Miracle-dz/UniFAR-FTUs huggingface dataset:
Chesapeake legacies: the importance of legacy nitrogen to improving Chesapeake Bay water quality

In the Chesapeake Bay, excess nitrogen (N) from both landscape and atmospheric sources has for decades fueled algal growth, disrupted aquatic ecosystems, and negatively impacted coastal economies. Since the 1980s, Chesapeake Bay Program partners have worked to implement a wide range of measures across the region—from the upgrading of wastewater treatment plants to implementation of farm-level best management practices—to reduce N fluxes to the Bay. Despite widespread implementation of such measures and notable reductions in N inputs, water quality across the region has been slow to improve. Such lack of response has in some cases been attributed to N legacies—accumulations of surplus N in soils and groundwater—that can contribute to time lags between implementation of conservation measures and improvements in water quality. Here, we use the ELEMeNT-N modeling framework to explore the role of legacy N in slowing reductions in N loading to the Bay, and to provide estimates of the time required to meet water quality goals in nine major tributary watersheds. Our results first show that recent improvements in water quality can be attributed to decreases in N surplus magnitudes that began to occur in the 1970s and 1980s, and that such improvements will continue in the coming decades. Future simulations suggest that, even with no additional changes in current management practices, goals to reduce N loads across the region by 25% can nearly be met within the next two decades. The present results also suggest that time lags to achieving water quality may vary considerably in the individual study watersheds, with the longest lag times being found in the highly agricultural Choptank watershed, where N surplus magnitudes remain high and where legacy N remains a major control on water quality.
Numerical ultrasonic full waveform inversion (FWI) for complex structures in coupled 2D solid/fluid media

Undetected inclusions in engineering components cause tremendous industrial expenses in maintenance and repairs each year, with additional risks of catastrophic failures. This paper introduces a powerful method for inclusion imaging and reconstruction in irregularly-shaped components, based on a cutting-edge imaging technique—full waveform inversion (FWI). We propose an ultrasonic scanning setup for nondestructive evaluation (NDE) that fits a variety of components with different shapes and sizes. The FWI theoretical expressions are summarized, aiming for creating clear explanations for the NDE and material characterization communities. Systematic analysis of the FWI performance using different setups has been conducted, and a variety of case studies show different aspects of complexity that the FWI technique can address. Multiple inclusions have been successfully reconstructed in gears, exhibiting the potential of applying the proposed technique in overcoming various NDE challenges related to the rapidly growing structural and material complexity nowadays.
Bayesian Quickest Detection Problems for Some Diffusion Processes

We study the Bayesian problems of detecting a change in the drift rate of an observable diffusion process with linear and exponential penalty costs for a detection delay. The optimal times of alarms are found as the first times at which the weighted likelihood ratios hit stochastic boundaries depending on the current observations. The proof is based on the reduction of the initial problems into appropriate three-dimensional optimal stopping problems and the analysis of the associated parabolic-type free-boundary problems. We provide closed-form estimates for the value functions and the boundaries, under certain nontrivial relations between the coefficients of the observable diffusion.
University of DelawareDepartment of Electrical and Computer EngineeringComputer Architecture and Parallel Systems

