Miracle-dz/UniFAR-FTUs huggingface dataset:
Novel High-Gain Circularly Polarized Lens Antenna Using Single-Layer Transmissive Metasurface

Abstract A high-gain lens antenna employing single-layer focusing metasurface (MS) is proposed in this article. The single-layer element achieves a 360° transmission phase range with a transmission magnitude better than 0.9. And the focusing MS consists of 169 elements was designed by utilizing the technique of varying rotation angle to compensate the phase delay. Thus, a lens antenna is constructed by placing a circularly polarized (CP) patch antenna at the focal point of the MS. The fabricated lens antenna demonstrates a good performance of 4.6 % 3-dB axial ratio bandwidth and 6 % 1-dB gain bandwidth, respectively. Moreover, the maximum gain is 18.3 dBic at 15 GHz, which is enhanced by 11.4 dBic compared with the patch antenna. Due to the single-layer structure, this design has a low profile and easy fabrication process compared with the conventional designs, making it an attractive alternative to compact high-gain antenna.
Applications of Functional Analysis to a Class of Elliptic PDES

In this report we discuss about the eigensolutions of an eigenvalue problem for the $p-$Laplace operator by investigating the underlying variational problem. Mostly the discussion is restricted to $p=1$ case however it can be extended to other values of $p$ also. When we have constrained minimizers subject to $L^1-$ norm, it is considered as the eigenvalue problem of $1-$Laplace operator. Several theorems are stated in the report which strongly support the existence of solutions to the variational problem, hence eigensolutions and also the sequence of eigensolutions. Solutions are treated as critical points (for the variational problem) in the sense of weak slope. Finally an additional necessary condition is introduced which is derived using inner variations to filter the non-eigensolutions of the problem. Basic concepts of functional analysis and necessary prerequisites to understand the variational problem and minimizers are also included.
The encoding of countability and numerosity in nominal morphology

The aim of this research was to examine the role of Number morphology for what concerns the encoding of information about the numerosity and countability of referents. The issue was approached both from a theoretical and from an experimental point of view. Number morphology is a widespread category and only few languages in the world seem to completely lack it (Corbett, 2000). Why is Number such a common feature among natural languages? In general, it can be assumed that language grammaticalises only some of all the possible information present in the referential world. The fact that information about numerosity is rammaticalised in such a widespread way in natural languages may mirror the salient role that such information has from a biological point of view, i.e. the fact that this information stems from cognitive processes that are biologically relevant in order to behave successfully in a given environment (Hauser & Spelke 2004). Language provides the means to communicate salient information readily. Morphology is one of these means in general, and Number morphology is the one specifically set for the encoding of the information about numerosity of referents. Number morphology is designed to convey salient information expressing numerosities, but this possibility takes place only when the noun is linked to a countable interpretation. Within morphological Number systems, countability plays a crucial role: in fact, in absence of countability, nouns are not inflected but assigned a Number value by default. Although the great amount of interest dedicated to countability both by theoretical and experimental approaches, no account has fully succeeded in explaining countability and its relation with morphological Number. In the present thesis we propose a formal model and provide empirical data - collected in quantitative morphology, psycholinguistics and language acquisition – in order to support the idea that in encoding countability more than one factor comes into play: namely, core grammar rules, effects of non-strictly grammatical processing of linguistic stimuli, and effects related to non-verbal cognitive processes that deal with the information encoded into language.
Czech-ing the News: Article Trustworthiness Dataset for Czech

We present the Verifee dataset: a multimodal dataset of news articles with fine-grained trustworthiness annotations. We bring a diverse set of researchers from social, media, and computer sciences aboard to study this interdisciplinary problem holistically and develop a detailed methodology that assesses the texts through the lens of editorial transparency, journalist conventions, and objective reporting while penalizing manipulative techniques. We collect over 10,000 annotated articles from 60 Czech online news sources. Each item is categorized into one of the 4 proposed classes on the credibility spectrum – ranging from entirely trustworthy articles to deceptive ones – and annotated of its manipulative attributes. We fine-tune prominent sequence-to-sequence language models for the trustworthiness classification task on our dataset and report the best F-1 score of 0.53. We open-source the dataset, annotation methodology, and annotators’ instructions in full length at https://www.verifee. ai/research/ to enable easy build-up work.
Transfer Learning for Code-Mixed Data: Do Pretraining Languages Matter?

