Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Building a Food Knowledge Graph for Internet of Food

Background: The deployment of various networks (e.g., Internet of Things (IoT) and mobile networks) and databases (e.g., nutrition tables and food compositional databases) in the food system generates massive information silos due to the well-known data harmonization problem. The food knowledge graph provides a uniﬁed and standardized conceptual terminology and their relationshipsinastructuredformandthuscantransformtheseinformationsilosacrossthewholefoodsystemtoamorereusablegloballydigitallyconnectedInternetofFood,enablingevery stageofthefoodsystemfromfarm-to-fork. Scope and approach: We review the evolution of food knowledge organization, from food clas-siﬁcation, food ontology to food knowledge graphs. We then discuss the progress in food knowledge graphs from several representative applications. We ﬁnally discuss the main challenges and future directions. Key ﬁndings and conclusions: Our comprehensive summary of current research on food knowledge graphs shows that food knowledge graphs play an important role in food-oriented applications, including food search and Question Answering (QA), personalized dietary recommendation, food analysis and visualization, food traceability, and food machinery intelligent manufacturing. Future directions for food knowledge graphs cover several ﬁelds such as multimodal food knowledge graphs and food intelligence.
Transformer-Based Behavioral Representation Learning Enables Transfer Learning for Mobile Sensing in Small Datasets

While deep learning has revolutionized research and applications in NLP and computer vision, this has not yet been the case for behavioral modeling and behavioral health applications. This is because the domain's datasets are smaller, have heterogeneous datatypes, and typically exhibit a large degree of missingness. Therefore, off-the-shelf deep learning models require significant, often prohibitive, adaptation. Accordingly, many research applications still rely on manually coded features with boosted tree models, sometimes with task-specific features handcrafted by experts. Here, we address these challenges by providing a neural architecture framework for mobile sensing data that can learn generalizable feature representations from time series and demonstrates the feasibility of transfer learning on small data domains through finetuning. This architecture combines benefits from CNN and Trans-former architectures to (1) enable better prediction performance by learning directly from raw minute-level sensor data without the need for handcrafted features by up to 0.33 ROC AUC, and (2) use pretraining to outperform simpler neural models and boosted decision trees with data from as few a dozen participants.
Bosonization of Majorana modes and edge states

We present a bosonization procedure which replaces fermions with generalized spin variables subject to local constraints. It requires that the number of Majorana modes per lattice site matches the coordination number modulo two. If this condition is not obeyed, then bosonization introduces additional fermionic excitations not present in the original model. In the case of one Majorana mode per site on a honeycomb lattice, we recover a sector of Kitaev's model. We discuss also decagonal and rectangular geometries and present bosonization of the Hubbard model. For geometries with a boundary we find that certain fermionic edge modes naturally emerge. They are of different nature than edge modes encountered in topological phases of matter. Euclidean representation for the unconstrained version of a spin system of the type arising in our construction is derived and briefly studied by computing some exact averages for small volumes.
Radiation from a dipole perpendicular to the interface between two planar semi-infinite magnetoelectric media

We consider two semi-infinite magnetoelectric media with constant dielectric permittivity separated by a planar interface, whose electromagnetic response is described by non-dynamical axion electrodynamics and investigate the radiation of a point-like electric dipole located perpendicularly to the interface. We start from the exact Green's function for the electromagnetic potential, whose far-field approximation is obtained using a modified steepest descent approximation. We compute the angular distribution of the radiation and the total radiated power finding different interference patterns, depending on the relative position dipole-observer, and polarization mixing effects which are all absent in the standard dipole radiation. They are a manifestation of the magnetoelectric effect induced by axion electrodynamics. We illustrate our findings with some numerical estimations employing realistic media as well as some hypothetical choices in order to illuminate the effects of the magnetoelectric coupling which is usually very small.
CentripetalText: An Efficient Text Instance Representation for Scene Text Detection

Scene text detection remains a grand challenge due to the variation in text curvatures, orientations, and aspect ratios. One of the hardest problems in this task is how to represent text instances of arbitrary shapes. Although many methods have been proposed to model irregular texts in a flexible manner, most of them lose simplicity and robustness. Their complicated post-processings and the regression under Dirac delta distribution undermine the detection performance and the generalization ability. In this paper, we propose an efficient text instance representation named CentripetalText (CT), which decomposes text instances into the combination of text kernels and centripetal shifts. Specifically, we utilize the centripetal shifts to implement pixel aggregation, guiding the external text pixels to the internal text kernels. The relaxation operation is integrated into the dense regression for centripetal shifts, allowing the correct prediction in a range instead of a specific value. The convenient reconstruction of text contours and the tolerance of prediction errors in our method guarantee the high detection accuracy and the fast inference speed, respectively. Besides, we shrink our text detector into a proposal generation module, namely CentripetalText Proposal Network, replacing Segmentation Proposal Network in Mask TextSpotter v3 and producing more accurate proposals. To validate the effectiveness of our method, we conduct experiments on several commonly used scene text benchmarks, including both curved and multi-oriented text datasets. For the task of scene text detection, our approach achieves superior or competitive performance compared to other existing methods, e.g., F-measure of 86.3% at 40.0 FPS on Total-Text, F-measure of 86.1% at 34.8 FPS on MSRA-TD500, etc. For the task of end-to-end scene text recognition, our method outperforms Mask TextSpotter v3 by 1.1% on Total-Text.
Least-Squares Linear Dilation-Erosion Regressor Trained using Stochastic Descent Gradient or the Difference of Convex Methods

