Low-complexity Problems Research Articles

Energy landscapes of peptide-MHC binding.

Molecules of the Major Histocompatibility Complex (MHC) present short protein fragments on the cell surface, an important step in T cell immune recognition. MHC-I molecules process peptides from intracellular proteins; MHC-II molecules act in antigen-presenting cells and present peptides derived from extracellular proteins. Here we show that the sequence-dependent energy landscapes of MHC-peptide binding encode class-specific nonlinearities (epistasis). MHC-I has a smooth landscape with global epistasis; the binding energy is a simple deformation of an underlying linear trait. This form of epistasis enhances the discrimination between strong-binding peptides. In contrast, MHC-II has a rugged landscape with idiosyncratic epistasis: binding depends on detailed amino acid combinations at multiple positions of the peptide sequence. The form of epistasis affects the learning of energy landscapes from training data. For MHC-I, a low-complexity problem, we derive a simple matrix model of binding energies that outperforms current models trained by machine learning. For MHC-II, higher complexity prevents learning by simple regression methods. Epistasis also affects the energy and fitness effects of mutations in antigen-derived peptides (epitopes). In MHC-I, large-effect mutations occur predominantly in anchor positions of strong-binding epitopes. In MHC-II, large effects depend on the background epitope sequence but are broadly distributed over the epitope, generating a bigger target for escape mutations due to loss of presentation. Together, our analysis shows how an energy landscape of protein-protein binding constrains the target of escape mutations from T cell immunity, linking the complexity of the molecular interactions to the dynamics of adaptive immune response.

Exploring the trade-off between computational power and energy efficiency: An analysis of the evolution of quantum computing and its relation to classical computing

Quantum computing is considered a revolutionary technology due to its ability to solve computational problems that are beyond the capabilities of classical computers. However, quantum computing requires great amounts of energy to run. Therefore, a factor in deciding whether to use quantum computing should be not only the complexity of the problem to be solved, but also the energy required to solve it. This paper presents an empirical study developed with the aim of comparing classical and quantum computing in terms of energy efficiency to determine whether the increased power of quantum computers is offset by their higher energy consumption. To achieve this, a variety of problems with different levels of complexity were tested on both types of computers. Specifically, we used the IBM Quantum computers with a maximum of 5 qubits and an Intel i7, as a classical computer. In addition to this we have also analysed the evolution of the quantum computers, performing measurements on three time periods. Our empirical study showed that there is a variability of results obtained in the three time periods and that quantum computing is not recommended for low-complexity problems, given its high energy consumption, particularly when compared to traditional computing.

Dynamic Regret Bounds for Constrained Online Nonconvex Optimization Based on Polyak–Lojasiewicz Regions

Online optimization problems are well-understood in the convex case, where algorithmic performance is typically measured relative to the best fixed decision. In this paper, we shed light on online nonconvex optimization problems in which algorithms are evaluated against the optimal decision at each time using the more useful notion of dynamic regret. The focus is on loss functions which are arbitrarily nonconvex, but have global solutions that are slowly time-varying. We address this problem by first analyzing the region around the global solution at each time to define time-varying target sets, which contain the global solution and exhibit desirable properties under the projected gradient descent algorithm. All points in a target set satisfy the proximal Polyak-Łojasiewicz inequality, among other conditions. Then, we introduce two algorithms and prove that the dynamic regret for each algorithm is bounded by a function of the temporal variation in the optimal decision. The first algorithm assumes that the decision maker has some prior knowledge about the initial objective function and may query the gradient repeatedly at each time. This algorithm ensures that decisions are within the target set at every time. The second algorithm makes no assumption about prior knowledge. It instead relies on random sampling and memory to find and then track the target sets over time. In this case, the landscape of the loss functions determines the likelihood that the dynamic regret will be small. Numerical experiments validate these theoretical results and highlight the impact of a single low-complexity problem early in the sequence.

