Miracle-dz/UniFAR-FTUs huggingface dataset:
Cardiac Complications of Marrow Transplantation

Cardiac complications are common, but usually subclinical, after blood and mar row transplantation. The most frequent causes of serious, life-threatening cardiac complica tions, such as severe heart failure, effusive pericarditis, and arrhythmias, are regimen-related toxicities due to cyclophosphamide and ifosfamide. These are most common in the presence of pre-existing anthracycline cardiac toxicity. Patients with baseline ejection fractions less than 50% have a higher incidence of cardiac toxicity. Radiation and other transplant-associated agents (etoposide, taxol, etc.) are known to have potential, but less frequent, cardiac toxicities. It may be clinically difficult to distinguish cardiac problems from other processes, such as pul monary toxicity, volume overload, or sepsis-related myocardial depression. Additionally, peri cardial, myocardial, and valvular infections are potential, rare causes of complications and may pose diagnostic challenges. While there is no consensus regarding the pre-transplant evaluation of left ventricular function, general guidelines for management issues, both before and after transplant, can be made.
RT-PLRU: A New Paging Scheme for Real--Time Execution of Program Codes on NAND Flash Memory for Portable Media Players

NAND flash memory has been widely used as a nonvolatile storage for storing data. However, it is challenging to execute program codes on NAND flash memory, since NAND flash memory only supports page-based reads, not byte-level random reads. This paper proposes an automated process to find the optimal paging strategy called RT-PLRU (Real-Time constrained combination of Pinning and LRU) that allows program codes stored in NAND flash memory to be executed satisfying real-time requirements with minimal usage of RAM. Moreover, the proposed process optimally configure the RT-PLRU in a developer-transparent way without giving any burden to the program developer. The developed technique is specifically applied to a media player program targeting a portable media player (PMP). To the best of our knowledge, this is the first effort to use NAND flash memory as a code storage for storing and executing real-time programs with minimal usage of RAM.
An extended multivariate autoregressive framework for EEG-based information flow analysis of a brain network

Recently effective connectivity studies have gained significant attention among the neuroscience community as Electroencephalography (EEG) data with a high time resolution can give us a wider understanding of the information flow within the brain. Among other tools used in effective connectivity analysis Granger Causality (GC) has found a prominent place. The GC analysis, based on strictly causal multivariate autoregressive (MVAR) models does not account for the instantaneous interactions among the sources. If instantaneous interactions are present, GC based on strictly causal MVAR will lead to erroneous conclusions on the underlying information flow. Thus, the work presented in this paper applies an extended MVAR (eMVAR) model that accounts for the zero lag interactions. We propose a constrained adaptive Kalman filter (CAKF) approach for the eMVAR model identification and demonstrate that this approach performs better than the short time windowing-based adaptive estimation when applied to information flow analysis.
All-Optical Wavelength-Routed Architecture for a Power-Efficient Network on Chip

In this paper, we propose a new architecture for nanophotonic Networks on Chip (NoC), named 2D-HERT, which consists of optical data and control planes. The proposed data plane is built upon a new topology and all-optical switches that passively route optical data streams based on their wavelengths. Utilizing wavelength routing method, the proposed deterministic routing algorithm, and Wavelength Division Multiplexing (WDM) technique, the proposed data plane eliminates the need for optical resource reservation at the intermediate nodes. For resolving end-point contention, we propose an all-optical request-grant arbitration architecture which reduces optical losses compared to the alternative arbitration schemes. By performing a series of simulations, we study the efficiency of the proposed architecture, its power and energy consumption, and the data transmission delay. Moreover, we compare the proposed architecture with electrical NoCs and alternative optical NoCs under various synthetic traffic patterns. Averaged across different traffic patterns, 2D-HERT reduces data transmission delay by 24, 15, 18, 4, and 70 percent and achieves average per-packet power reduction of 58, 47, 52, 45, and 95 percent over Phastlane, Firefly, Corona architecture, λ-router, and electrical Torus, respectively.
The Linkage between Imported Energy and Trade in Taiwan