This paper presents a hybrid morphological neural network for regression tasks called linear dilation-erosion regression ( (cid:96) -DER). In few words, an (cid:96) -DER model is given by a convex combination of the composition of linear and elementary morphological operators. As a result, they yield continuous piecewise linear functions and, thus, are universal approximators. Apart from introducing the (cid:96) -DER models, we present three approaches for training these models: one based on stochastic descent gradient and two based on the diﬀerence of convex programming problems. Finally, we evaluate the performance of the (cid:96) -DEP model us-ing 13 regression tasks. Although the approach based on SDG revealed faster than the other two, the (cid:96) -DER trained using a disciplined convex-concave programming problem outperformed the others in terms of the least mean absolute error score.
Interpreting Criminal Charge Prediction and Its Algorithmic Bias via Quantum-Inspired Complex Valued Networks

While predictive policing has become increasingly common in assisting with decisions in the criminal justice system, the use of these results is still controversial. Some software based on deep learning lacks accuracy (e.g., in F-1), and importantly many decision processes are not transparent, causing doubt about decision bias, such as perceived racial and age disparities. This paper addresses bias issues with post-hoc explanations to provide a trustable prediction of whether a person will receive future criminal charges given one's previous criminal records by learning temporal behavior patterns over twenty years. Bi-LSTM relieves the vanishing gradient problem, attentional mechanisms allow learning and interpretation of feature importance, and complex-valued networks inspired quantum physics to facilitate a certain level of transparency in modeling the decision process. Our approach shows a consistent and reliable prediction precision and recall on a real-life dataset. Our analysis of the importance of each input feature shows the critical causal impact on decision-making, suggesting that criminal histories are statistically significant factors, while identifiers, such as race and age, are not. Finally, our algorithm indicates that a suspect tends to rather than suddenly increase crime severity level over time gradually.
Predictions for local PNG bias in the galaxy power spectrum and bispectrum and the consequences for f NL constraints

We use hydrodynamical separate universe simulations with the IllustrisTNG model to predict the local primordial non-Gaussianity (PNG) bias parameters b ϕ and b ϕδ, which enter at leading order in the galaxy power spectrum and bispectrum. This is the first time that b ϕδ is measured from either gravity-only or galaxy formation simulations. For dark matter halos, the popular assumption of universality overpredicts the b ϕδ(b 1) relation in the range 1 ≲ b 1 ≲ 3 by up to Δ b ϕδ ∼ 3 (b 1 is the linear density bias). The adequacy of the universality relation is worse for the simulated galaxies, with the relations b ϕ(b 1) and b ϕδ(b 1) being generically redshift-dependent and very sensitive to how galaxies are selected (we test total, stellar and black hole mass, black hole mass accretion rate and color). The uncertainties on b ϕ and b ϕδ have a direct, often overlooked impact on the constraints of the local PNG parameter f NL, which we study and discuss. For a survey with V = 100 Gpc3/h3 at z=1, uncertainties Δ b ϕ ≲ 1 and Δ b ϕδ ≲ 5 around values close to the fiducial can yield relatively unbiased constraints on f NL using power spectrum and bispectrum data. We also show why priors on galaxy bias are useful even in analyses that fit for products f NL b ϕ and f NL b ϕδ. The strategies we discuss to deal with galaxy bias uncertainties can be straightforwardly implemented in existing f NL constraint analyses (we provide fits for some of the bias relations). Our results motivate more works with galaxy formation simulations to refine our understanding of b ϕ and b ϕδ towards improved constraints on f NL.
Heat dissipation in few-layer MoS2 and MoS2/hBN heterostructure

