Miracle-dz/UniFAR-FTUs huggingface dataset:
What shapes climate change perceptions in Africa? A random forest approach

Climate change perceptions are fundamental for adaptation and environmental policy support. Although Africa is one of the most vulnerable regions to climate change, little research has focused on how climate change is perceived in the continent. Using random forest methodology, we analyse Afrobarometer data (N = 45,732), joint with climatic data, to explore what shapes climate change perceptions in Africa. We include 5 different dimensions of climate change perceptions: awareness, belief in its human cause, risk perception, need to stop it and self-efficacy. Results indicate that perceived agriculture conditions are crucial for perceiving climate change. Country-level factors and long-term changes in local weather conditions are among the most important predictors. Moreover, education level, access to information, poverty, authoritarian values, and trust in institutions shape individual climate change perceptions. Demographic effects -- including religion -- seem negligible. These findings suggest policymakers and environmental communicators how to frame climate change in Africa to raise awareness, gather public support and induce adaptation.
On Performance of Energy Harvested Cooperative NOMA Under Imperfect CSI and Imperfect SIC

With the advent of 5G and the need for energy-efficient massively connected wireless networks, in this work, we consider an energy harvesting (EH) based multi-relay downlink cooperative non-orthogonal multiple access (NOMA) system with practical constraints. The base station serves NOMA users with the help of decode-and-forward based multiple EH relays, where relays harvest the energy from the base station's radio frequency. A relay is selected from the multiple <inline-formula><tex-math notation="LaTeX">$K$</tex-math></inline-formula>-relays by using a partial relay selection protocol. The system is considered to operate in half-duplex mode over a generalized independent and identical Nakagami<inline-formula><tex-math notation="LaTeX">$-m$</tex-math></inline-formula> fading channel. The closed-form expression of outage probability and ergodic rate are derived for users, under the assumption of imperfect channel state information (CSI) and imperfect successive interference cancellation (SIC) at the receiver node. Expression of outage probability and ergodic rate for two users under the assumption of perfect CSI and perfect SIC are also presented. Further, the asymptotic expression for the outage probability is also shown. The derived analytical expressions are verified through Monte-Carlo simulations.
Assessing the detectability of the secondary spin in extreme mass-ratio inspirals with fully relativistic numerical waveforms

Extreme mass-ratio inspirals~(EMRIs) detectable by the Laser Inteferometric Space Antenna~(LISA) are unique probes of astrophysics and fundamental physics. Parameter estimation for these sources is challenging, especially because the waveforms are long, complicated, known only numerically, and slow to compute in the most relevant regime, where the dynamics is relativistic. We perform a time-consuming Fisher-matrix error analysis of the EMRI parameters using fully-relativistic numerical waveforms to leading order in an adiabatic expansion on a Kerr background, taking into account the motion of the LISA constellation, higher harmonics, and also including the leading correction from the spin of the secondary in the post-adiabatic approximation. We pay particular attention to the convergence of the numerical derivatives in the Fisher matrix and to the numerical stability of the covariance matrix, which for some systems requires computing the numerical waveforms with approximately $90$-digit precision. Our analysis confirms previous results (obtained with approximated but much more computationally efficient waveforms) for the measurement errors on the binary's parameters. We also show that the inclusion of higher harmonics improves the errors on the luminosity distance and on the orbital angular momentum angles by one order and two orders of magnitude, respectively, which might be useful to identify the environments where EMRIs live. We particularly focus on the measurability of the spin of the secondary, confirming that it cannot be measured with sufficient accuracy. However, due to correlations, its inclusion in the waveform model can deteriorate the accuracy on the measurements of other parameters by orders of magnitude, unless a physically-motivated prior on the secondary spin is imposed.
Superposition type coherent states in all dimensional loop quantum gravity