Monolinguals make up a minority of the world’s speakers, and yet most language technologies lag behind in handling linguistic behaviours produced by bilingual and multilingual speakers. A commonly observed phenomenon in such communities is code-mixing, which is prevalent on social media, and thus requires attention in NLP research. In this work, we look into the ability of pretrained language models to handle code-mixed data, with a focus on the impact of languages present in pretraining on the downstream performance of the model as measured on the task of sentiment analysis. Ultimately, we find that the pretraining language has little effect on performance when the model sees code-mixed data during downstream finetuning. We also evaluate the models on code-mixed data in a zero-shot setting, after task-specific finetuning on a monolingual dataset. We find that this brings out differences in model performance that can be attributed to the pretraining languages. We present a thorough analysis of these findings that also looks at model performance based on the composition of participating languages in the code-mixed datasets.
Terahertz range profile of the tilted-plate

The range profile is studied first in the microwave band. With the advent of the terahertz radar technology, the range profile is studied from microwave band to the terahertz band, which is the terahertz range profile (TRP). The outstanding feature of TRP is that it can obtain the 3-D shape and the range information of the target by one ultra-short pulse without scanning system. In this paper, terahertz range profile theory and simulation is investigated. Terahertz range profile simulation is studied based on the theory of beam scattering by rough target, pulse wave scattering theory and the radar equation, the calculated formula of range profile is obtained. This equation is, in part, dependent upon the target’s scattering strength which is quantified by its radar cross section. As examples, the range profile simulations are done for tilted-plate. It is indicated that the influence of pulse width, beam parameters, transmit-receive angle and target shape, roughness on the simulation results is also analyzed. This paper is offer theory bases and simulation method for abstraction and identification target feature on terahertz waveband.
You Are What You Read: Inferring Personality From Consumed Textual Content

In this work we use consumed text to infer Big-5 personality inventories using data we have collected from the social media platform Reddit. We test our model on two datasets, sampled from participants who consumed either fiction content (N = 913) or news content (N = 213). We show that state-of-the-art models from a similar task using authored text do not translate well to this task, with average correlations of r=. 06 between the model’s predictions and ground-truth personality inventory dimensions. We propose an alternate method of generating average personality labels for each piece of text consumed, under which our model achieves correlations as high as r=. 34 when predicting personality from the text being read.
Can Sequence-to-Sequence Transformers Naturally Understand Sequential Instructions?

While many real-life tasks require reasoning over multi-step sequential instructions, collecting fine-grained annotations for each intermediate step can be prohibitively expensive. In this work, we study how general pretrained sequence-to-sequence transformers perform under varying types of annotation for sequential instruction understanding. We conduct experiments using T5 (Raffel et al., 2020) on a commonly-used multi-step instruction understanding dataset SCONE (Long et al., 2016) that includes three sub-tasks. First, we show that with only gold supervision for the final step of a multi-step instruction sequence, depending on the sequential properties of different tasks, transformers may exhibit extremely bad performance on intermediate steps, in stark contrast with their performance on the final step. Next, we explore two directions to relieve this problem. We show that with the same limited annotation budget, using supervision uniformly distributed across different steps (instead of only final-step supervision), we can greatly improve the performance on intermediate steps with a drop in final-step performance. Further, we explore a contrastive learning approach to provide training signals on intermediate steps with zero intermediate gold supervision. This, however, achieves mixed results. It significantly improves the model’s bad intermediate-step performance on one subtask, but also shows decreased performance on another subtask.
Mann” is to “Donna asis to Reine Adapting the Analogy Task for Multilingual and Contextual Embeddings

How does the word analogy task fit in the modern NLP landscape? Given the rarity of comparable multilingual benchmarks and the lack of a consensual evaluation protocol for contextual models, this remains an open question. In this paper, we introduce MATS: a multilingual analogy dataset, covering forty analogical relations in six languages, and evaluate human as well as static and contextual embedding performances on the task. We find that not all analogical relations are equally straightforward for humans, static models remain competitive with contextual embeddings, and optimal settings vary across languages and analogical relations. Several key challenges remain, including creating benchmarks that align with human reasoning and understanding what drives differences across methodologies.
Communicating Climate Change: A Comparison Between Tweets and Speeches by German Members of Parliament

