Intractable Problems Research Articles

Chance-Constrained Optimal Design of PV-Based Microgrids under Grid Blackout Uncertainties

A grid blackout is an intractable problem with serious economic consequences in many developing countries. Although it has been proven that microgrids (MGs) are capable of solving this problem, the uncertainties regarding when and for how long blackouts occur lead to extreme difficulties in the design and operation of the related MGs. This paper addresses the optimal design problem of the MGs considering the uncertainties of the blackout starting time and duration utilizing the kernel density estimator method. Additionally, uncertainties in solar irradiance and ambient temperature are also considered. For that, chance-constrained optimization is employed to design residential and industrial PV-based MGs. The proposed approach aims to minimize the expected value of the levelized cost of energy (LCOE), where the restriction of the annual total loss of power supply (TLPS) is addressed as a chance constraint. The results show that blackout uncertainties have a considerable effect on calculating the size of the MG’s components, especially the battery bank size. Additionally, it is proven that considering the uncertainties of the input parameters leads to an accurate estimation for the LCOE and increases the MG reliability level.

Non-Invasive Self-Adaptive Information States' Acquisition inside Dynamic Scattering Spaces.

Pushing the information states' acquisition efficiency has been a long-held goal to reach the measurement precision limit inside scattering spaces. Recent studies have indicated that maximal information states can be attained through engineered modes; however, partial intrusion is generally required. While non-invasive designs have been substantially explored across diverse physical scenarios, the non-invasive acquisition of information states inside dynamic scattering spaces remains challenging due to the intractable non-unique mapping problem, particularly in the context of multi-target scenarios. Here, we establish the feasibility of non-invasive information states' acquisition experimentally for the first time by introducing a tandem-generated adversarial network framework inside dynamic scattering spaces. To illustrate the framework's efficacy, we demonstrate that efficient information states' acquisition for multi-target scenarios can achieve the Fisher information limit solely through the utilization of the external scattering matrix of the system. Our work provides insightful perspectives for precise measurements inside dynamic complex systems.

Data-driven quasi-convex method for hit rate optimization of process product quality in digital twin

Hit rate is an important quantitative criterion for the process product quality prediction of the integrated industrial processes. The hit rate indicates the percentage of product quantities accepted by the downstream process within the controlled range of the product quality. The optimization model of the hit rate criterion is a non-convex intractable problem. In order to improve the hit rate of the predicted product quality, we define a hit rate optimization problem, and propose a data-driven quasi-convex approach, which converts the original problem into a set of convex feasible problems and achieves the optimal hit rate. The proposed approach combines factorial hidden Markov models, multitask elastic net and quasi-convex optimization. In order to illustrate the advantages of the proposed approach, a Monte Carlo simulation experiment is designed to verify the convex optimization property. Another experiment is carried out on two actual steel production datasets for the temperature prediction in molten iron dispatch. The results confirm that the proposed approach not only exhibits superior performance with the controlled hit rate, but also improves the hit rate by at least 41.11 % and 31.01 %, respectively, compared with the classical models on two real datasets.

Adaptively resettable microfluidic patch for sweat rate and electrolytes detection

Skin-interfaced microfluidic patch has become a reliable device for sweat collection and analysis. However, the intractable problems of emptying the microchannel for reuse, and the channel's volumetric capacity limited by the size of the patch, directly hinder the practical application of sweat sensors. Herein, we report an adaptively resettable microfluidic sweat patch (Art-Sweat patch) capable of continuously monitoring both sweat rate (0.2–4.0 μL min−1) and total ionic charge concentration (10–200 mmol L−1). We develop a platform with a vertical and horizontal microchannel combined strategy, enabling repeatedly filling sweat and emptying the microchannel for autonomously resetting and detecting. The variation in the emptied volume is designed to be adaptively identified by the sensor, resulting in enhanced stability and an enlarged volumetric capacity of over 300 μL. By integrating with self-designed wireless transmission modules, the proposed Art-Sweat patch shows product-level wearability and high performance in monitoring variations in regional sweat rate and concentration for hydration status assessment.

