Miracle-dz/UniFAR-FTUs huggingface dataset:
Supplementary Material: Simulating terahertz ﬁeld-induced ferroelectricity in quantum paraelectric SrTiO 3

ferroelectricity in quantum paraelectric SrTiO3 Dongbin Shin, 2, ∗ Simone Latini, Christian Schäfer, Shunsuke A. Sato, 1 Edoardo Baldini, Umberto De Giovannini, 5 Hannes Hübener, and Angel Rubio 5, 6, † Max Planck Institute for the Structure and Dynamics of Matter and Center for Free Electron Laser Science, 22761 Hamburg, Germany Department of Physics and Photon Science, Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Republic of Korea Center for Computational Sciences, University of Tsukuba, Tsukuba 305-8577, Japan Department of Physics, The University of Texas at Austin, 78712 Austin, Texas Massachusetts, USA Nano-Bio Spectroscopy Group, Departamento de Fisica de Materiales, Universidad del Páıs Vasco UPV/EHU20018 San Sebastián, Spain Center for Computational Quantum Physics (CCQ), The Flatiron Institute, 162 Fifth avenue, New York NY 10010.
Effect of the Pes Planus on Vertical Jump Height and Lower Extremity Muscle Activation in Gymnasts*

The aim of this study was to examine the effect of the pes planus vertical jump height and muscle activation in gymnasts. Forty- four gymnasts (mean age, 12.47±4.34 years) were included in the study. Pes planus was evaluated using a podoscope. Muscle activation was measured with a surface electromyography device during maximum voluntary isometric contraction (MVIC) and vertical jump test. Vertical jump height was evaluated using a contact mat. There was no significant difference in MVIC values of gastrocnemius medialis (GM), soleus (S), peroneus longus (PL), tibialis anterior (TA) muscles of gymnasts with and without pes planus (GM p=0.526; S p=0.157; PL p=0.696; TA p=0.223). In athletes with pes planus, GM muscle in rising phase of vertical jump; in the landing phase, the activation of the GM and S muscles was found to be lower (GMrising phase p=0.004; GMlanding phase p=0.014; Slanding phase p=0.017). In conclusion, it was found that the pes planus had no effect on vertical jump and MVIC values. It was observed that the activation of GM and S muscles was low during vertical jump in gymnasts with pes planus.
Effects of Augmented Reality-Based Dual-Task Program on Physical Ability by Cognitive Stage with Developmental Disabilities

People diagnosed with developmental disabilities are less likely to participate in physical activities even if they are provided opportunities. This study aimed to examine the effects of dual-task exercise-based augmented reality (AR) on muscle strength, muscle endurance, balance ability, and flexibility among people with developmental disabilities. Twenty-seven patients with developmental disabilities were included in the study. The intervention was based on an AR-based rehabilitation program and lasted for 8 weeks. The results showed a statistically significant improvement in muscle strength, muscle endurance, balance ability, and flexibility after the intervention (p < 0.05). The AR-based dual-task program increased interest and motivation in the high-cognitive-stage groups, while less interest and motivation were observed in the low-cognitive-stage groups. Our results suggest that an AR-based dual-task program can be an effective method to improve physical ability in patients with high cognitive levels.
Learning geospatially aware place embeddings via weak-supervision

Understanding and representing real-world places (physical locations where drivers can deliver packages) is key to successfully and efficiently delivering packages to customer's doorstep. Prerequisite to this is the task of capturing similarity and relatedness between places. Intuitively, places that belong to a same building should have similar characteristics in geospatial as well as textual space. However, these assumptions fail in practice as existing methods use customer address text as a proxy for places. While providing the address text, customers tend to miss-out on key tokens, use vernacular content or place synonyms and do not follow a standard structure making them inherently ambiguous. Thus, modelling the problem from linguistic perspective alone is not sufficient. To overcome these shortcomings, we adapt various state-of-the-art embedding learning techniques to geospatial domain and propose Places-FastText, Places-Bert, and Places-GraphSage. We train these models using weak-supervision by innovatively leveraging different geospatial signals already available from historical delivery data. Our experiments and intrinsic evaluation demonstrate the significance of utilizing these signals and neighborhood information in learning geospatially aware place embeddings. Conclusions are further validated by observing significant improvements in two domain specific tasks viz., Pair-wise Matching (recall@95precision improves by 29%) and Candidate Generation (avg recall@k improves by 10%) as evaluated on UAE addresses.
E FFICIENT S ECOND -O RDER O PTIMIZATION FOR D EEP L EARNING WITH K ERNEL M ACHINES