Twitter and parliamentary speeches are very different communication channels, but many members of parliament (MPs) make use of both. Focusing on the topic of climate change, we undertake a comparative analysis of speeches and tweets uttered by MPs in Germany in a recent six-year period. By keyword/hashtag analyses and topic modeling, we find substantial differences along party lines, with left-leaning parties discussing climate change through a crisis frame, while liberal and conservative parties try to address climate change through the lens of climate-friendly technology and practices. Only the AfD denies the need to adopt climate change mitigating measures, demeaning those concerned about a deteriorating climate as climate cult or fanatics. Our analysis reveals that climate change communication does not differ substantially between Twitter and parliamentary speeches, but across the political spectrum.
Identifying Quantifiably Verifiable Statements from Text

Humans often describe complex quantitative data using trend-based patterns. Trend-based patterns can be interpreted as higher order functions and relations over numerical data such as extreme values, rates of change, or cyclical repetition. One application where trends abound are descriptions of numerical tabular data. Therefore, the alignment of numerical tables and textual description of trends enables easier interpretations of tables. Most existing approaches can align quantities in text with tabular data but are unable to detect and align trend-based patterns about data. In this paper, we introduce the initial steps for aligning trend-based patterns about the data, i.e. the detection of textual description of trends and the alignment of trends with a relevant table. We introduce the problem of identifying quantifiably verifiable statements (QVS) in the text and aligning them with tables and datasets. We define the structure of these statements and implement a structured based detection. In our experiments, we demonstrate our method can detect and align these statements from several domains and compare favorably with traditional sequence labeling methods.
A Survey of Challenges and Methods in the Computational Modeling of Multi-Party Dialog

Advances in conversational AI systems, powered in particular by large language models, have facilitated rapid progress in understanding and generating dialog. Typically, task-oriented or open-domain dialog systems have been designed to work with two-party dialog, i.e., the exchange of utterances between a single user and a dialog system. However, modern dialog systems may be deployed in scenarios such as classrooms or meetings where conversational analysis of multiple speakers is required. This survey will present research around computational modeling of “multi-party dialog”, outlining differences from two-party dialog, challenges and issues in working with multi-party dialog, and methods for representing multi-party dialog. We also provide an overview of dialog datasets created for the study of multi-party dialog, as well as tasks that are of interest in this domain.
Correlation between structure-specific ground motion intensity measures and seismic response of 3D R/C buildings

The present paper investigates the correlation between a number of structure-specific ground motion intensity measures and the damage state of 3D R/C buildings. To accomplish this purposefour 5-story R/C buildings are studied. The buildings are analyzed by nonlinear time history analysis using 20 bidirectional earthquake ground motions. The two horizontal accelerograms of each ground motion are applied along the structural axes of the buildings. The structural damage is expressed in terms of the maximum interstory drift as well as the overall structural damage index. For each individual pair of accelerograms the values of the aforementioned seismic damage measures are determined. Then, they are correlated with severalstrong motion intensity measures that take into account both earthquake and structural characteristics. The research identified certain intensity measures which exhibited strong correlation with the seismic damage of the four buildings. However, the degree of correlation between them and the seismic damage depends on the response parameter adopted. Furthermore, it was confirmed that the widely used spectral acceleration at the fundamental period of the structure is a good indicator of the expected earthquake damage level.
Changes in nonprejudiced motivations track shifts in the U.S. sociopolitical climate

Recently, major societal events have shaped perceptions of race relations in the US. The current work argues that people’s motivations to be nonprejudiced toward Black people have changed in concert with these broader societal forces. Analyses of two independent archival datasets reveal that nonprejudiced motivations changed predictably in accordance with shifts in the social milieu over the last 15 years. In one dataset (N = 13,395), we track movement in internal and external motivations to respond without prejudice from 2004 to 2017. Internal motivation initially decreased before ticking upward following multiple events suggesting worsening race relations (e.g., noteworthy killings of unarmed Black men, resurgent racialized politics). Conversely, external motivation initially increased but reversed course across the same time span. A second dataset (N = 2,503) corroborates these trends in two conceptually related nonprejudiced motivations. Results suggest that changes in nonprejudiced motivations may reflect broader shifts in the sociopolitical climate.
Proud to support social equality: Investigating the roles of pride, guilt, anger, and disgust in attitudes towards immigrants