State-of-the-art fabrication and characterisation techniques have been employed to measure the thermal conductivity of suspended, single-crystalline MoS2 and MoS2/hBN heterostructures. Two-laser Raman scattering thermometry was used combined with real time measurements of the absorbed laser power. Measurements on MoS2 layers with thicknesses of 5 and 14 nm exhibit thermal conductivity in the range between 12 Wm−1 K−1 and 24 Wm−1 K−1. Additionally, after determining the thermal conductivity of the latter MoS2 sample, an hBN flake was transferred onto it and the effective thermal conductivity of the heterostructure was subsequently measured. Remarkably, despite that the thickness of the hBN layer was less than a hal of the thickness of the MoS2 layer, the heterostructure showed an almost eight-fold increase in the thermal conductivity, being able to dissipate more than ten times the laser power without any visible sign of damage. These results are consistent with a high thermal interface conductance G between MoS2 and hBN and an efficient in-plane heat spreading driven by hBN. Indeed, we estimate G ∼ 70 MW m−2 K−1 for hBN layer thermal conductivity of 450 Wm−1 K−1 which is significantly higher than previously reported values. Our work therefore demonstrates that the insertion of hBN layers in potential MoS2-based devices holds the promise for efficient thermal management.
Composition of choreography automata

Choreography automata are an automata-based model of choreographies, that we show to be a compositional one. Choreography automata represent global views of choreographies (and rely on the well-known model of communicating finite-state machines to model local behaviours). The projections of well-formed global views are live as well as lock- and deadlock-free. In the class of choreography automata we define an internal operation of {\em composition}, which connects two global views via roles acting as interfaces. We show that under mild conditions the composition of well-formed choreography automata is well-formed. The composition operation enables for a flexible modular mechanism at the design level.
Polynomially tractable cases in the popular roommates problem

The input of the popular roommates problem consists of a graph $G = (V, E)$ and for each vertex $v\in V$, strict preferences over the neighbors of $v$. Matching $M$ is more popular than $M'$ if the number of vertices preferring $M$ to $M'$ is larger than the number of vertices preferring $M'$ to $M$. A matching $M$ is called popular if there is no matching $M'$ that is more popular than $M$. Only recently Faenza et al. and Gupta et al. resolved the long-standing open question on the complexity of deciding whether a popular matching exists in a popular roommates instance and showed that the problem is NP-complete. In this paper we identify a class of instances that admit a polynomial-time algorithm for the problem. We also test these theoretical findings on randomly generated instances to determine the existence probability of a popular matching in them.
Thermodynamics of a continuous quantum heat engine: Interplay between population and coherence

We present a detailed thermodynamic analysis of a three-level quantum heat engine coupled continuously to hot and cold reservoirs. The system is driven by an oscillating external field and is described by the Markovian quantum master equation. We use the general form of the dissipator which is consistent with thermodynamics. We calculate the heat, power, and efficiency of the system as a heat engine and also examine the thermodynamic uncertainty relation. The efficiency of the system is strongly dependent on the structure of the dissipator and the correlations between different levels can be an obstacle to an ideal operation. In quantum systems, the heat flux is decomposed into the population and coherent parts. The coherent part is specific to quantum systems and, in contrast to the population part, the standard thermodynamic scenario cannot be applied even in the linear response regime. We discuss how the interplay between the population and coherent parts affects the performance of the heat engine.
Intersection Problems in Extremal Combinatorics: Theorems, Techniques and Questions Old and New

The study of intersection problems in Extremal Combinatorics dates back perhaps to 1938, when Paul Erdős, Chao Ko and Richard Rado proved the (first) ‘Erdős-Ko-Rado theorem’ on the maximum possible size of an intersecting family of k-element subsets of a finite set. Since then, a plethora of results of a similar flavour have been proved, for a range of different mathematical structures, using a wide variety of different methods. Structures studied in this context have included families of vector subspaces, families of graphs, subsets of finite groups with given group actions, and of course uniform hypergraphs with stronger or weaker intersection conditions imposed. The methods used have included purely combinatorial methods such as shifting/compressions, algebraic methods (including linear-algebraic, Fourier analytic and representation-theoretic), and more recently, analytic, probabilistic and regularity-type methods. As well as being natural problems in their own right, intersection problems have revealed connections with many other parts of Combinatorics and with Theoretical Computer Science (and indeed with many other parts of Mathematics), both through the results themselves, and the methods used. In this survey paper, we discuss both old and new results (and both old and new methods), in the field of intersection problems. Many interesting open problems remain; we will discuss several such. For expositional and pedagogical purposes, we also take this opportunity to give slightly streamlined versions of proofs (due to others) of several classical results in the area. This survey is intended to be useful to graduate students, as well as to more established researchers. It is an expanded version of a survey paper that will appear in the Proceedings of the 29th British Combinatorial Conference, University of Lancaster, 11th-15th July 2022.
Minimizing the Risk of Spreading Processes via Surveillance Schedules and Sparse Control