IoT Edge-Computing-Enabled Efficient Localization via Robust Optimal Estimation

Source localization within wireless sensor networks (WSNs) is one of the critical technologies in the Internet of Things (IoT). As the number of network nodes increases, so does the amount of data and computational requirement. It is imperative to introduce edge computing. However, there are still two issues when running existing wireless location algorithms on edge nodes: 1) conventional low-complexity approaches are easily affected by the bias generated in complex environments, leading to low locating accuracy and 2) the optimization algorithms considering the bias have good performances, but they are calculation-efficiency low on edge nodes. This study proposes a computationally efficient and high-precision location method to tackle the troubles. Precisely, we first introduce our previous research to construct a bias-considered nonconvex problem with a linear objective. Then, we propose an angle-assisted Taylor series with zero truncation error to linearize the second-order cone (SOC) constraint in the established problem. Next, we resort to the mini-max criterion to eliminate the angular uncertainty and get a robust linear programming (LP) problem with an optimal solution. So far, we have obtained a convex problem of low complexity. To ensure the calculated efficiency of the proposed problem on edge nodes, we proceed to give the solving process of the problem. Moreover, we provide a constraints tracking mechanism to reduce the number of iterations in the solution procedure, improving computational efficiency. Simulations and experiments demonstrate that the proposed method with similar locating accuracy to state-of-the-art optimization algorithms exhibits much higher computing efficiency on edge nodes.

Solution of large-scale supply chain models using graph sampling & coarsening

We present a graph sampling and coarsening scheme (gSC) for computing lower and upper bounds for large-scale supply chain models. An edge sampling scheme is used to build a low-complexity problem that is used to finding an approximate (but feasible) solution for the original model and to compute a lower bound (for a maximization problem). This scheme is similar in spirit to the so-called sample average approximation scheme, which is widely used for the solution of stochastic programs. A graph coarsening (aggregation) scheme is used to compute an upper bound and to estimate the optimality gap of the approximate solution. The coarsening scheme uses node sampling to select a small set of support nodes that are used to guide node/edge aggregation and we show that the coarsened model provides a relaxation of the original model and a valid upper bound. We provide evidence that gSC can yield significant improvements in solution time and memory usage over state-of-the-art solvers. Specifically, we study a supply chain design model (a mixed-integer linear program) that contains over 38 million variables and show that gSC finds a solution with an optimality gap of <0.5% in less than 22 minutes.

Rapid Response! Investigating the Effects of Problem Definition on the Characteristics of Additively Manufactured Solutions for COVID-19

Abstract Designers from around the world have proposed numerous engineering design solutions for problems related to the COVID-19 pandemic, many of which leverage the rapid prototyping and manufacturing capabilities of additive manufacturing (AM). While some of these solutions are motivated by complex and urgent requirements (e.g., face masks), others are motivated by simpler and less urgent needs (e.g., hands-free door openers). Previous research suggests that problem definition influences the creativity of solutions generated for the problem. In this study, we investigate the relationship between the definition of problems related to the COVID-19 pandemic and the characteristics of AM solutions that were openly shared for these problems. Specifically, we analyze 26 AM solutions spanning three categories: (1) hands-free door openers (low complexity problem), (2) face shields (moderate complexity problem), and (3) face masks (high complexity problem). These designs were compared on (1) DfAM utilization, (2) manufacturability (i.e., build time, cost, and material usage), and (3) creativity. We see that the solutions designed for the high complexity problem, i.e., face masks, were least suitable for AM. Moreover, we see that solutions designed for the moderate complexity problem, i.e., face shields, had the lowest build time, build cost, and material consumption. Finally, we observe that the problem definition did not relate to the creativity of the AM solutions. In light of these findings, designers must sufficiently emphasize the AM suitability and manufacturability of their solutions when designing for urgent and complex problems in rapid response situations.

Capability Development through Just-in-Time Access to Knowledge in Document Repositories: A Longitudinal Examination of Technical Problem Solving

With knowledge and expertise increasingly being recognized as important, firms have made significant investments in document repositories as part of their knowledge management initiatives. Document repositories are intended to enhance the ability to access codified knowledge and help improve task performance through knowledge reuse. However, it is not clear what effects they have on how knowledge workers perform their tasks. Using longitudinal data on repository accesses and calls to technical support by field technicians in an engineering firm, we examine how just-in-time access to codified knowledge affects patterns of help-seeking from technical support. We find evidence that greater accessing of codified knowledge reduces calls for help to technical support. The type of codified knowledge accessed from the repository affects field technicians’ calling behavior. Accessing general knowledge reduces calls to support for low-complexity problems, while accessing procedural knowledge reduces calls related to high-complexity problems. Further, accessing procedural knowledge is significantly associated with promotion, suggesting that the use of document repositories can help individuals build firm-specific human capital. Building on the insights of cognitive load theory, this study suggests that making information available just in time through document repositories reduces the cognitive load involved for task performance and enables learning. This work contributes to a greater understanding of the value of knowledge management systems and suggests that, beyond the efficiencies gained from knowledge reuse, just-in-time access to knowledge repositories builds problem solving capabilities and contributes to human capital development.

