Miracle-dz/UniFAR-FTUs huggingface dataset:
1 Neighborhood Effects on Food Consumption

Food consumption behavior is likely a result of environmental stimuli, access, and personal preferences, making policy aimed at increasing the nutritional content of food consumption challenging. We examine the dual role of the social and physical neighborhood environment as they relate to the eating behaviors of residents of a low-income minority urban neighborhood. We find that both proximity to different types of food sources (a characteristic of the physical neighborhood environment) and dietary intake of neighbors (a characteristic of the neighborhood’s social environment) are related to dietary intake. The relationships are most robust for fruits and vegetables consumption. Proximity to fast food sources is related to less fruits and vegetables consumption while the opposite is found for individuals residing closer to fresh food sources. Additionally, individuals whose neighbors report increased fruits and vegetables intake also report higher fruits and vegetables consumption, while controlling for proximity to food sources. Instrumental variable and quasi-experimental robustness checks suggest that correlation in neighbors’ fruits and vegetables consumption is likely due to social interactions among neighboring residents. The results elucidate important inter-relationships between access and social norms that influence dietary behavior.
Title : The effect of phonotactics on alternation learning

This study investigates whether alternation learning is facilitated by a matching phonotactic generalization. In a series of artificial grammar learning experiments, English learners were trained on artificial languages evincing categorical vowel harmony alternations across morpheme boundaries. These languages differed in the degree of harmony within stems (disharmonic, semiharmonic and harmonic), and thus the degree of phonotactic support for the alternation. Results indicate that alternation learning was best when supported by matching stem phonotactics (harmonic language; Experiment 1). Learners, however, were reluctant to extend a learnt phonotactic constraint to novel unseen alternations (Experiments 2 and 3). Taken together, the results are consistent with the hypothesis that alternation learning is facilitated by a matching static phonotactic generalization, but that learners are conservative in positing alternations in the absence of overt evidence for them. * *Acknowledgements: This work has benefited from discussion and feedback from Megha Sundara, Kie Zuraw, Bruce Hayes, Robert Daland, Sharon Peperkamp, Karen Jesney, Stephanie Shih, Sharon Inkelas, Ricardo Bermúdez-Otero, Jamie White, and the audiences at the University of Melbourne, UCLA, U. Hawai’i, NYU, QMUL, the University of Manchester, Keio-ICU Colloquium and LSA 2017 in Austin. I would also like to thank Eleanor Glewwe for recording stimuli, Audrey Kim for helping with initial piloting of experiment, as well as Michael Becker (Stony Brook) and Daniel Szeredi for assistance with Experigen. This work was funded by a UCLA Dissertation Year Fellowship and appeared as a chapter in the author’s 2017 UCLA dissertation. The study protocol was certified exempt by UCLA Institutional Review Board: IRB#15-000223.
Process Resilient Fault and Error Tolerant DLL for Supporting Multi-Die Clock Synchronization

Abstract—A Delay-Locked Loop (DLL) circuit is often useful for the clock synchronization in a chip incorporating multiple functional dies. Previously we have shown in [14] that TripleModule Redundancy (TMR) technique cannot work alone to provide fault and error tolerance for a DLL design unless it is enhanced by a timing correction scheme to nullify the adversary effect caused by the voter circuit’s delay. However, even such a timing correction scheme still has a serious drawback it cannot track the process variation automatically. Therefore, in this work, we propose a process resilient fault and error tolerant DLL architecture featuring a new “dynamic timing correction scheme”. The process variation or even temperature effects can now be dealt with during runtime operations and thereby significantly improved performance can be achieved in terms of the overall phase error between the DLL’s input and output clock signals. Post-layout simulation under 5 process corners indicates that the worst-case phase error can be further slashed from 20ps down to only 10ps, or 50% improvement.
International Journal of Instruction

