Miracle-dz/UniFAR-FTUs huggingface dataset:
Cholesterol content and fatty acid composition in Longissimus dorsi muscle of purebred and crossbred pigs

The fat and cholesterol contents, and fatty acid composition in Longissimus dorsi (LD) muscle differ between purebred and crossbred pigs was determined. Duroc purebred (D, n=10), two-way crossbred (Largewhite x Landrace; LWLR, n = 10), and three-way crossbred [Duroc x (Largewhite x Landrace); DLWLR, n=10] pigs were used in this study. Each group consisted of 2 genders (5 barrows and 5 gilts). All pigs were reared under the same housing and feeding conditions for 6 months until slaughtered at 110 ± 5 kg live weight, following which, LD muscle was collected from the left side of each carcass for subsequent analyses. The results showed that D had higher (P<0.01) fat percentage than LWLR and DLWLR. Neither pig group nor gender affected cholesterol content (P > 0.05). For fatty acid composition, D presented highest concentrations of C14:0, C16:0, C16:1, C18:0, C18:1n9c, C18:2n6c, C18:2n6c, C20:1, SFA (saturated fatty acid), MUFA (monounsaturated fatty acid), and PUFA (polyunsaturated fatty acid) than those recorded in samples obtained from DLWLR and LWLR (P<0.01). DLWLR had significantly higher concentrations of C16:1, C18:1n9c and MUFA (P<0.05), with a tendency of higher concentration of C16:0, C18:0, C18:2n6c, C20:1, SFA, and PUFA than LWLR (P<0.1). The ratio of unsaturated to saturated fatty acid (P/S) was more favorable in LWLR compared with those of D and DLWLR (P<0.01). There was a significant group x gender interaction for the concentration of C23:0 (P<0.05). In Duroc purebred pigs, higher amount of C23:0 was observed in gilts than that in the barrow. However, barrow of DLWLR showed higher C23:0 than the amount recorded in the gilts. Fat percentage positively correlated with the concentrations of C14:0, C16:0, C16:1, C18:0, C18:1n9c, C18:2n6c, C20:1, C23:0, SFA, MUFA, PUFA, and P/S (P <0.01) but negatively correlated with C23:0 (P<0.05).
Word recognition and sound merg centering diphthongs i

Sociolinguistic and phonetic studies of New Zealand English have indicated an ongoing merger of NEAR and SQUARE vowels on the closer form. This paper explores the consequences of this change for spoken word recognition, using an auditory lexical decision experiment with semantic priming. Subjects were young NZE speakers, a group who predominantly merge the vowels in their own speech. These subjects responded to NEAR forms as though they were homophones giving access to the meanings of both NEAR and SQUARE words. Their responses to SQUARE forms, still encountered amongst more conservative speakers, appear to reflect access only to SQUARE meanings. This asymmetry in the pattern of lexical decision responses is compatible with the changein-progress towards the N E A R vowel, and provides psycholinguistic support for earlier studies of the merger.
Effect of Paraffin Oil and Polyethylene Packaging on " Manfaluty " Pomegranate Fruits During Storage

This investigation aimed to study the effect of paraffin oil and polyethylene bags used separately or in combination on storage quality of "Manfaluty" pomegranate fruits during storage throughout 2012 and 2013 seasons. Fruits were stored at 5 °C with 85-90% RH and room temperature at 22 ± 5 C°. Generally, the storage period in the present study was 45 days at room temperature and 90 days at cold storage. All treatments, namely, coating with paraffin oil, packing with polyethylene bags and combination of both coating and packing, significantly decreased weight loss % and decay % of stored Manfalouty fruits compared with untreated fruits (control) under both storage conditions during 2012 and 2013 seasons. Concerning the chemical properties, total soluble solids (T.S.S) % and acidity gradually increased with the progress of storage period, under all storage conditions during both tested seasons. At the end of storage period the treated fruits with polyethylene bags + paraffin oil contained the lowest values of T.S. S and acidity % while the untreated fruits (control) had the highest percentage of both properties. Juice volume decreased with the extending of storage period and reached the minimum values after 45 days at room temperature and 90 days under cold storage during both investigated seasons. The highest juice content was obtained by polyethylene packing + paraffin oil coating, while the lowest content was found in untreated fruits (control).
Oxadiazon Dissipation in Water and Topsoil in Flooded and Dry-Seeded Rice Fields

