Miracle-dz/UniFAR-FTUs huggingface dataset:
Embedding manifold structures into Kalman filters

—Error-state Kalman ﬁlter is an elegant and effective ﬁltering technique for robotic systems operating on manifolds. However, to implement an error-state Kalman ﬁlter for a certain robotic system, one usually needs to derive each step of the ﬁlter by switching between the original and the error state, a tedious and prone-to-error process. To avoid such repetitive derivations, this paper proposes a generic symbolic representation for error-state Kalman ﬁlters on manifolds. Utilizing the (cid:1) \ (cid:12) operations and further deﬁning a ⊕ operation on the respective manifold, we propose a canonical representation of the robotic system. Such a canonical form enables us to separate the manifold structures from the system descriptions in each step of the Kalman ﬁlter, ultimately leading to a generic, symbolic, and manifold-embedding Kalman ﬁlter framework. A major advantage of the proposed manifold-embedding Kalman ﬁlter framework is that users need only to cast the system model into the canonical form without going through the cumbersome hand-derivation of the on-manifold Kalman ﬁlter. This is particularly useful when the robotic system is of high dimension (e.g., augmented internal state, multiple extrinsic parameters, swarms). Furthermore, the manifold-embedding Kalman ﬁlter is implemented as a toolkit in C ++ packages with which an user needs only to deﬁne the system, and call the respective ﬁlter steps (e.g., propagation, update) according to the events (e.g., reception of input, reception of measurement). The existing implementation supports full iterated Kalman ﬁltering for systems on manifold S = R m × SO (3) ×· · ·× SO (3) × S 2 ×· · ·× S 2 or any of its sub-manifolds, and is extend-able to other types of manifold when necessary. The proposed symbolic Kalman ﬁlter and the developed toolkit are veriﬁed by implementing a tightly-coupled lidar-inertial navigation system. Results show that the developed toolkit leads to superior ﬁltering performances and computation efﬁciency comparable to hand-engineered counterparts. Finally, the
Neural Data-to-Text Generation with LM-based Text Augmentation

For many new application domains for data-to-text generation, the main obstacle in training neural models consists of a lack of training data. While usually large numbers of instances are available on the data side, often only very few text samples are available. To address this problem, we here propose a novel few-shot approach for this setting. Our approach automatically augments the data available for training by (i) generating new text samples based on replacing specific values by alternative ones from the same category, (ii) generating new text samples based on GPT-2, and (iii) proposing an automatic method for pairing the new text samples with data samples. As the text augmentation can introduce noise to the training data, we use cycle consistency as an objective, in order to make sure that a given data sample can be correctly reconstructed after having been formulated as text (and that text samples can be reconstructed from data). On both the E2E and WebNLG benchmarks, we show that this weakly supervised training paradigm is able to outperform fully supervised sequence-to-sequence models with less than 10% of the training set. By utilizing all annotated data, our model can boost the performance of a standard sequence-to-sequence model by over 5 BLEU points, establishing a new state-of-the-art on both datasets.
Conditions de Kan sur les nerfs des $\omega$-cat\'egories

We show that the Street nerve of a strict $\omega$-category $C$ is a Kan complex (respectively a quasi-category) if and only if the $n$-cells of $C$ for $n\geq 1$ (respectively $n>1$) are weakly invertible. Moreover, we equip $\mathcal{N}(C)$ with a structure of saturated complicial set where the $n$-simplices correspond to morphisms from the $n^{th}$ oriental to $C$ sending the unique non-trivial $n$-cell of the domain to a weakly invertible cell of $C$.
Opinion Dynamics Incorporating Higher-Order Interactions

The issue of opinion sharing and formation has received considerable attention in the academic literature, and a few models have been proposed to study this problem. However, existing models are limited to the interactions among nearest neighbors, ignoring those second, third, and higherorder neighbors, despite the fact that higher-order interactions occur frequently in real social networks. In this paper, we develop a new model for opinion dynamics by incorporating long-range interactions based on higher-order random walks. We prove that the model converges to a fixed opinion vector, which may differ greatly from those models without higherorder interactions. Since direct computation of the equilibrium opinions is computationally expensive, which involves the operations of huge-scale matrix multiplication and inversion, we design a theoretically convergence-guaranteed estimation algorithm that approximates the equilibrium opinion vector nearly linearly in both space and time with respect to the number of edges in the graph. We conduct extensive experiments on various social networks, demonstrating that the new algorithm is both highly efficient and effective.
Learning to Decode Protograph LDPC Codes

