Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparison Between Manufacturer’s Suggested Retail Price and Minimum Advertised Price in a Distribution Channel and the Effect of Competition

This paper investigates two coordination mechanisms in a simple distribution channel: 1) the manufacturer's suggested retail price (MSRP) and 2) the minimum advertised price (MAP). With a MSRP, the retailer can engage in a price discount policy to maintain control of the selling price. With a MAP, the manufacturer pays part of the retailer's advertising expenses. Our results illustrate that when the consumer’s price sensitivity is lower than their advertising sensitivity, both players prefer a MAP. Otherwise, the adoption of a MSRP is only profit-Pareto-improving when the retailer's transfer price is large. We contrast the findings with a distribution channel that adopts a classic wholesale price contract (WPC). When compared to a classic WPC, firms are indifferent between selecting a WPC or a MSRP, while they prefer a MAP to a WPC. We explore two competitive scenarios to investigate the impact of competition on the preferences among these contracts.
Differential Effects of Experience and Information Cues on Metacognitive Judgments About Others’ Change Detection Abilities

This study explored the interaction between visual metacognitive judgments about others and cues related to the workings of System 1 and System 2. We examined how intrinsic cues (i.e., saliency of a visual change) and experience cues (i.e., detection/blindness) affect people’s predictions about others’ change detection abilities. In Experiment 1, 60 participants were instructed to notice a subtle and a salient visual change in a magic trick that exploits change blindness, after which they estimated the probability that others would detect the change. In Experiment 2, 80 participants watched either the subtle or the salient version of the trick and they were asked to provide predictions for the experienced change. In Experiment 1, participants predicted that others would detect the salient change more easily than the subtle change, which was consistent with the actual detection reported in Experiment 2. In Experiment 2, participants’ personal experience (i.e., whether they detected the change) biased their predictions. Moreover, there was a significant difference between their predictions and offline predictions from Experiment 1. Interestingly, change blindness led to lower predictions. These findings point to joint contributions of experience and information cues on metacognitive judgments about other people’s change detection abilities.
Gold nanorods enhance different immune cells and allow for efficient targeting of CD4+ Foxp3+ Tregulatory cells

Gold nanoparticles (AuNPs) hold great promise in nanomedicine, yet their successful clinical translation has not been realized. Some challenges include effective AuNP targeting and delivery to improve modulation of immune cells of interest while limiting potential adverse effects. In order to overcome these challenges, we must fully understand how AuNPs impact different immune cell subsets, particularly within the dendritic cell and T cell compartments. Herein, we show that polyethylene glycol coated (PEG) gold nanorods (AuNRs) and PEG AuNRs covered with a thin layer of silver (AuNR/Ag) may enhance the immune response towards immune suppression or activation. We also studied the ability to enhance CD4+ Foxp3+ Tregs in vitro using AuNRs functionalized with interleukin 2 (IL2), a cytokine that is important in Treg development and homeostasis. Our results indicate that AuNRs enhance different immune cells and that NP composition matters in immune targeting. This knowledge will help us understand how to better design AuNRs to target and enhance the immune system.
Control Scenarios for Probabilistic SIR Epidemics on Social-Connection Graphs

A probabilistic approach to epidemic evolution on realistic social-connection graphs allows for characteristic diﬀerences among subjects, including the individual structure and number of social contacts (‘ego networks’) and the individual infection and recovery rates according to age or medical preconditions. Within our probabilistic Susceptible-Infectious-Removed (SIR) model on graphs, we compare the infection load and proxy cost of control scenarios by conﬁnement, vaccination, and their combinations. The epidemic diﬀusion is explored on random social-connection graphs designed for more realistic person-person characteristics
LOGISTICS OF RURAL TOURISM

