Query: Analyzing EEG signals to detect unexpected obstacles during walking

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Lower Extremity Exoskeletons and Active Orthoses: Challenges and State-of-the-Art <=SEP=> In the nearly six decades since researchers began to explore methods of creating them, exoskeletons have progressed from the stuff of science fiction to nearly commercialized products. While there are still many challenges associated with exoskeleton development that have yet to be perfected, the advances in the field have been enormous. In this paper, we review the history and discuss the state-of-the-art of lower limb exoskeletons and active orthoses. We provide a design overview of hardware, actuation, sensory, and control systems for most of the devices that have been described in the literature, and end with a discussion of the major advances that have been made and hurdles yet to be overcome.

Brain–machine interfaces: past, present and future <=SEP=> Since the original demonstration that electrical activity generated by ensembles of cortical neurons can be employed directly to control a robotic manipulator, research on brain-machine interfaces (BMIs) has experienced an impressive growth. Today BMIs designed for both experimental and clinical studies can translate raw neuronal signals into motor commands that reproduce arm reaching and hand grasping movements in artificial actuators. Clearly, these developments hold promise for the restoration of limb mobility in paralyzed subjects. However, as we review here, before this goal can be reached several bottlenecks have to be passed. These include designing a fully implantable biocompatible recording device, further developing real-time computational algorithms, introducing a method for providing the brain with sensory feedback from the actuators, and designing and building artificial prostheses that can be controlled directly by brain-derived signals. By reaching these milestones, future BMIs will be able to drive and control revolutionary prostheses that feel and act like the human arm.

A point process framework for relating neural spiking activity to spiking history, neural ensemble, and extrinsic covariate effects. <=SEP=> Multiple factors simultaneously affect the spiking activity of individual neurons. Determining the effects and relative importance of these factors is a challenging problem in neurophysiology. We propose a statistical framework based on the point process likelihood function to relate a neuron's spiking probability to three typical covariates: the neuron's own spiking history, concurrent ensemble activity, and extrinsic covariates such as stimuli or behavior. The framework uses parametric models of the conditional intensity function to define a neuron's spiking probability in terms of the covariates. The discrete time likelihood function for point processes is used to carry out model fitting and model analysis. We show that, by modeling the logarithm of the conditional intensity function as a linear combination of functions of the covariates, the discrete time point process likelihood function is readily analyzed in the generalized linear model (GLM) framework. We illustrate our approach for both GLM and non-GLM likelihood functions using simulated data and multivariate single-unit activity data simultaneously recorded from the motor cortex of a monkey performing a visuomotor pursuit-tracking task. The point process framework provides a flexible, computationally efficient approach for maximum likelihood estimation, goodness-of-fit assessment, residual analysis, model selection, and neural decoding. The framework thus allows for the formulation and analysis of point process models of neural spiking activity that readily capture the simultaneous effects of multiple covariates and enables the assessment of their relative importance.

Power assist method for HAL-3 using EMG-based feedback controller <=SEP=> We have developed the exoskeletal robotics suite HAL (Hybrid Assisitve Leg) which is integrated with human and assists suitable power for ‘lower limb of people with goit disorder. This study proposes the method of assist motion and ossist torque to realize a power assist corresponding to the operator’s intention. In the method of a,ssist motion, we adopted Phase Sequence control which generates a series of assist m,otions hi/ transiti.n,q .some simple basic motions called Phase. w e used the feedback controller to adjust the assist torque to m.ointain myoelectricity signals which were generated while performing the power assist uiolking. The experiment xsults showed the effective power assist according to operator’s intention b y using these control methods.

Adaptive control of a variable-impedance ankle-foot orthosis to assist drop-foot gait <=SEP=> An active ankle-foot orthoses (AAFO) is presented where the impedance of the orthotic joint is modulated throughout the walking cycle to treat drop-foot gait. During controlled plantar flexion, a biomimetic torsional spring control is applied where orthotic joint stiffness is actively adjusted to minimize forefoot collisions with the ground. Throughout late stance, joint impedance is minimized so as not to impede powered plantar flexion movements, and during the swing phase, a torsional spring-damper control lifts the foot to provide toe clearance. To assess the clinical effects of variable-impedance control, kinetic and kinematic gait data were collected on two drop-foot participants wearing the AAFO. For each participant, zero, constant, and variable impedance control strategies were evaluated and the results were compared to the mechanics of three age, weight, and height matched normals. We find that actively adjusting joint impedance reduces the occurrence of slap foot allows greater powered plantar flexion and provides for less kinematic difference during swing when compared to normals. These results indicate that a variable-impedance orthosis may have certain clinical benefits for the treatment of drop-foot gait compared to conventional ankle-foot orthoses having zero or constant stiffness joint behaviors.

