Flash trends: Challenges and future

Stroke is a leading cause of disability and impaired arm function is a common consequence. Constraint-induced movement treatment is a technique used to increase weak arm use and to decrease motor deficits resulting from stroke. Eleven stroke survivors participated in a constraint-induced movement treatment protocol of 3 ½ hours of treatment per day for 8 days. Participants experienced significantly increased use of the weak arm in daily activities, which was measured by the Motor Activity Log, and a trend toward improved coordination in the weak arm, which was measured by the Wolf Motor Function Test. Participants did not report significant improvements in average performance or satisfaction in self-identified occupational performance problems. When these problems were analyzed individually, no significant differences were seen immediately post-treatment. However, significant improvements were seen for satisfaction at 4 to 6 months post-treatment. The occupational performance problems identified by the participants were equally divided between problems related to hand use and problems not related to hand use. Approximately half of the non-hand use problems involved endurance. Although participants in this study did make improvements in arm use and coordination, they did not identify improvements in average occupational performance or satisfaction as an outcome of constraint-induced movement treatment.

Cochlear Implantation in Inner Ear Malformations: Systematic Review of Speech Perception Outcomes and Intraoperative Findings.

Outdoor air ventilation rates vary considerably between and within buildings, and may be too low in some spaces. The purpose of this study was to evaluate the potential work performance benefits of increased ventilation. We analyzed the literature relating work performance with ventilation rate and employed statistical analyses with weighting factors to combine the results of different studies. The studies included in the review assessed performance of various tasks in laboratory experiments and measured performance at work in real buildings. Almost all studies found increases in performance with higher ventilation rates. The studies indicated typically a 1-3% improvement in average performance per 10 l/s-person increase in outdoor air ventilation rate. The performance increase per unit increase in ventilation was bigger with ventilation rates below 20 l/s-person and almost negligible with ventilation rates over 45 l/s-person. The performance increase was statistically significant with increased ventilation rates up to 15 l/s-person with 95% CI and up to 17 l/s-person with 90% CI. Practical Implications We have demonstrated a quantitative relationship between work performance and ventilation within a wide range of ventilation rates. The model shows a continuous increase in performance per unit increase in ventilation rate from 6.5 l/s-person to 65 l/s-person. The increase is statistically significant up to 15 l/s-person. This relationship has a high level of uncertainty; however, use of this relationship in ventilation design and feasibility studies may be preferable to the current practice, which ignores the relationship between ventilation and productivity.

In this work, the creation of a dataset labeled in a pixel-wise manner for the uncommon domain of stain detection on patterned laundry is described. The unique properties of images in this dataset—stains are small and sometimes occur in large amounts—led to the creation of noisy labels. Indeed, the training of a fully convolutional neural network for salient object detection with this dataset revealed that the model predicts stains missed by human labelers. Thus, the reduction in label noise by adding overlooked regions with the help of the model’s predictions is examined in two different experiments. In the model-assisted labeling experiment, a simulation is ran where a human selects correct regions from the predictions. In the self-training experiment, regions of high certainty are automatically selected from the predictions. Re-training the model with the revised labels shows that model-assisted labeling leads to an average improvement in performance by 8.52%. In contrast, with self-training, the performance increase is generally lower (2.58% on average) and a decrease is even possible since regions of high certainty are often false positives.

Abstract: An architectural paradigm designed to accelerate streaming operations on mixed-width data is presented and evaluated. The described Complex Streamed Instruction (CSI) set contains instructions that process data streams of arbitrary length. The number of bits or elements that will be processed in parallel is, therefore, not visible to the programmer, so no recompilation is needed in order to benefit from a wider datapath. CSI also eliminates many overhead instructions (such as instructions needed for data alignment and reorganization) often needed in applications utilizing media ISA extensions such as MMX and VIS by replacing them by a hardware mechanism. Simulation results using several multimedia kernels demonstrate that CSI provides a factor of up to 9.9 (4.0 on average) performance improvement when compared to Sun's VIS extension. For complete applications, the performance gain is 9% to 36% with an average of 20%.

