Miracle-dz/UniFAR-FTUs huggingface dataset:
Color changes of blackberry as affected by freezing rate

Fast frozen blackberry partially change its surface colour from characteristic black to copper-redish colour. This change decreases the market value, especially when the product is intended for export. For these reasons classic tunnels for slow freezing are being more widely used nowadays. In order to clarify mentioned phenomena, a detailed monitoring of preparation process and freezing, as well as changes of frozen blackberry fruits in industrial conditions was carried out. Along with sensory evaluation of surface colour changes, chemical analysis of reference parameters of fresh and frozen fruits were performed. The results of sensory evaluation confirmed evident colour change of fruits frozen by slow (and fast) treatment; the chemical analysis confirmed unchanged total solids and anthocyanin content, as well as changes in pH value and vitamin C, in comparison to fresh blackberry fruits.
Proton NMR studies of a polybenzimidazole in solution

Molecular aggregation of poly(4,4′-diphenyl ether-5,5′-bibenzimidazole)(PBI) in solution has been studied by high resolution proton NMR. PBI and model compounds have been synthesized, purified, and characterized. Proton resonances in the NMR spectrum of PBI are assigned by comparison with the proton resonances of the model compounds. Spectra are studied by total line-shape analysis, assuming each absorption curve to be Lorentzian. For PBI in N,N-dimethylacetamide (DMAc), the resonance due to the proton of a hydroxyl group formed by proton exchange between the imino group of PBI and the carbonyl group of DMAc is observed. The activation energy for the proton exchange, obtained from Arrhenius plots of the temperature dependence of the chemical shifts of the hydroxyl proton and the imino proton, was found to increase in the order corresponding to dissociation energy of the NH···OC hydrogen bond. The chemical shifts in the NMR spectra of PBI-DMAc solutions on the addition of LiCl are strongly dependent on the polymer-salt ratio; and thereby the coordination position of LiCl to PBI is tentatively identified, assuming a pseudocontact LiCl-induced shift. The dependence of the chemical shifts of protons in PBI on the dielectric constant of the solvent is demonstrated by using polar solvents of varying dielectric constant, such as N-methylpyrrolidone, dimethylsulfoxide, and formic acid. The viscosity of the PBI-DMAc solutions is reported at various temperatures and concentrations of LiCl. The results from viscometry are explicable in terms of the NMR observations.
Water‐soluble organic compounds in biomass burning aerosols over Amazonia 2. Apportionment of the chemical composition and importance of the polyacidic fraction

[1] Chemical characterization was performed on carbonaceous aerosols from Rondonia in the Brazilian Amazon region as part of the European contribution to the Large-Scale Biosphere-Atmosphere Experiment in Amazonia (LBA-EUSTACH). The sampling period (October 1999) included the peak of the burning season as well as the dry-to-wet season transition. Characterization of the carbonaceous material was performed by using a thermal combustion method. This enabled determination of aerosol total carbon (TC), black carbon (BC), and organic carbon (OC). A significant fraction of the BC material (on average about 50%) seemed to be highly refractory organic material soluble in water. A more detailed analysis of the water-soluble organic carbon (WSOC) fraction of the TC was undertaken, involving measurements of WSOC content, high-performance liquid chromatography (HPLC) separation (with UV detection) of the water-soluble components, and characterization of individual components by gas chromatography/mass spectrometry (GC/MS). The WSOC fraction accounted for 45−75% of the OC. This high WSOC fraction suggests an aerosol derived mainly from smoldering combustion. Using GC/MS, many different compounds, containing hydroxy, carboxylate, and carbonyl groups, were detected. The fraction of the WSOC identified by GC/MS was about 10%. Three classes of compounds were separated by HPLC/UV: neutral compounds (N), monocarboxylic and dicarboxylic acids (MDA), and polycarboxylic acids (PA). The sum of these three groups accounted for about 70% of the WSOC, with MDA and PA being most abundant (about 50%). Good correlations (r2 between 0.84 and 0.99) of BCwater (BC after water extraction) and levoglucosan (both indicators of biomass combustion) with the water-soluble species (i.e., WSOC, N, MDA, and PA), and their increase in concentrations during the burning period provided strong evidence that biomass burning is a major source of the WSOC. Particularly interesting is that PA and therefore, probably, humic-like substances (due to their polyacidic nature) are generated in significant amounts during biomass burning. These substances, due to their water solubility and surface tension-lowering effects, may play an important role in determining the overall cloud condensation nuclei activity of biomass burning aerosols and, consequently, could be important in cloud processes and climate forcing.
Counting Problems on Quantum Graphs: Parameterized and Exact Complexity Classifications

