Processing

Many application domains can benefit from hybrid transaction/analytical processing (HTAP) by executing queries on real-time datasets produced by concurrent transactions. However, with the increasingly speedy transactions and queries thanks to large memory and fast interconnect, commodity HTAP systems have to make a tradeoff between data freshness and performance degradation. Fortunately, we observe that the backups for high availability in modern distributed OLTP systems can be retrofitted to bridge the analytical queries and transactions in HTAP workloads. In this paper, we present VEGITO, a distributed in-memory HTAP system that embraces freshness and performance with the following three techniques: (1) a lightweight gossip-style scheme to apply logs on backups consistently; (2) a block-based design for multi-version columnar backups; (3) a two-phase concurrent updating mechanism for the tree-based index of backups. They collectively make the backup fresh, columnar, and fault-tolerant, even facing millions of concurrent transactions per second. Evaluations show that VEGITO can perform 1.9 million TPC-C NEWORDER transactions and 24 TPC-Hequivalent queries per second simultaneously, which retain the excellent performance of specialized OLTP and OLAP counterparts (e.g., DrTM+H and MonetDB). These results outperform state-of-the-art HTAP systems by several orders of magnitude on transactional performance, while just incurring little performance slowdown (5% over pure OLTP workloads) and still enjoying data freshness for analytical queries (less than 20 ms of maximum delay) in the failure-free case. Further, VEGITO can recover from cascading machine failures by using the columnar backup in less than 60 ms.
MapperX: Adaptive Metadata Maintenance for Fast Crash Recovery of DM-Cache Based Hybrid Storage Devices

DM-cache is a component of the device mapper of Linux kernel, which has been widely used to map SSDs and HDDs onto higher-level virtual block devices that take fast SSDs as a cache for slow HDDs to achieve high I/O performance at low monetary cost. While enjoying the beneﬁt of persistent caching where SSDs accelerate normal I/O without affecting durability, the current design of DM-cache suffers from long crash recovery times (at the scale of hours) and low availability. This is because its metadata of dirty bits has to be asynchronously persisted for high I/O performance, which consequently causes all cached data on SSDs to be assumed dirty and to be recovered after the system is restarted. This paper presents MapperX, a novel extension to DM-cache that uses an on-disk adaptive bit-tree (ABT) to synchronously maintain the metadata of dirty bits in a hierarchical manner. Leveraging spatial locality of block writes, Map-perX achieves controlled metadata persistence overhead with fast crash recovery by adaptively adding/deleting leaves in the ABT where different levels represent the states of blocks with different granularity. We have implemented MapperX for Linux DM-cache module. Experimental results show that the MapperX based hybrid storage device outperforms the original DM-cache based hybrid device by orders of magnitude in crash recovery times while only introducing negligible metadata persistence overhead.
Fast, Self‐Driven, Air‐Stable, and Broadband Photodetector Based on Vertically Aligned PtSe2/GaAs Heterojunction

Group‐10 layered transitional metal dichalcogenides including PtS2, PtSe2, and PtTe2 are excellent potential candidates for optoelectronic devices due to their unique properties such as high carrier mobility, tunable bandgap, stability, and flexibility. Large‐area platinum diselenide (PtSe2) with semiconducting characteristics is far scarcely investigated. Here, the development of a high‐performance photodetector based on vertically aligned PtSe2‐GaAs heterojunction which exhibits a broadband sensitivity from deep ultraviolet to near‐infrared light, with peak sensitivity from 650 to 810 nm, is reported. The Ilight/Idark ratio and responsivity of photodetector are 3 × 104 and 262 mA W−1 measured at 808 nm under zero bias voltage. The response speed of τr/τf is 5.5/6.5 µs, which represents the best result achieved for Group‐10 TMDs based optoelectronic device thus far. According to first‐principle density functional theory, the broad photoresponse ranging from visible to near‐infrared region is associated with the semiconducting characteristics of PtSe2 which has interstitial Se atoms within the PtSe2 layers. It is also revealed that the PtSe2/GaAs photodetector does not exhibit performance degradation after six weeks in air. The generality of the above good results suggests that the vertically aligned PtSe2 is an ideal material for high‐performance optoelectronic systems in the future.
Fruit Freshness Detection Using Raspberry PI