Tourism has been developing intensively in recent decades due to various social and economic trends. Rural tourism, which includes various tourist activities in rural areas (tourism in households, ecotourism, recreational, adventure, sports, health, artistic, heritage tourism, etc.) is also gaining in importance. The main instrument for the efficient realization of flows of people, goods and information in the Rural Tourism Supply Chain (RTSC) and increasing the competitiveness of tourism products is logistics. Logistics provides a material and a nonmaterial basis for rural tourism services. As suppliers of raw materials and finished products to other actors, but also as creators of their own tourism offer, households are significant actors in the supply chain and generators of logistics flows in rural tourism. A specific form of rural tourism related to households and agricultural production is called agritourism. This paper analyzes the critical issues and structure of the RTSC, structure the logistics of agritourism and analyzing particular areas in terms of logistics flows, processes, and activities.
The Effectiveness of Pictorial Aesthetics Based on Multiview Parallel Neural Networks in Art-Oriented Teaching

How to effectively improve the effectiveness of art teaching has always been one of the hot topics concerned by all sectors of society. Especially, in art teaching, situational interaction helps improve the atmosphere of art class. However, there are few attempts to quantitatively evaluate the aesthetics of ink painting. Ink painting expresses images through ink tone and stroke changes, which is significantly different from photos and paintings in visual characteristics, semantic characteristics, and aesthetic standards. For this reason, this study proposes an adaptive computational aesthetic evaluation framework for ink painting based on situational interaction using deep learning techniques. The framework extracts global and local images as multiple input according to the aesthetic criteria of ink painting and designs a model named MVPD-CNN to extract deep aesthetic features; finally, an adaptive deep aesthetic evaluation model is constructed. The experimental results demonstrate that our model has higher aesthetic evaluation performance compared with baseline, and the extracted deep aesthetic features are significantly better than the traditional manual design features, and its adaptive evaluation results reach a Pearson height of 0.823 compared with the manual aesthetic. In addition, art classroom simulation and interference experiments show that our model is highly resistant to interference and more sensitive to the three painting elements of composition, ink color, and texture in specific compositions.
Stockade: Hardware Hardening for Distributed Trusted Sandboxes

Recent studies showed that a cloud application consists of multiple distributed modules provided by mutually distrustful parties. For trusted services, such applications can use trusted execution environments (TEEs) communicating through software-encrypted memory channels. Such an emerg-ing TEE execution model requires a new type of bi-directional protection: protecting the rest of the system from the enclave module with sandboxing and protecting the enclave module from third-party modules and the operating system. However, the current TEE model cannot efﬁciently represent such distributed sandbox applications. To overcome the lack of hardware supports, this paper proposes an extended TEE model called S TOCKADE , which supports distributed sandboxes hardened by hardware. S TOCKADE proposes new three key techniques. First, it extends the hardware-based memory isolation in SGX to conﬁne a user software module only within its TEE (enclave). Second, it proposes a trusted monitor enclave that ﬁlters and validates systems calls from enclaves. Finally, it allows hardware-protected memory sharing between a pair of enclaves for efﬁcient protected communication without software-based encryption. Using an emulated SGX platform with the proposed extensions, this paper shows that distributed sandbox applications can be effectively supported with small changes of SGX hardware.
Association of Mental Health Problems and Socio-Demographic Factors Among Adolescents in Indonesia

Mental health in adolescents represent a considerable public health issue in developing countries. These problems are considered a source of problem for adolescents’ quality of life. The study objective was to determine the association between mental health problems and sociodemographic variables among adolescents. A cross-sectional study was conducted from October to December 2016 using a school-based survey in 4 districts and 3 cities in West Java Province, Indonesia. Stratified multistage random sampling was used to select junior and senior high school students and self-administered questionnaires were used to collect study data which included: personal data, socioeconomic status scale, and self-reported version of Strengths and Difficulties Questionnaire (SDQ) which divided into 5 subscales. About 3452 adolescents of 32 schools were included in this study, 59% female, 56% junior high school, 56% district residence, most parents’ education were senior high school. The prevalence of mental health problems based on SDQ subscale as total difficulties, conduct problem, emotional symptoms, peer problems, hyperactivity-inattention, were abnormal 31.6, 38.9, 30, 29.3, 15.6, respectively. Mental health problems were more prevalent amongst adolescents in junior high school and residence in district area, with RR and 95% CI were 1.14 (1.04-1.26), and 1.18 (1.08-1.30). As conclusions mental health problems in adolescents were associated with education level and residence area
On local minimizers of generalized trust-region subproblem