Chip multiprocessor designs are the most common types of architectures seen in Network Processors. As the Network Processors are used to implement increasingly complicated applications, task distribution among the cores is becoming an important problem. In this paper, we propose a new task allocation scheme for such architectures. This scheme relies on the inherent modular nature of the networking applications and intelligently distributes modules among different execution cores. Additionally, we selectively replicate modules to parallelize execution of tasks having longer processing time. We have developed a technique that uses the probability distribution of the execution times of different modules in the networking applications. The proposed schemes result in resource utilization of up to 95%, 89%, and 84% on average for the processors with 2, 4, and 8 cores, respectively. The schemes are highly scalable and can improve the throughput by 6.72 times for 8 core processors, aggregated over four representative applications. The combination of selective replication of modules and variation-aware task allocation result in up to 12.5% (9.9% on average) performance improvement as compared to a scheme based on just mean processing time.

Nonvolatile memory such as magnetic RAM (MRAM) offers high cell density and low leakage power while suffering from long write latency and high write energy, compared with SRAM. 3-D integration technology using through-silicon vias enables stacking disparate memory technologies (e.g., SRAM and MRAM) together onto chip-multiprocessors (CMPs). The use of hybrid memories as an on-chip cache can take advantage of the best characteristics that each technology offers. However, the inherent high power density and heat removal limitation in 3-D integrated circuits may incur temperature-related problems. In this paper, we propose a runtime thermal management method for CMPs with the 3-D stacked hybrid SRAM/MRAM L2 cache. The proposed method combines dynamic cache management such as resource allocation, way-based power gating, and data migration with dynamic voltage and frequency scaling of processing cores in a temperature- and energy-aware manner. Experimental results show that the proposed runtime method with the 3-D stacked hybrid L2 cache offers up to 107.37% (55.28% on average) performance improvement and 88.47% (47.65% on average) energy efficiency improvement compared with existing thermal management methods with 3-D stacked SRAM-based L2 cache.

Mapping matrix operations on SIMD processors brings a large amount of data rearrangement that decrease the system performance. This paper proposes a Configurable Matrix Register File (CMRF) that supports both row-wise and column-wise accesses. The CMRF can be dynamically configured into different operating modes in which one or several sub-matrices can be accessed in parallel. Experimental results show that, compared with the traditional Vector Register File (VRF) and the MRF, the CMRF can respectively achieve about 2.21x and 1.6x average performance improvement. Compared with TMS320C64x+, our SIMD processor can achieve about 5.65x to 7.71x performance improvement by employing the CMRF.

Thermal management of DRAM memory has become a critical issue for server systems. We have done, to our best knowledge, the first study of software thermal management for memory subsystem on real machines. Two recently proposed DTM (Dynamic Thermal Management) policies have been improved and implemented in Linux OS and evaluated on two multicore servers, a Dell PowerEdge 1950 server and a customized Intel SR1500AL server testbed. The experimental results first confirm that a system-level memory DTM policy may significantly improve system performance and power efficiency, compared with existing memory bandwidth throttling scheme. A policy called DTM-ACG (Adaptive Core Gating) shows performance improvement comparable to that reported previously. The average performance improvements are 13.3% and 7.2% on the PowerEdge 1950 and the SR1500AL (vs. 16.3% from the previous simulation-based study), respectively. We also have surprising findings that reveal the weakness of the previous study: the CPU heat dissipation and its impact on DRAM memories, which were ignored, are significant factors. We have observed that the second policy, called DTM-CDVFS (Coordinated Dynamic Voltage and Frequency Scaling), has much better performance than previously reported for this reason. The average improvements are 10.8% and 15.3% on the two machines (vs. 3.4% from the previous study), respectively. It also significantly reduces the processor power by 15.5% and energy by 22.7% on average.