In this paper, we present the superstrand architecture and it's underlying execution model. A superstrand architecture exploits the notion of a strand { a block of instructions grouped together by a compiler to become a scheduling quantum of execution | a strand is enabled at runtime if all necessary dependence (data and control) constraints are satis ed. The code is partitioned into strands so that (1) source and destination of a \long latency" data dependence will be placed into di erent strands; (2) the source (e.g. a test) and destinations of a branch operation will be placed into di erent strands. We show that partitioning strands in this way gives the hardware access to a pool of strands resulting in a large \window" of instructions, which is necessary for su cient instruction level parallelism, but reduces the number of potential dependences which the hardware must check, speculate and resolve at runtime, leading to signi cant savings in hardware complexity. The main results of this paper include: The superstrand program execution model is proposed and presented. An unique feature is: under this model, a strand, once scheduled for execution, can proceed to completion without stalls due to latency for branching or data access and synchronization. (This feature simpli es the superstrand architecture design | see the non-preemptivity feature below.) We demonstrate that programs can be partitioned into strands to be executed under the superstrand execution model with a reasonable programming e ort and compiler optimization. We present an outline for an architecture model and support for the superstrand architecture model. One unique feature of this architecture model is that it makes use of \explicit" dependence information in the inter-strand synchronization and scheduling lookahead, which is in contrast to register scoreboarding where implicit dependence information in the sequential code needs to be discovered and resolved at runtime. i
Understanding, Discovering, and Mitigating Habitual Smartphone Use in Young Adults

People, especially young adults, often use their smartphones out of habit: They compulsively browse social networks, check emails, and play video-games with little or no awareness at all. While previous studies analyzed this phenomena qualitatively, e.g., by showing that users perceive it as meaningless and addictive, yet our understanding of how to discover smartphone habits and mitigate their disruptive effects is limited. Being able to automatically assess habitual smartphone use, in particular, might have different applications, e.g., to design better “digital wellbeing” solutions for mitigating meaningless habitual use. To close this gap, we first define a data analytic methodology based on clustering and association rules mining to automatically discover complex smartphone habits from mobile usage data. We assess the methodology over more than 130,000 phone usage sessions collected from users aged between 16 and 33, and we show evidence that smartphone habits of young adults can be characterized by various types of links between contextual situations and usage sessions, which are highly diversified and differently perceived across users. We then apply the proposed methodology in Socialize, a digital wellbeing app that (i) monitors habitual smartphone behaviors in real time and (ii) uses proactive notifications and just-in-time reminders to encourage users to avoid any identified smartphone habits they consider as meaningless. An in-the-wild study with 20 users (ages 19–31) demonstrates that Socialize can assist young adults in better controlling their smartphone usage with a significant reduction of their unwanted smartphone habits.
Rietveld Refinement of Disordered Illite-Smectite Mixed-Layer Structures by a Recursive Algorithm. I: One-Dimensional Patterns

X-ray diffraction patterns of oriented mounts of clay minerals are often used in clay mineralogy for qualitative and quantitative purposes. Fequently occurring stacking defects, in particular, can be characterized by this technique. Modeling of these diffraction profiles has become an important tool in obtaining structural information about the nature of stacking order. Manual matching of calculated and observed patterns is time consuming and user dependent. Automatic refinement procedures are, therefore, desirable. An improved approach for the treatment of disordered layer structures within a Rietveld refinement is presented here. The recursive calculation of structure factors, similar to that of the simulation program DIFFaX, was introduced in the Rietveld code BGMN. Complete implementation is formulated within the interpreter language of the Rietveld code and is transparent as well as flexible. Such a method has opened the application of Rietveld refinement to patterns of oriented mounts where only basal reflections of stacking disordered structures were recorded. The DIFFaX code was used to simulate basal reflections of illite-smectite mixed layers (I-S) with different ratios of illitic and smectitic layers and with different degrees of long-range ordering (Reichweite). Rietveld refinements with these simulated patterns were used to evaluate the application of this new approach. Several I-S with different degrees of ordering were also chosen as tests for the refinement of basal reflections. The samples were prepared as standard airdried and ethylene glycol-solvated, oriented specimens. Realistic structural parameters were obtained for the composition and ordering of the I-S.
Robot-Assisted Therapy and Constraint-Induced Movement Therapy for Motor Recovery in Stroke: Results From a Randomized Clinical Trial