Second-order optimization has been recently explored in neural network training. However, the recomputation of the Hessian matrix in the second-order optimization posts much extra computation and memory burden in the training. There have been some attempts to address the issue by approximation on the Hessian matrix, which unfortunately degrades the performance of the neural models. To address the issue, we propose Kernel Stochastic Gradient Descent (Kernel SGD) which projects the optimization problem to a transformed space with the Hessian matrix of kernel machines. Kernel SGD eliminates the recomputation of the Hessian matrix and requires a much smaller memory cost which can be controlled via the mini-batch size. The additional advantage of Kernel SGD is its ability to converge to better solutions according to our theoretical analysis. Kernel SGD is theoretically guaranteed to converge. Experimental results on tabular, image and text data show that Kernel SGD converges up to 30 times faster than the existing second-order optimization techniques, and also shows remarkable performance in generalization.
Library of behaviors and tools for robot manipulation

Learned policies often fail to generalize across environment variations, such as, different objects, object arrangements, or camera viewpoints. Moreover, most policies are trained and tested in simulation environments, and the simtoreal gap remains large under weak visual representations that do not disentangle the scene from the objects in it. We first propose a visually-grounded library of behaviors approach for learning to manipulate diverse objects across varying initial and goal configurations and camera placements. Our key innovation is to disentangle the standard image-to-action mapping into two separate modules that use different types of perceptual input: (1) a behavior selector which conditions on intrinsic and semantically-rich object appearance features to select the behaviors that can successfully perform the desired tasks on the object in hand, and (2) a library of behaviors each of which conditions on extrinsic and abstract object properties, such as object location and pose, to predict actions to execute over time. We test our framework on pushing and grasping diverse objects in simulation as well as transporting rigid, granular, and liquid food ingredients in a real robot setup. Our model outperforms image-to-action mappings that do not factorize static and dynamic object properties. We then propose an end-to-end learning framework that jointly learns to choose different tools and deploy tool-conditioned policies with a limited amount of human demonstrations directly on a real robot platform. It is important to correctly switch between and deploy suitable tools in object rearrangement and cleaning tasks in complex scenes. We evaluate our method on parallel gripper and suction cup picking and placing, sweeping with a brush, and household rearrangement tasks, generalizing to different configurations, novel objects, and cluttered scenes in the real world. Finally, we show a long-horizon planning framework that could utilize our multiple tool setup to manipulate elastoplastic objects successfully, such as a dough.
Temperature Sensitivity of Wild-Type, Mutant and Genetic Sexing Strains of Ceratitis capitata