The interaction between humans and computers is one of the most pressing challenges in contemporary society. Subtleties of this communication require that human-computer interfaces are conceptualized and tested by highly specialized experts. Here we describe a novel academic course for human-oriented designers and project managers, with the emphasis being put on the communication between the human brain and computer. Outcomes of the course were validated on 12 course participants both: qualitatively – educational experience, and quantitatively – outcomes of a project and final exam. T-test comparison revealed that students who participated in the course gained specialized knowledge, as compared to students who did not take part in the course. Moreover, students generally agreed that the course was helpful and interesting, however, about one-third of them considered the course hard. All in all, we find our brain-computer interfaces course useful in instructing specialist oriented on the human-factor of the communication between human and the machine.
1 The COVID-19 Pandemic and Eating Disorders in Children , Adolescents , and Emerging Adults : Recommendations from the Canadian Consensus Panel

Jennifer Couturier*1, Danielle Pellegrini1, Catherine Miller2, Neera Bhatnagar1, Ahmed Boachie3, Kerry Bourret4, Melissa Brouwers5, Jennifer S. Coelho6, Gina Dimitropoulos7, Sheri Findlay1, Catherine Ford8, Josie Geller6, Seena Grewal3, Joanne Gusella9, Leanna Isserlin5, Monique Jericho7, Natasha Johnson1, Debra K. Katzman3, Melissa Kimber1, Adele Lafrance10, Anick Leclerc11, Rachel Loewen12, Techiya Loewen13, Gail McVey3, Mark Norris5, David Pilon9, Wendy Preskow14, Wendy Spettigue5, Cathleen Steinegger3, Elizabeth Waite13, Cheryl Webb1
Munich Personal RePEc Archive Spurious Regression

The spurious regression phenomenon in Least Squares occurs for a wide range of Data Generating Processes, such as driftless unit roots, unit roots with drift, long memory, trend and broken-trend stationarity. Indeed, spurious regressions have played a fundamental role in the building of modern time series econometrics and have revolutionized many of the procedures used in applied macroeconomics. Spin-offs from this research range from unit-root tests to cointegration and error-correction models. This paper provides an overview of results about spurious regression, pulled from disperse sources, and explains their implications.
Determination of adenosine in plasma in rabbit by HPLC

OBJECTIVE To determine the concentration of adenosine in plasma by RP-HPLC with an attempt to study the stability,and also providing an aid for exploring the mechanism of some clinical drugs and evaluating therapeutic efficiency.METHODS An isocratic elution of a phosphate buffer and methanol (80∶20,pH 5.9) was used as mobile phase at a flow rate of 1 mL min -1.Detection was carried out on YWG-C 18 column(10 μm,250 mm×4.6 mm)at 35 ℃ with a UV detector monitored at 254 nm.RESULTS The curve was linear over the range of 0.50- 40.00 mg·L -1 (r= 0.998 0). The limits of detection (LOD) was 0.50 mg·L -1.CONCLUSION The application of sampling and pretreatment established in this study could decrease the inactivation of adenosine by components in blood.The assay in this study is sensitive,specific,simple and rapid.It could be a good assistant method for the purpose of exploring the mechanism of some therapeutic drugs.
Age & Gender Classification Using Histogram of Oriented Gradients and Back Propagation Neural Network

of the first group and because they have used a small database, it was difficult for the evaluation of the system performance. Another approach for age estimation is proposed by Geng et al [6] and is termed as Aging Pattern subspace (AGES). Geng used the sequential facial images of the individual distributed over time to create the model. For the encoding of face images of the subject around 200 AAMs features are used. They have used FG-NET Database in their research and calculated a Mean Absolute Error of 6.77. The problem found is that facial wrinkles for senior peoples are not well represented by AAM. Lanitis et al [5] has given a method for estimation of age by giving a function age=f (b) known as Aging Function . The variation on a face due to age can easily be represented by this function. For the above function age represent subject real age, f represent aging function and feature vector is represented by b which can be calculated with the help of AAM and it has 50 raw model attributes [2]. A database of 500 facial images was used to carry out the experiment and contains 60 subject images these subjects are mainly in the range of 1 to 35 years in age. An accuracy of around 71% is found in the recognition of the age. Hayashi et al [7] came out with another approach in the age estimation technique.
Overlapping Spaces for Compact Graph Representations