The recent development of deep learning methods provides a new approach to optimize the belief propagation (BP) decoding of linear codes. However, the limitation of existing works is that the scale of neural networks increases rapidly with the codelength, thus they can only support short to moderate codelengths. From the point view of practicality, we propose a high-performance neural min-sum (MS) decoding method that makes full use of the lifting structure of protograph low-density parity-check (LDPC) codes. By this means, the size of the parameter array of each layer in the neural decoder only equals the number of edge-types for arbitrary codelengths. In particular, for protograph LDPC codes, the proposed neural MS decoder is constructed in a special way such that identical parameters are shared by a bundle of edges derived from the same edge-type. To reduce the complexity and overcome the vanishing gradient problem in training the proposed neural MS decoder, an iteration-by-iteration (i.e., layer-by-layer in neural networks) greedy training method is proposed. With this, the proposed neural MS decoder tends to be optimized with faster convergence, which is aligned with the early termination mechanism widely used in practice. To further enhance the generalization ability of the proposed neural MS decoder, a codelength/rate compatible training method is proposed, which randomly selects samples from a set of codes lifted from the same base code. As a theoretical performance evaluation tool, a trajectory-based extrinsic information transfer (T-EXIT) chart is developed for various decoders. Both T-EXIT and simulation results show that the optimized MS decoding can provide faster convergence and up to 1dB gain compared with the plain MS decoding and its variants with only slightly increased complexity. In addition, it can even outperform the sum-product algorithm for some short codes.
Antioxidative Activity of Volatile Compounds in Flower of Chrysanthemum indicum, C. morifolium, and C. zawadskii

감국, 국화 및 구절초 등 3종의 국화과 식물에 대해 향기성분을 분석하고 향기성분 추출물의 항산화활성을 측정하고자 3종의 국화과 식물에 대한 향기특성을 SDE법으로 추출하여 GC/MS로 분석한 결과 camphene, 1,8-cineole, benzene, pinocarvone, bicyclo-2,2,1-heptan-2-ol, trans-caryophyllene, 3-cyclohexen-1-ol, $.
Generation-recombination noise in MOSFETs

A new analytical approach to the low frequency noise analysis in MOSFETs has been developed. Local levels contributing to generation-recombination noise are classified into three groups. For deep levels located near the midgap the approach of zero free carrier concentration inside the space charge region can be used. Simple analytical expressions and conditions of their applicability are derived. Parameters of levels located near the conduction and valence bands can be determined analytically. Analytical expressions for such a situation are also derived. For the intermediate case the level parameters can be only determined numerically. The low frequency noise in n- and p-MOSFETs has been studied experimentally at 220 K < T < 425 K over frequency range 2 Hz < f < Hz. Experimental noise spectra are analysed on the basis of developed theoretical model.
Biases obscure whether sexes and ages of window-killed fall migrants die in proportion to their frequency in the migrating population

ABSTRACT The timing of migratory passage for sexes and ages of birds is difficult to document because migration is spread out over time and space. We compared sex and age ratios from 10 species of birds that were banded in Wisconsin during fall migration to a sample of autumn window-killed birds from Chicago, Illinois. We assumed that these specimens were a random sample of the migrating population because dates of passage in Wisconsin either predate or were coincident with specimens recovered in Chicago. More juveniles than adults collided with windows, which is expected because of the higher number of juveniles in the fall migratory population. For Connecticut Warbler (Oporornis agilis), Dark-eyed Junco (Junco hyemalis), Ovenbird (Seiurus aurocapilla), and White-throated Sparrow (Zonotrichia albicollis), statistically fewer adults collided with windows than expected. It is possible that adults of these species, who have made at least 2 prior successful migratory journeys, might have experienced some degree of learning to avoid windows. For specimen data, ratios of males to females were approximately equal, except for Swamp Sparrow (Melospiza georgiana; 1:0.82). However, banders were unable to determine sex for a high percentage of each species, ranging from 98.5% undetermined sex for Hermit Thrush (Catharus guttatus) to 48% for Common Yellowthroat (Geothlypis trichas). Taken at face value, banding records for Common Yellowthroat, Dark-eyed Junco, and White-throated Sparrow indicated a much higher percentage of males, whereas for Song Sparrow (Melospiza melodia), females were much more common; 6 species showed no apparent difference in sex ratio between the banding and window samples. In species for which banders declined to identify an individual's sex for a high percentage of birds, we questioned whether the identification of sex in the remaining small percentage was accurate. Thus, we considered the alternative possibility that the specimen data were in fact the most appropriate null hypothesis, and if so, it would suggest that banders have misidentified sexes consistently in 4 species, and ages in 7 species.
Modulation of electron energy distributions and discharge parameters in a dual frequency ICP discharge