Quantum graphs, as defined by Lovász in the late 60s, are formal linear combinations of simple graphs with finite support. They allow for the complexity analysis of the problem of computing finite linear combinations of homomorphism counts, the latter of which constitute the foundation of the structural hardness theory for parameterized counting problems: The framework of parameterized counting complexity was introduced by Flum and Grohe, and McCartin in 2002 and forms a hybrid between the classical field of computational counting as founded by Valiant in the late 70s and the paradigm of parameterized complexity theory due to Downey and Fellows which originated in the early 90s. The problem of computing homomorphism numbers of quantum graphs subsumes general motif counting problems and the complexity theoretic implications have only turned out recently in a breakthrough regarding the parameterized subgraph counting problem by Curticapean, Dell and Marx in 2017. We study the problems of counting partially injective and edge-injective homomorphisms, counting induced subgraphs, as well as counting answers to existential first-order queries. We establish novel combinatorial, algebraic and even topological properties of quantum graphs that allow us to provide exhaustive parameterized and exact complexity classifications, including necessary, sufficient and mostly explicit tractability criteria, for all of the previous problems.
The Effect of Some Nitriles as Electrolyte Additives in Li-Ion Batteries

The effect of the nitrile electrolyte additives, succinonitrile (SN), adiponitrile (AN) and pimelonitrile (PN) was studied using High Precision Charger at Dalhousie University, automated cell storage and AC impedance. Li[Ni1/3Mn1/3Co1/3]O2/graphite (NMC111/graphite) cells containing the nitrile electrolyte additives showed no significant effect on the cycling between 2.8 V and 4.2 V and storage at 4.2 V at 60. ± 0.1◦C. However, when the high cutoff voltage increased to 4.5 V in Li[Ni0.4Mn0. 4Co0.2]O2/graphite (NMC442/graphite) cells, the addition of 2 wt% SN and 2 wt% vinylene carbonate (VC) reduced the reversible capacity loss and gas generation during storage experiments at 60. ± 0.1◦C compared to 2 wt% VC alone. The addition of 2 wt% SN also caused a large impedance growth during the 4.5 V storage period, but that could be suppressed by the addition of 1 wt% ethylene sulfite (ES) + 1 wt% tris(trimethlysilyl) phosphite (TTSPi) or 2 wt% trimethylene sulfate (TMS) + 2 wt% TTSPi. This work shows that SN leads to meaningful improvements in storage properties at voltages higher than 4.4 V. This also suggests that SN can enhance the long-term cycle life of the high voltage NMC/graphite cells by suppressing electrolyte oxidation at the positive electrode. © The Author(s) 2015. Published by ECS. This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 License (CC BY, http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse of the work in any medium, provided the original work is properly cited. [DOI: 10.1149/2.0651503jes] All rights reserved.
Synthesis of Stably Water-Soluble Gold Nanoparticles Protected by Porphyrin-Thiol Derivative

Meso-tetraphenylporphyrin derivative having four thioacetyl groups was synthesized using Lindsay's method. Monodisperse small gold (Au) nanoparticles passivated by the synthesized ligand were prepared by a liquid phase synthesis. The nanoparticles were extremely stable and can be homogeneously dispersed in basic aqueous solution. [DOI: 10.1380/ejssnt.2005. 30]
The Acidity of Malondialdehyde and the Stability of its Complexes with Nickel(II) and Copper(II)