Simple Summary The Mediterranean fruit fly (medfly), Ceratitis capitata, is a major agricultural insect pest species worldwide. The sterile insect technique (SIT), as a component of area-wide integrated management (AW-IPM) programmes, is currently used to control populations of this pest. SIT is based on the mass production and release of sexually sterile insects, ideally males, over a target area. Male-only releases can be achieved by using genetic sexing strains (GSS) such as the medfly VIENNA 8 GSS. Females of this strain are homozygous for a mutation in the temperature-sensitive lethal (tsl) gene, which kills them when exposed to high temperatures during the embryonic stage. In the present study, we employed a temperature-sensitive lethal test (TSLT) to determine the temperature sensitivity or tolerance of twenty-seven Ceratitis capitata wild-type, genetic sexing, and tsl mutant strains. Significant differences were detected among the strains studied with respect to egg hatching, pupal, and adult recovery rates. We discussed our findings in the context of SIT applications and climate change. Abstract Area-wide integrated pest management (AW-IPM) programmes with a sterile insect technique component (SIT) are used to control populations of insect pests worldwide, including the Mediterranean fruit fly, Ceratitis capitata. SIT consists of the mass rearing, radiation-induced sterilization, handling, and release of sterile insects over the target area. Although SIT can be performed by using both sterile males and females, male-only releases significantly increase the efficiency and cost-effectiveness of SIT applications. Male-only releases can be achieved by using genetic sexing strains (GSS). The medfly VIENNA 8 GSS is based on two selectable markers, the white pupae (wp) gene, and the temperature-sensitive lethal (tsl) genes. The latter allows the elimination of females by exposing embryos to elevated temperatures. This study assessed the temperature sensitivity of twenty-seven medfly strains through a TSLT. Our results indicated significant differences among the strains regarding egg hatching as well as pupal and adult recovery rates due to the presence or absence of the tsl mutation and/or the genetic background of the strains. Our findings are discussed in the context of SIT applications, the importance of the tsl gene for developing genetic sexing strains, and climate change.
On the Robustness of Deep Learning-Based Speech Enhancement

In this paper, we present the design of a robust deep neural network based speech enhancement (DNNSE) solution for joint noise reduction and dereverberation under real-world acoustic conditions. This makes our proposed solution suitable for smart-speaker products that encounter a wide variety of acoustic challenges during their real-world deployment. We provide a systematic introduction to the acoustic challenges involved in real-world products and perform a detailed analysis to compare DNNSE models using metrics such as short-time objective intelligibility (STOI), scale-invariant signal-to-distortion-ratio (SI-SDR), and generalizability to unseen acoustic conditions. We then develop a robust DNNSE solution along with a robust training procedure that are well suited for the acoustic challenges specified in the paper. Through detailed analysis, we demonstrate that our DNNSE solution performs and generalizes better than a baseline solution that is 5x-6x times larger.
Don't Learn What You Already Know Scheme-Aware Modeling for Profiling Side-Channel Analysis against Masking

Over the past few years, deep-learning-based attacks have emerged as a de facto standard, thanks to their ability to break implementations of cryptographic primitives without pre-processing, even against widely used counter-measures such as hiding and masking. However, the recent works of Bronchain and Standaert at Tches 2020 questioned the soundness of such tools if used in an uninformed setting to evaluate implementations protected with higher-order masking. On the opposite, worst-case evaluations may be seen as possibly far from what a real-world adversary could do, thereby leading to too conservative security bounds. In this paper, we propose a new threat model that we name scheme-aware benefiting from a trade-off between uninformed and worst-case models. Our scheme-aware model is closer to a real-world adversary, in the sense that it does not need to have access to the random nonces used by masking during the profiling phase like in a worst-case model, while it does not need to learn the masking scheme as implicitly done by an uninformed adversary. We show how to combine the power of deep learning with the prior knowledge of scheme-aware modeling. As a result, we show on simulations and experiments on public datasets how it sometimes allows to reduce by an order of magnitude the profiling complexity, i.e., the number of profiling traces needed to satisfyingly train a model, compared to a fully uninformed adversary.
Image Synthesis with Appearance Decomposition

Our visual world is compositional and its appearance can be decomposed into various components. Leveraging these components can be beneficial for challenging image synthesis tasks. To this end, this thesis focuses on studying how appearance decomposition can improve image synthesis methods using two examples. (1) Structural decomposition: we introduce a periodicity-aware single image framework to synthesize a scene of near-periodic patterns (NPP). In particular, the appearance of an NPP scene is decomposed into motifs and their corresponding periodicities (i.e., arrangement), which are injected into the proposed framework as a prior to synthesize the NPP scene. The proposed method can interpolate and extrapolate NPP images, in-paint large and arbitrarily shaped regions, recover blurry regions when images are remapped, segment periodic and non-periodic regions, in planar and multi-planar scenes. (2) Intrinsic decomposition: we propose a novel approach to decompose a single panorama of an empty indoor environment into four appearance components: specular, direct sunlight, diffuse and diffuse ambient without direct sunlight. This appearance decomposition enables multiple image synthesis applications including sun direction estimation, virtual furniture insertion, floor material replacement, and sun direction change. We conduct extensive experiments to demonstrate the effectiveness of both methods.
Motivation to Learn Online during and after the Covid-19 Pandemic