Botanical extracts control the fungal pathogen Colletotrichum boninense in smallholder production of common bean

Anthracnose caused by Colletotrichum spp. remains an intractable problem in the most common bean (Phaseolus vulgaris) production areas worldwide and can cause total yield loss. Many smallholder farmers are familiar with using botanical extracts to control insect pests; however, there is less familiarity with their use to control fungal diseases due to a lack of evidence. Here, we demonstrate that anthracnose could be controlled effectively by pesticidal plant species that are used for insect control. In laboratory trials, water extracts from 11 plant species could inhibit fungal growth (100%) and spore germination (75–100%) equally well to two commercially available fungicides, the synthetic Mancolaxyl and biofungicide Bioderma. In screenhouse trials, anthracnose disease was reduced by the extracts of three plant species. Moreover, bean crop growth in these botanical treatments did not differ significantly from that observed in the commercial fungicide treated plants. Field trials in a smallholder community reporting severe problems with anthracnose showed an effect similar to the screenhouse results. Field trials resulted in bean seed yields approximately 350 kg/ha higher in bean plants treated with Azadirachta indica and Lippia javanica at 10% w/v compared to the negative control untreated plants. In all trials, botanical extracts were as effective as commercially available fungicides, suggesting that these botanical extracts could provide dual-purpose pest and disease management for anthracnose and crop pest insects. The outcomes of this research show that prospects for using locally available resources to control anthracnose on common bean are credible and can be combined with controlling insect pests.

Splitting and parallelizing of quantum convolutional neural networks for learning translationally symmetric data

The quantum convolutional neural network (QCNN) is a promising quantum machine learning (QML) model that is expected to achieve quantum advantages in classically intractable problems. However, the QCNN requires a large number of measurements for data learning, limiting its practical applications in large-scale problems. To alleviate this requirement, we propose an architecture called split-parallelizing QCNN (sp-QCNN), which exploits the prior knowledge of quantum data to design an efficient model. This architecture draws inspiration from geometric quantum machine learning and targets translationally symmetric quantum data commonly encountered in physics and quantum computing science. By splitting the quantum circuit based on translational symmetry, the sp-QCNN can substantially parallelize the conventional QCNN without increasing the number of qubits and improve the measurement efficiency by an order of the number of qubits. To demonstrate its effectiveness, we apply the sp-QCNN to a quantum phase recognition task and show that it can achieve comparable classification accuracy to the conventional QCNN while considerably reducing the measurement resources required. Due to its high measurement efficiency, the sp-QCNN can mitigate statistical errors in estimating the gradient of the loss function, thereby accelerating the learning process. These results open up possibilities for incorporating the prior data knowledge into the efficient design of QML models, leading to practical quantum advantages. Published by the American Physical Society 2024

Anomalous Political Discursive Narratives on Indigeneity: In Ethiopia's Regional States

AbstractThe major purpose of this article is to critically probe the competing political narratives on indigeneity‐ in the form of autochthony‐ the sons of the soil, and its instrumentalization for discrimination against non‐indigenous groups in Ethiopia's regional states. To this end, the article argues that the political discourses (narratives) determine Ethiopia's ethnic‐based state structure and politics that dichotomize citizens as indigenes and non‐indigenes, which, in reality, is not viable. Each ethnic group, which forms the region, has a warranty of sovereign political power and territorial autonomy. Inside the regions, except for the Amhara and the South Nations, Nationalities, and Peoples Regions, the privileged ethnic group (the indigenous) has been empowered to hold prominent political positions. Non‐indigenous ethnic groups, on the other hand, are discriminated against in politics, economy, and social affairs. Thus, these political narratives have created a system of inequality and discrimination that has persisted even after the end of EPRDF era. To address these intractable problems, Ethiopians seek to balance diversity and unity in order to create an inclusive and all‐empowering environment in society through multiculturalism.

Fast Clustering With Anchor Guidance.

