Individual Planning in Agent Populations: Exploiting Anonymity and Frame-Action Hypergraphs

Interactive partially observable Markov decision processes (I-POMDP) provide a formal framework for planning for a self-interested agent in multiagent settings. An agent operating in a multiagent environment must deliberate about the actions that other agents may take and the effect these actions have on the environment and the rewards it receives. Traditional I-POMDPs model this dependence on the actions of other agents using joint action and model spaces. Therefore, the solution complexity grows exponentially with the number of agents thereby complicating scalability. In this paper, we model and extend anonymity and context-specific independence -- problem structures often present in agent populations -- for computational gain. We empirically demonstrate the efficiency from exploiting these problem structures by solving a new multiagent problem involving more than 1,000 agents.Comment: 8 page article plus two page appendix containing proofs in Proceedings of 25th International Conference on Autonomous Planning and Scheduling, 201

Palladium nanoparticles by electrospinning from poly(acrylonitrile-co-acrylic acid)-PdCl2 solutions. Relations between preparation conditions, particle size, and catalytic activity

Catalytic palladium (Pd) nanoparticles on electrospun copolymers of acrylonitrile and acrylic acid (PAN-AA) mats were produced via reduction of PdCl2 with hydrazine. Fiber mats were electrospun from homogeneous solutions of PAN-AA and PdCl2 in dimethylformamide (DMF). Pd cations were reduced to Pd metals when fiber mats were treated in an aqueous hydrazine solution at room temperature. Pd atoms nucleate and form small crystallites whose sizes were estimated from the peak broadening of X-ray diffraction peaks. Two to four crystallites adhere together and form agglomerates. Agglomerate sizes and fiber diameters were determined by scanning and transmission electron microscopy. Spherical Pd nanoparticles were dispersed homogeneously on the electrospun nanofibers. The effects of copolymer composition and amount of PdCl2 on particle size were investigated. Pd particle size mainly depends on the amount of acrylic acid functional groups and PdCl2 concentration in the spinning solution. Increasing acrylic acid concentration on polymer chains leads to larger Pd nanoparticles. In addition, Pd particle size becomes larger with increasing PdCl2 concentration in the spinning solution. Hence, it is possible to tune the number density and the size of metal nanoparticles. The catalytic activity of the Pd nanoparticles in electrospun mats was determined by selective hydrogenation of dehydrolinalool (3,7-dimethyloct-6- ene-1-yne-3-ol, DHL) in toluene at 90 °C. Electrospun fibers with Pd particles have 4.5 times higher catalytic activity than the current Pd/Al2O3 catalyst

Can bounded and self-interested agents be teammates? Application to planning in ad hoc teams

Planning for ad hoc teamwork is challenging because it involves agents collaborating without any prior coordination or communication. The focus is on principled methods for a single agent to cooperate with others. This motivates investigating the ad hoc teamwork problem in the context of self-interested decision-making frameworks. Agents engaged in individual decision making in multiagent settings face the task of having to reason about other agents’ actions, which may in turn involve reasoning about others. An established approximation that operationalizes this approach is to bound the infinite nesting from below by introducing level 0 models. For the purposes of this study, individual, self-interested decision making in multiagent settings is modeled using interactive dynamic influence diagrams (I-DID). These are graphical models with the benefit that they naturally offer a factored representation of the problem, allowing agents to ascribe dynamic models to others and reason about them. We demonstrate that an implication of bounded, finitely-nested reasoning by a self-interested agent is that we may not obtain optimal team solutions in cooperative settings, if it is part of a team. We address this limitation by including models at level 0 whose solutions involve reinforcement learning. We show how the learning is integrated into planning in the context of I-DIDs. This facilitates optimal teammate behavior, and we demonstrate its applicability to ad hoc teamwork on several problem domains and configurations

Three-Dimensional Radiofrequency Tissue Tightening: A Proposed Mechanism and Applications for Body Contouring

The use of radiofrequency energy to produce collagen matrix contraction is presented. Controlling the depth of energy delivery, the power applied, the target skin temperature, and the duration of application of energy at various soft tissue levels produces soft tissue contraction, which is measurable. This technology allows precise soft tissue modeling at multiple levels to enhance the result achieved over traditional suction-assisted lipectomy as well as other forms of energy such as ultrasonic and laser-generated lipolysis