The research aims to define the model and the role of motivation to learn in the online classrooms. As online courses become prevalent during the present pandemic caused by COVID-19, it is very important to search for the answers to the following questions: How can instructors design their online courses to optimize student motivation? And how can students identify and take advantage of motivational strengths in online courses? The modified version of the Motivation to Learn Online Questionnaire (MLOQ) was used to assess college students' motivation to learn during online classes. The MLOQ is adapted, in part, from the Motivated Strategies for Learning Questionnaire (MSLQ) and it was designed to assess the following dimensions: intrinsic and extrinsic goal orientation, self-efficacy and task value. These factors are based on a general cognitive view of motivation and are well-documented as influencing the quality of instruction in a traditional classroom, but they might function differently in online learning environments. Two other constructs: instructor support and social engagement are part of the MLOQ since they are relevant to the motivation to learn online. The sample consists of 184 university students recruited from 30 different online courses taught at two private universities in Skopje, the International Balkan University and University American College Skopje. Results from the confirmatory factor analysis show that the hypothesized 6-factor model has an acceptable fit to the data from this research and it can be considered when determining the optimal approach to online learning.
Electrochemical Enrichment and Isolation of Electrogenic Bacteria from 0.22 µm Filtrate

Ultramicrobacteria (UMB) that can pass through a 0.22 µm filter are attractive because of their novelty and diversity. However, isolating UMB has been difficult because of their symbiotic or parasitic lifestyles in the environment. Some UMB have extracellular electron transfer (EET)-related genes, suggesting that these symbionts may grow on an electrode surface independently. Here, we attempted to culture from soil samples bacteria that passed through a 0.22 µm filter poised with +0.2 V vs. Ag/AgCl and isolated Cellulomonas sp. strain NTE-D12 from the electrochemical reactor. A phylogenetic analysis of the 16S rRNA showed 97.9% similarity to the closest related species, Cellulomonas algicola, indicating that the strain NTE-D12 is a novel species. Electrochemical and genomic analyses showed that the strain NTE-D12 generated the highest current density compared to that in the three related species, indicating the presence of a unique electron transfer system in the strain. Therefore, the present study provides a new isolation scheme for cultivating and isolating novel UMB potentially with a symbiotic relationship associated with interspecies electron transfer.
The Effect of a Spiral Density Wave on the Galaxy (cid:48) s Rotation Curve, as Applied to the Andromeda Galaxy (M31)

: The rotational velocity curve, which is the circular velocity proﬁle of the stars and gas in a spiral galaxy as a function of their distance from the galactic center, plays an important role in the kinematic and dynamic investigation of spiral galaxies. There are observations of approximately ﬂat rotation curves (RC) at large distances that have introduced mass discrepancy between the theoretically derived RC and the observed one. In this paper, we derive a rotational velocity expression using a nonlinear spiral density wave solution for the surface mass density (SMD) within the disk. We show that the proposed nonlinear spiral solution is able to support the observed ﬂat rotational velocity curve for large distances with no mass deﬁciency. The aim of the paper is to conﬁrm the crucial importance of the mass distribution on the rotation curve proﬁle. Although the model is limited by the ﬂuid description of the galactic disk, it provides an improved rotational velocity expression and a rotation curve with no mass discrepancy in the outer part of the disk due to the inclusion of the spiral mass distribution. The disk mass has not been averaged within the exponential disk approximation, but it rather follows the observed spiral pattern given by the analytical solution of the nonlinear equation. The M31 galaxy has been chosen as the closest and well mapped spiral galaxy, similar in many aspects to our host galaxy, in order to apply a rotational velocity expression that accounts for nonlinear effects and derive RC. The obtained result can have a strong inﬂuence on large-scale gravity dynamics, as well.
Determinants of Intra-SSA Tourism Demand