The acidity of malondialdehyde as well as the stability of its 1 :1-complexes with NiII and CuII have been determined (Table). In comparison with the enolates of aliphatic β-dike-tones the anion C3H3O2− has not only a strikingly reduced basicity, but is also a grossly inferior ligand for metal cations.
LATTICE DESIGN OF A RAPID CYCLING MEDICAL SYNCHROTRON FOR CARBON/PROTON THERAPY*

We present a design of the ion Rapid Cycling Medical Synchrotron (iRCMS) for carbon/proton cancer therapy facility. The facility design, produced at Brookhaven National Laboratory (BNL) at the Collider Accelerator Department (CAD), with BEST Medical International, Inc., will be able to treat the cancer patients with carbon, lighter ions and protons. The low energy injector system accelerates ions and protons to the kinetic energy of 8 MeV/u. It consists of a laser driven ion source (for either fully stripped carbon ions or protons), matching solenoid, Radio-Frequency Quadrupole (RFQ) and linac. The 8 MeV beam is injected into a fast cycling synchrotron (iRCMA). The lattice design is a racetrack, with zero dispersion with two parallel straight sections. There are 24 combined function magnets in the two arcs with a bending radius of ~5 meters and maximum magnetic field of Bmax~1.3 T. The acceleration is performed with a frequency of 30 Hz up to required energy for the treatment with a maximum depth of 27 cm with the spot scanning technique. The maximum energy for carbon ions is 400 MeV/u. Ions are extracted in a single turn and fed to different beam lines for patient treatment.
Crystallization and Preliminary Crystallographic Study of Bacterial α-Amylases

: Crystallization of bacterial alpha-amylases has been achieved by the hanging-drop vapor diffusion method. The crystals of Bacillus licheniformis and B. licheniformis 584 amylases are isomorphous to each other. The crystals of B. licheniformis amylase belong to the tetragonal system, space group P4(2)2(1)2 with cell dimensions of a = 119.3 and c = 85.4 A. The asymmetric unit contains one molecule of amylase, with a volume per molecular mass, Vm, of 2.75 A3/Da. The crystals of B. licheniformis and B. licheniformis 584 amylases diffract beyond 2.5 A resolution and are suitable for X-ray diffraction analysis. The crystals of B. amyloliquefaciens amylase are orthorhombic, and have space group C222(1), with cell dimensions of a = 154, b = 298, and c = 90 A. The asymmetric unit contains three to five molecules. In the crystallization of B. licheniformis and B. licheniformis 584 amylases, the addition of EDTA was indispensable to obtain large single crystals, while it had an adverse effect on the crystallization of B. amyloliquefaciens amylase, producing a large amount of small crystals.
Rice grain discoloration effect on physical properties and head rice yield in three rice cultivars

Rice grain discoloration is a complex disease which develops by a range of micro-organisms. Effects of fungal agents such as Alternaria sp. , Curvularia sp. , Cladosporium spp. , Bipolaris spp. on the physical properties and head rice yield (HRY) were examined during the cropping season 2013-2014. The experiment was conducted in factorial design with two factors; three rice cultivars of Hashemi, Gohar and Khazar, and two grain disease severity levels (high and low) based on a randomised complete design with four replications. In order to attain high disease severity level, water was sprayed at the heading stage of the crop. The grains axial dimensions, geometric and arithmetic mean diameters, sphericity, surface area, volume, slenderness ratio, 1000-grain weight, as well as HRY were measured at a moisture content of 9% (wet basis). The average of length, width, thickness, surface area, volume and 1000-grain weight at the high disease severity were found to be 7.64, 2.04, 1.67 mm, 20.375 mm2, 13.796 mm3 and 2.
Reinforcement Learning Beyond Expectation