Macrophages Recognize Size and Shape of Their Targets

Recognition by macrophages is a key process in generating immune response against invading pathogens. Previous studies have focused on recognition of pathogens through surface receptors present on the macrophage's surface. Here, using polymeric particles of different geometries that represent the size and shape range of a variety of bacteria, the importance of target geometry in recognition was investigated. The studies reported here reveal that attachment of particles of different geometries to macrophages exhibits a strong dependence on size and shape. For all sizes and shapes studied, particles possessing the longest dimension in the range of 2–3 µm exhibited highest attachment. This also happens to be the size range of most commonly found bacteria in nature. The surface features of macrophages, in particular the membrane ruffles, might play an important role in this geometry-based target recognition by macrophages. These findings have significant implications in understanding the pathogenicity of bacteria and in designing drug delivery carriers

Characterizing Heterogeneity in Neuroimaging, Cognition, Clinical Symptoms, and Genetics Among Patients With Late-Life Depression

Importance: Late-life depression (LLD) is characterized by considerable heterogeneity in clinical manifestation. Unraveling such heterogeneity might aid in elucidating etiological mechanisms and support precision and individualized medicine. Objective: To cross-sectionally and longitudinally delineate disease-related heterogeneity in LLD associated with neuroanatomy, cognitive functioning, clinical symptoms, and genetic profiles. Design, Setting, and Participants: The Imaging-Based Coordinate System for Aging and Neurodegenerative Diseases (iSTAGING) study is an international multicenter consortium investigating brain aging in pooled and harmonized data from 13 studies with more than 35 000 participants, including a subset of individuals with major depressive disorder. Multimodal data from a multicenter sample (N = 996), including neuroimaging, neurocognitive assessments, and genetics, were analyzed in this study. A semisupervised clustering method (heterogeneity through discriminative analysis) was applied to regional gray matter (GM) brain volumes to derive dimensional representations. Data were collected from July 2017 to July 2020 and analyzed from July 2020 to December 2021. Main Outcomes and Measures: Two dimensions were identified to delineate LLD-associated heterogeneity in voxelwise GM maps, white matter (WM) fractional anisotropy, neurocognitive functioning, clinical phenotype, and genetics. Results: A total of 501 participants with LLD (mean [SD] age, 67.39 [5.56] years; 332 women) and 495 healthy control individuals (mean [SD] age, 66.53 [5.16] years; 333 women) were included. Patients in dimension 1 demonstrated relatively preserved brain anatomy without WM disruptions relative to healthy control individuals. In contrast, patients in dimension 2 showed widespread brain atrophy and WM integrity disruptions, along with cognitive impairment and higher depression severity. Moreover, 1 de novo independent genetic variant (rs13120336; chromosome: 4, 186387714; minor allele, G) was significantly associated with dimension 1 (odds ratio, 2.35; SE, 0.15; P = 3.14 ×10⁸) but not with dimension 2. The 2 dimensions demonstrated significant single-nucleotide variant–based heritability of 18% to 27% within the general population (N = 12 518 in UK Biobank). In a subset of individuals having longitudinal measurements, those in dimension 2 experienced a more rapid longitudinal change in GM and brain age (Cohen ƒ² = 0.03; P = .02) and were more likely to progress to Alzheimer disease (Cohen ƒ² = 0.03; P = .03) compared with those in dimension 1 (N = 1431 participants and 7224 scans from the Alzheimer’s Disease Neuroimaging Initiative [ADNI], Baltimore Longitudinal Study of Aging [BLSA], and Biomarkers for Older Controls at Risk for Dementia [BIOCARD] data sets). Conclusions and Relevance: This study characterized heterogeneity in LLD into 2 dimensions with distinct neuroanatomical, cognitive, clinical, and genetic profiles. This dimensional approach provides a potential mechanism for investigating the heterogeneity of LLD and the relevance of the latent dimensions to possible disease mechanisms, clinical outcomes, and responses to interventions

GAMER MRI: Gated-attention mechanism ranking of multi-contrast MRI in brain pathology.