Generalized trust-region subproblem (GT) is a nonconvex quadratic optimization with a single quadratic constraint. It reduces to the classical trust-region subproblem (T) if the constraint set is a Euclidean ball. (GT) is polynomially solvable based on its inherent hidden convexity. In this paper, we study local minimizers of (GT). Unlike (T) with at most one local nonglobal minimizer, we can prove that two-dimensional (GT) has at most two local nonglobal minimizers, which are shown by example to be attainable. The main contribution of this paper is to prove that, at any local nonglobal minimizer of (GT), not only the strict complementarity condition holds, but also the standard second-order sufficient optimality condition remains necessary. As a corollary, finding all local nonglobal minimizers of (GT) or proving the nonexistence can be done in polynomial time. Finally, for (GT) in complex domain, we prove that there is no local nonglobal minimizer, which demonstrates that real-valued optimization problem may be more difficult to solve than its complex version.
Photophysics of Two-Dimensional Semiconducting Organic-Inorganic Metal-Halide Perovskites.

Two-dimensional organic-inorganic hybrid perovskites (2DHPs) consist of alternating anionic metal-halide and cationic organic layers. They have widely tunable structural and optical properties. We review the role of the organic cation in defining the structural and optical properties of 2DHPs through example lead iodide 2DHPs. Even though excitons reside in the metal-halide layers, the organic and inorganic frameworks cannot be separated-they must be considered as a single unit to fully understand the photophysics of 2DHPs. We correlate cation-induced distortion and disorder in the inorganic lattice with the resulting optical properties. We also discuss the role of the cation in creating and altering the discrete excitonic structure that appears at cryogenic temperatures in some 2DHPs, including the cation-dependent presence of hot-exciton photoluminescence. We conclude our review with an outlook for 2DHPs, highlighting existing gaps in fundamental knowledge as well as potential future applications. Expected final online publication date for the Annual Review of Physical Chemistry, Volume 73 is April 2022. Please see http://www.annualreviews. org/page/journal/pubdates for revised estimates.
Simultaneous Nuclear Instance and Layer Segmentation in Oral Epithelial Dysplasia

Oral epithelial dysplasia (OED) is a pre-malignant histopathological diagnosis given to lesions of the oral cavity. Predicting OED grade or whether a case will transition to malignancy is critical for early detection and appropriate treatment. OED typically begins in the lower third of the epithelium before progressing upwards with grade severity, thus we have suggested that segmenting intra-epithelial layers, in addition to individual nuclei, may enable researchers to evaluate important layer-specific morphological features for grade/malignancy prediction. We present HoVer-Net+, a deep learning framework to simultaneously segment (and classify) nuclei and (intra-)epithelial layers in H&E stained slides from OED cases. The proposed architecture consists of an encoder branch and four decoder branches for simultaneous instance segmentation of nuclei and semantic segmentation of the epithelial layers. We show that the proposed model achieves the state-of-the-art (SOTA) performance in both tasks, with no additional costs when compared to previous SOTA methods for each task. To the best of our knowledge, ours is the first method for simultaneous nuclear instance segmentation and semantic tissue segmentation, with potential for use in computational pathology for other similar simultaneous tasks and for future studies into malignancy prediction.
Through the Looking Glass: Diminishing Occlusions in Robot Vision Systems with Mirror Reflections

The quality of robot vision greatly affects the performance of automation systems, where occlusions stand as one of the biggest challenges. If the target is occluded from the sensor, detecting and grasping such objects become very challenging. For example, when multiple robot arms cooperate in a single workplace, occlusions will be created under the robot arm itself and hide objects underneath. While occlusions can be greatly reduced by installing multiple sensors, the increase in sensor costs cannot be ignored. Moreover, the sensor placements must be rearranged every time the robot operation routine and layout change.To diminish occlusions, we propose the first robot vision system with tilt-type mirror reflection sensing. By instantly tilting the sensor itself, we obtain two sensing results with different views: conventional direct line-of-sight sensing and non-line-of-sight sensing via mirror reflections. Our proposed system removes occlusions adaptively by detecting the occlusions in the scene and dynamically configuring the sensor tilt angle to sense the detected occluded area. Thus, sensor rearrangements are not required even after changes in robot operation or layout. Since the required hardware is the tilt-unit and a commercially available mirror, the cost increase is marginal. Through experiments, we show that our system can achieve a similar detection accuracy as systems with multiple sensors, regardless of the single-sensor implementation.
Non-minimally coupled vector boson dark matter

