Miracle-dz/UniFAR-FTUs huggingface dataset:
Stream Cipher Systems Using aChaotic Sequence of I.I.D Random Variables i.i.d. カオス系列を用いたストリーム暗号システム

Asequence of i.i. d. binary random variables has found significant applications in modern digital communication systems, such as aspreading spectrum sequence for a DS/CDMA system and akey-stream sequence for astream cipher system. In this paper, after reviewing the generation method of sequences of i.i. d. binary random variables generated by chaotic dynamics we give astream cipher system using asequence of i.i. d. binary random variables which has cryptographic resistance for several kinds of cryptanalysis based on correlation functions. $\mathrm{K}\mathrm{e}\mathrm{y}\mathrm{w}\mathrm{o}\mathrm{r}\mathrm{d}_{\mathrm{S}}-\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{e}\mathrm{a}\mathrm{m}$ cipher, keystream sequences, random sequence, i.i. d. sequence, chaotic dynamics, Cryptanalysis, aut0-/cross-correlation function, secret key 1Introduction Asequence of independent and identically distributed $(i.i.d.)$ binary random variables $[1]-[4]$ has found significant applications in modern digital communication systems such as in spread spectrum (SS) communication systems [5] or cryptosystems $[6, 7]$ as well as in computational applications requiring random numbers $[8, 9]$ . Such abinary sequence can be generated in various ways. Nevertheless, linear feedback shift register (LFSR) sequences which have already been thoroughly investigated based on finite field theory are employed in nearly aU the methods [10]. It is, however, $\mathrm{w}\mathrm{e}\mathrm{u}$ known from probability theory $[1]-[3]$ and ergodic theory [4] that coin tossing is atheoretic model that generates asequence of i.i. d. binary random variables in the following sense. Imagine $n$ independent tosses of afair coin. If we label 1and 0instead of head and tail, then the resulting binary sequence of length $n$ gives uniquely both the associated dyadically rational number and one of 2’$l$ disjoint half-0pen subintervals of widt $\mathrm{h}$ 数理解析研究所講究録 1240巻 2001年 74-87 74
Abstract. Y-box binding protein-1 (YB-1) facilitates cancer chemoresistance through the upregulation of ATP-binding cassette (ABC) transporters associated with multidrug resistance, which is one of the primary obstacles in cancer treatment. Since aberrant Janus kinase (JAK)/signal transducer and act

Y-box binding protein-1 (YB-1) facilitates cancer chemoresistance through the upregulation of ATP-binding cassette (ABC) transporters associated with multidrug resistance, which is one of the primary obstacles in cancer treatment. Since aberrant Janus kinase (JAK)/signal transducer and activator of transcription (STAT) signaling is also implicated in chemoresistance in numerous human malignancies, the interaction between YB-1 and JAK/STAT signaling was explored underlying the chemoresistance of NUGC3 gastric cancer cells. It was demonstrated that YB-1 translocated into the nuclei of NUGC3 cells exposed to doxorubicin hydrochloride, suggesting its important role in chemoresistance. Consistently, knockdown of YB-1 significantly decreased the chemoresistance of cells to doxorubicin hydrochloride and epirubicin hydrochloride, as evidenced by a decrease in cell viability. Notably, JAK inhibitor AG490 treatment further decreased the cell viability caused by YB-1 inhibition and doxorubicin hydrochloride. It was also observed that YB-1 transcriptionally regulated the ABCC3 transporter, whereas STAT3 modulated ABCC2 transporter levels. These findings suggest that YB-1 and STAT3 act together to facilitate chemoresistance via modulating the expression of different ABC transporters in NUGC3 cells. Notably, siYB-1 did not exhibit any significant effect on STAT3 expression. Similarly, siSTAT3 failed to alter YB-1 expression, suggesting that the two may not regulate each other in a mutual manner. However, double knockdown of YB-1 and STAT3 led to a synergistic inhibition of cell invasion in NUGC3 cells. Nonetheless, the combined treatment of YB-1 antagonists with STAT3 inhibitors may serve as an effective therapy in gastric cancer.
DUNNING-KRUGER EFFECT FOR STUDENTS TAKING STATISTICS COURSES