Comparison of Parametric and Nonparametric Techniques for Non-peak Traffic Forecasting <=SEP=> Accurately predicting non-peak traffic is crucial to daily traffic for all forecasting models. In the paper, least squares support vector machines (LS-SVMs) are investigated to solve such a practical problem. It is the first time to apply the approach and analyze the forecast performance in the domain. For comparison purpose, two parametric and two non-parametric techniques are selected because of their effectiveness proved in past research. Having good generalization ability and guaranteeing global minima, LS-SVMs perform better than the others. Providing sufficient improvement in stability and robustness reveals that the approach is practically promising. Keywords—Parametric and Nonparametric Techniques, Non-peak Traffic Forecasting

Ultrasound-Assisted Evaluation ofthe Airway in Clinical AnesthesiaPractice: Past, Present and Future <=SEP=> Introduction: The incidence of difficulties encountered in perioperative airway management has been reported to range from 1% to 4%. In patients with head and neck cancers, the incidence can be dramatically higher. Because of high quality of imaging, non-invasiveness and relatively low cost, ultrasonography has been utilized as a valuable adjunct to the clinical assessment of the airway. A review of the literature was conducted with the objective of summarizing the available evidence concerning the use of ultrasound (US) for assessment of the airway, with special emphasis on head and neck cancers. Methods and Materials: A systematic search of the literature in the MEDLINE database was performed. A total of 42 manuscripts from 329 searched articles were included in this review. Results: Ultrasonography was found to give high-resolution images of the anatomic structures of the upper airway comparable to computed tomography and magnetic resonance imaging. Several ultrasonographic parameters (soft tissue thickness at level of hyoid bone, epiglottis and vocal cords, visibility of hyoid bone in sublingual ultrasound, hyomental distance in head-extended position and hyomental distance ratio) were found to be independent predictors of difficult laryngoscopy in obese and non-obese patients. In conjunction with elastosonography, it also provided valuable information regarding tumors, infiltration, and edema as well as fibrosis of the head and neck. Conclusion: Ultrasound-assisted evaluation of the difficult airway offers many important advantages. The ready availability of US machines in anesthesiology departments, familiarity of anesthesia providers with USguided procedures and the portability of US machines allow real-time, point-of-care assessment. It will undoubtedly become more popular and will greatly contribute to improve perioperative patient safety.

Time series forecasting using Artificial Neural Networks vs. evolving models <=SEP=> Time series forecasting plays an important role in many fields such as economics, finance, business intelligence, natural sciences, and the social sciences. This forecasting task can be achieved by using different techniques such as statistical methods or Artificial Neural Networks (ANN). In this paper, we present two different approaches to time series forecasting: evolving Takagi-Sugeno (eTS) fuzzy model and ANN. These two different methods will be compared taking into account the different characteristic of each approach.

Practice Makes Perfect ? When Does Massed Learning Improve Product Usage Proficiency ? <=SEP=> Previous research has shown that spacing of information (over time) leads to better learning of product information. We develop a theoretical framework to describe how massed or spaced learning schedules interact with different learning styles to influence product usage proficiency. The core finding is that with experiential learning, proficiency in a product usage task is better under massed conditions, whereas with verbal learning, spacing works better. This effect is demonstrated for usage proficiency assessed via speed as well as quality of use. Further, massed learning also results in better usage proficiency on transfer tasks, for both experiential and verbal learning. We also find that massed learning in experiential learning conditions leads not only to better usage proficiency but also to positive perceptions of the product. Overall, the pattern of results is consistent with a conceptual mapping account, with massed experiences leading to a superior mental model of usage and thus to better usage proficiency.