The aim of the present study was to assess the behavior of oxadiazon in topsoil and waters of paddy fields cultivated according to the two rice seeding systems: conventional water seeding (CON) and dry seeding (DRY). The study was carried out from 2012 to 2013 in Vercelli, north-west of Italy, the most important rice growing area of Europe. Variations in oxadiazon concentrations were studied over time in topsoil, paddy water, inlet water and outlet water. Over the two years, the estimated oxadiazon topsoil half-life was 3.27 days, without significant differences between systems and years. Oxadiazon dissipation in the paddy water was rapid during the first week, but less pronounced late in the season. Residues of oxadiazon were frequently detected in inlet waters. Transfer of oxadiazon residues in outlet waters occurred over all the growing seasons, with important peaks in the first weeks after the treatment. A possible way to reduce the transfer of oxadiazon to water bodies could be increasing the residence time of the herbicide in the paddy fields during the first 7–10 days after spraying, avoiding the release of water from them.
A Distriubuted Implementation for Reinforcement Learning

In this CME323 project, we implement a distributed algorithm for model-free reinforcement learning. Each processor independently makes decision, interacts with the environment, and update one Q-function that is shared by all processors. After updating, processors again make decisions according to this shared Q-function. The distributed structure can be considered multi-agent learning, since each processor is an independent learner but sharing some information with others. Empirical test on one benchmark problem shows that this distributed implementation indeed accelerates the convergence to optimal policy. 1. Reinforcement Learning In this section, we review the basic concepts of reinforcement learning and the model-free method that will be paralleled in later section. 1.
A SELF-CONFIGURING GENETIC ALGORITHM FOR THE AUTOMATED DESIGN OF SEMI-SUPERVISED ARTIFICIAL NEURAL NETWORKS 1

Semi-supervised techniques based on using both labelled and unlabelled examples can be an efficient tool for solving real-world problems with large amounts of unlabelled data. The usual method of solving such problems demands the long work of human experts in its initial stages to prepare the learning data, a process which includes such complex tasks as the labelling of large numbers of examples. In this case, it is not necessary to label all of these many examples, but just a few of them. In this paper, a new self-configuring genetic algorithm (SelfCGA) for the automated design of semi-supervised artificial neural networks (ANN) is presented. Firstly, the performance and behaviour of the proposed semi-supervised ANNs were studied under common experimental settings and their workability was established. Then their efficiency was evaluated on a real-world problem.
Modified Strichartz Estimate of the Periodic Fourth Order NLS

We prove modified Strichartz estimates on the one-dimensional torus, that are adapted to a fourth-order dispersion relation, and use them to show global well-posedness of nonlinear fourth-order Schrodinger equations. This extends the (low regularity) existence theory of the adiabatic transition of the Quantum Zakharov system to NLS. We show that the solutions behave continuously with respect to the quantum parameter in every compact time interval. Globally in time, however, we also show that such continuous dependence is generally not uniform.
Context Petri Nets : Definition , Analysis , Debugging and Execution of Context-aware Systems