The inputs and preferences of human users are important considerations in situations where these users interact with autonomous cyber or cyber-physical systems. In these scenarios, one is often interested in aligning behaviors of the system with the preferences of one or more human users. Cumulative prospect theory (CPT) is a paradigm that has been empirically shown to model a tendency of humans to view gains and losses differently. In this paper, we consider a setting where an autonomous agent has to learn behaviors in an unknown environment. In traditional reinforcement learning, these behaviors are learned through repeated interactions with the environment by optimizing an expected utility. In order to endow the agent with the ability to closely mimic the behavior of human users, we optimize a CPT-based cost. We introduce the notion of the CPT-value of an action taken in a state, and establish the convergence of an iterative dynamic programming-based approach to estimate this quantity. We develop two algorithms to enable agents to learn policies to optimize the CPT-value, and evaluate these algorithms in environments where a target state has to be reached while avoiding obstacles. We demonstrate that behaviors of the agent learned using these algorithms are better aligned with that of a human user who might be placed in the same environment, and is significantly improved over a baseline that optimizes an expected utility.
The potential of soya oil and egg-yolk as sources of fat in beef sausages

A study was conducted to assess the effects of less-saturated fats [Egg yolk (EY) and soya oil (SO)] on characteristics and acceptability of beef sausages. Each of the fats was incorporated in beef sausages at three levels (5%, 10% and 15%), and compared with products formulated with only lean beef (Control). Fresh boneless beef was obtained and thoroughly trimmed of excess visible fats, and then minced. The minced beef was divided into portions of 4kg, and were randomly assigned to the fats. The fats were then mixed with the minced meats and allowed 15 minutes for the meat to absorb the fat. The products were formulated and evaluated for their sensory characteristics using BSI (1993) method, and storability by laboratory analyses. Addition of both fats up to 15% improved (P 0.05) on flavour liking and reduced acceptability of the EY3 products. Increasing levels of SO improved (P 0.05) effect on product storability. Comparatively, inclusion of SO up to 15% in beef sausages was preferred (P<0.001) to that of EY. SO could be used up to 15% in beef sausages for improved sensory characteristics and acceptability.
Online Learning Probabilistic Event Calculus Theories in Answer Set Programming

Complex Event Recognition (CER) systems detect event occurrences in streaming time-stamped input using predefined event patterns. Logic-based approaches are of special interest in CER, since, via Statistical Relational AI, they combine uncertainty-resilient reasoning with time and change, with machine learning, thus alleviating the cost of manual event pattern authoring. We present a system based on Answer Set Programming (ASP), capable of probabilistic reasoning with complex event patterns in the form of weighted rules in the Event Calculus, whose structure and weights are learnt online. We compare our ASP-based implementation with a Markov Logic-based one and with a number of state-of-the-art batch learning algorithms on CER data sets for activity recognition, maritime surveillance and fleet management. Our results demonstrate the superiority of our novel approach, both in terms of efficiency and predictive performance. This paper is under consideration for publication in Theory and Practice of Logic Programming (TPLP).
NeuralRecon: Real-Time Coherent 3D Reconstruction from Monocular Video

We present a novel framework named NeuralRecon for real-time 3D scene reconstruction from a monocular video. Unlike previous methods that estimate single-view depth maps separately on each key-frame and fuse them later, we propose to directly reconstruct local surfaces represented as sparse TSDF volumes for each video fragment sequentially by a neural network. A learning-based TSDF fusion module based on gated recurrent units is used to guide the network to fuse features from previous fragments. This de-sign allows the network to capture local smoothness prior and global shape prior of 3D surfaces when sequentially reconstructing the surfaces, resulting in accurate, coherent, and real-time surface reconstruction. The experiments on ScanNet and 7-Scenes datasets show that our system outperforms state-of-the-art methods in terms of both ac-curacy and speed. To the best of our knowledge, this is the first learning-based system that is able to reconstruct dense coherent 3D geometry in real-time. Code is available at the project page: https://zju3dv.github. io/neuralrecon/.
The Exact Computation of A Real Hyperbolic Structure on the Complement of the Borromean Rings