Background: Stroke is one of the leading causes of adult disability, and up to 80% of stroke survivors undergo upper extremity motor dysfunction. Constraint-Induced Movement Therapy (CIMT) and Robot-Assisted Therapy (RT) are used for upper limb stroke rehabilitation. Although CIMT and RT are different techniques, both are beneficial; however, their results must be compared. The objective is to establish the difference between RT and CIMT after a rehabilitation program for chronic stroke patients. Method: This is a randomized clinical trial, registered at ClinicalTrials.gov (ID number NCT02700061), in which patients with stroke received sessions of RT or CIMT protocol, combined with a conventional rehabilitation program for 12 weeks. The primary outcome was measured by Wolf Motor Function Test (WMFT) and Fugl-Meyer Assessment—Upper Limb (FMA-UL). Activities of daily living were also assessed. Results: Fifty one patients with mild to moderate upper limb impairment were enrolled in this trial, 25 women and 26 men, mean age of 60,02 years old (SD 14,48), with 6 to 36 months after stroke onset. Function significantly improved regardless of the treatment group. However, no statistical difference was found between both groups as p-values of the median change of function measured by WMFT and FMA were 0.293 and 0.187, respectively. Conclusion: This study showed that Robotic Therapy (RT) was not different from Constraint-Induced Movement Therapy (CIMT) regardless of the analyzed variables. There was an overall upper limb function, motor recovery, functionality, and activities of daily living improvement regardless of the interventions. At last, the combination of both techniques should be considered in future studies.
Yes! Maximizers Maximize Almost Everything: The Decision-Making Style Is Consistent in Different Decision Domains

The decision-making literature distinguishes one maximizing style from another satisficing decision-making style, but it is unknown whether these styles remain stable or are variable depending on the occasion. One way to approach it is to verify eventual generalization of these styles in behavior of people in different decision domains. Some incipient results with University students from the United States and Austria suggest that these styles would remain in three different domains. However, it is unknown if this is the case in adults, other cultures, or vital areas of great relevance, such as health and personal finances. The objective here is to identify if Chilean Latin American participants of different sex and age maintain their decision-making style in five different decision domains. The sample was 343 volunteers, 52.6% men, from two regions of central-southern Chile (Maule and Ñuble), aged between 20 and 90 years (M = 45.47; SD = 16.05), who answered the Maximization Tendency Scale, and 45 items corresponding to five different decision domains: health, life decision, finances, services and experiences, and consumer's good. An apparent coherence of decision-making style—maximizing and satisficing—was obtained in the five domains. The health domain stands out for being the one in which it is maximized and with greater internal homogeneity.
Topological Study on the Design of Soft Strain Sensors for Simultaneous Multi-point Contact Localization

Soft strain sensors have been widely used for the development of electronic skins both for robotic and wearable applications. To sense contact location on a wide surface, the standard methodology consists of square grids of strain fibers that are able to detect single contact points but fail to detect multiple ones simultaneously. To avoid such a problem, state-of-the-art technologies implement sequential sampling that isolates each sensing node, but at the cost of a lower sampling rate. This theoretical study proposes a design methodology for multi-touch detection for parallel processed grid-based strain sensors. The fundamental idea is to add diagonal grids of varying orientations on top of the standard architecture to achieve multi-touch detection. The maximum number of detectable points and the number of required strain fibers and the overall geometry of the sensor are studied along with the error introduced when trying to sense more contact points than designed for. Overall, compared with state-of-the-art design methodologies, our work provides a guideline for more efficient grid-based architectures that are able to simultaneously detect up to a fixed finite number of contact points.
Privacy-Preserving Efficient Data Retrieval in IoMT Based on Low-Cost Fog Computing