Context-Oriented Programming (COP) [8] is a programming paradigm that allows dynamic software adaptations to the surrounding execution environment of a system. Over the past few years effort has been set into the definition of programming languages and their abstractions that enable the definition of such dynamic adaptations. All existing COP languages, through different abstractions and techniques, provide one way or another to reify the situations on the surrounding execution environment of the system, being the must prominent contexts and layers. This abstractions are usually first-class entities of the system to which partial behavior definitions can be associated. We regard contexts (or layers) together with such definitions by the more general term of adaptation. Adaptations are made dynamically available or unavailable in the system by means of the activation or deactivation constructs respectively. Whenever an adaptation is activated (in the dynamic scope of the activation construct or globally) the behavior defined for the adaptation can be used by the system. If an adaptation is deactivated, its associated behavior will no longer be visible in the system. We have identified some issues that still need to be tackled by the COP community, and in our vision is holding it back from moving forward. First, the dynamicity with which adaptations are made available, based on the execution environment of the system, can rise inconsistencies in the system’s behavior. Different approaches have been proposed to mitigate behavior inconsistencies by defining dependency relations between adaptations [4, 6, 7]. Such approaches allow to define constraints on interaction between adaptations when they are activated. However, none, or only manual verification of the interaction between adaptations is provided, which means that the definition of the interaction between adaptations needs to be foreseen before hand. Bearing in mind that COP systems may define multiple adaptations and that their activation occurs dynamically and unannounced, foreseeing all possible interactions between adaptations might be to cumbersome or even un-
Quantum oscillations in acoustic phonons in Weyl semimetals

We theoretically study the modification of the energy spectrum of long-wavelength acoustic phonons due to the electron-phonon interaction in a three-dimensional topological Weyl semimetal under the influence of quantizing magnetic fields. We find that the dispersion and attenuation of phonons show striking oscillatory behaviors when varying the magnetic field at low temperatures. These oscillations are distinct when the Fermi energy is in different energy regimes. Moreover, the van Hove singularity of the Weyl spectrum can manifest as a transition between different oscillation patterns when increasing the magnetic field. These phonon behaviors could provide testable fingerprints of the Fermi-surface morphology and relativistic feature of the Weyl semimetal.
A Novel Adaptive Controller for Robot Manipulators Based on Active Inference

More adaptive controllers for robot manipulators are needed, which can deal with large model uncertainties. This letter presents a novel active inference controller (AIC) as an adaptive control scheme for industrial robots. This scheme is easily scalable to high degrees-of-freedom, and it maintains high performance even in the presence of large unmodeled dynamics. The proposed method is based on active inference, a promising neuroscientific theory of the brain, which describes a biologically plausible algorithm for perception and action. In this work, we formulate active inference from a control perspective, deriving a model-free control law which is less sensitive to unmodeled dynamics. The performance and the adaptive properties of the algorithm are compared to a state-of-the-art model reference adaptive controller (MRAC) in an experimental setup with a real 7-DOF robot arm. The results showed that the AIC outperformed the MRAC in terms of adaptability, providing a more general control law. This confirmed the relevance of active inference for robot control.
Laparoscopy Surgery CO2 Removal via Generative Adversary Network and Dark Channel Prior

Laparoscopic surgery uses a thin tube with a camera called a laparoscope, which is inserted into the abdomen through a small incision in the skin during surgery. This allows to a surgeon to see inside of the body without causing significant injury to the patient. These characteristics make laparoscopy a widely used technique. In laparoscopic surgery, image quality can be severely degraded by surgical smoke caused by the use of tissue dissection tools which reduces the visibility of the observed organs and tissues. This lack of visibility increases the possibility of errors and surgery time with the consequences that this may have on the patient's health. In this paper, we introduce a novel hybrid approach for computational smoke removal which is based on the combination of a widely dehazing method used: the dark channel prior (DCP) and a pixel-to-pixel neural network approach: Generative Adversary Network (GAN). The experimental results have proven that the proposed method achieves a better performance than the individual results of the DCP and GAN in terms of restoration quality, obtaining a PSNR value of 25 and SSIM index of 0.88 over a test set of synthetic images.
The Differentiable Cross-Entropy Method