Dunning-Kruger (D-K) effect refers to “low-performing people tend to overestimate their performance to a much greater extent than high-performing people underestimate their performance”. Many studies have revealed similar as well as different, and even reverse results depending on culture and task. In this paper, we examine the D-K effect for students taking statistics courses at Michigan State University. An attitude survey, an assessment instrument and questions related to the D-K effect were administered in statistics classes. The D-K effect are investigated and compared between different genders and different learning styles.
A Study on Measuring Public and Private Hospital Employees’ Organizational Commitment and Organizational Cynicism Levels: The Case of Konya

In today’s businesses, where knowledge is an important power, human resources are seen as an important investment asset for the efficiency and effectiveness of businesses instead of cost elements. It is obvious that watching organizational goals will be very easy with employees who come to work on time, avoid absenteeism and have adopted the organization they work for. On the other hand, employees who cherish negative feelings towards their workplace, do not trust their directors and think they are being exploited contribute negatively or very little to the organization. In this study, organizational commitment and organizational cynicism levels of health employees working at public and private hospitals in the province of Konya will be investigated and the relationships between these two variables will be explored. To this end, the scale developed by Dean et al. (1998) for organizational cynicism, and the scale developed by Meyer and Allen (1991) for organizational commitment were used. The scales were dealt with in three dimensions. In the scale developed by Allen and Meyer, the commitments of employees will be investigated as affective commitment, continuance commitment and normative commitment whereas in the cynicism scale developed by Dean et al. (1998), cynical attitudes of employees will be examined as cognitive, affective and behavioral attitudes. In the first part of the study, definitions of organizational commitment and organizational cynicism were made, their conceptual frameworks were drawn in detail and then findings about the study were used. The population of the study was determined as employees working at hospitals in Konya and the study sample was identified as 270 people. Questionnaire forms were sent to hospital employees using the random sampling method. “5-point likert type” questionnaire method was used in collecting research data. In the responses given to the questionnaire questions, 1 means “I totally disagree”, whereas 5 means “I totally agree”. When the questionnaire procedure was completed, 255 questionnaire forms had been returned. 8 questionnaires were excluded from evaluation because responses were not complete. The responses in the returned questionnaire forms were encoded and analyzed in accordance with the statistical analysis program "SPSS (Statistical Package for Social Sciences)/Windows 15.0 Version". Factor Analysis, reliability test, frequency analysis, one-way variance analysis (anova test) Mann Whitney U Test and Pearson correlation analysis were used to analyze the data.
The Bacterial Amyloid-Like Hfq Promotes In Vitro DNA Alignment

The Hfq protein is reported to be involved in environmental adaptation and virulence of several bacteria. In Gram-negative bacteria, Hfq mediates the interaction between regulatory noncoding RNAs and their target mRNAs. Besides these RNA-related functions, Hfq is also associated with DNA and is a part of the bacterial chromatin. Its precise role in DNA structuration is, however, unclear and whether Hfq plays a direct role in DNA-related processes such as replication or recombination is controversial. In previous works, we showed that Escherichia coli Hfq, or more precisely its amyloid-like C-terminal region (CTR), induces DNA compaction into a condensed form. In this paper, we evidence a new property for Hfq; precisely we show that its CTR influences double helix structure and base tilting, resulting in a strong local alignment of nucleoprotein Hfq:DNA fibers. The significance of this alignment is discussed in terms of chromatin structuration and possible functional consequences on evolutionary processes and adaptation to environment.
Free radical scavenging activity and antioxidative potential of various solvent extracts of Mussaenda macrophylla Wall: An in vitro and ex vivo study