Reducing CO2 emissions through pricing, planning, and subsidizing rail freight

Modal shift from road to rail is considered a promising way to reduce CO2 emissions from the transportation industry. To promote this modal shift, a profit-maximization model that integrates two dimensions is proposed. Internally, the competitiveness of rail freight is enhanced by dynamic freight pricing, backlog controlling, and flexible dispatching. Externally, a subsidy policy is considered to stimulate rail freight. Based on Lyapunov optimization, this multi-slot global optimization problem is converted into a series of low-complexity problems that only require knowledge of the instantaneous system state. The equivalence of this transformation is proved theoretically. Then Harmony search algorithm is used to solve these low-complexity models. A case study based on the transportation network composed of five central cities in China is conducted. Compared with the application of the actual fixed rail freight rates, a CO2 emission reduction of 3261 t can be achieved with the dynamic pricing strategy. Based on jointly optimizing rail freight price and service plan, an additional subsidy of 0.1–0.6 RMB/TEU-km to rail freight operators can achieve an 23.78–25.38% MS from road to rail, as well as a 24.08–26.12% CO2 emission reduction in this service chain.

Energy Efficient Computing for Smart Phones in Cloud Assisted Environment

In recent years, the employment of smart mobile phones has increased enormously and are concerned as an area of human life. Smartphones are capable to support immense range of complicated and intensive applications results shortened power capability and fewer performance. Mobile cloud computing is the newly rising paradigm integrates the features of cloud computing and mobile computing to beat the constraints of mobile devices. Mobile cloud computing employs computational offloading that migrates the computations from mobile devices to remote servers. In this paper, a novel model is proposed for dynamic task offloading to attain the energy optimization and better performance for mobile applications in the cloud environment. The paper proposed an optimum offloading algorithm by introducing new criteria such as benchmarking for offloading decision making. It also supports the concept of partitioning to divide the computing problem into various sub-problems. These sub-problems can be executed parallelly on mobile device and cloud. Performance evaluation results proved that the proposed model can reduce around 20% to 53% energy for low complexity problems and up to 98% for high complexity problems.

Energy Efficient Computing for Smart Phones in Cloud Assisted Environment

In recent years, the employment of smart mobile phones has increased enormously and are concerned as an area of human life. Smartphones are capable to support immense range of complicated and intensive applications results shortened power capability and fewer performance. Mobile cloud computing is the newly rising paradigm integrates the features of cloud computing and mobile computing to beat the constraints of mobile devices. Mobile cloud computing employs computational offloading that migrates the computations from mobile devices to remote servers. In this paper, a novel model is proposed for dynamic task offloading to attain the energy optimization and better performance for mobile applications in the cloud environment. The paper proposed an optimum offloading algorithm by introducing new criteria such as benchmarking for offloading decision making. It also supports the concept of partitioning to divide the computing problem into various sub-problems. These sub-problems can be executed parallelly on mobile device and cloud. Performance evaluation results proved that the proposed model can reduce around 20% to 53% energy for low complexity problems and up to 98% for high complexity problems.

Deep Learning Methods for Improved Decoding of Linear Codes

The problem of low complexity, close to optimal, channel decoding of linear codes with short to moderate block length is considered. It is shown that deep learning methods can be used to improve a standard belief propagation decoder, despite the large example space. Similar improvements are obtained for the min-sum algorithm. It is also shown that tying the parameters of the decoders across iterations, so as to form a recurrent neural network architecture, can be implemented with comparable results. The advantage is that significantly less parameters are required. We also introduce a recurrent neural decoder architecture based on the method of successive relaxation. Improvements over standard belief propagation are also observed on sparser Tanner graph representations of the codes. Furthermore, we demonstrate that the neural belief propagation decoder can be used to improve the performance, or alternatively reduce the computational complexity, of a close to optimal decoder of short BCH codes.