Using a radio frequency (RF) compensated Langmuir probe, modulations in electron energy distribution (EED) and plasma potential are investigated in a discharge produced by a large-area dual frequency/dual antenna inductively coupled plasma source. The discharge is ignited using two frequencies (2 and 13.56 MHz). It is observed that the EEDs can be tailored by varying the power ratio of the two frequencies. Increasing the power level of the low frequency (P2 MHz) enhances the population density of high-energy electrons; however, increasing the high-frequency power (P13.56 MHz) increases the low-energy electron population density. At a fixed total power (P2 MHz + P13.56 MHz), the higher the low-frequency power (P2 MHz) content, the higher the population density of high-energy electrons; however, this trend reverses with high-frequency power (P13.56 MHz). The influence of power ratio on plasma density (ne), plasma temperature (Te) and plasma potential (Vp) has also been studied. It is found out that the plasma parameters have similar trends with RF power irrespective of its frequency. The value of ne increases, and Te and Vp decrease with increasing power. At a fixed P2 MHz, Vp increases with increasing P13.56 MHz. However, Vp decreases with increasing P2 MHz at a fixed value of P13.56 MHz.
Strongly Coupled Bi2S3@CNT Hybrids for Robust Lithium Storage

DOI: 10.1002/aenm.201400798 Despite a high initial delivery, the capacity retention is insuffi cient for practice. Recently, Jung et al. reported that the Bi 2 S 3 /C (7:3 by mass) nanocomposite could achieved a high capacity of ≈490 mAh g −1 over 100 cycles. [ 14 ] It was also demonstrated that the Bi 2 S 3 /C nanomesh afforded a capacity of 472 mAh g −1 or 3210 mAh cm −3 over 50 cycles. [ 15 ] However, neither the cycling or rate capability of the Bi 2 S 3 materials is suffi cient for real battery application and necessitate further improvement. In this work, we have designed a strongly coupled Bi 2 S 3 and carbon nanotube (CNT) hybrid (denoted as Bi 2 S 3 @CNT) comprising Bi 2 S 3 nanocrystals intimately anchored on CNT backbones, in an attempt to mitigate the above issues of Bi 2 S 3 . [ 16,17 ]
Effect of Interpersonal Communication and Motivation on Employee Work Effectiveness in BAPPEDA, Karawang Regency

This study aimed to determine, analyze, and explain the effect of interpersonal communication and motivation on work effectiveness at BAPPEDA Karawang Regency. Quantitative descriptive analysis methods, scale techniques, and path analysis, with the results of research, conducted it can be concluded:interpersonal Communication, in general, went well, motivation, in general, went well, work effectiveness of employees, in general, went well, the correlation coefficient between interpersonal communication and motivation has a correlation value of 0.352, the level of this relationship is included in the low criteria, but the value is positive, this means that if Interpersonal Communication is getting better, then the motivation of employees will also be better, the partial influence of interpersonal communication partially on work effectiveness of 0.369 or 36.9% which is a contribution from the direct effect of 0.5342 = 0.285 and indirect contribution of 0.534 x 0.352 x 0.445 = 0.084. While the partial influence of motivation on work effectiveness is 0.282, or 28.2% which is a contribution from the direct effect of 0.4452 = 0.198 and indirect contribution of 0.534 x 0.352 x 0.445 = 0.084, the simultaneous influence of Interpersonal communication and motivation on work effectiveness is 0.651 or 65.1% which is a contribution of partial influence of interpersonal communication on work effectiveness of 0.369 or 36.9% and the effect of partial motivation on effectiveness work of 0.282, or 28.2%, the remaining 34.39% is the contribution of other variables (ε) that are not examined.
Characterization of optical collectors for concentration photovoltaic applications

The design and characterization of the collector of a photovoltaic concentrator system is commonly carried out for a given receiver, the optical parameters of the collector being linked to it. This paper, which has substantial tutorial content, deals with the characterization of collectors for concentrator photovoltaic systems, independently of any receiver, and providing the necessary parameters for the design of a system. This strategy allows the parameters related to the collector and the receiver, which are usually manufactured by different industries, to be totally separated. It also allows the optical collectors coming from non‐photovoltaic industries to be evaluated. The information that the mirror and lens manufacturers should provide for a photovoltaic concentrator application can be summarized under three characteristics: overall optical efficiency; light distribution; and acceptance angle. Theory, equipment, and procedures to carry out the optical characterization of the collectors are explained. Copyright © 2003 John Wiley & Sons, Ltd.
Multivariate Identification of Functional Neural Networks Underpinning Humorous Movie Viewing