Growing on-chip wire delays are motivating architectural features that expose on-chip communication to the compiler. EDGE architectures are one example of communication-exposed microarchitectures in which the compiler forms dataflow graphs that specify how the microarchitecture maps instructions onto a distributed execution substrate. This paper describes a compiler scheduling algorithm called spatial path scheduling that factors in previously fixed locations - called anchor points - for each placement. This algorithm extends easily to different spatial topologies. We augment this basic algorithm with three heuristics: (1) local and global ALU and network link contention modeling, (2) global critical path estimates, and (3) dependence chain path reservation. We use simulated annealing to explore possible performance improvements and to motivate the augmented heuristics and their weighting functions. We show that the spatial path scheduling algorithm augmented with these three heuristics achieves a 21% average performance improvement over the best prior algorithm and comes within an average of 5% of the annealed performance for our benchmarks.

Moore's Law improvement in transistor density is driving a rapid increase in the number of cores per processor. DRAM device capacity and energy efficiency are increasing at a slower pace, so the importance of DRAM power is increasing. This problem presents system designers with two nominal options when designing future systems: 1) decrease off-chip memory capacity and bandwidth per core or 2) increase the fraction of system power allocated to main memory. Reducing capacity and bandwidth leads to imbalanced systems with poor processor utilization for noncache-resident applications, so designers have chosen to increase DRAM power budget. This choice has been viable to date, but is fast running into a memory power wall. To address the looming memory power wall problem, we propose a novel iso-power tiered memory architecture that supports 2-3X more memory capacity for the same power budget as traditional designs by aggressively exploiting low-power DRAM modes. We employ two "tiers" of DRAM, a "hot" tier with active DRAM and a "cold" tier in which DRAM is placed in self-refresh mode. The DRAM capacity of each tier is adjusted dynamically based on aggregate workload requirements and the most frequently accessed data are migrated to the "hot" tier. This design allows larger memory capacities at a fixed power budget while mitigating the performance impact of using low-power DRAM modes. We target our solution at server consolidation scenarios where physical memory capacity is typically the primary factor limiting the number of virtual machines a server can support. Using iso-power tiered memory, we can run 3× as many virtual machines, achieving a 250 percent improvement in average aggregate performance, compared to a conventional memory design with the same power budget.

Unconventional completions in North America have seen a paradigm shift in volumes of proppant pumped since 2014. There is a clear noticeable trend in both oil prices and proppant volumes – thanks to low product and service costs that accompanied the oil price crash in early 2015. As the industry continues to recover, operators are reevaluating completion designs to understand if these proppant volumes are beyond what is optimal. This paper analyzes trends in completion sizes and types across all major unconventional oil and gas plays in the US since 2011 and tracks their impact on well productivity. Completion and production data since 2011 from more than 70,000 horizontal wells in seven major basins (Gulf Coast, Permian, Appalachian, Anadarko, Haynesville, Williston and Denver Julesburg basins) and 11 major oil/gas producing formations were analyzed to examine developments in proppant and fluid volumes. Average concentration of proppant per gallon of fluid pumped was used to understand transitional trends in fracturing fluid types with time. Production performance indicators such as First month, Best 3 or Best 12 months of oil and gas production were mapped against completion volumes to evaluate if there are added economic advantages to pumping larger designs. In general, all major basins have seen progressive improvements in average well performance since 2011, with the Permian Basin showing the highest improvement, increasing from an average first-six-months oil production of 25,000 bbl in 2011 to 75,000 bbl in 2017. The Gulf Coast basin, where the Eagle Ford formation is located, has seen a 6-fold increase in proppant volumes pumped per foot of lateral since 2011 while the Permian and Appalachian basins hit peak proppant volumes in 2015 and 2016 respectively. In Permian and Eagleford wells, higher proppant volumes in general have resulted in better production up to a certain concentration. In Williston and Denver basins, most operators are moving away from gelled fluids, and reduced average proppant concentration per fluid volume pumped shows inclination toward hybrid or slickwater designs. While some of these observations are tied to reservoir quality, proppant volumes have begun to peak as operators have either reached an optimal point or are in the process of reducing volumes. Demand for proppant is expected to nearly double by 2020. As oil prices continue to recover, well AFEs continue to increase, despite multiple efforts to improve capital efficiency. The need for enhanced fracture conductivity and extended half-lengths on EURs are been discussed by combining actual observed production data and sensitivities using calibrated production models. The industry is moving toward large-volume slickwater fracturing operations using smaller proppants, but he operating landscape is expected to see a correction when such designs become less economical.