Clustering aims to partition a set of objects into different groups through the internal nature of these objects. Most existing methods face intractable hyper-parameter problems triggered by various regularization terms, which degenerates the applicability of models. Moreover, traditional graph clustering methods always encounter the expensive time overhead. To this end, we propose a Fast Clustering model with Anchor Guidance (FCAG). The proposed model not only avoids trivial solutions without extra regularization terms, but is also suitable to deal with large-scale problems by utilizing the prior knowledge of the bipartite graph. Moreover, the proposed FCAG can cope with out-of-sample extension problems. Three optimization methods Projected Gradient Descent (PGD) method, Iteratively Re-Weighted (IRW) algorithm and Coordinate Descent (CD) algorithm are proposed to solve FCAG. Extensive experiments verify the superiority of the optimization method CD. Besides, compared with other bipartite graph models, FCAG has the better performance with the less time cost. In addition, we prove through theory and experiment that when the learning rate of PGD tends to infinite, PGD is equivalent to IRW.

Efficacy and safety of topical photodynamic therapy for genital warts: experience review in China

SignificanceHigh recurrence rate and adverse effects were common shortcomings in treatments for genital warts, and the inaccessibility of some deep meatal lesions remains an intractable problem. This study aims to evaluate the treatment of PDT for genital warts in comparison to alternative treatments. ApproachSeven databases and ClinicalTrials. gov were searched and seven randomized controlled trials were finally included. ResultsThere was no significant difference in clearance rate of urethral lesions between the PDT group and the CO2 laser therapy (CO2-LT group) (Odds ratio [OR] 0.676, 95% confidence interval [CI] 0.071–6.426, P = 0.734), but the overall clearance rate of all genital warts was slightly lower for PDT (OR 0.574, CI 0.335–0.985, P = 0.044). The recurrence rate after PDT was significantly lower than after CO2-LT (OR 0.318, CI 0.220–0.460, P < 0.001), and adverse effects were also slighter after PDT (OR 0.015, CI 0.003–0.074, P < 0.001). In addition, a modification of parameters of light irradiation provides the potential to alleviate pain. This study shows that PDT is an effective treatment for genital warts with good tolerability and safety, especially for cavitary lesions. Some modified protocols probably have the potential to reduce adverse effects. ConclusionsIn all, this study demonstrates that PDT is a favorable treatment for genital warts with high efficacy, low recurrence rate and mild adverse effect, especially for urethral and other cavitary lesions. Besides, some modified protocols probably had the potential to reduce adverse effects, but still needed further investigation.

Phytotherapy of Vulvovaginal Candidiasis: A Narrative Review.

Vulvovaginal candidiasis (VVC) is a real gynecological problem among women of reproductive age from 15 to 49. A recent analysis showed that 75% of women will have an occurrence at least once per year, while 5% are observed to have recurrent vaginal mycosis-these patients may become unwell four or more times a year. This pathology is caused in 85-90% of cases by fungi of the Candida albicans species. It represents an intractable medical problem for female patients due to pain and pruritus. Due to the observation of an increasing number of strains resistant to standard preparations and an increase in the recurrence of this pathology when using local or oral preferential therapy, such as fluconazole, an analysis was launched to develop alternative methods of treating VVC using herbs such as dill, turmeric, and berberine. An in-depth analysis of databases that include scientific articles from recent years made it possible to draw satisfactory conclusions supporting the validity of herbal therapy for the pathology in question. Although phytotherapy has not yet been approved by the Food and Drug Administration, it appears to be a promising therapeutic solution for strains that are resistant to existing treatments. There is research currently undergoing aimed at comparing classical pharmacotherapy and herbal therapy in the treatment of vaginal candidiasis for the purpose of increasing medical competence and knowledge for the care of the health and long-term comfort of gynecological patients.

Irruption and Absorption: A 'Black-Box' Framework for How Mind and Matter Make a Difference to Each Other.