We propose a new kind of coherent state for the general $SO(D+1)$ formulation of loop quantum gravity in the $(1+D)$-dimensional space-time. Instead of Thiemann's coherent state for $SO(D+1)$ gauge theory, our coherent spin-network state is given by constructing proper superposition over quantum numbers of the spin-networks with vertices labelled by the coherent intertwiners. Such superposition type coherent states are labelled by the so-called generalized twisted geometric variables which capture the geometric meaning of discretized general relativity. We study the basic properties of this kind of coherent states, i.e., the completeness and peakedness property. Moreover, we show that the superposition type coherent states are consistent with Thiemann's coherent state for $SO(D+1)$ gauge theory in large $\eta$ limit.
Determination of moisture content and moisture content profiles in wood during drying by low-field nuclear magnetic resonance

ABSTRACT In this article, the low-field nuclear magnetic resonance was used to measure the moisture content (MC) and MC profiles in poplar wood during real-time drying. The T2 distribution curve at each drying stage measured using the Carr–Purcell–Meiboom–Gill pulse sequence provided detailed information in the dynamic change of free water as well as bound water of the whole wood sample. In addition, a new developed SE-SPI pulse sequence was first used to evaluate the spatially resolved T2 distribution of the successive nondestructive sliced layer of wood. Combined with the area integration method, the moisture content in each layer was calculated, and the change of MC profiles within wood at the MC above and below fiber saturation point was well reflected.
Be Causal: De-Biasing Social Network Confounding in Recommendation

In recommendation systems, the existence of the missing-not-at-random (MNAR) problem results in the selection bias issue, degrading the recommendation performance ultimately. A common practice to address MNAR is to treat missing entries from the so-called “exposure” perspective, i.e., modeling how an item is exposed (provided) to a user. Most of the existing approaches use heuristic models or re-weighting strategy on observed ratings to mimic the missing-at-random setting. However, little research has been done to reveal how the ratings are missing from a causal perspective. To bridge the gap, we propose an unbiased and robust method called DENC (De-Bias Network Confounding in Recommendation), inspired by confounder analysis in causal inference. In general, DENC provides a causal analysis on MNAR from both the inherent factors (e.g., latent user or item factors) and auxiliary network’s perspective. Particularly, the proposed exposure model in DENC can control the social network confounder meanwhile preserve the observed exposure information. We also develop a deconfounding model through the balanced representation learning to retain the primary user and item features, which enables DENC generalize well on the rating prediction. Extensive experiments on three datasets validate that our proposed model outperforms the state-of-the-art baselines.
3D Two-Hop Cellular Networks with Wireless Backhauled UAVs: Modeling and Fundamentals

In this paper, we characterize the performance of a three-dimensional (3D) two-hop cellular network in which terrestrial base stations (BSs) coexist with unmanned aerial vehicles (UAVs) to serve a set of ground user equipment (UE). In particular, a UE connects either directly to its serving terrestrial BS by an access link or connects first to its serving UAV which is then wirelessly backhauled to a terrestrial BS (joint access and backhaul). We consider realistic antenna radiation patterns for both BSs and UAVs using practical models developed by the third generation partnership project (3GPP). We assume a probabilistic channel model for the air-to-ground transmission, which incorporates both line-of-sight (LoS) and non-line-of-sight (NLoS) links. Assuming the max-power association policy, we study the performance of the network in both amplify-and-forward (AF) and decode-and-forward (DF) relaying protocols. Using tools from stochastic geometry, we analyze the joint distribution of distance and zenith angle of the closest (and serving) UAV to the origin in a 3D setting. Further, we identify and extensively study key mathematical constructs as the building blocks of characterizing the received signal-to-interference-plus-noise ratio (SINR) distribution. Using these results, we obtain exact mathematical expressions for the coverage probability in both AF and DF relaying protocols. Furthermore, considering the fact that backhaul links could be quite weak because of the downtilted antennas at the BSs, we propose and analyze the addition of a directional uptilted antenna at the BS that is solely used for backhaul purposes. The superiority of having directional antennas with wirelessly backhauled UAVs is further demonstrated via simulation.
NeLF: Practical Novel View Synthesis with Neural Light Field