Depth-wise pruning accelerates LLM inference in resource-constrained scenarios but suffers from performance degradation due to indiscriminate removal of entire Transformer layers. This paper reveals ``Patch-Like'' redundancy across layers via correlation analysis of the outputs of different layers in reproducing kernel Hilbert space, demonstrating consecutive layers exhibit high functional similarity. Building on this observation, this paper proposes Sliding-Window Merging (SWM) - a dynamic compression method that selects consecutive layers from top to bottom using a pre-defined similarity threshold, and compacts patch-redundant layers through a parameter consolidation, thereby simplifying the model structure while maintaining its performance. Extensive experiments on LLMs with various architectures and different parameter scales show that our method outperforms existing pruning techniques in both zero-shot inference performance and retraining recovery quality after pruning. In particular, in the experiment with 35\% pruning on the Vicuna-7B model, our method achieved a 1.654\% improvement in average performance on zero-shot tasks compared to the existing method. Moreover, we further reveal the potential of combining depth pruning with width pruning to enhance the pruning effect.

Due to the prevalence of music performance anxiety and the emergence of new biofeedback technologies used to decrease anxiety, student musicians were recruited to participate in an experimental repeated-measures study to identify effects of heart rate variability coherence biofeedback training and emotional self-regulation techniques on music performance anxiety and music performance. Fourteen students were assigned randomly to a treatment or control group following a 5-minute unaccompanied baseline performance. Treatment group participants received 4–5 heart rate variability training sessions of 30–50 minutes each. Training included bibliotherapy, computerized heart rate variability biofeedback training, emotional regulation exercises, and use of a portable heart rate variability training device. Measures included the State-Trait Anxiety Inventory, Performance Anxiety Inventory, Flow State Scale, average heart rate, and heart rate variability. Quade's rank-transformed analysis of covariance was used to evaluate treatment and no-treatment group comparisons. Combined music performance anxiety scores showed statistically significant improvement at a p = .05 level with a large effect size of ηp2 = .320. State anxiety measurement showed a large effect size of ηp2 = .291. The Performance Anxiety Inventory showed a large effect size of ηp2 = .149. Heart rate showed a large effect size of ηp2 = .143. Heart rate variability showed statistical significance at p = .001 level and a large effect size of ηp2 = .698. The treatment group average subjective decrease in music performance anxiety was 71%, and the treatment group average subjective improvement in performance was 62%. This study demonstrated statistical and practical/clinical significance of a relatively quick and inexpensive biofeedback training that had a large effect on decreasing mental, emotional, and physiological aspects of music performance anxiety and subjective improvement of performance for university students.

Transform domain communication system (TDCS) using transform domain processing is demonstrated as having enhanced interference avoidance capability under adverse environmental conditions. Many transform methods can be used in the TDCS, a method is proposed in the paper which is based on random-ambiguity transform (RAT) plus fractional Fourier transform (FRFT). The proposed TDCS and its response to nonstationary interference scenarios are modeled and simulation results obtained using MATLAB. The proposed method has a good ability to anti any narrowband continuous nonstationary jammers. The system was evaluated using an E <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</sub> /N <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sub> of -7.0 dB and interference energy-to-signal energy (J/E) ratios ranging from 0 dB to 16.0 dB. For BCSK (binary cyclic shift key) data modulation cases, the average TDCS bit error performance improvement is more than 14 dB relatives to a DSSS system according to different J/E. Collectively, these results indicate that the proposed TDCS using RAT-FRFT for interference mitigating is a viable option in nonstationary jammers scenarios