This study examines the Granger causality between Taiwan's aggregate and disaggregate energy consumption and trade variables, including total imports (IM), total exports (EX), total energy consumption (ENERGY), oil and petroleum products consumption (OIL), coal and coal products consumption (COAL), natural gas consumption (GAS), export value of the industrial sector (EX_I), export value of heavy-chemical industrial products (EX_HI), and export value of non-heavy-chemical industrial products (EX_NHI) with monthly data during 1998–2009. Via applying Hsiao's version of the Granger causality method, the results find causality running from ENERGY to IM, EX, EX_I, and EX_HI. The impulse-response simulations show that the above relations have positive responses at the initial period. OIL Granger causes all trade variables. The impulse directions to IM and EX_NHI are negative, whereas others are positive. On the other hand, COAL responds to impulses in all trade variables. The impulse-response simulations show that these relations have positive responses at the initial period except for causality running from EX_HI and EX_NHI to COAL. GAS positively responds to impulses in EX, EX_I, and EX_HI at the initial period. The bidirectional Granger causality between pairs of variables (such as GAS and EX_NHI as well as GAS and EX_HI) is found.
Path-Based Partitioning Methods for 3D Networks-on-Chip with Minimal Adaptive Routing

Combining the benefits of 3D ICs and Networks-on-Chip (NoCs) schemes provides a significant performance gain in Chip Multiprocessors (CMPs) architectures. As multicast communication is commonly used in cache coherence protocols for CMPs and in various parallel applications, the performance of these systems can be significantly improved if multicast operations are supported at the hardware level. In this paper, we present several partitioning methods for the path-based multicast approach in 3D mesh-based NoCs, each with different levels of efficiency. In addition, we develop novel analytical models for unicast and multicast traffic to explore the efficiency of each approach. In order to distribute the unicast and multicast traffic more efficiently over the network, we propose the Minimal and Adaptive Routing (MAR) algorithm for the presented partitioning methods. The analytical and experimental results show that an advantageous method named Recursive Partitioning (RP) outperforms the other approaches. RP recursively partitions the network until all partitions contain a comparable number of switches and thus the multicast traffic is equally distributed among several subsets and the network latency is considerably decreased. The simulation results reveal that the RP method can achieve performance improvement across all workloads while performance can be further improved by utilizing the MAR algorithm. Nineteen percent average and 42 percent maximum latency reduction are obtained on SPLASH-2 and PARSEC benchmarks running on a 64-core CMP.
Architecting against Software Cache-Based Side-Channel Attacks

Using cache-like architectural components including data caches, instruction caches, or branch target buffers as a side channel, software cache-based side-channel attacks are able to derive secret keys used in cryptographic operations through legitimate software activities. Existing software solutions are typically application specific and incur substantial performance overhead. Recent hardware proposals against attacks on data caches, although effective in reducing performance overhead, may still be vulnerable to advanced attacks. Furthermore, efficient defenses against attacks on other cache structures, including instruction caches and branch target buffers, are missing. In this paper, we propose hardware-software integrated approaches to defend against software cache-based attacks comprehensively. For attacks on data caches, we propose to use preloading, informing loads, and informing loads with software random permutation to secure the partition-locked cache (PLcache), the random permutation (RPcache) and regular caches, respectively. These approaches present different tradeoffs between hardware complexity and performance overhead. To defend against attacks on instruction caches, we show that the PLcache with preloading and the RPcache provide good protection. To defend against attacks based on branch target buffers, we propose to adopt a new update policy to eliminate potential information leaking. Our experiments show that the proposed schemes not only provide strong security protection but also incur small performance overhead.
Improving Space Efficiency With Path Length Prediction for Finding $k$  Shortest Simple Paths

Finding \mbi k shortest simple paths in a directed graph is a fundamental problem in many engineering applications. Most existing algorithms such as Yen's algorithm and its variants have polynomial worst-case time complexity, but their average-case running time is very high. The heuristic algorithm MPS can run significantly faster in practice. However, it requires an excessive amount of memory space. In this paper, we provide a new heuristic algorithm that achieves high space efficiency while maintaining similar average-case running time. We first propose a sidetrack representation of path, with which a path can be stored in \mbi O(1) space. We then show how to categorize a candidate path as either partial or complete, and restrict the number of paths added to the queue. In addition, we provide an empirical equation that can very accurately predict the \mbi kth shortest path length, provided that a much smaller number of shortest paths have been found. Extensive experiments prove that our algorithm can achieve an \mbi O(n) speedup in practice over Yen's algorithm. In comparison with MPS, it runs up to three times faster and uses less space by an order of magnitude.
Software Support and Evaluation of Hardware Transactional Memory on Blue Gene/Q

This paper describes an end-to-end system implementation of a transactional memory (TM) programming model on top of the hardware transactional memory (HTM) of the Blue Gene/Q machine. The TM programming model supports most C/C++ programming constructs using a best-effort HTM and the help of a complete software stack including the compiler, the kernel, and the TM runtime. An extensive evaluation of the STAMP and the RMS-TM benchmark suites on BG/Q is the first of its kind in understanding characteristics of running TM workloads on real hardware TM. The study reveals several interesting insights on the overhead and the scalability of BG/Q HTM with respect to sequential execution, coarse-grain locking, and software TM.
Reliable Multicast in Data Center Networks