Multi-Modal Fashion Product Retrieval <=SEP=> Finding a product in the fashion world can be a daunting task. Everyday, e-commerce sites are updating with thousands of images and their associated metadata (textual information), deepening the problem. In this paper, we leverage both the images and textual metadata and propose a joint multi-modal embedding that maps both the text and images into a common latent space. Distances in the latent space correspond to similarity between products, allowing us to effectively perform retrieval in this latent space. We compare against existing approaches and show significant improvements in retrieval tasks on a largescale e-commerce dataset.

QUOTA: The Quantile Option Architecture for Reinforcement Learning <=SEP=> In this paper, we propose the Quantile Option Architecture (QUOTA) for exploration based on recent advances in distributional reinforcement learning (RL). In QUOTA, decision making is based on quantiles of a value distribution, not only the mean. QUOTA provides a new dimension for exploration via making use of both optimism and pessimism of a value distribution. We demonstrate the performance advantage of QUOTA in both challenging video games and physical robot simulators.

A 3.4 – 6.2 GHz Continuously tunable electrostatic MEMS resonator with quality factor of 460–530 <=SEP=> In this paper we present the first MEMS electrostatically-tunable loaded-cavity resonator that simultaneously achieves a very high continuous tuning range of 6.2 GHz:3.4 GHz (1.8:1) and quality factor of 460–530 in a volume of 18×30×4 mm3 including the actuation scheme and biasing lines. The operating principle relies on tuning the capacitance of the loaded-cavity by controlling the gap between an electrostatically-actuated membrane and the cavity post underneath it. Particular attention is paid on the fabrication of the tuning mechanism in order to avoid a) quality factor degradation due to the biasing lines and b) hysteresis and creep issues. A single-crystal silicon membrane coated with a thin gold layer is the key to the success of the design.

Recommendations for the Assessment of Blend and Content Uniformity: Modifications to Withdrawn FDA Draft Stratified Sampling Guidance <=SEP=> The following paper describes the International Society for Pharmaceutical Engineering (ISPE)-sponsored Blend Uniformity and Content Uniformity Group’s proposed modifications to the withdrawn FDA draft guidance document for industry “Powder Blends and Finished Dosage Units—Stratified In-Process Dosage Unit Sampling and Assessment.” The modifications targeted FDA’s primary concerns that led to the withdrawal of the draft guidance document, which were insufficient blend uniformity testing and that a one-time passing of the criteria stated in USP General Chapter <905> Uniformity of Dosage Units testing lacks confidence to ensure the content uniformity of a batch. The Group’s approach discusses when triplicate blend samples should be analyzed and the importance of performing variance component analysis on the data to identify root causes of non-uniformity. The Group recommends the use of statistically based approaches, acceptance criteria, and sampling plans for assessing content uniformity for batch release that provide increased confidence that future samples drawn from the batch will comply with USP <905>. Alternative statistical approaches, sampling plans, and acceptance criteria, including modern analytical method (e.g., process analytical technology (PAT)) sampling plans, may be substituted for those mentioned in this paper, with justification. This approach also links blend and content uniformity testing to the three stages of the life cycle process validation approach. A framework for the assessment of blend and content uniformity that provides greater assurance of passing USP <905> is presented.

Digital Beamforming on Receive: Techniques and Optimization Strategies for High-Resolution Wide-Swath SAR Imaging <=SEP=> Synthetic Aperture Radar (SAR) is a well-proven imaging technique for remote sensing of the Earth. However, conventional SAR systems are not capable of fulfilling the increasing demands for improved spatial resolution and wider swath coverage. To overcome these inherent limitations, several innovative techniques have been suggested which employ multiple receive-apertures to gather additional information along the synthetic aperture. These digital beamforming (DBF) on receive techniques are reviewed with particular emphasis on the multi-aperture signal processing in azimuth and a multi-aperture reconstruction algorithm is presented that allows for the unambiguous recovery of the Doppler spectrum. The impact of Doppler aliasing is investigated and an analytic expression for the residual azimuth ambiguities is derived. Further, the influence of the processing on the signal-to-noise ratio (SNR) is analyzed, resulting in a pulse repetition frequency (PRF) dependent factor describing the SNR scaling of the multi-aperture beamforming network. The focus is then turned to a complete high-resolution wide-swath SAR system design example which demonstrates the intricate connection between multi-aperture azimuth processing and the system architecture. In this regard, alternative processing approaches are compared with the multi-aperture reconstruction algorithm. In a next step, optimization strategies are discussed as pattern tapering, prebeamshaping-on-receive, and modified processing algorithms. In this context, the analytic expressions for both the residual ambiguities and the SNR scaling factor are generalized to cascaded beamforming networks. The suggested techniques can moreover be extended in many ways. Examples discussed are a combination with ScanSAR burst mode operation and the transfer to multistatic sparse array configurations.