Various non-trivial spaces are becoming popular for embedding structured data such as graphs, texts, or images. Following spherical and hyperbolic spaces, more general product spaces have been proposed. However, searching for the best conﬁguration of a product space is a resource-intensive procedure, which reduces the practical applicability of the idea. We generalize the concept of product space and introduce an overlapping space that does not have the conﬁguration search problem. The main idea is to allow subsets of coordinates to be shared between spaces of different types (Euclidean, hyperbolic, spherical). As a result, we often need fewer coordinates to store the objects. Additionally, we propose an optimization algorithm that automatically learns the optimal conﬁguration. Our experiments conﬁrm that overlapping spaces outperform the competitors in graph embedding tasks with different evaluation metrics. We also perform an empirical analysis in a realistic information retrieval setup, where we compare all spaces by incorporating them into DSSM. In this case, the proposed overlapping space consistently achieves nearly optimal results without any conﬁguration tuning. This allows for reducing training time, which can be essential in large-scale applications.
Temporal Nuances of Coordination Networks

Current network-based methods for detecting coordinated inauthentic behaviour on social media focus primarily on inferring links between accounts based on common"behavioural traces"[19], such as retweeting the same tweet or posting the same URL. Assuming the goal of coordination is amplification, boosting a message within a constrained period, most approaches use a temporal window to ensure the co-activity occurs within a specific timeframe [9, 14, 19, 24]. Real-world application requires considering near real-time processing, creating performance requirements, which also highlight gaps in the semantics of coordination in the literature. These methods could all exploit temporal elements of coordinated activity. We describe preliminary research regarding coordination network semantics, coordination network construction, relevant observations in three political Twitter datasets and the role of cheerleaders in revealing social bots.
On computing bound states of the Dirac and Schr\"odinger Equations

We cast the quantum chemistry problem of computing bound states as that of solving a set of auxiliary eigenvalue problems for a family of parameterized compact integral operators. The compactness of operators assures that their spectrum is discrete and bounded with the only possible accumulation point at zero. We show that, by changing the parameter, we can always find the bound states, i.e., the eigenfunctions that satisfy the original equations and are normalizable. While for the non-relativistic equations these properties may not be surprising, it is remarkable that the same holds for the relativistic equations where the spectrum of the original relativistic operators does not have a lower bound. We demonstrate that starting from an arbitrary initialization of the iteration leads to the solution, as dictated by the properties of compact operators.
The rapid destruction of toroidal magnetic surfaces

An ideal magnetic evolution can cause the development of an exponentially large variation between the distance of closest approach and greatest separation between neighboring pairs of magnetic field lines. When this occurs, a fast magnetic reconnection naturally arises on the evolution time scale of magnetic field multiplied by a factor that depends only logarithmically on the strength of the non-ideal effects. An obvious example arises when the magnetic evolution is driven by footpoint motion, as in the solar corona. A similar effect can be responsible for the sudden loss of magnetic surfaces during a tokamak disruption. In almost all magnetic surfaces in a tokamak, a magnetic field line never closes on itself as its trajectory ~x(φ) is followed in the toroidal angle φ, and the line comes arbitrarily close to every point in the surface. A single field-line trajectory ~x(φ) is shown to give γψ ≡ ln(∆max/∆min), where ∆max/∆min is the ratio of the maximum to the minimum separation of neighboring magnetic surfaces. The exponentiation, γψ, increases the more an ideal perturbation displaces the magnetic surfaces. When this displacement exceeds the distance between low-order rational magnetic surfaces, the surface contortion becomes so great that ∆max/∆min can exceed the ratio consistent with magnetic surface preservation. That ratio is the distance between the rational surfaces and the minimum distance over which two neighboring field lines can remain distinguishable, ∆d, in the presence of plasma resistivity and electron inertia.
Integrating Circle Kernels into Convolutional Neural Networks