(e rapid development of Internet of Medical (ings (IoMT) is remarkable. However, IoMT faces many problems including privacy disclosure, long delay of service orders, low retrieval efficiency of medical data, and high energy cost of fog computing. For these, this paper proposes a data privacy protection and efficient retrieval scheme for IoMT based on low-cost fog computing. First, a fog computing system is located between a cloud server and medical workers, for processing data retrieval requests of medical workers and orders for controlling medical devices. Simultaneously, it preprocesses physiological data of patients uploaded by IoMT, collates them into various data sets, and transmits them to medical institutions in this way. It makes the entire execution process of low latency and efficient. Second, multidimensional physiological data are of great value, and we use ciphertext retrieval to protect privacy of patient data in this paper. In addition, this paper uses range tree to build an index for storing physiological data vectors, and meanwhile a range retrieval method is also proposed to improve data search efficiency. Finally, bat algorithm (BA) is designed to allocate cost on a fog server group for significant energy cost reduction. Extensive experiments are conducted to demonstrate the efficiency of the proposed scheme.
Interleaver in Coded Short Packets Transmission: A Preliminary Result

In wireless high-performance communications (WirelessHP) target industrial applications, impulse interference is an important source that may cause burst errors in a transmitted packet. By concatenating interleaver with channel coding, this paper investigates the improvement of reliability for short packets transmission in WirelessHP. Based on our constructed hardware platform for WirelessHP protocols, the packets error rate (PER) of interleaved coded packets transmission for convolutional codes (CC) is tested with detailed analysis. Through practical experiments, we shown that interleavers can improve the PER performance in factory environments, the interleaver structure and code rate are also important factors affecting the improvement.
Light and Scanning Electron Miscroscopic Studies of Waxy and Nonwaxy Endosperm Sorghum Varieties

Microscopic studies of the endosperm of waxy and nonwaxy sorghums have indicated a change in distribution of protein and starch of the peripheral endosperm area in the waxy grain. Nonwaxy sorghums contained small starch granules embedded in a dense proteinaceous matrix in the peripheral area. In contrast, the waxy grain had a less dense peripheral endosperm with larger starch granules with considerably less protein in the peripheral endosperm area. These findings were confirmed in several waxy vs. nonwaxy comparisons with different genetic backgrounds. However, one waxy line had a denser peripheral endosperm area than other waxy lines. This indicates there may be differences among the waxy lines. Waxy starch granules are more susceptible to enzyme degradation than nonwaxy starch granules. The combination of increased starch susceptibility and the alteration in structure of waxy sorghum accounts for the improved feed efficiency of waxy grain over nonwaxy in feeding trials
LSP: Collective Cross-Page Prefetching for NVM

As an emerging technique, non-volatile memory (NVM) provides valuable opportunities for boosting the memory system, which is vital for the computing system performance. However, one challenge preventing NVM from replacing DRAM as the main memory is that NVM row activation's latency is much longer (by approximately 10x) than that of DRAM. To address this issue, we present a collective cross-page prefetching scheme that can accurately open an NVM row in advance and then prefetch the data blocks from the opened row with low overhead. We identify a memory access pattern (referred to as a ladder stream) to facilitate prefetching that can cross page boundary, and propose the ladder stream prefetcher (LSP) for NVM. In LSP, two crucial components have been well designed. Collective Prefetch Table is proposed to reduce the interference with demand requests caused by prefetching through speculatively scheduling the prefetching according to the states of the memory queue. It is implemented with low overhead by using single entry to track multiple prefetches. Memory Mapping Table is proposed to accurately prefetch future pages by maintaining the mapping between physical and virtual addresses. Experimental evaluations show that LSP improves the memory system performance with no prefetching by 66%, and the improvement over the state-of-the-art prefetchers, Access Map Pattern Matching Prefetcher (AMPM), Best-Offset Prefetcher (BOP) and Signature Path Prefetcher (SPP) is 26.6%. 21.7% and 27.4%. respectively.
Device- and Temperature Dependency of Systematic Fault Injection Results in Artix-7 and iCE40 FPGAs