Symmetric Nonnegative Matrix Factorization: Algorithms and Applications to Probabilistic Clustering <=SEP=> Nonnegative matrix factorization (NMF) is an unsupervised learning method useful in various applications including image processing and semantic analysis of documents. This paper focuses on symmetric NMF (SNMF), which is a special case of NMF decomposition. Three parallel multiplicative update algorithms using level 3 basic linear algebra subprograms directly are developed for this problem. First, by minimizing the Euclidean distance, a multiplicative update algorithm is proposed, and its convergence under mild conditions is proved. Based on it, we further propose another two fast parallel methods: α-SNMF and β -SNMF algorithms. All of them are easy to implement. These algorithms are applied to probabilistic clustering. We demonstrate their effectiveness for facial image clustering, document categorization, and pattern clustering in gene expression.

Child maltreatment and the developing brain : A review of neuroscience perspectives ☆ <=SEP=> a r t i c l e i n f o Keywords: Child maltreatment Neuroscience Brain plasticity Stress system dysregulation Brain development In this article we review neuroscience perspectives on child maltreatment to facilitate understanding of the rapid integration of neuroscience knowledge into the academic, clinical, and lay literature on this topic. Seminal articles from developmental psychology and psychiatry, a discussion of brain plasticity, and a summary of recent reviews of research on stress system dysregulation are presented with some attention to methodological issues. A common theme is that maltreatment during childhood is an experience that may affect the course of brain development, potentially leading to differences in brain anatomy and functioning with lifelong consequences for mental health. The design of prevention and intervention strategies for child maltreatment may benefit from considering neuroscience perspectives along with those of other disciplines.

WHY SUMMARIES OF RESEARCH ON PSYCHOLOGICAL THEORIES ARE OFTEN UNINTERPRETABLE <=SEP=> —Null hypothesis testing of correlational predictions from weak substantive theories in soft psychology is subject to the influence of ten obfuscating factors whose effects are usually (1) sizeable, (2) opposed, (3) variable, and (4) unknown The net epistemic effect of these ten obfuscating influences is that the usual research literature review is well nigh uninterpretable Major changes in graduate education, conduct of research, and editorial policy are proposed

An Improved Variable On-Time Control Strategy for a CRM Flyback PFC Converter <=SEP=> The traditional critical conduction mode (CRM) flyback PFC converter with constant on-time control strategy usually suffers low power factor (PF) and high total harmonic distortion (THD) due to the nonsinusoidal input current waveform. In order to solve this problem, an improved variable on-time control strategy for the CRM flyback PFC converter is proposed in this letter. A simple analog divider circuit consisting of an operational amplifier, two signal switches, and an RC filter is proposed to modulate the turn-on time of the primary switch, and the PF and THD of the CRM flyback PFC converter can be evidently improved. The theoretical analysis is presented and the experiment results verify the advantages of the proposed control scheme.

Who does what during a code review? Datasets of OSS peer review repositories <=SEP=> We present four datasets that are focused on the general roles of OSS peer review members. With data mined from both an integrated peer review system and code source repositories, our rich datasets comprise of peer review data that was automatically recorded. Using the Android project as a case study, we describe our extraction methodology, the datasets and their application used for three separate studies. Our datasets are available online at http://sdlab.naist.jp/reviewmining/.

Range-Based Localization in Wireless Networks Using Density-Based Outlier Detection <=SEP=> Node localization is commonly employed in wireless networks. For example, it is used to improve routing and enhance security. Localization algorithms can be classified as range-free or range-based. Range-based algorithms use location metrics such as ToA, TDoA, RSS, and AoA to estimate the distance between two nodes. Proximity sensing between nodes is typically the basis for range-free algorithms. A tradeoff exists since range-based algorithms are more accurate but also more complex. However, in applications such as target tracking, localization accuracy is very important. In this paper, we propose a new range-based algorithm which is based on the density-based outlier detection algorithm (DBOD) from data mining. It requires selection of the K-nearest neighbours (KNN). DBOD assigns density values to each point used in the location estimation. The mean of these densities is calculated and those points having a density larger than the mean are kept as candidate points. Different performance measures are used to compare our approach with the linear least squares (LLS) and weighted linear least squares based on singular value decomposition (WLS-SVD) algorithms. It is shown that the proposed algorithm performs better than these algorithms even when the anchor geometry about an unlocalized node is poor.