Multicast benefits data center group communication in both saving network traffic and improving application throughput. Reliable packet delivery is required in data center multicast for data-intensive computations. However, existing reliable multicast solutions for the Internet are not suitable for the data center environment, especially with regard to keeping multicast throughput from degrading upon packet loss, which is norm instead of exception in data centers. We present RDCM, a novel reliable multicast protocol for data center network. The key idea of RDCM is to minimize the impact of packet loss on the multicast throughput, by leveraging the rich link resource in data centers. A multicast-tree-aware backup overlay is explicitly built on group members for peer-to-peer packet repair. The backup overlay is organized in such a way that it causes little individual repair burden, control overhead, as well as overall repair traffic. RDCM also realizes a window-based congestion control to adapt its sending rate to the traffic status in the network. Simulation results in typical data center networks show that RDCM can achieve higher application throughput and less traffic footprint than other representative reliable multicast protocols. We have implemented RDCM as a user-level library on Windows platform. The experiments on our test bed show that RDCM handles packet loss without obvious throughput degradation during high-speed data transmission, gracefully respond to link failure and receiver failure, and causes less than 10% CPU overhead to data center servers.
The Lived Experience of Socialization Among African American Nursing Students in a Predominantly White University

Purpose: This study explored the phenomenon of socialization among African American nursing students in predominantly White universities. Recruitment and retention of African American nursing students is in lower percentages compared with other groups. Many nursing schools reflect a culture of the White middle class, which may present a barrier to minority students by requiring students to socialize to a culture different from their own. Overcoming such barriers cannot begin until the experience of socialization is understood. Design: A sample consisting of eight co-researchers was used who all attended nursing school at a predominantly White university. Interviews were analyzed using Colaizzi’s phenomenological method. Results: The following six themes emerged: Theme 1—The Strength to Pursue More, Theme 2—Encounters With Discrimination, Theme 3—Pressure to Succeed, Theme 4—Isolation and Sticking Together, Theme 5—To Fit In and Talk White, and Theme 6—To Learn With New Friends and Old Ones. Conclusions: This study found a strongly consistent process of socialization to the dominant norm, and raised questions about the effects of this process on African American nursing students and its impact on improving patient care.
Using Culturally Competent Community-Based Participatory Research With Older Diabetic Chinese Americans

The purpose of this article is to share culturally competent strategies and lessons learned from a study that used a community-based participatory research (CBPR) approach with older adult diabetic Chinese Americans. This approach was essential to gain insight into the health beliefs, attitudes, and practices of selected communities. The vulnerable population conceptual model (VPCM) provided a framework for the study. The CBPR, a collaborative research approach, and the VPCM provided the basis for the development of culturally competent research strategies. Strategies and lessons learned to be particularly effective for this CBPR study included (a) developing an in-depth understanding of the community ethnic culture; (b) developing mutual respect and trust with community members and study participants; (c) appreciating and praising community partners’ knowledge, expertise, and experiences; (d) developing a sense of ownership by incorporating their needs, ideas, suggestions, and opinions and empowering study participants to make decisions concerning the study approach and wording; (e) soliciting participant feedback and clarification of study results and involving them in disseminating the study findings to their community; and (f) having fun with them and encouraging them to have fun.
Elastic Power-Aware Resource Provisioning of Heterogeneous Workloads in Self-Sustainable Datacenters

While major Cloud service operators have taken various initiatives to operate their datacenters with renewable energy partially or completely, it is challenging to effectively utilize the renewable energy since its generation depends on dynamic natural conditions. In this paper, we propose and develop an elastic power-aware resource provisioning approach (ePower) for heterogeneous workloads in self-sustainable datacenters that completely rely on renewable energy. We aim to maximize the system goodput and control the system power consumption with respect to green power supply. ePower takes challenges and advantages of dynamic power supply, heterogeneous workload characteristics and QoS requirements, and automatically optimizes elastic resource allocations to workloads. The core of ePower design is a novel power-aware simulated annealing algorithm with fuzzy performance modeling for the efficient search of an optimal resource allocation. We have implemented ePower in a university cloud testbed hosting Gridmix2 and RUBiS benchmark applications. We utilize real weather data traces to simulate the green power generation and supply in the experiments. Experimental results demonstrate ePower can achieve near-to-optimal system performance while being resilient to dynamic power availability. It outperforms a representative resource provisioning approach for heterogeneous workloads by at least 24% in improving system goodput and 35 percent in reducing QoS violations.
Reducing Static Energy in Supercomputer Interconnection Networks Using Topology-Aware Partitioning