We consider a simple abelian vector dark matter (DM) model, where only the DM (X̃μ) couples non-minimally to the scalar curvature (R̃) of the background spacetime via an operator of the form ∼X̃μ X̃μ R̃. By considering the standard freeze-out scenario, we show, it is possible to probe such a non-minimally coupled DM in direct detection experiments for a coupling strength ξ∼𝒪(1030) and DM mass m X ≲ 55 TeV, satisfying Planck observed relic abundance and perturbative unitarity. We also discuss DM production via freeze-in, governed by the non-minimal coupling, that requires ξ ≲10-5 to produce the observed DM abundance over a large range of DM mass depending on the choice of the reheating temperature. We further show, even in the absence of the non-minimal coupling, it is possible to produce the whole observed DM abundance via 2-to-2 scattering of the bath particles mediated by massless gravitons.
The Horn Non-Clausal Class and its Polynomiality

The expressiveness of propositional non-clausal (NC) formulas is exponentially richer than that of clausal formulas. Yet, clausal efficiency outperforms non-clausal one. Indeed, a major weakness of the latter is that, while Horn clausal formulas, along with Horn algorithms, are crucial for the high efficiency of clausal reasoning, no Horn-like formulas in non-clausal form had been proposed. To overcome such weakness, we define the hybrid class $\mathbb{H_{NC}}$ of Horn Non-Clausal (Horn-NC) formulas, by adequately lifting the Horn pattern to NC form, and argue that $\mathbb{H_{NC}}$, along with future Horn-NC algorithms, shall increase non-clausal efficiency just as the Horn class has increased clausal efficiency. Secondly, we: (i) give the compact, inductive definition of $\mathbb{H_{NC}}$; (ii) prove that syntactically $\mathbb{H_{NC}}$ subsumes the Horn class but semantically both classes are equivalent, and (iii) characterize the non-clausal formulas belonging to $\mathbb{H_{NC}}$. Thirdly, we define the Non-Clausal Unit-Resolution calculus, $UR_{NC}$, and prove that it checks the satisfiability of $\mathbb{H_{NC}}$ in polynomial time. This fact, to our knowledge, makes $\mathbb{H_{NC}}$ the first characterized polynomial class in NC reasoning. Finally, we prove that $\mathbb{H_{NC}}$ is linearly recognizable, and also that it is both strictly succincter and exponentially richer than the Horn class. We discuss that in NC automated reasoning, e.g. satisfiability solving, theorem proving, logic programming, etc., can directly benefit from $\mathbb{H_{NC}}$ and $UR_{NC}$ and that, as a by-product of its proved properties, $\mathbb{H_{NC}}$ arises as a new alternative to analyze Horn functions and implication systems.
Obliquity Constraints on the Planetary-mass Companion HD 106906 b

We constrain the angular momentum architecture of HD 106906, a 13 ± 2 Myr old system in the ScoCen complex composed of a compact central binary, a widely separated planetary-mass tertiary HD 106906 b, and a debris disk nested between the binary and tertiary orbital planes. We measure the orientations of three vectors: the companion spin axis, companion orbit normal, and disk normal. Using near-IR high-resolution spectra from Gemini/IGRINS, we obtain a projected rotational velocity of vsinip = 9.5 ± 0.2 km s−1 for HD 106906 b. This measurement together with a published photometric rotation period implies the companion is viewed nearly pole-on, with a line-of-sight spin axis inclination of i p = 14° ± 4° or 166° ± 4°. By contrast, the debris disk is known to be viewed nearly edge on. The likely misalignment of all three vectors suggests HD 106906 b formed by gravitational instability in a turbulent environment, either in a disk or cloud setting.
𝓁p-Spread Properties of Sparse Matrices