The square kernel is a standard unit for contemporary Convolutional Neural Networks (CNNs), as it ﬁts well on the tensor computation for the convolution operation. However, the receptive ﬁeld in the human visual system is actually isotropic like a circle. Motivated by this observation, we propose using circle kernels with isotropic receptive ﬁelds for the convolution, and our training takes approximately equivalent amount of calculation when compared with the corresponding CNN with square kernels. Our preliminary experiments demonstrate the rationality of circle kernels. We then propose a kernel boosting strategy that integrates the circle kernels with square kernels for the training and inference, and we further let the kernel size/radius be learnable during the training. Note that we reparameterize the circle kernels or integrated kernels before the inference, thus taking no extra computation as well as the number of parameter overhead for the testing. Extensive experiments on several standard datasets, ImageNet, CIFAR-10 and CIFAR-100, using the circle kernels or integrated kernels on typical existing CNNs, show that our approach exhibits highly competitive performance. Speciﬁcally, on ImageNet with standard data augmentation, our approach dramatically boosts the performance of MobileNetV3-Small by 5.20% top-1 accuracy and 3.39% top-5 accuracy, and boosts the performance of MobileNetV3-Large by 2.16% top-1 accuracy and 1.18% top-5 accuracy.
Shapes as Product Differentiation: Neural Network Embedding in the Analysis of Markets for Fonts

Many differentiated products have key attributes that are unstructured and thus high-dimensional (e.g., design, text). Instead of treating unstructured attributes as unobservables in economic models, quantifying them can be important to answer interesting economic questions. To propose an analytical framework for these types of products, this paper considers one of the simplest design products-fonts-and investigates merger and product differentiation using an original dataset from the world's largest online marketplace for fonts. We quantify font shapes by constructing embeddings from a deep convolutional neural network. Each embedding maps a font's shape onto a low-dimensional vector. In the resulting product space, designers are assumed to engage in Hotelling-type spatial competition. From the image embeddings, we construct two alternative measures that capture the degree of design differentiation. We then study the causal effects of a merger on the merging firm's creative decisions using the constructed measures in a synthetic control method. We find that the merger causes the merging firm to increase the visual variety of font design. Notably, such effects are not captured when using traditional measures for product offerings (e.g., specifications and the number of products) constructed from structured data.
Geometrical postural optimisation of 7-DoF limb-like manipulators

Robots are moving towards applications in less structured environments, but their model-based controllers are challenged by the tasks’ complexity and intrinsic environmental unpredictability. Studying biological motor control can provide insights into overcoming these limitations due to the high dexterity and stability observable in humans and animals. This work presents a geometrical solution to the postural optimisation of 7-DoF limbs-like mechanisms, which are robust to singularities and computationally efficient. The theoretical formulation identified two separate decoupled optimisation strategies. The shoulder and elbow strategy align the plane of motion with the expected plane of motion and guarantee the reachability of the end-posture. The wrist strategy ensures the end-effector orientation, which is essential to retain manipulability when nearing a singular configuration. The numerical results confirmed the theoretical observations and allowed us to identify the effect of different grasp strategies on system manipulability. The geometrical method was numerically tested in thousands of configurations proving to be both robust and accurate. The tested scenarios include left and right arm postures, singular configurations, and walking scenarios. The proposed geometrical approach can find application in developing efficient and robust interaction controllers that could be applied in computational neuroscience and robotics.
Autonomous Robotic Endoscope Control based on Semantically Rich Instructions