In this paper, we present a practical and robust deep learning solution for the novel view synthesis of complex scenes. In our approach, a continuous scene is represented as a light ﬁeld, i.e., a set of rays, each of which has a corresponding color. We adopt a 4D parameterization of the light ﬁeld. We then formulate the light ﬁeld as a 4D function that maps 4D coordinates to corresponding color values. We train a deep fully connected network to optimize this function. Then, the scene-speciﬁc model is used to synthesize novel views. Previous light ﬁeld approaches usually require dense view sampling to reliably render high-quality novel views. Our method can render novel views by sampling rays and querying the color for each ray from the network directly; thus enabling fast light ﬁeld rendering with a very sparse set of input images. Our method achieves state-of-the-art novel view synthesis results while maintaining an interactive frame rate.
On the Distributional Properties of Adaptive Gradients

Adaptive gradient methods have achieved remarkable success in training deep neural networks on a wide variety of tasks. However, not much is known about the mathematical and statistical properties of this family of methods. This work aims at providing a series of theoretical analyses of its statistical properties justified by experiments. In particular, we show that when the underlying gradient obeys a normal distribution, the variance of the magnitude of the \textit{update} is an increasing and bounded function of time and does not diverge. This work suggests that the divergence of variance is not the cause of the need for warm up of the Adam optimizer, contrary to what is believed in the current literature.
The Low-Dimensional Linear Geometry of Contextualized Word Representations

Black-box probing models can reliably extract linguistic features like tense, number, and syntactic role from pretrained word representations. However, the manner in which these features are encoded in representations remains poorly understood. We present a systematic study of the linear geometry of contextualized word representations in ELMO and BERT. We show that a variety of linguistic features (including structured dependency relationships) are encoded in low-dimensional subspaces. We then refine this geometric picture, showing that there are hierarchical relations between the subspaces encoding general linguistic categories and more specific ones, and that low-dimensional feature encodings are distributed rather than aligned to individual neurons. Finally, we demonstrate that these linear subspaces are causally related to model behavior, and can be used to perform fine-grained manipulation of BERT’s output distribution.
Node Selection Toward Faster Convergence for Federated Learning on Non-IID Data

Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancies between the global and local objectives, making the FL model slow to converge. In this paper, we proposed <monospace>Optimal Aggregation</monospace> algorithm for better aggregation, which finds out the optimal subset of local updates of participating nodes in each global round, by identifying and excluding the adverse local updates via checking the relationship between the local gradient and the global gradient. Then, we proposed a <monospace>P</monospace>robabilistic <monospace>N</monospace>ode <monospace>S</monospace>election framework (<monospace>FedPNS</monospace>) to dynamically change the probability for each node to be selected based on the output of <monospace>Optimal Aggregation</monospace>. <monospace>FedPNS</monospace> can preferentially select nodes that propel faster model convergence. The convergence rate improvement of <monospace>FedPNS</monospace> over the commonly adopted Federated Averaging (<monospace>FedAvg</monospace>) algorithm is analyzed theoretically. Experimental results demonstrate the effectiveness of <monospace>FedPNS</monospace> in accelerating the FL convergence rate, as compared to <monospace>FedAvg</monospace> with random node selection.
SU(5)L × U(1)Y electroweak unification

We propose here - for the first time in the literature, to our best knowledge - an electroweak unification based on the SU(5)L × U(1)Y gauge group. The spontaneous symmetry breaking takes place in the manner SU(5)L × U(1)Y → U(1)em, due to a particular Higgs sector consisting of five scalar quintuplets. Each scalar quintuplet acquires its own vacuum expectation value, by means of a proper parametrization which is worked out once an overall vacuum expectation value in the model is established. The decoupling of the low energy regime (corresponding to the Standard Model) from the high scale (required by our model here) is straightforwardly achieved in order to preserve the consistency with the present experimental data. Finally, a promising phenomenological outcome is derived by simply tuning a single free parameter. Our results include, besides a viable one-parameter mass spectrum, also the prediction of precisely three generations in the fermion sector and the electric charge quantization.
Data Augmentation for Sign Language Gloss Translation