Random subspaces of R= of dimension proportional to = are, with high probability, wellspread with respect to the l? -norm (for ? ∈ [1, 2]). Namely, every nonzero G ∈ is “robustly non-sparse” in the following sense: G is ‖G‖? -far in l? -distance from all =-sparse vectors, for positive constants , bounded away from 0. This “l? -spread” property is the natural counterpart, for subspaces over the reals, of the minimum distance of linear codes over finite fields, and, for ? = 2, corresponds to being a Euclidean section of the l1 unit ball. Explicit l? spread subspaces of dimensionΩ(=), however, are not known except for ? = 1. The construction for ? = 1, as well as the best known constructions for ? ∈ (1, 2] (which achieve weaker spread properties), are analogs of low density parity check (LDPC) codes over the reals, i.e., they are kernels of certain sparse matrices. Motivated by this, we study the spread properties of the kernels of sparse random matrices. Rather surprisingly, we prove that with high probability such subspaces contain vectors G that are >(1) · ‖G‖2-close to >(=)-sparse with respect to the l2-norm, and in particular are not l2spread. This is strikingly different from the case of random LDPC codes, whose distance is asymptotically almost as good as that of (dense) random linear codes. On the other hand, for ? < 2 we prove that such subspaces are l? -spread with high probability. The spread property of sparse random matrices thus exhibits a threshold behavior at ? = 2. Our proof for ? < 2 moreover shows that a random sparse matrix has the stronger restricted isometry property (RIP) with respect to the l? norm. In fact, we show that RIP follows solely from the unique expansion of a random biregular graph, yielding a somewhat unexpected generalization of a similar result for the l1 norm [BGI +08]. Instantiating this with suitable explicit expanders, we obtain the first explicit constructions of l? -spread subspaces and l? -RIP matrices for 1 ≤ ? < ? 0, where 1 < ? 0 < 2 is an absolute constant.
A CRISPR/Cas9‐based method for seamless N‐terminal protein tagging in Saccharomyces cerevisiae

Protein tagging is an effective method for characterizing a gene of interest. Tagging can be accomplished in vivo in Saccharomyces cerevisiae by chromosomal integration of a PCR‐amplified cassette. However, common tagging cassettes are not suitable for in situ N‐terminal tagging when we aim to preserve the gene's endogenous promoter. Existing methods require either two rounds of homologous recombination or a relatively complex cloning process to construct strains with N‐terminal protein tags. Here, we describe a simple CRISPR/Cas9‐based method for seamless N‐terminal tagging of yeast genes that preserves their endogenous promoter. This method enables the generation of N‐terminally tagged strains by introducing an expression vector containing the cas9 gene and a specific gRNA for cleaving the 5′ end of the target gene's protein‐coding sequence, along with donor DNA containing the tag sequence and homology arms. gRNA cloning was executed by inverse PCR instead of the conventional method. After verifying the tag, the Cas9 and gRNA expression plasmids were eliminated without using antibiotic‐containing medium. By this method, we generated strains that express N‐terminally tagged subunits of the TORC1 protein kinase complex and found that these strains are comparable to strains made by conventional methods. Thus, our method provides a cost‐effective alternative for seamless N‐terminal tagging in baker's yeast.
Image Denoising Inspired by Quantum Many-Body physics

Decomposing an image through Fourier, DCT or wavelet transforms is still a common approach in digital image processing, in number of applications such as denoising. In this context, data-driven dictionaries and in particular exploiting the redundancy withing patches extracted from one or several images allowed important improvements. This paper proposes an original idea of constructing such an image-dependent basis inspired by the principles of quantum many-body physics. The similarity between two image patches is introduced in the formalism through a term akin to interaction terms in quantum mechanics. The main contribution of the paper is thus to introduce this original way of exploiting quantum many-body ideas in image processing, which opens interesting perspectives in image denoising. The potential of the proposed adaptive decomposition is illustrated through image denoising in presence of additive white Gaussian noise, but the method can be used for other types of noise such as image-dependent noise as well. Finally, the results show that our method achieves comparable or slightly better results than existing approaches.