In this paper we present a classical construction of the Hyperbolic structure of the complement of a link in the sense of Thurston for the particular case of the Borromean rings link. As this is nothing new, the aim of this paper is to complete the literature about this topic in the particular case of the Borromean rings complement.
Using Graph Theory to Derive Inequalities for the Bell Numbers

The Bell numbers count the number of different ways to partition a set of $n$ elements while the graphical Bell numbers count the number of non-equivalent partitions of the vertex set of a graph into stable sets. This relation between graph theory and integer sequences has motivated us to study properties on the average number of colors in the non-equivalent colorings of a graph to discover new non trivial inequalities for the Bell numbers. Example are given to illustrate our approach.
Sedimentation and Diffusion in Suspensions of Sterically Stabilized Colloidal Platelets

Ultracentrifuge and light scattering measurements are performed to study static and dynamic properties for a suspension of well-defined platelike colloids to first order in concentration. Both the form factor and the diffusion coefficient at infinite dilution match theoretical expressions if polydispersity is incorporated. This yields values for the polydispersity corresponding with values obtained from electron microscopy. Application of the generalized Stokes-Einstein relation to the measured concentration dependence of sedimentation and collective diffusion coefficients yields a second virial coefficient B2 for the platelets which is slightly smaller than the theoretical value for hard disks.
Passive Inter-Photon Imaging

Digital camera pixels measure image intensities by converting incident light energy into an analog electrical current, and then digitizing it into a fixed-width binary representation. This direct measurement method, while conceptually simple, suffers from limited dynamic range and poor performance under extreme illumination — electronic noise dominates under low illumination, and pixel full-well capacity results in saturation under bright illumination. We propose a novel intensity cue based on measuring inter-photon timing, defined as the time delay between detection of successive photons. Based on the statistics of inter-photon times measured by a time-resolved single-photon sensor, we develop theory and algorithms for a scene brightness estimator which works over extreme dynamic range; we experimentally demonstrate imaging scenes with a dynamic range of over ten million to one. The proposed techniques, aided by the emergence of single-photon sensors such as single-photon avalanche diodes (SPADs) with picosecond timing resolution, will have implications for a wide range of imaging applications: robotics, consumer photography, astronomy, microscopy and biomedical imaging.
Limit Theorems for Default Contagion and Systemic Risk

We consider a general tractable model for default contagion and systemic risk in a heterogeneous financial network subjected to an exogenous macroeconomic shock. We show that under certain regularity assumptions, the default cascade model can be transformed into a death process problem represented by a balls-and-bins model. We state various limit theorems regarding the final size of default cascades. Under appropriate assumptions on the degree and threshold distributions, we prove that the final sizes of default cascades have asymptotically Gaussian fluctuations. We next state limit theorems for different system-wide wealth aggregation functions, which enable us to provide systemic risk measures in relation to the structure and heterogeneity of the financial network. Lastly, we demonstrate how these results can be utilized by a social planner to optimally target interventions during a financial crisis given a budget constraint and under partial information of the financial network.
Gaussian Process Convolutional Dictionary Learning

Convolutional dictionary learning (CDL), the problem of estimating shift-invariant templates from data, is typically conducted in the absence of a prior/structure on the templates. In data-scarce or low signal-to-noise ratio (SNR) regimes, learned templates overfit the data and lack smoothness, which can affect the predictive performance of downstream tasks. To address this limitation, we propose GPCDL, a convolutional dictionary learning framework that enforces priors on templates using Gaussian Processes (GPs). With the focus on smoothness, we show theoretically that imposing a GP prior is equivalent to Wiener filtering the learned templates, thereby suppressing high-frequency components and promoting smoothness. We show that the algorithm is a simple extension of the classical iteratively reweighted least squares algorithm, independent of the choice of GP kernels. This property allows one to experiment flexibly with different smoothness assumptions. Through simulation, we show that GPCDL learns smooth dictionaries with better accuracy than the unregularized alternative across a range of SNRs. Through an application to neural spiking data, we show that GPCDL learns a more accurate and visually-interpretable smooth dictionary, leading to superior predictive performance compared to non-regularized CDL, as well as parametric alternatives.