This study investigates the phytochemical composition, free radical scavenging activities, and antioxidative potential of various extracts of Mussaenda macrophylla. Phytochemicals such as alkaloids, cardiac glycosides, saponins, steroids, tannins, and terpenoids were found to be present in various extracts of M. macrophylla. Aqueous extract of M. macrophylla has the highest total phenolic (387.61 ± 14.10 mg gallic acid equivalent/g) and flavonoid (5,761.65 ± 38.5 mg quercetin equivalent/g) contents. The antioxidative potential of M. macrophylla extracts was measured by their ability to scavenge 1,1-diphenyl-2-picrylhydrazyl (DPPH), superoxide anions (O2• −), and 2, 2′-azino-bis-(3ethylbenzothiazoline-6-sulfonic acid) (ABTS) in a cell free system. The aqueous extract of M. macrophylla possessed the highest scavenging activities for DPPH, O2• −, and ABTS with IC50 of 25.92 ± 0.33 μg/ml, 4.12 ± 0.94 μg/ml, and 17.20 ± 0.50 μg/ml, respectively. Furthermore, the scavenging activities of the aqueous extract of M. macrophylla against ABTS and O2• − were found to be more effective than ascorbic acid which was used as standard. The total reducing power of M. macrophylla extracts was also determined by measuring the transformation of Fe3+ into Fe2+ and the methanolic extract was found to exhibit the highest reducing power. The aqueous extract of M. macrophylla showed the highest inhibitory activities against mice erythrocyte hemolysis and lipid peroxidation in the liver homogenate with an inhibition rate of 80.53% and 65.33%, respectively.
Describing Sensory Experience : The Genre of Wine

The purpose of the article is to shed light on how experiences of sensory perceptions in the domains o f VISION, SMELL, TASTE and TOUCH are recast into text and discourse in the genre of wine reviews. Because of the alleged paucity of sensory vocabular ies, in particular in the olfactory domain, it is o f particular interest to investigate what resources l anguage has to offer in order to describe those experiences. We show that the main resources are, o n the one hand, words evoking properties that are applicable cross-modally and properties of objects that range over more than one domain, and on the other, vivid imagery that compares the characterist ics of the wine with people, building, animals and the hustle and bustle of market places and other ev nts. The second goal is to account for the construals of the meanings of the expressions used in the recontextualization into written discourse i n the light of their apparent flexibility across the d scriptions of the sensory experiences. In contras t to a large body of the literature on sensory meanings in language, we argue that the descriptors of properties such as sharp, soft, lemon and cherry used to describe a wine’s qualities across the sens ory domains are not polysemous synesthetic metaphors, b ut monosemous synesthetic metonymizations, more precisely zone activations. With regard to the imagery used, the construals represented cover both similes, metaphorizations and metonymizations proper.
Welcome Bank Regulation , Credit Ratings , and Systematic Risk

This paper’s model shows that when regulatory capital and deposit insurance are based on credit ratings that reflect expected default losses, a bank’s shareholder value increases when it chooses similarly-rated loans and bonds with greater systematic risk. This moral hazard arises if loan and bond credit spreads incorporate systematic risk premia not accounted for by credit ratings. Consequently, credit rating-based regulations subsidize banks’ systematically risky investments, leading banks to herd into such assets. We confirm the model’s critical assumption by providing evidence that similarly-rated bonds have significantly higher credit spreads when their issuers have higher systematic risk as measured by “debt beta. ” We also show that if a bank chooses the higher-yielding bonds within a given Basel Accord credit rating class, its systematic risk rises by an economically significant amount. Our theory explains why banks and capital-regulated insurance companies were motivated to take excessive systematic risks documented in prior research.
RedShift: Transparent SNARKs from List Polynomial Commitment IOPs