We study the cross-entropy method (CEM) for the non-convex optimization of a continuous and parameterized objective function and introduce a differentiable variant that enables us to differentiate the output of CEM with respect to the objective function's parameters. In the machine learning setting this brings CEM inside of the end-to-end learning pipeline where this has otherwise been impossible. We show applications in a synthetic energy-based structured prediction task and in non-convex continuous control. In the control setting we show how to embed optimal action sequences into a lower-dimensional space. DCEM enables us to fine-tune CEM-based controllers with policy optimization.
On the Approximation Ratio of the k-Opt and Lin-Kernighan Algorithm for Metric TSP

The $k$-Opt and Lin-Kernighan algorithm are two of the most important local search approaches for the metric TSP. Both start with an arbitrary tour and make local improvements in each step to get a shorter tour. We show that, for any fixed $k\geq 3$, the approximation ratio of the $k$-Opt algorithm is $O(\sqrt[k]{n})$. Assuming the Erdős girth conjecture, we prove a matching lower bound of $\Omega(\sqrt[k]{n})$. Unconditionally, we obtain matching bounds for $k=3,4,6$ and a lower bound of $\Omega(n^{\frac{2}{3k-3}})$. Our most general bounds depend on the values of a function from extremal graph theory and are sharp up to a logarithmic factor unconditionally. Moreover, all the upper bounds also apply to a parameterized version of the Lin-Kernighan algorithm with appropriate parameter.
Realism Index: Interpolation in Generative Models With Arbitrary Prior

In order to perform plausible interpolations in the latent space of a generative model, we need a measure that credibly reflects if a point in an interpolation is close to the data manifold being modelled, i.e. if it is convincing. In this paper, we introduce a realism index of a point, which can be constructed from an arbitrary prior density, or based on FID score approach in case a prior is not available. We propose a numerically efficient algorithm that directly maximises the realism index of an interpolation which, as we theoretically prove, leads to a search of a geodesic with respect to the corresponding Riemann structure. We show that we obtain better interpolations then the classical linear ones, in particular when either the prior density is not convex shaped, or when the soap bubble effect appears.
The Impact of Bestseller Rank on Demand : Evidence From a Software Market

This paper uses daily data on the sales ranking of the top 100 applications sold through Apple’s App Store to provide evidence of the impact of today’s bestseller rank information on tomorrow’s demand. The estimates indicate that the willingness to pay of consumers is about $4.50 greater for a top ranked product than for the same product when it is unranked. The results also indicate that the effects of bestseller status on demand decline steeply with rank at the top ranks, but remain economically significant for apps in the first half of the bestseller list.
Joint Optimization of Execution Latency and Energy Consumption for Mobile Edge Computing with Data Compression and Task Allocation

This paper studies the mobile edge offloading scenario consisting of one mobile device (MD) with multiple independent tasks and various remote edge devices. In order to save energy, the user’s device can offload the tasks to available access points for edge computing. Data compression is exploited to reduce offloaded data size prior to wireless transmission to minimize the execution latency. The problem of jointly optimizing the task allocation decision and the data compression ratio to minimize the total tasks’ execution latency and the MD’s energy consumption concurrently is proposed. We show that the design problem is a non-convex optimization one but it can be recasted into a convex one through a semidefinite relaxation (SDR) based approach. Numerical simulations demonstrate the outperformance of the proposed scheme compared to the benchmark one.
Edge Expansion and Spectral Gap of Nonnegative Matrices