During the last decade, a multitude of novel quantitative and semiquantitative MRI techniques have provided new information about the pathophysiology of neurological diseases. Yet, selection of the most relevant contrasts for a given pathology remains challenging. In this work, we developed and validated a method, Gated-Attention MEchanism Ranking of multi-contrast MRI in brain pathology (GAMER MRI), to rank the relative importance of MR measures in the classification of well understood ischemic stroke lesions. Subsequently, we applied this method to the classification of multiple sclerosis (MS) lesions, where the relative importance of MR measures is less understood. GAMER MRI was developed based on the gated attention mechanism, which computes attention weights (AWs) as proxies of importance of hidden features in the classification. In the first two experiments, we used Trace-weighted (Trace), apparent diffusion coefficient (ADC), Fluid-Attenuated Inversion Recovery (FLAIR), and T1-weighted (T1w) images acquired in 904 acute/subacute ischemic stroke patients and in 6,230 healthy controls and patients with other brain pathologies to assess if GAMER MRI could produce clinically meaningful importance orders in two different classification scenarios. In the first experiment, GAMER MRI with a pretrained convolutional neural network (CNN) was used in conjunction with Trace, ADC, and FLAIR to distinguish patients with ischemic stroke from those with other pathologies and healthy controls. In the second experiment, GAMER MRI with a patch-based CNN used Trace, ADC and T1w to differentiate acute ischemic stroke lesions from healthy tissue. The last experiment explored the performance of patch-based CNN with GAMER MRI in ranking the importance of quantitative MRI measures to distinguish two groups of lesions with different pathological characteristics and unknown quantitative MR features. Specifically, GAMER MRI was applied to assess the relative importance of the myelin water fraction (MWF), quantitative susceptibility mapping (QSM), T1 relaxometry map (qT1), and neurite density index (NDI) in distinguishing 750 juxtacortical lesions from 242 periventricular lesions in 47 MS patients. Pair-wise permutation t-tests were used to evaluate the differences between the AWs obtained for each quantitative measure. In the first experiment, we achieved a mean test AUC of 0.881 and the obtained AWs of FLAIR and the sum of AWs of Trace and ADC were 0.11 and 0.89, respectively, as expected based on previous knowledge. In the second experiment, we achieved a mean test F1 score of 0.895 and a mean AW of Trace = 0.49, of ADC = 0.28, and of T1w = 0.23, thereby confirming the findings of the first experiment. In the third experiment, MS lesion classification achieved test balanced accuracy = 0.777, sensitivity = 0.739, and specificity = 0.814. The mean AWs of T1map, MWF, NDI, and QSM were 0.29, 0.26, 0.24, and 0.22 (p < 0.001), respectively. This work demonstrates that the proposed GAMER MRI might be a useful method to assess the relative importance of MRI measures in neurological diseases with focal pathology. Moreover, the obtained AWs may in fact help to choose the best combination of MR contrasts for a specific classification problem

Fair and Sound Secret Sharing from Homomorphic Time-Lock Puzzles

Achieving fairness and soundness in non-simultaneous rational secret sharing schemes has proved to be challenging. On the one hand, soundness can be ensured by providing side information related to the secret as a check, but on the other, this can be used by deviant players to compromise fairness. To overcome this, the idea of incorporating a time delay was suggested in the literature: in particular, time-delay encryption based on memory-bound functions has been put forth as a solution. In this paper, we propose a different approach to achieve such delay, namely using homomorphic time-lock puzzles (HTLPs), introduced at CRYPTO 2019, and construct a fair and sound rational secret sharing scheme in the non-simultaneous setting from HTLPs. HTLPs are used to embed sub-shares of the secret for a predetermined time. This allows to restore fairness of the secret reconstruction phase, despite players having access to information related to the secret which is required to ensure soundness of the scheme. Key to our construction is the fact that the time-lock puzzles are homomorphic so that players can compactly evaluate sub-shares. Without this efficiency improvement, players would have to independently solve each puzzle sent from the other players to obtain a share of the secret, which would be computationally inefficient. We argue that achieving both fairness and soundness in a non-simultaneous scheme using a time delay based on CPU-bound functions rather than memory-bound functions is more cost effective and realistic in relation to the implementation of the construction