We introduce an efficient transformation from univariate polynomial commitment based zk-SNARKs to their fully transparent counterparts. The transformation is achieved with the help of a new IOP primitive which we call a list polynomial commitment. This primitive is applicable for preprocessing zkSNARKs over both prime and binary fields. We present the primitive itself along with a soundness analysis of the transformation and instantiate it with an existing universal proof system. We also present benchmarks for a proof of concept implementation alongside a comparison with a non-transparent alternative based on Kate commitments. Our results show competitive efficiency both in terms of proof size and generation times at large security levels.
GRANGER CAUSALITY AND STRUCTURAL CAUSALITY IN CROSS-SECTION AND PANEL DATA

Granger noncausality in distribution is fundamentally a probabilistic conditional independence notion that can be applied not only to time series data but also to crosssection and panel data. In this paper, we provide a natural definition of structural causality in cross-section and panel data and forge a direct link between Granger (G−) causality and structural causality under a key conditional exogeneity assumption. To put it simply, when structural effects are well defined and identifiable, G−non-causality follows from structural noncausality, and with suitable conditions (e.g., separability or monotonicity), structural causality also implies G−causality. This justifies using tests of G−non-causality to test for structural noncausality under the key conditional exogeneity assumption for both cross-section and panel data. We pay special attention to heterogeneous populations, allowing both structural heterogeneity and distributional heterogeneity. Most of our results are obtained for the general case, without assuming linearity, monotonicity in observables or unobservables, or separability between observed and unobserved variables in the structural relations.
Revisiting Few-Shot Learning for Facial Expression Recognition

Most of the existing deep neural nets on automatic facial expression recognition focus on a set of predefined emotion classes, where the amount of training data has the biggest impact on performance. However, in the standard setting over-parameterised neural networks are not amenable for learning from few samples as they can quickly over-fit. In addition, these approaches do not have such a strong generalisation ability to identify a new category, where the data of each category is too limited and significant variations exist in the expression within the same semantic category. We embrace these challenges and formulate the problem as a low-shot learning, where once the base classifier is deployed, it must rapidly adapt to recognise novel classes using a few samples. In this paper, we revisit and compare existing few-shot learning methods for the low-shot facial expression recognition in terms of their generalisation ability via episode-training. In particular, we extend our analysis on the cross-domain generalisation, where training and test tasks are not drawn from the same distribution. We demonstrate the efficacy of low-shot learning methods through extensive experiments.
Phase/Amplitude Synchronization of Brain Signals During Motor Imagery BCI Tasks

In the last decade, functional connectivity (FC) has been increasingly adopted based on its ability to capture statistical dependencies between multivariate brain signals. However, the role of FC in the context of brain-computer interface applications is still poorly understood. To address this gap in knowledge, we considered a group of 20 healthy subjects during an EEG-based hand motor imagery (MI) task. We studied two well-established FC estimators, i.e. spectral- and imaginary-coherence, and we investigated how they were modulated by the MI task. We characterized the resulting FC networks by extracting the strength of connectivity of each EEG sensor and we compared the discriminant power with respect to standard power spectrum features. At the group level, results showed that while spectral-coherence based network features were increasing in the sensorimotor areas, those based on imaginary-coherence were significantly decreasing. We demonstrated that this opposite, but complementary, behavior was respectively determined by the increase in amplitude and phase synchronization between the brain signals. At the individual level, we eventually assessed the potential of these network connectivity features in a simple off-line classification scenario. Taken together, our results provide fresh insights into the oscillatory mechanisms subserving brain network changes during MI and offer new perspectives to improve BCI performance.
Effective Data Augmentation Approaches to End-to-End Task-Oriented Dialogue

The training of task-oriented dialogue systems is often confronted with the lack of annotated data. In contrast to previous work which augments training data through expensive crowd-sourcing efforts, we propose four different automatic approaches to data augmentation at both the word and sentence level for end-to-end task-oriented dialogue and conduct an empirical study on their impact. Experimental results on the CamRest676 and KVRET datasets demonstrate that each of the four data augmentation approaches is able to obtain a significant improvement over a strong baseline in terms of Success F1 score and that the ensemble of the four approaches achieves the state-of-the-art results in the two datasets. In-depth analyses further confirm that our methods adequately increase the diversity of user utterances, which enables the end-to-end model to learn features robustly.
High-precision analysis of Feshbach resonances in a Mott insulator