Systematic fault injection into the configuration memory of SRAM-based FPGAs promises to gain insight into the criticality of individual configuration bits. Current approaches implicitly assume that results obtained on one FPGA device can be generalized to all devices of that type and hence allow to parallelize fault injection. This work, to the best of our knowledge, is the first to challenge this assumption. To that end, a synthetic test design was subjected to systematic fault injection on 16 Xilinx Artix-7 as well as 10 Lattice iCE40 FPGAs for which bitstream documentation is publicly available. The results of these experiments indicate that the derived sets of critical configuration bits vary from device to device of the same type, especially if the interconnect is targeted. Furthermore, temperature is observed to influence the fault injection results on Artix-7. Suggestions for dealing with the implications in future fault injection experiments are provided.
Securing Named Data Networking routing using Decentralized Identifiers

Named Data Networking (NDN) is a realization of the Information-Centric Networking (ICN) paradigm, where routing is based on content identifiers rather than on network location identifiers. The routing state in NDN can grow exponentially, not only due to the huge number of content identifiers (as opposed to network addresses) but also because it is difficult to detect "fake" routing advertisements. For example, in contrast to IP-based routing, a potentially valid routing entry in NDN can be advertised from multiple network locations, making NDN susceptible to Denial-of-Service attacks at the routing layer. In this paper, we leverage Decentralized Identifiers (DIDs) to build self-verifiable "content advertisements. " With our solution, any router can verify that a content advertisement originates from an "authorized" entity, without requiring any trusted third party. We implement our solution and we evaluate it in a scenario where filtering is implemented by the edge routers. We show that our solution reduces fake routing advertisements with minimal computational overhead.
A Sonification of the zCOSMOS Galaxy Dataset

The sonification of scientific data is useful not only for the work of visually impaired scientists but also for having a feeling of the global behaviour of a set of data and has also an intrinsic artistic value. Here we present the sonification of the zCOSMOS spectroscopic survey aimed to study the evolution of galaxies within the last 6 billions years of the Universe's life. The goals of such an initiative are multiple: providing a sound-based description of the dataset in order to make hidden features emerge, hybridizing science with art in a cross-domain framework, and treating scientific data as cultural heritage to be preserved and enhanced, thus breaking down the barriers between scientists and the general audience.
Efficient Detection of Botnet Traffic by Features Selection and Decision Trees

Botnets are one of the online threats with the most significant presence, causing billionaire losses to global economies. Nowadays, the increasing number of devices connected to the Internet makes it necessary to analyze extensive network traffic data. In this work, we focus on increasing the performance of botnet traffic classification by selecting those features that further increase the detection rate. For this purpose, we use two feature selection techniques, i.e., Information Gain and Gini Importance, which led to three pre-selected subsets of five, six and seven features. Then, we evaluate the three feature subsets and three models, i.e., Decision Tree, Random Forest and k-Nearest Neighbors. To test the performance of the three feature vectors and the three models, we generate two datasets based on the CTU-13 dataset, namely QB-CTU13 and EQB-CTU13. Finally, we measure the performance as the macro averaged F1 score over the computational time required to classify a sample. The results show that the highest performance is achieved by Decision Trees using a five feature set, which obtained a mean F1 score of 85% classifying each sample in an average time of 0.78 microseconds.
Long Short-Term Transformer for Online Action Detection

We present Long Short-term TRansformer (LSTR), a temporal modeling algorithm for online action detection, which employs a long- and short-term memory mechanism to model prolonged sequence data. It consists of an LSTR encoder that dynamically leverages coarse-scale historical information from an extended temporal window (e.g., 2048 frames spanning of up to 8 minutes), together with an LSTR decoder that focuses on a short time window (e.g., 32 frames spanning 8 seconds) to model the fine-scale characteristics of the data. Compared to prior work, LSTR provides an effective and efficient method to model long videos with fewer heuristics, which is validated by extensive empirical analysis. LSTR achieves state-of-the-art performance on three standard online action detection benchmarks, THUMOS'14, TVSeries, and HACS Segment. Code has been made available at: https://xumingze0308.github. io/projects/lstr