The study examines factors that influence demand for intra- sub-Saharan Africa countries’ tourism for 46 bilateral countries using a dynamic panel (system GMM) data model technique. The study employs most common macroeconomic variables and some critical social variables that help explain demand for African tourism. Results from the study suggest that while most common variables are significant, intra-SSA tourists consider internet usage important and prefer to visit less urbanized regions in SSA. However, there is enough evidence in the study to conclude that reasons for travelling within Africa are not much different than for international tourists.
Efficient Public Key Searchable Encryption Schemes from Standard Hard Lattice Problems for Cloud Computing

Cloud storage and computing oﬀers signiﬁcant convenience and management ef-ﬁciency in the information era. Privacy protection is a major challenge in cloud computing. Public key encryption with keyword search (PEKS) is an ingenious tool for ensuring privacy and functionality in certain scenario, such as ensuring privacy for data retrieval appearing in the cloud computing. Despite many attentions received, PEKS schemes still face several challenges in practical applications, such as low computational eﬃciency, high end-to-end delay, vulnerability to inside keyword guessing attacks(IKGA) and key management defects in the multi-user environment. In this work, we introduce three Ring-LWE/ISIS based PEKS schemes: (1) Our basic PEKS scheme achieves high level security in the standard model. (2) Our PAEKS scheme utilizes the sender’s private key to generate an authentication when encrypting, which can resist IKGA. (3) Our IB-PAEKS scheme not only can resist IKGA, but also signiﬁcantly reduces the complexity of key management in practical applications. Experimental results indicate that the ﬁrst scheme provides lower end-to-end delay and higher computational eﬃciency compared to similar ones, and that our last two schemes can provide more secure properties with little additional overhead.
Treewidth-Two Graphs as a Free Algebra ∗

We give a new and elementary proof that the graphs of treewidth at most two can be seen as a free algebra. This result was originally established through an elaborate analysis of the structure of K 4 -free graphs, ultimately reproving the well-known fact that the graphs of treewidth at most two are precisely those excluding K 4 as a minor. Our new proof is based on a conﬂuent and terminating rewriting system for term-labeled graphs and does not involve graph minors anymore. The new strategy is simpler and robust in the sense that it can be adapted to subclasses of treewidth-two graphs, e
Cas9 gRNA and Homologous Recombinant Plasmid Integrate Inducible HA-N-ICER in the AAVS1 Locus of SK-MEL-24 Cells

Melanoma is one of the most aggressive forms of cancer and is prone to metastasis. Once melanoma metastasizes, it is difficult to treat as patients often become immune to MAPK inhibitors and other therapeutic agents. Small molecules, such as immune checkpoint inhibitors, have demonstrated success as an adjunct to melanoma treatment. As a small, 18kDa molecule with tumor suppressing properties, research using ICER continues to provide positive anti-tumor effects. ICER regulates cAMP-induced transcription as a powerful, dominant-negative transcriptional repressor. Recent research demonstrated that the fusion tag hemagglutinin (HA), when fused to ICER’s N-terminus, increased ICER’s half-life and apoptotic activity in SK-MEL24 cells. If a stable, inducible HAN-ICER cell line is established, much more research will be able to take place to further understand the role of ICER in melanoma cells. Until now, a stable HAN-ICER cell line has not been developed despite many efforts to accomplish this in the laboratory. Here, it was demonstrated that co-transfection with a homologous recombination (HR) plasmid and a Cas9 gRNA plasmid which targets the AAVS1 safe harbor locus were used to incorporate EGFP into the genome of SK-MEL-24 cells as a proof of concept. Results were confirmed by fluorescent microscopy and Sanger sequencing data. Additionally, HAN-ICER was integrated into the SK-MEL-24 cellular genome, and results were confirmed by western blot and immunocytochemistry (ICC).
Spectral Theory of Matrices. I. General Matrices.

Abstract : This is the first of our survey reports on the spectral theory of matrices. The report is self contained from the matrix point of view. The main subject of this paper is the theory of analytic similarity of matrices. This topic of matrix theory is very important in the study of systems of ordinary differential equations having singularities either in time or a parameter. (Author)