We show that recent high-precision measurements of relative on-site interaction energies $\Delta U$ in a Mott insulator require a theoretical description beyond the standard Hubbard-model interpretation, when combined with an accurate coupled-channels calculation. In contrast to more sophisticated lattice models, which can be elaborate especially for parameter optimization searches, we introduce an easy to use effective description of $U$ valid over a wide range of interaction strengths modeling atomic pairs confined to single lattice sites. This concise model allows for a straightforward combination with a coupled-channels analysis. With this model we perform such a coupled-channels analysis of high-precision $^7$Li spectroscopic data on the on-site interaction energy $U$, which spans over four Feshbach resonances and provide an accurate and consistent determination of the associated resonance positions. Earlier experiments on three of the Feshbach resonances are consistent with this new analysis. Moreover, we verify our model with a more rigorous numerical treatment of the two atom system in an optical lattice.
Uplink-Downlink Channel Covariance Transformations and Precoding Design for FDD Massive MIMO

A large majority of cellular networks deployed today make use of Frequency Division Duplexing (FDD) where, in contrast with Time Division Duplexing (TDD), the channel reciprocity does not hold and explicit downlink (DL) probing and uplink (UL) feedback are required in order to achieve spatial multiplexing gain. In order to support massive MIMO, i.e., a very large number of antennas at the base station (BS) side, the overhead incurred by conventional DL probing and UL feedback schemes scales linearly with the number of BS antennas and, therefore, may be very large. In this paper, we present a new approach to achieve a very competitive tradeoff between spatial multiplexing gain and probing-feedback overhead in such systems. Our approach is based on two novel methods: (i)an efficient regularization technique based on Deep Neural Networks (DNN) that learns the Angular Spread Function (ASF) of users channels and permits to estimate the DL covariance matrix from the noisy i.i. d. channel observations obtained freely via UL pilots (UL-DL covariance transformation), (ii) a novel "sparsifying precoding" technique that uses the estimated DL covariance matrix from (i) and imposes a controlled sparsity on the DL channel such that given any assigned DL pilot dimension, it is able to find an optimal sparsity level and a corresponding sparsifying precoder for which the "effective" channel vectors after sparsification can be estimated at the BS with a low mean-square error. We compare our proposed DNN-based method in (i) with other methods in the literature via numerical simulations and show that it yields a very competitive performance. We also compare our sparsifying precoder in (ii) with the state-of-the-art statistical beamforming methods under the assumption that those methods also have access to the covariance knowledge in the DL and show that our method yields higher spectral efficiency since it uses in addition the instantaneous channel information after sparsification.
Reorienting Objects in 3D Space Using Pivoting

We consider the problem of reorienting a rigid object with arbitrary known shape on a table using a two-finger pinch gripper. Reorienting problem is challenging because of its non-smoothness and high dimensionality. In this work, we focus on solving reorienting using pivoting, in which we allow the grasped object to rotate between fingers. Pivoting decouples the gripper rotation from the object motion, making it possible to reorient an object under strict robot workspace constraints. We provide detailed mechanical analysis to the 3D pivoting motion on a table, which leads to simple geometric conditions for its stability. To solve reorienting problems, we introduce two motion primitives: pivot-on-support and roll-on-support, and provide an efficient hierarchical motion planning algorithm with the two motion primitives to solve for the gripper motions that reorient an object between arbitrary poses. To handle the uncertainties in modeling and perception, we make conservative plans that work in the worst case, and propose a robust control strategy for executing the motion plan. Finally we discuss the mechanical requirements on the robot and provide a "two-phase" gripper design to implement both pivoting grasp and firm grasp. We demonstrate the effectiveness of our method in simulations and multiple experiments. Our algorithm can solve more reorienting problems with fewer making and breaking contacts, when compared to traditional pick-and-place based methods.