The key to reducing static energy in supercomputers is switching off their unused components. Routers are the major components of a supercomputer. Whether routers can be effectively switched off or not has become the key to static energy management for supercomputers. For many typical applications, the routers in a supercomputer exhibit low utilization. However, there is no effective method to switch the routers off when they are idle. By analyzing the router occupancy in time and space, for the first time, we present a routing-policy guided topology partitioning methodology to solve this problem. We propose topology partitioning methods for three kinds of commonly used topologies (mesh, torus and fat-tree) equipped with the three most popular routing policies (deterministic routing, directionally adaptive routing and fully adaptive routing). Based on the above methods, we propose the key techniques required in this topology partitioning based static energy management in supercomputer interconnection networks to switch off unused routers in both time and space dimensions. Three topology-aware resource allocation algorithms have been developed to handle effectively different job-mixes running on a supercomputer. We validate the effectiveness of our methodology by using Tianhe-2 and a simulator for the aforementioned topologies and routing policies. The energy savings achieved on a subsystem of Tianhe-2 range from 3.8 to 79.7 percent. This translates into a yearly energy cost reduction of up to half a million US dollars for Tianhe-2.
Equine-Assisted Intervention for People with Dementia

ABSTRACT The purpose of this exploratory study was to determine the feasibility and effectiveness of using guided interactions with horses as a nonpharmaceutical intervention to improve the physiological and behavioral states of persons with dementia. A convenience sample of persons with dementia was recruited from an adult day health center (n = 16). A multi-component intervention was implemented comprised of opportunities for grooming, painting, and leading horses. Using a randomized pretest-posttest crossover design, researchers compared participants receiving the equine-assisted intervention with participants receiving treatment as usual. Older persons with Alzheimer's disease and related dementias engaged positively in animal-assisted therapy with horses. A reduction in behavioral problems was found post intervention in contrast to the comparison group. Pre-intervention measures showed that participants exhibited lower levels of disruptive behaviors compared with the control group on the days they were scheduled to work with the horses. Interestingly, cortisol levels, used as a physiological measure of coping with stress, were elevated after the intervention in participants with higher Mini Mental State Examination scores. Equine-assisted interventions are feasible and possibly beneficial for adults with Alzheimer's disease or a related dementia disorder, such as those enrolled in adult day health programs. Future studies should utilize multiple methods of assessing impact and include process measures to delineate which specific activities seem to provide the most benefit.
An Ethnographic Exploration of Postoperative Pain Experiences Among Ghanaian Surgical Patients

Background: The experience of pain associated with surgery has been a challenge for health care professionals for many years, and culture is said to influence pain. Purpose: This study focused on patients’ experiences of postoperative pain (POP) and factors that affect POP. Design: The study employed qualitative ethnographic principles. Method: Data were collected through individual face-to-face interviews. Data were saturated after analyzing data from 13 patients from two hospitals in Ghana. Findings: Themes that emerged were the subjective nature of pain, which described pain dimensions and communication; psycho-sociocultural factors, such as personal inclinations and sociocultural background; and health system factors, such as personnel attitudes and health financing. Implications for Future Research and Practice: Health professionals need to understand the sociocultural effects of pain in order to give effective care. Conclusion: The study highlighted the need for patient education and the importance that health care professionals understand context-specific factors that influence POP management.
Me-CLOCK:A Memory-Efficient Framework to Implement Replacement Policies for Large Caches

Solid State Drives (SSDs) have been extensively deployed as the cache of hard disk-based storage systems. The SSD-based cache generally supplies ultra-large capacity, whereas managing so large a cache introduces excessive memory overhead, which in turn makes the SSD-based cache neither cost-effective nor energy-efficient. This work targets to reduce the memory overhead introduced by the replacement policy of SSD-based cache. Traditionally, data structures involved in cache replacement policy reside in main memory. While these in-memory data structures are not suitable for SSD-based cache any more since the cache is much larger than ever. We propose a memory-efficient framework which keeps most data structures in SSD while just leaving the memory-efficient data structure (i.e., a new bloom proposed in this work) in main memory. Our framework can be used to implement any LRU-based replacement policies under negligible memory overhead. We evaluate our proposals via theoretical analysis and prototype implementation. Experimental results demonstrate that, our framework is practical to implement most replacement policies for large caches, and is able to reduce the memory overhead by about <inline-formula><tex-math notation="LaTeX">$10 \times$</tex-math><alternatives> <inline-graphic xlink:type="simple" xlink:href="xiao-ieq1-2495182.gif"/></alternatives></inline-formula>.