In this article, we propose an optimization framework that combines surveillance schedules and sparse control to bound the risk of spreading processes, such as epidemics and wildfires. Here, risk is the product of the probability of an undetected outbreak and the impact of that outbreak. The aim is to bound or minimize the risk by resource allocation and persistent monitoring schedules. The presented framework utilizes the properties of positive systems and exponential cone programming to provide scalable algorithms for combined surveillance and intervention problems. We demonstrate via epidemic and wildfire examples how the method can incorporate practically relevant parameters and scenarios such as a vaccination strategy for epidemics and the effect of vegetation and outbreak rate on a wildfire. Furthermore, we show how the method can be integrated with algorithms for robotic path planning to generate persistent surveillance motion plans.
Dephasing superchannels

We characterise a class of environmental noises that decrease coherent properties of quantum channels by introducing and analysing the properties of dephasing superchannels. These are defined as superchannels that affect only non-classical properties of a quantum channel $\mathcal{E}$, i.e., they leave invariant the transition probabilities induced by $\mathcal{E}$ in the distinguished basis. We prove that such superchannels $\Xi_C$ form a particular subclass of Schur-product supermaps that act on the Jamiolkowski state $J(\mathcal{E})$ of a channel $\mathcal{E}$ via a Schur product, $J'=J\circ C$. We also find physical realizations of general $\Xi_C$ through a pre- and post-processing employing dephasing channels with memory, and show that memory plays a non-trivial role for quantum systems of dimension $d>2$. Moreover, we prove that coherence generating power of a general quantum channel is a monotone under dephasing superchannels. Finally, we analyse the effect dephasing noise can have on a quantum channel $\mathcal{E}$ by investigating the number of distinguishable channels that $\mathcal{E}$ can be mapped to by a family of dephasing superchannels. More precisely, we upper bound this number in terms of hypothesis testing channel divergence between $\mathcal{E}$ and its fully dephased version, and also relate it to the robustness of coherence of $\mathcal{E}$.
Droplet epitaxy symmetric InAs/InP quantum dots for quantum emission in the third telecom window: morphology, optical and electronic properties

Abstract The rapidly developing quantum communication technology requires deterministic quantum emitters that can generate single photons and entangled photon pairs in the third telecom window, in order to be compatible with existing optical fiber networks and on-chip silicon photonic processors. InAs/InP quantum dots (QDs) are among the leading candidates for this purpose, due to their high emission efficiency in the required spectral range. However, fabricating versatile InAs/InP QD-based quantum emitters is challenging, especially as these QDs typically have asymmetric profiles in the growth plane, resulting in a substantial bright-exciton fine structure splitting (FSS). This hinders the generation of entangled photon pairs and thus, compromises the versatility of InAs/InP QDs. We overcome this by implementing droplet epitaxy (DE) synthesis of low surface density (2.8 × 108 cm−2) InAs x P1−x QDs with x = (80 ± 15)% on an (001)-oriented InP substrate. The resulting QDs are located in etched pits, have concave bases, and most importantly, have symmetric in-plane profiles. We provide an analytical model to explain the kinetics of pit formation and QD base shape modification. Our theoretical calculations of electronic states reveal the properties of neutral and charged excitons and biexcitons confined in such QDs, which agree with the optical investigations of individual QDs. The optical response of QDs' ensemble suggests that FSS may indeed be negligible, as reflected in the vanishing degree of linear polarization. However, single QD spectrum gathered from an etched mesa shows moderate FSS of (50 ± 5) µeV that we link to destructive changes made in the QD environment during the post-growth processing. Finally, we show that the studied DE QDs provide a close-to-ideal single-photon emission purity of (92.5 ± 7.5)% in the third telecom window.
The EXO-200 detector, part II: auxiliary systems

The EXO-200 experiment searched for neutrinoless double-beta decay of 136Xe with a single-phase liquid xenon detector. It used an active mass of 110 kg of 80.6%-enriched liquid xenon in an ultra-low background time projection chamber with ionization and scintillation detection and readout. This paper describes the design and performance of the various support systems necessary for detector operation, including cryogenics, xenon handling, and controls. Novel features of the system were driven by the need to protect the thin-walled detector chamber containing the liquid xenon, to achieve high chemical purity of the Xe, and to maintain thermal uniformity across the detector.
The Ameyalli-Rule: Logical Universality in a 2D Cellular Automaton

We present a new spontaneously emergent glider-gun in a 2D Cellular Automaton and build the logical gates NOT, AND and OR required for logical universality. The Ameyalli-rule is not based on survival/birth logic but depends on 102 isotropic neighborhood groups making an iso-rule, which can drive an interactive input-frequency histogram for visualising iso-group activity and dependent functions for filtering and mutation. Neutral inputs relative to logical gates are identified which provide an idealized striped-down form of the iso-rule.