Zika virus impairs growth in human neurospheres and brain organoids <=SEP=> Since the emergence of Zika virus (ZIKV), reports of microcephaly have increased considerably in Brazil; however, causality between the viral epidemic and malformations in fetal brains needs further confirmation. We examined the effects of ZIKV infection in human neural stem cells growing as neurospheres and brain organoids. Using immunocytochemistry and electron microscopy, we showed that ZIKV targets human brain cells, reducing their viability and growth as neurospheres and brain organoids. These results suggest that ZIKV abrogates neurogenesis during human brain development.

Research on non-invasive glucose concentration measurement by NIR transmission <=SEP=> Diabetes is a widely spreading disease which is known as one of the life threatening disease in the world. It occurs not only among adults and elderly, but also among infants and children. Blood glucose measurements are indispensable to diabetes patients to determine their insulin dose intake. Invasive blood glucose measurement ways which are high in accuracy are common but they are uncomfortable and have higher risk of infections especially for elders, pregnant and children. As a change, non-invasive blood glucose measurement techniques are introduced to provide a reliable and pain free method for monitoring glucose level without puncturing the skin. In this paper, a non-invasive glucose monitoring setup was developed using near infrared by detecting the transmission laser power. The detecting system included the semiconductor laser diode as light source, the S302C light power probe which detected the incident light and, the PM100USB transmit data to the computer. The specific infrared spectrum (1310 nm) was used as the incident beam. A proportional relationship between the laser power and the glucose concentration was proved by comparing the resulting laser power for a few of glucose aqueous solution samples with glucose concentration estimated value at the same circumstances.

LSD-induced entropic brain activity predicts subsequent personality change. <=SEP=> Personality is known to be relatively stable throughout adulthood. Nevertheless, it has been shown that major life events with high personal significance, including experiences engendered by psychedelic drugs, can have an enduring impact on some core facets of personality. In the present, balanced-order, placebo-controlled study, we investigated biological predictors of post-lysergic acid diethylamide (LSD) changes in personality. Nineteen healthy adults underwent resting state functional MRI scans under LSD (75µg, I.V.) and placebo (saline I.V.). The Revised NEO Personality Inventory (NEO-PI-R) was completed at screening and 2 weeks after LSD/placebo. Scanning sessions consisted of three 7.5-min eyes-closed resting-state scans, one of which involved music listening. A standardized preprocessing pipeline was used to extract measures of sample entropy, which characterizes the predictability of an fMRI time-series. Mixed-effects models were used to evaluate drug-induced shifts in brain entropy and their relationship with the observed increases in the personality trait openness at the 2-week follow-up. Overall, LSD had a pronounced global effect on brain entropy, increasing it in both sensory and hierarchically higher networks across multiple time scales. These shifts predicted enduring increases in trait openness. Moreover, the predictive power of the entropy increases was greatest for the music-listening scans and when "ego-dissolution" was reported during the acute experience. These results shed new light on how LSD-induced shifts in brain dynamics and concomitant subjective experience can be predictive of lasting changes in personality. Hum Brain Mapp 37:3203-3213, 2016. © 2016 Wiley Periodicals, Inc.

Data Structures and Algorithms for Nearest Neighbor Search in General Metric Spaces <=SEP=> We consider the computational problem of nding nearest neighbors in general metric spaces Of particular interest are spaces that may not be conveniently embedded or approxi mated in Euclidian space or where the dimensionality of a Euclidian representation is very high Also relevant are high dimensional Euclidian settings in which the distribution of data is in some sense of lower di mension and embedded in the space The vp tree vantage point tree is introduced in several forms together with associated algorithms as an improved method for these di cult search problems Tree construc tion executes in O n log n time and search is under certain circumstances and in the limit O log n expected time The theoretical basis for this approach is developed and the results of several experiments are reported In Euclidian cases kd tree performance is compared