Sign language translation (SLT) is often decomposed into video-to-gloss recognition and gloss to-text translation, where a gloss is a sequence of transcribed spoken-language words in the order in which they are signed. We focus here on gloss-to-text translation, which we treat as a low-resource neural machine translation (NMT) problem. However, unlike traditional low resource NMT, gloss-to-text translation differs because gloss-text pairs often have a higher lexical overlap and lower syntactic overlap than pairs of spoken languages. We exploit this lexical overlap and handle syntactic divergence by proposing two rule-based heuristics that generate pseudo-parallel gloss-text pairs from monolingual spoken language text. By pre-training on this synthetic data, we improve translation from American Sign Language (ASL) to English and German Sign Language (DGS) to German by up to 3.14 and 2.20 BLEU, respectively.
Real-time Detection of Practical Universal Adversarial Perturbations

Universal Adversarial Perturbations (UAPs) are a prominent class of adversarial examples that exploit the systemic vulnerabilities and enable physically realizable and robust attacks against Deep Neural Networks (DNNs). UAPs generalize across many different inputs; this leads to realistic and effective attacks that can be applied at scale. In this paper we propose HyperNeuron, an efficient and scalable algorithm that allows for the real-time detection of UAPs by identifying suspicious neuron hyper-activations. Our results show the effectiveness of HyperNeuron on multiple tasks (image classification, object detection), against a wide variety of universal attacks, and in realistic scenarios, like perceptual ad-blocking and adversarial patches. HyperNeuron is able to simultaneously detect both adversarial mask and patch UAPs with comparable or better performance than existing UAP defenses whilst introducing a significantly reduced latency of only 0.86 milliseconds per image. This suggests that many realistic and practical universal attacks can be reliably mitigated in real-time, which shows promise for the robust deployment of machine learning systems.
Singularity for bifractional and trifractional Brownian motions based on their Hurst indices

with 0 < H < 1 is called a fractional Brownian motion with Hurst index H. This process is a standard Brownian motion if H = 12 . Probabilistic properties of fractional Brownian motion (fBm) and self-similar processes and their application to modeling phenomena with longrange dependence is discussed in Doukhan et al. (2003). Some recent results in statistical inference for processes driven by fBm were surveyed in Prakasa Rao (2010).
From Masked Language Modeling to Translation: Non-English Auxiliary Tasks Improve Zero-shot Spoken Language Understanding

The lack of publicly available evaluation data for low-resource languages limits progress in Spoken Language Understanding (SLU). As key tasks like intent classification and slot filling require abundant training data, it is desirable to reuse existing data in high-resource languages to develop models for low-resource scenarios. We introduce xSID, a new benchmark for cross-lingual (x) Slot and Intent Detection in 13 languages from 6 language families, including a very low-resource dialect. To tackle the challenge, we propose a joint learning approach, with English SLU training data and non-English auxiliary tasks from raw text, syntax and translation for transfer. We study two setups which differ by type and language coverage of the pre-trained embeddings. Our results show that jointly learning the main tasks with masked language modeling is effective for slots, while machine translation transfer works best for intent classification.
Invariant measures in simple and in small theories

We give examples of (i) a simple theory with a formula (with parameters) which does not fork over the empty set but has mu measure 0 for every automorphism invariant Keisler measure mu, and (ii) a definable group G in a simple theory such that G is not definably amenable, i.e. there is no translation invariant Keisler measure on G We also discuss paradoxical decompositions both in the setting of discrete groups and of definable groups, and prove some positive results about small theories, including the definable amenability of definable groups, and nontriviality of the graded Grothendieck ring.
Emergence of kinship structures and descent systems: multi-level evolutionary simulation and empirical data analysis