Cognitive science is confronted by several fundamental anomalies deriving from the mind-body problem. Most prominent is the problem of mental causation and the hard problem of consciousness, which can be generalized into the hard problem of agential efficacy and the hard problem of mental content. Here, it is proposed to accept these explanatory gaps at face value and to take them as positive indications of a complex relation: mind and matter are one, but they are not the same. They are related in an efficacious yet non-reducible, non-observable, and even non-intelligible manner. Natural science is well equipped to handle the effects of non-observables, and so the mind is treated as equivalent to a hidden 'black box' coupled to the body. Two concepts are introduced given that there are two directions of coupling influence: (1) irruption denotes the unobservable mind hiddenly making a difference to observable matter, and (2) absorption denotes observable matter hiddenly making a difference to the unobservable mind. The concepts of irruption and absorption are methodologically compatible with existing information-theoretic approaches to neuroscience, such as measuring cognitive activity and subjective qualia in terms of entropy and compression, respectively. By offering novel responses to otherwise intractable theoretical problems from first principles, and by doing so in a way that is closely connected with empirical advances, irruption theory is poised to set the agenda for the future of the mind sciences.

Sterically Wrapped Oxygen‐Bridged Boron Derivatives for High‐Performance Blue Organic Light‐Emitting Diodes

AbstractNear ultraviolet (NUV) organic light‐emitting diodes (OLEDs) have great advantages in the field of light excitation sources, chemical and biological sensors, etc. However, the molecular design of NUV emitters still faces the challenges of spectral broadening and red‐shift. In this study, attaching weak oxygen‐bridged triarylboron acceptor to weak carbazolyl donor is able to address intractable problems by weakening donor–acceptor (D–A) charge transfer. Simultaneously, sterically wrapped and modified oxygen‐bridged triarylboron acceptor by bulk substituents (multiple carbazolyl) can suppress intermolecular π–π stacking through steric hindrance effect. These design strategies are able to obtain short‐wavelength, narrow‐band emission, and great color purity. As a result, carbazolyl derivatives xCz‐BO‐based (x = 2, 3, 4, 5) emitters show EL emission peaks from 406 to 422 nm in the NUV light region and small full‐width at half‐maximums (FWHMs) of 32–41 nm over a wide range of dopant concentrations (10–40 wt.%). The most violet Commission International de I'Eclairage (CIE) color coordinate is (0.16, 0.03). Moreover, 5Cz‐BO can be employed as host for vacuum‐/solution‐processed blue OLEDs and these devices exhibit high performance with EQEs of over 20%.

DreamIdentity: Enhanced Editability for Efficient Face-Identity Preserved Image Generation

While large-scale pre-trained text-to-image models can synthesize diverse and high-quality human-centric images, an intractable problem is how to preserve the face identity and follow the text prompts simultaneously for conditioned input face images and texts. Despite existing encoder-based methods achieving high efficiency and decent face similarity, the generated image often fails to follow the textual prompts. To ease this editability issue, we present DreamIdentity, to learn edit-friendly and accurate face-identity representations in the word embedding space. Specifically, we propose self-augmented editability learning to enhance the editability for projected embedding, which is achieved by constructing paired generated celebrity's face and edited celebrity images for training, aiming at transferring mature editability of off-the-shelf text-to-image models in celebrity to unseen identities. Furthermore, we design a novel dedicated face-identity encoder to learn an accurate representation of human faces, which applies multi-scale ID-aware features followed by a multi-embedding projector to generate the pseudo words in the text embedding space directly. Extensive experiments show that our method can generate more text-coherent and ID-preserved images with negligible time overhead compared to the standard text-to-image generation process.

Robust Blind Text Image Deblurring via Maximum Consensus Framework

The blind text image deblurring problem presents a formidable challenge, requiring the recovery of a clean and sharp text image from a blurry version with an unknown blur kernel. Sparsity-based strategies have demonstrated their efficacy by emphasizing the sparse priors of the latent image and kernel. However, these existing strategies have largely neglected the influence of additional noise, imposing limitations on their performance. To overcome this limitation, we propose a novel framework designed to effectively mitigate the impact of extensive noise prevalent in blurred images. Our approach centers around a robust Maximum Consensus Framework, wherein we optimize the quantity of interest from the noisy blurry image based on the maximum consensus criterion. Furthermore, we propose the integration of the Alternating Direction Method of Multipliers (ADMM) and the Half-Quadratic Splitting (HQS) method to address the computationally intractable L0 norm problem. This innovative strategy enables improvements in the deblurring performance of blurry text images with the additional synthetic noise. Experimental evaluations conducted on various noisy blurry text images demonstrate the superiority of the proposed approach over existing methods.