The role of visual attention in saccadic eye movements. <=SEP=> The relationship between saccadic eye movements and covert orienting or visual spatial attention was investigated in two experiments. In the first experiment, subjects were required to make a saccade to a specified location while also detecting a visual target presented just prior to the eye movement. Detection accuracy was highest when the location of the target coincided with the location of the saccade, suggesting that subjects use spatial attention in the programming and/or execution of saccadic eye movements. In the second experiment, subjects were explicitly directed to attend to a particular location and to make a saccade to the same location or to a different one. Superior target detection occurred at the saccade location regardless of attention instructions. This finding shows that subjects cannot move their eyes to one location and attend to a different one. The result of these experiments suggest that visuospatial attention is an important mechanism in generating voluntary saccadic eye movements.

Three stage 6–18 GHz high gain and high power amplifier based on GaN technology <=SEP=> A monolithic three stage HPA has been developed for wide band applications. This MMIC is fabricated on UMS 0.25 µm GaN technology based on SiC substrate. At 18GHz, the MMIC achieved in CW mode 10W of output power with 20dB linear gain and 20% power added efficiency. The HPA provided 6 to 10W output power over 6 to 18GHz with minimum small signal gain of 18dB. These obtained performances are very promising and very close to the simulations; this will allow a very short term further improvement. This demonstration is the first MMIC on the UMS 0.25µm GaN technology.

Low-loss ultra-wideband transition between conductor-backed coplanar waveguide and substrate integrated waveguide <=SEP=> A novel transition between conductor-backed coplanar waveguide (CBCPW) and substrate integrated waveguide (SIW) is presented for microwave and millimeter-wave integrated circuit design. The proposed integrated transition that can provide simultaneous field and impedance matching, exhibits outstanding low-loss performances over an ultra-wideband range (entire Ka-band in our case). In this work, a generalized impedance inverter whose parameters are accurately extracted by the use of a numerical Thru-Reflection-Line (TRL) calibration technique is utilized to design the transition. Measured results for the fabricated transition in back-to-back configuration show that the insertion loss is better than 0.4 dB while the return loss is better than 20 dB over the entire Ka-band.

Data fusion schemes for cooperative spectrum sensing in cognitive radio networks <=SEP=> Cooperative spectrum sensing has proven its efficiency to detect spectrum holes in cognitive radio network (CRN) by combining sensing information of multiple cognitive radio users. In this paper, we study different fusion schemes that can be implemented in fusion center. Simulation comparison between these schemes based on hard, quantized and soft fusion rules are conducted. It is shown through computer simulation that the soft combination scheme outperforms the hard one at the cost of more complexity; the quantized combination scheme provides a good tradeoff between detection performance and complexity. In the paper, we also analyze a quantized combination scheme based on a tree-bit quantization and compare its performance with some hard and soft combination schemes.

Optimization by simulated annealing. <=SEP=> There is a deep and useful connection between statistical mechanics (the behavior of systems with many degrees of freedom in thermal equilibrium at a finite temperature) and multivariate or combinatorial optimization (finding the minimum of a given function depending on many parameters). A detailed analogy with annealing in solids provides a framework for optimization of the properties of very large and complex systems. This connection to statistical mechanics exposes new information and provides an unfamiliar perspective on traditional optimization problems and methods.

Deriving optimal weights in deep neural networks <=SEP=> Training deep neural networks generally requires massive amounts of data and is very computation intensive. We show here that it may be possible to circumvent the expensive gradient descent procedure and derive the parameters of a neural network directly from properties of the training data. We show that, near convergence, the gradient descent equations for layers close to the input can be linearized and become stochastic equations with noise related to the covariance of data for each class. We derive the distribution of solutions to these equations and discover that it is related to a “supervised principal component analysis.” We implement these results on image datasets MNIST, CIFAR10 and CIFAR100 and find that, indeed, pretrained layers using our findings performs comparable or superior to neural networks of the same size and architecture trained with gradient descent. Moreover, our pretrained layers can often be calculated using a fraction of the training data, owing to the quick convergence of the covariance matrix. Thus, our findings indicate that we can cut the training time both by requiring only a fraction of the data used for gradient descent, and by eliminating layers in the costly backpropagation step of the training. Additionally, these findings partially elucidate the inner workings of deep neural networks and allow us to mathematically calculate optimal solutions for some stages of classification problems, thus significantly boosting our ability to solve such problems efficiently.