: This paper sheds light on the advancements made in the agricultural industry. Digital image processing techniques are now widely used for the maturity estimation of fruits. This work aimed to study and analyze the various algorithms and feature extraction techniques that are now used for the extracting features from the captured digital images. Thus, it is important for the suppliers to label the quality of fruits. In this paper, we are inspecting the quality of fruits based on size, shape and color and also by its weight. All these algorithms are implemented using RASPBERRY PI development board which will become an independent and cost effective system. All the interfacing of the components will be carried out and will make a cost effective embedded system prototype for the determination of size, shape and color of the fruit. Same system can be utilized for other fruits also. Advantages and disadvantages of various classifiers have been classified. It was observed that for achieving high accuracy a compromise had to be made with high computational complexity.
Mass Spectrometric and Rapid Electrochemical Detection and Reductive Dehalogenation of Triclosan

Triclosan is a chlororganic substance used as a bactericide in numerous cosmetics. Intensive triclosan use results in the exposure into the environment; therefore, it is vital to analyze and detoxify triclosan-contaminated wastewater, sludge and soil. This study investigates two analytical strategies. The first is a rapid and inexpensive electrochemical screening technique based on commercial Screen-Printed Electrodes (SPE), which delivers initial results suitable for field measurements and screening. The second is Gas Chromatography–Mass Spectrometry (GC-MS), which is more sophisticated, accurate and sensitive. This latter method can provide more reliable determination of triclosan, e.g., for suspicious samples. We tested the limits of triclosan detection for different electrochemical methods and compared these to mass spectrometry with and without derivatization of triclosan. In addition to these analytical techniques, gas phase dehalogenation was used to dechlorinate triclosan. Reductive dechlorination of triclosan occurs efficiently at moderately elevated temperatures (350-550°C) with zero valent iron. In contrast to oxidative processes, reductive dehalogenation leads to harmless and completely dechlorinated products.
Bayesian parameter inference for Individual-Based Models 1 using Particle Markov chain Monte Carlo (PMCMC) 2

22 Parameter estimation for agent-based and individual-based models (ABMs/IBMs) is often performed 23 by manual tuning and model uncertainty assessment is often ignored. Bayesian inference can jointly 24 address these issues. However, due to high computational requirements of these models and technical 25 difficulties in applying Bayesian inference to stochastic models, the exploration of its application to 26 ABMs/IBMs has just started. We demonstrate the feasibility of Bayesian inference for ABMs/IBMs with 27 a Particle Markov Chain Monte Carlo (PMCMC) algorithm developed for state-space models. The 28 algorithm profits from the model’s hidden Markov structure by jointly estimating the system state and 29 the marginal likelihood of the parameters using time-series observations. The PMCMC algorithm 30 performed well when tested on a simple predator-prey IBM using artificial observation data. Hence, it 31 offers the possibility for Bayesian inference for ABMs/IBMs. This yields additional insights into model 32 behaviour and uncertainty and extends the usefulness of ABMs/IBMs in ecological and environmental 33 research.
Generalized LASSO in Array Signal Processing diploma thesis

Radar, seismic and and wireless communication systems observe waves (hidden in noise) by sensor arrays. Those systems infer the originating (spatially-sparse) set of sources, within a minimum prescribed resolution and with as few sensors as feasible. These requirements lead to under-determined systems of equations. Compressed sensing is an active research field which treats the recovery of a set of sources from an under-determined system of equations exploiting sparsity. Naturally two main questions arise in this context. Firstly, what is the minimum number of equations/sensors for which reconstruction can be guaranteed and secondly, how to achieve efficient reconstruction regarding the sensors’ observations? Solutions to the first question provide insights into the design of linear measurements and use quantities like the restricted isometry property or coherence to describe well-behaved matrices, such as equiangular tight frames. Algorithms as answers to the second question mostly rely on the assumption, that the measurements were made in accordance to the reconstruction guarantees. Suppose that one has to work with existing data acquisition systems, the measurement matrix is given a priori and algorithms studied under too idealistic assumptions are prone to failure. This thesis shows that well known greedy algorithms like orthogonal matching pursuit are not suited for array processing problems. We devise an algorithm based on the generalized Least Absolute Shrinkage and Selection Operator (LASSO), which is a penalized least squares problem. The heuristically chosen `1 penalty term ensures strict convexity and that strong duality holds. The corresponding dual problem is interpretable as a weighted conventional beamformer acting on the residuals of the LASSO. Based on physical insights provided by the dual problem’s solution, three procedures for single snapshot reconstruction and one for sequential online reconstruction are proposed and analysed. The sequential procedure assumes a weighted Laplace-like prior for the sources such that the maximum a posteriori source estimate at the current time step is the solution to a generalized LASSO problem. For the sequential implementation, the posterior distribution is fitted to the Laplace-like density by use of the dual solution.