While univariate functional magnetic resonance imaging (fMRI) data analysis methods have been utilized successfully to map brain areas associated with cognitive and emotional functions during viewing of naturalistic stimuli such as movies, multivariate methods might provide the means to study how brain structures act in concert as networks during free viewing of movie clips. Here, to achieve this, we generalized the partial least squares (PLS) analysis, based on correlations between voxels, experimental conditions, and behavioral measures, to identify large-scale neuronal networks activated during the first time and repeated watching of three ∼5-min comedy clips. We identified networks that were similarly activated across subjects during free viewing of the movies, including the ones associated with self-rated experienced humorousness that were composed of the frontal, parietal, and temporal areas acting in concert. In conclusion, the PLS method seems to be well suited for the joint analysis of multi-subject neuroimaging and behavioral data to quantify a functionally relevant brain network activity without the need for explicit temporal models.
Targeting MYCN in Pediatric and Adult Cancers

The deregulation of the MYC family of oncogenes, including c-MYC, MYCN and MYCL occurs in many types of cancers, and is frequently associated with a poor prognosis. The majority of functional studies have focused on c-MYC due to its broad expression profile in human cancers. The existence of highly conserved functional domains between MYCN and c-MYC suggests that MYCN participates in similar activities. MYC encodes a basic helix-loop-helix-leucine zipper (bHLH-LZ) transcription factor (TF) whose central oncogenic role in many human cancers makes it a highly desirable therapeutic target. Historically, as a TF, MYC has been regarded as “undruggable”. Thus, recent efforts focus on investigating methods to indirectly target MYC to achieve anti-tumor effects. This review will primarily summarize the recent progress in understanding the function of MYCN. It will explore efforts at targeting MYCN, including strategies aimed at suppression of MYCN transcription, destabilization of MYCN protein, inhibition of MYCN transcriptional activity, repression of MYCN targets and utilization of MYCN overexpression dependent synthetic lethality.
Phytoradiotherapy: An Integrative Approach to Cancer Treatment by Combining Radiotherapy With Phytomedicines

Radiotherapy (RT) is an effective method of cancer treatment, but like any other method of cancer treatment, there are inherent limitations. While technological advances and a growing understanding of its biological effects have improved its results dramatically, the use of RT is still limited to certain patient populations and by normal tissue toxicities. The harmful side effects of treating patients with radiation can offset its therapy benefits, limiting its use in certain cases. Phyto, or plant-based, medicines offer a way to add to radiation treatment, while also protecting patients from its toxic side effects. Phytomedicines such as cannabinoids (CBD) and bitter melon extract have demonstrated therapeutic properties, including the ability to activate apoptotic death in cancer cells, diminish tumor progression, and generally decrease the incidence of several cancer types. In addition, herbal drugs have been shown to be powerful antioxidants with the ability to decrease toxicity of RT without the adverse side effects found in synthetic drugs. Furthermore, a number of phytomedicines have been shown to mitigate hypoxic conditions within the tumor microenvironment, creating a more radiosensitive disease and preventing tumorigenesis. The purpose of this article is to examine the merits and demerits of employing phytomedicines during RT. Results from studies that have tested the effects of combining radiotherapy with supplemental herbal treatment are discussed along with perspectives on where additional research is needed to advance “Phytoradiotherapy”. Overall, experimental evidence points to the fact that phytomedicines have significant potential to enhance RT, with need for cross-disciplinary collaborations to establish optimal dosing combinations with evidence-base for clinical translation.
COVID-19 Stress and Addictive Social Media Use (SMU): Mediating Role of Active Use and Social Media Flow

The ongoing COVID-19 pandemic is likely to enhance the risk of addictive social media use (SMU) as people spend more time online maintaining connectivity when face-to-face communication is limited. Stress is assumed to be a critical predictor of addictive SMU. However, the mechanisms underlying the association between stress and addictive SMU in crises like the current COVID-19 situation remain unclear. The present study aimed to understand the relationship between COVID-19 stress and addictive SMU by examining the mediating role of active use and social media flow (i.e., an intensive, enjoyable experience generated by SMU that perpetuates media use behaviors). A sample of 512 Chinese college students (Mage = 22.12 years, SD = 2.47; 62.5% women) provided self-report data on COVID-19 stress and SMU variables (i.e., time, active use, flow, addictive behavior) via an online survey from March 24 to April 1, 2020. The results showed that COVID-19 stress was positively associated with tendencies toward addictive SMU. Path analyses revealed that this relationship was significantly serially mediated by active use and social media flow, with SMU time being controlled. Our findings suggest that individuals who experience more COVID-19 stress are at increased risk of addictive SMU that may be fostered by active use and flow experience. Specific attention should be paid to these high-risk populations and future interventions to reduce addictive SMU could consider targeting factors of both active use and social media flow.