In many indigenous societies, people are categorized into several cultural groups, or clans, within which they believe they share ancestors. Clan attributions provide certain rules for marriage and descent. Such rules between clans constitute kinship structures. Anthropologists have revealed several kinship structures. Here, we propose an agent-based model of indigenous societies to reveal the evolution of kinship structures. In the model, several societies compete. Societies themselves comprise multiple families with parameters for cultural traits and mate preferences. These values determine with whom each family cooperates and competes, and they are transmitted to a new generation with mutation. The growth rate of each family is determined by the number of cooperators and competitors. Through this multi-level evolution, family traits and preferences diverge to form clusters that can be regarded as clans. Subsequently, kinship structures emerge, including dual organization and generalized or restricted exchange, as well as patrilineal, matrilineal and double descent systems. These structures emerge depending on the necessity of cooperation and the strength of mating competition. Their dependence is also estimated analytically. Finally, statistical analysis using the Standard Cross-Cultural Sample, a global ethnographic database, empirically verified the theoretical results. Such collaboration between theoretical and empirical approaches will unveil universal features in anthropology.
Excitations in cubic BaSnO3: a consistent picture revealed by combining theory and experiment

Among the transparent conducting oxides, the perovskite barium stannate is most promising for various electronic applications due to its outstanding carrier mobility achieved at room temperature. Most important characteristics however, i.e., its band gap, effective masses, and absorption edge remain controversial. Here, we provide a fully consistent picture by combining state-of-the-art ab initio methodology with forefront electron energy-loss spectroscopy (EELS) and optical absorption measurements. Onand off-axis valence EELS spectra, featuring signals originating from band gap transitions, are acquired on defect-free sample regions of a BaSnO3 single crystal. These highenergy-resolution measurements are able to capture also very weak excitations below the optical gap, attributed to indirect transitions. By temperature-dependent optical absorption measurements, we assess band-gap renormalization effects induced by electron-phonon coupling. Overall, we find for the effective electronic mass, the direct and the indirect gap, the optical gap as well as the absorption onsets and spectra excellent agreement between both experimental techniques and the theoretical many-body results, supporting also the picture of a phonon-mediated mechanism where indirect transitions are activated by phonon-induced symmetry lowering. This work demonstrates a fruitful connection between different high-level theoretical and experimental methods for exploring the characteristics of advanced materials.
PGRP-LB: An Inside View into the Mechanism of the Amidase Reaction

Peptidoglycan recognition proteins (PGRPs) are ubiquitous among animals and play pivotal functions in insect immunity. Non-catalytic PGRPs are involved in the activation of immune pathways by binding to the peptidoglycan (PGN), whereas amidase PGRPs are capable of cleaving the PGN into non-immunogenic compounds. Drosophila PGRP-LB belongs to the amidase PGRPs and downregulates the immune deficiency (IMD) pathway by cleaving meso-2,6-diaminopimelic (meso-DAP or DAP)-type PGN. While the recognition process is well analyzed for the non-catalytic PGRPs, little is known about the enzymatic mechanism for the amidase PGRPs, despite their essential function in immune homeostasis. Here, we analyzed the specific activity of different isoforms of Drosophila PGRP-LB towards various PGN substrates to understand their specificity and role in Drosophila immunity. We show that these isoforms have similar activity towards the different compounds. To analyze the mechanism of the amidase activity, we performed site directed mutagenesis and solved the X-ray structures of wild-type Drosophila PGRP-LB and its mutants, with one of these structures presenting a protein complexed with the tracheal cytotoxin (TCT), a muropeptide derived from the PGN. Only the Y78F mutation abolished the PGN cleavage while other mutations reduced the activity solely. Together, our findings suggest the dynamic role of the residue Y78 in the amidase mechanism by nucleophilic attack through a water molecule to the carbonyl group of the amide function destabilized by Zn2+.