Solving Satisfiability Modulo Counting for Symbolic and Statistical AI Integration with Provable Guarantees

Satisfiability Modulo Counting (SMC) encompasses problems that require both symbolic decision-making and statistical reasoning. Its general formulation captures many real-world problems at the intersection of symbolic and statistical AI. SMC searches for policy interventions to control probabilistic outcomes. Solving SMC is challenging because of its highly intractable nature (NP^PP-complete), incorporating statistical inference and symbolic reasoning. Previous research on SMC solving lacks provable guarantees and/or suffers from suboptimal empirical performance, especially when combinatorial constraints are present. We propose XOR-SMC, a polynomial algorithm with access to NP-oracles, to solve highly intractable SMC problems with constant approximation guarantees. XOR-SMC transforms the highly intractable SMC into satisfiability problems by replacing the model counting in SMC with SAT formulae subject to randomized XOR constraints. Experiments on solving important SMC problems in AI for social good demonstrate that XOR-SMC outperforms several baselines both in solution quality and running time.

Abstract 4877: Morphological detection of chemotherapy resistance in cancer cell lines using AI-based analysis

Abstract Chemoresistance can develop during the course of treatment of many cancers, which can limit the utility of standard therapeutics and lead to poor outcomes. The mechanism by which resistance arises is not well understood, and developing models to study resistance can be challenging. Resistance can spontaneously arise in cell lines, however, it is difficult to separate resistant from sensitive cells using traditional biomarkers, hampering their utility for studying mechanisms. Morphology presents another modality to examine resistance, independently of protein markers, and may solve a previously intractable problem of separating resistant from sensitive cells for further study. Here we sought to study the mechanism of chemoresistance in cell lines using multi-parameter morphology in combination with gene expression and other standard molecular approaches. We induced resistance to specific chemotherapeutic drugs in parent cancer cell lines from lung, ovarian, and melanoma cancers. IC-50s of each drug increased by ≥1 order of magnitude in the resistant cells. Whole exome sequencing and bulk RNAseq were performed to examine differentially expressed genes and pathways between the parent and resistant cell lines. Each cell line was analyzed using multi-dimensional morphology using the Deepcell platform, which extracts &amp;gt;100 morphological features from images of unlabeled single cells using artificial intelligence (AI), advanced imaging, and microfluidics. The morphology features represent both Deep Learning (DL)-derived features and human-interpretable morphometric features, such as size and shape. Unique morphotypes were observed differentiating each combination of parent and resistant cell lines, with notable changes in granularity distinguishing them. A subset of images was used to train a random forest classifier to predict the resistant state of each cell image, which performed with up to 85% accuracy on independent cell images. Strikingly, the combination of DL plus morphometric features outperformed the use of morphometric features alone, demonstrating the increased utility of combined analysis. The top gene pathways and top morphological features were examined to infer potential mechanisms underlying chemoresistance. This work demonstrates that morphology alone can be used to distinguish drug resistant vs. parent populations without the use of markers. Morphology analysis can be applied to understanding complex phenotypes, and future platform improvements will enable sorting of these resistant populations to better identify molecular pathways in both cell lines and in primary tumor samples. Citation Format: Andreja Jovic, Manisha Ray, Ryan Carelli, Kiran Saini, Tiffine Pham, Christian Corona, Stephane C. Boutet, Chassidy Johnson, Mahyar Salek, Maddison Masaeli, Matt Barnes, Cyril Y. Ramathal. Morphological detection of chemotherapy resistance in cancer cell lines using AI-based analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4877.