In keyhole interventions, surgeons rely on a colleague to act as a camera assistant when their hands are occupied with surgical instruments. This often leads to reduced image stability, increased task completion times and sometimes errors. Robotic endoscope holders (REHs), controlled by a set of basic instructions, have been proposed as an alternative
Oxidative-Extractive Desulfurization of Liquid Fuel by Dimethyl Sulfoxide and ZnCl2 Based Ionic Liquid

Abstract In this study, oxidative-extractive desulfurization (OEDS) of liquid fuel has been studied using an ionic liquid prepared by coordination of dimethyl sulfoxide (DMSO) with ZnCl2. Synthetic model liquid fuel was prepared by dissolving dibenzothiophene (DBT) in iso-octane. In addition, actual diesel was also tested for total sulfur removal. Oxidation in the OEDS process was achieved by adding hydrogen peroxide and acetic acid to the mixture. Different parameters such as oil to ionic liquid ratio, oxidant to sulfur ratio and temperature were optimized. Kinetic study was performed at different temperatures so as to calculate the reaction rate constants. Optimized conditions were also tested for their applicability on actual diesel. At the optimized conditions (oxidant to sulfur ratio = 6, oil to ionic liquid ratio = 3 and temperature = 30°C), 78% sulfur removal was observed from model oil (initial sulfur concentration = 1,000 mg/L) and 65% sulfur removal was observed for actual diesel oil (initial sulfur concentration = 140 mg/L).
A Short Note on the Relationship of Information Gain and Eluder Dimension

Eluder dimension and information gain are two widely used methods of complexity measures in bandit and reinforcement learning. Eluder dimension was originally proposed as a general complexity measure of function classes, but the common examples of where it is known to be small are function spaces (vector spaces). In these cases, the primary tool to upper bound the eluder dimension is the elliptic potential lemma. Interestingly, the elliptic potential lemma also features prominently in the analysis of linear bandits/reinforcement learning and their nonparametric generalization, the information gain. We show that this is not a coincidence – eluder dimension and information gain are equivalent in a precise sense for reproducing kernel Hilbert spaces.
Consistency in the description of quantum measurement: Quantum theory can consistently describe the use of itself

Recent extended formulations of the Wigner's friend thought experiment throw the measurement problem of quantum mechanics into sharper relief. Here I respond to an invitation by Renner to provide a consistent and concrete set of rules for quantum mechanics which can avoid the apparent paradox formulated by Frauchiger and Renner [Nat. Comm. 9, 3711 (2018)]. I propose a slight addition to standard textbook quantum mechanics, in the form of two rules, which avoids the paradox. The first specifies when a given quantum dynamics can be interpreted as a measurement. Potentially any dynamics can, but doing so depends on the context of other performed operations. The second requires that a joint context be used to determine whether several different dynamical evolutions can all be interpreted as measurement. The paradox is then avoided because not every participant in the Frauchiger-Renner thought experiment regards the stated dynamical descriptions of the experiment as measurements.
Flare-induced decay-less transverse oscillations in solar coronal loops

Evidence of flare induced, large-amplitude, decay-less transverse oscillations is presented. A system of multi-thermal coronal loops as observed with the Atmospheric Imaging Assembly (AIA), exhibit decay-less transverse oscillations after a flare erupts nearby one of the loop footpoints. Measured oscillation periods lie between 4.2 min and 6.9 min wherein the displacement amplitudes range from 0.17 Mm to 1.16 Mm. A motion-magnification technique is employed to detect the pre-flare decay-less oscillations. These oscillations have similar periods (between 3.7 min and 5.0 min) like the previous ones but their amplitudes (0.04 Mm to 0.12 Mm) are found to be significantly smaller. No phase difference is found among oscillating threads of a loop when observed through a particular AIA channel or when their multi-channel signatures are compared. These features suggest that the occurrence of a flare in this case neither changed the nature of these oscillations (decaying vs decay-less) nor the oscillation periods. The only effect the flare has is to increase the oscillation amplitudes.