The classic graphical Cheeger inequalities state that if $M$ is an $n\times n$ symmetric doubly stochastic matrix, then \[ \frac{1-\lambda_{2}(M)}{2}\leq\phi(M)\leq\sqrt{2\cdot(1-\lambda_{2}(M))} \] where $\phi(M)=\min_{S\subseteq[n],|S|\leq n/2}\left(\frac{1}{|S|}\sum_{i\in S,j\not\in S}M_{i,j}\right)$ is the edge expansion of $M$, and $\lambda_{2}(M)$ is the second largest eigenvalue of $M$. We study the relationship between $\phi(A)$ and the spectral gap $1-\text{Re}\lambda_{2}(A)$ for any doubly stochastic matrix $A$ (not necessarily symmetric), where $\lambda_{2}(A)$ is a nontrivial eigenvalue of $A$ with maximum real part. Fiedler showed that the upper bound on $\phi(A)$ is unaffected, i.e., $\phi(A)\leq\sqrt{2\cdot(1-\text{Re}\lambda_{2}(A))}$. With regards to the lower bound on $\phi(A)$, there are known constructions with \[ \phi(A)\in\Theta\left(\frac{1-\text{Re}\lambda_{2}(A)}{\log n}\right), \] indicating that at least a mild dependence on $n$ is necessary to lower bound $\phi(A)$. In our first result, we provide an exponentially better construction of $n\times n$ doubly stochastic matrices $A_{n}$, for which \[\phi(A_{n})\leq\frac{1-\text{Re}\lambda_{2}(A_{n})}{\sqrt{n}}. \] In fact, all nontrivial eigenvalues of our matrices are $0$, even though the matrices are highly nonexpanding. We further show that this bound is in the correct range (up to the exponent of $n$), by showing that for any doubly stochastic matrix $A$, \[\phi(A)\geq\frac{1-\text{Re}\lambda_{2}(A)}{35\cdot n}. \] Our second result extends these bounds to general nonnegative matrices $R$, obtaining a two-sided quantitative refinement of the Perron-Frobenius theorem in which the edge expansion $\phi(R)$ (appropriately defined), a quantitative measure of the irreducibility of $R$, controls the gap between the Perron-Frobenius eigenvalue and the next-largest real part of any eigenvalue.
Creating locally interacting Hamiltonians in the synthetic frequency dimension for photons

The recent emerging field of synthetic dimension in photonics offers a variety of opportunities for manipulating different internal degrees of freedom of photons such as the spectrum of light. While nonlinear optical effects can be incorporated into these photonic systems with synthetic dimensions, these nonlinear effects typically result in long-range interactions along the frequency axis. Thus it has been difficult to use of the synthetic dimension concept to study a large class of Hamiltonians that involves local interactions. Here we show that a Hamiltonian that is locally interacting along the synthetic dimension can be achieved in a dynamically-modulated ring resonator incorporating $\chi^{(3)}$ nonlinearity, provided that the group velocity dispersion of the waveguide forming the ring is specifically designed. As a demonstration we numerically implement a Bose-Hubbard model, and explore photon blockade effect, in the synthetic frequency space. Our work opens new possiblities for studying fundamental many-body physics in the synthetic space in photonics, with potential applications in optical quantum communication and quantum computation.
University of Birmingham A degree sequence version of the Kühn-Osthus tiling theorem

A fundamental result of K¨uhn and Osthus [The minimum degree threshold for perfect graph packings, Combinatorica, 2009] determines up to an additive constant the minimum degree threshold that forces a graph to contain a perfect H -tiling. We prove a degree sequence version of this result which allows for a signiﬁcant number of vertices to have lower degree.
TORM: Collision-Free Trajectory Optimization of Redundant Manipulator given an End-Effector Path

A redundant manipulator has multiple inverse kinematics solutions per an end-effector pose. Accordingly, there can be many trajectories for joints that follow a given end-effector path in a Cartesian space. In this paper, we present a trajectory optimization of a redundant manipulator (TORM) to synthesize a trajectory that follows a given end-effector path accurately, while achieving the smoothness and collision-free manipulation. Given these desirable properties, our method optimizes a trajectory using two-stage gradient descent to reduce potential competition between different properties during the update. To further improve the performance and avoid falling into local minima, we apply the quantum annealing that iteratively randomizes various configurations of the trajectory, followed by updating the trajectory. We first show benefits of our method with environments containing external obstacles. We then compare ours with the state-of-the-art methods in their favorable setting, environments without having obstacles. Our method robustly minimizes the pose error in a progressive manner while satisfying various desirable properties.