Effects of problem characteristics on the online helping behavior of shy individuals

The present research examined how problem characteristics might influence the online helping behavior of shy individuals. Three hundred undergraduates were recruited and assessed using the College Students’ Shyness Scale. According to the scores, 68 high-shy students and 68 low-shy students were selected to participate in simulated experiments designed to determine the influence of the shyness level, the presence of a foreseeable reward, the complexity of the problem, and the urgency of the problem influence online helping behavior. The results showed that (a) High-shy individuals tended to exhibit online helping behavior more for low-complexity problems and high-urgency problems than for high-complexity problems and low-urgency problems. (b) Low-shy individuals always exhibited online helping behavior less for high-complexity problems and when no reward was offered than they did for low-complexity problems and when a reward was offered. (c) High-shy individuals showed online helping behavior more than low-shy individuals did for no-reward, high-complexity problems and high-urgency problems. (d) Participants online had higher shyness scores for showing helping behavior than those offline.

Graph‐based low complexity detection algorithms in multiple‐input–multiple‐out systems: an edge selection approach

In this study, the problem of low complexity multiple-input-multiple-out signal detection based on belief propagation (BP) is addressed. The authors propose an edge selection approach that works on factor graph model to cut down the number of circles and high complexity of standard BP algorithm. The message passing from factor nodes to variable nodes is updated by only partial edges, and the mean feedback method is designed to compensate the information loss brought by the edge selection. Both binary and high-order modulations are considered, and the scheme of mapping between bit soft output and modulation symbols when computing the feedback information is discussed. In addition, a minimum mean-square error filter initialised algorithm is proposed, in which the initial message of BP detection is exploited. Both binary and high-order modulations are discussed as well when the authors design this initial message. Simulation results along with convergence and complexity analyses verify that the proposed edge selection approach can achieve good performance with low complexity, and significantly outperform the existing methods with comparative complexity. Moreover, our approach has asymptotic optimality and is a self-adapting scheme, which can achieve the trade-off between performance and complexity by varying the number of selected edges.

A randomized controlled trial of nurses vs. doctors in the resolution of acute disease of low complexity in primary care

To compare the effectiveness of care delivered by nurses to the usual care delivered by general practitioners, in adult patients requesting same day appointments in primary care practices in Catalonia (Spain). Same day appointments conducted by nurses are characterized by high patient satisfaction and a high resolution index. The profile of nursing and the organization of primary care services in our country differ from other countries. Multicentre, randomized, unblinded clinical trial with two parallel groups. Patients were randomized to an intervention group (seen by nurses trained to respond to low complexity problems) or a control group (seen by the general practitioner) using an automatic probabilistic function. 38 primary care practices in Catalonia, 142 general practitioners and 155 nurses participated. Population study: ≥ 18-year-old patients who requested a same day consultation. Recruitment period: January-May, 2009. Of the 1,461 randomized patients, 92.5% completed the study. resolution of symptoms and patient satisfaction 2 weeks after the visit. Seven hundred and fifty-three patients were assigned to the intervention group and 708 to the control group. Nurses successfully solved 86.3% of the cases. We did not observe any differences in resolution of symptoms or patient satisfaction between the groups. Nurses trained specifically to resolve acute health problems of low complexity give comparable quality of care to that provided by general practitioners in terms of resolution of the problem 15 days after the visit and in patient satisfaction with the visit.

Characterization of robust stability of a class of interconnected systems

We consider robust stability analysis of a class of large scale interconnected systems. The individual subsystems may be different but they are assumed to share a property that characterizes a set of interconnection matrices which lead to stable overall systems. The main contribution of the paper is to show that, for the case where the network interconnection matrix is normal, (robust) stability verification can be simplified to a low complexity problem of checking whether the frequency response of the individual subsystems and the eigenvalues of the interconnection matrix can be mutually separated using a class of quadratic forms. Most interestingly, it is shown that this criterion is also necessary, in the sense that if the criterion is violated, an interconnection matrix of the same eigenvalue distribution can be found to make the overall system unstable.

Semantics and Expressive Power of Nondeterministic Constructs in Deductive Databases

Nondeterministic extensions are needed in logic-based languages, such as first-order relational languages and Datalog, to enhance their expressive power and support the efficient formulation of low-complexity problems and database queries. In this paper, we study the semantics and expressive power of the various nondeterministic constructs proposed in the past, including various versions of the choice operator and the witness operator. The paper develops a model-theoretic semantics, a fixpoint semantics, and an operational semantics for these constructs, and characterizes their power of expressing deterministic and nondeterministic queries. The paper presents various soundness and completeness results and establishes an expressiveness hierarchy that correlates the various operators with each other and with other constructs such as negation and fixpoint.