Previous research has demonstrated that low social dominance orientation (social equality orientation) promotes empathy with disadvantaged group members. In three studies, we tested a model relating preference for egalitarianism to positive attitudes towards immigrants through emotional experiences (pride, guilt, moral anger/ anger, disgust). Studies 1 and 2 showed that social equality orientation was positively related to proimmigrant attitudes through increased pride in helping immigrants, controlling for participants’ gender, age, and political orientation. Such a preference for egalitarianism was unrelated to proimmigrant attitudes through guilt for not helping immigrants and moral anger concerning mistreatment of immigrants. By focusing on emotional experience concerning proimmigrant national initiatives (e.g., integrating immigrants into the labor market), Study 3 corroborated the indirect effect of social equality orientation on proimmigrant attitudes through increased pride, controlling for participants’ gender, age, political orientation, as well as competitive jungle and dangerous world beliefs. Although much weaker, we also found a positive association between social equality orientation and proimmigrant attitudes through reduced anger, while no significant association through guilt and disgust was found. Results suggest that, relative to guilt, anger, and disgust, pride is the key channel through which preference for egalitarianism is related to positive attitudes towards immigrants. Implications and future directions for research are discussed.
A Novel Method for Constructing Multi-Mode Deployable Polyhedron Mechanisms Using Symmetric Spatial RRR Compositional Units

A novel construction method is proposed to construct multimode deployable polyhedron mechanisms (DPMs) using symmetric spatial RRR compositional units, a serial kinematic chain in which the axes of the first and the third revolute (R) joints are perpendicular to the axis of the second R joint. Single-loop deployable linkages are first constructed using RRR units and are further assembled into polyhedron mechanisms by connecting single-loop kinematic chains using RRR units. The proposed mechanisms are over-constrained and can be deployed through two approaches. The prism mechanism constructed using two Bricard linkages and six RRR limbs has one degree-of-freedom (DOF). When removing three of the RRR limbs, the mechanism obtains one additional 1-DOF motion mode. The DPMs based on 8R and 10R linkages also have multiple modes, and several mechanisms are variable-DOF mechanisms. The DPMs can switch among different motion modes through transition positions. Prototypes are 3D-printed to verify the feasibility of the mechanisms.
Stationary crossflow vortices near the leading edge of three-dimensional boundary layers: the role of non-parallelism and excitation by surface roughness

Non-parallelism, i.e. the effect of the slow variation of the boundary-layer flow in the chordwise and spanwise directions, in general produces a higher-order correction to the growth rate of instability modes. Here we investigate stationary crossflow vortices, which arise due to the instability of the three-dimensional boundary layer over a swept wing, focusing on a region near the leading edge where non-parallelism plays a leading-order role in their development. In this regime, the vortices align themselves with the local wall shear at leading order, and so have a marginally separated triple-deck structure, consisting of the inviscid main boundary layer, an upper deck and a viscous sublayer. We find that the streamwise (and spanwise) variations of both the base flow and the modal shape must be accounted for. An explicit expression for the growth rate is derived that shows a neutral point occurs in this regime, downstream of which non-parallelism has a stabilising effect. Stationary crossflow vortices thus have a viscous and non-parallel genesis near the leading edge. If an ‘effective pressure minimum’ occurs within this region then the growth rate becomes unbounded, and so the previous analysis is regularised within a localised region around it. A new instability is identified. The mode maintains its three-tiered structure, but the pressure perturbation now plays a passive role. Downstream, the instability evolves into a Cowley, Hocking & Tutty (Phys. Fluids, vol. 28, 1985, pp. 441–443) instability associated with a critical layer located in the lower deck. Finally, we consider the receptivity of the flow in the non-parallel regime: generation of stationary crossflow modes by arrays of chordwise-localised, spanwise-periodic surface roughness elements. The flow responds differently to different Fourier spectral content of the roughness, giving the lower deck a two-part structure. We find that roughness elements with sharper edges generate stronger modes. For roughness elements of fairly moderate height, the resulting nonlinear forcing leads to the so-called super-linearity of receptivity, namely, the amplitude of the generated crossflow mode deviates from the linear dependence on the roughness height even though the perturbation in the boundary layer remains linear.