Cyber Resilience, Dependability and Security

There is a continuing skills shortage associated with digital security and DevSecOps (World Economic Forum, 2023), but this paper argues that is due to non-recognition that it is time for cyber security and/or digital security to be defined, and a further separation of specialisms in computing to be made apparent. This has become increasingly important when considering Artificial Intelligence. The problem is not new. This paper presents a refinement of the principles suggested by Milner (2007) of using a model to describe behaviour and organise software, grappling with seemingly intractable and complex problems which cross boundaries between different systems: engineering, technological, social, economic, legal, and political, each with a distinct perspective and goal. It emphasises Hoare’s (1996) assertion that system failures are largely due to failed analysis impacting development of resilient systems. It argues that there are dichotomies between resilience – a system security/safety perspective, dependability – a user/consumer perspective, and security – a technology perspective. Many proposed systems to date have conflated these perspectives in the secure by design paradigm which requires a depth of knowledge and expertise. Unicorns are rare. This paper suggests how to overcome the skills shortage utilising the skill sets that are available in a manner that maximises the contribution to digital security. Recognising that not everyone and everything needs to communicate with the world reduces complexity and can increase trust. Concentration on the operational purpose of a system, resulting in an Operational Design Domain (ODD) reduces complexity further. Additional reduction in complexity is achieved by placing resilience in an engineering and programming development context, grounded in acceptable behaviours, while accepting dependability as a user expectation of system behaviour, and cyber security as a separate specialism addressing access to systems and infrastructure. Much of this paper is a reversion to defensive programming through the ODD. There is a need for any solution to the skills shortage be scalable and economic, and this paper suggests how that can be achieved using existing skill sets targeted at their specialisms.

Real-Time Batch Optimization for the Stochastic Container Relocation Problem

The container relocation problem (CRP) is an important factor affecting the operation efficiency of container terminal yards, and it has attracted much attention for decades. The CRP during the pickup operations of import containers is still an intractable problem for two reasons: the first is that the solution efficiency of the algorithms developed in the existing literature cannot meet the real-time operation requirements; the second is that the pre-optimized operation plan cannot cope with the changes in the real-time operation scenarios caused by the uncertainty of the arrival time of external trucks. This paper proposes an optimization method for the real-time operation scenario which aims to solve the most reasonable operation plan quickly according to the arrivals of external trucks, in which a dynamic upper bound of the optimal solution is derived based on the dynamic programming model of the import containers’ CRP, and an approximate optimal solution can be obtained by minimizing this dynamic upper bound. A heuristic algorithm based on three relocation rules is developed to implement this method, considering the adjustment of the pickup sequence of the target containers. Numerical experiments show that (1) when the number of a batch of target containers is less than 10 (excluding target containers that can be directly picked up), the method proposed in this paper can solve the problem quickly to meet the demand of optimizing real-time pickup operations; (2) compared with other outstanding algorithms, the quality of the solutions obtained by this method is also improved; and (3) this method can be applied to the most container terminals for optimizing real-time pickup operations.

Why isn’t flow flowing? Metatheoretical issues in explanations of flow

A flow state describes intrinsically rewarding experiences of complete absorption in a task. While descriptive accounts of flow have gained prominence in “popular” psychology, scientific research has reached a crisis point due to perceived limitations of current theoretical explanations for the experience. Here, we evaluate key metatheoretical frameworks underpinning previous explanations of flow and situate the need for reconsidering the ontological status of flow experiences and the causal entailments that might be needed to explain them. We consider the possibility that the subject–object dualism implied, and the organismic asymmetry apparent in prevalent metatheoretical frameworks, may create intractable problems for explanations of flow. Finally, the suitability of the ecological metatheory and eco-physical variables for explaining flow experiences is discussed.

Eigenvalue-based quantum state verification of three-qubit W class states

In quantum many-body systems, W class states are typical examples of states with genuine multipartite entanglement. They have been found to be valuable resources in many quantum information processing tasks. However, the characterization and verification of W class states still remains an intractable problem. Here we first propose an eigenvalue-based verification protocol consisting of four practical adaptive local projective measurement tests, and obtain the optimal test probabilities with particle swarm optimization algorithm. The efficiency achieved by this protocol is far from satisfactory when compared with the theoretical upper bound. Then by utilizing the symmetry of the target states and a class of specific unitaries, we optimize the original verification strategy based on group theory. In this case, our new protocol can realize the efficient verification of three-qubit W class states.