Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Context-Free Ambiguity of Emoji: A Data-Driven Study of 1, 289 Emojis

Due to their pictographic nature, emojis come with baked-in, grounded semantics. Although this makes emojis promising candidates for new forms of more accessible communication, it is still unknown to what degree humans agree on the inherent meaning of emojis when encountering them outside of concrete textual contexts. To bridge this gap, we collected a crowdsourced dataset (made publicly available) of one-word descriptions for 1,289 emojis presented to participants with no surrounding text. The emojis and their interpretations were then examined for ambiguity. We find that, with 30 annotations per emoji, 16 emojis (1.2%) are completely unambiguous, whereas 55 emojis (4.3%) are so ambiguous that the variation in their descriptions is as high as that in randomly chosen descriptions. Most emojis lie between these two extremes. Furthermore, investigating the ambiguity of different types of emojis, we find that emojis representing symbols from established, yet not cross-culturally familiar code books (e.g., zodiac signs, Chinese characters) are most ambiguous. We conclude by discussing design implications.
Representational Continuity for Unsupervised Continual Learning

Continual learning (CL) aims to learn a sequence of tasks without forgetting the previously acquired knowledge. However, recent CL advances are restricted to supervised continual learning (SCL) scenarios. Consequently, they are not scalable to real-world applications where the data distribution is often biased and unannotated. In this work, we focus on unsupervised continual learning (UCL), where we learn the feature representations on an unlabelled sequence of tasks and show that reliance on annotated data is not necessary for continual learning. We conduct a systematic study analyzing the learned feature representations and show that unsupervised visual representations are surprisingly more robust to catastrophic forgetting, consistently achieve better performance, and generalize better to out-of-distribution tasks than SCL. Furthermore, we find that UCL achieves a smoother loss landscape through qualitative analysis of the learned representations and learns meaningful feature representations. Additionally, we propose Lifelong Unsupervised Mixup (LUMP), a simple yet effective technique that interpolates between the current task and previous tasks' instances to alleviate catastrophic forgetting for unsupervised representations.
Impact of classification difficulty on the weight matrices spectra in Deep Learning and application to early-stopping

Much research effort has been devoted to explaining the success of deep learning. Random Matrix Theory (RMT) provides an emerging way to this end: spectral analysis of large random matrices involved in a trained deep neural network (DNN) such as weight matrices or Hessian matrices with respect to the stochastic gradient descent algorithm. To have more comprehensive understanding of weight matrices spectra, we conduct extensive experiments on weight matrices in different modules, e.g., layers, networks and data sets. Following the previous work of \cite{martin2018implicit}, we classify the spectra in the terminal stage into three main types: Light Tail (LT), Bulk Transition period (BT) and Heavy Tail(HT). These different types, especially HT, implicitly indicate some regularization in the DNNs. A main contribution from the paper is that we identify the difficulty of the classification problem as a driving factor for the appearance of heavy tail in weight matrices spectra. Higher the classification difficulty, higher the chance for HT to appear. Moreover, the classification difficulty can be affected by the signal-to-noise ratio of the dataset, or by the complexity of the classification problem (complex features, large number of classes) as well. Leveraging on this finding, we further propose a spectral criterion to detect the appearance of heavy tails and use it to early stop the training process without testing data. Such early stopped DNNs have the merit of avoiding overfitting and unnecessary extra training while preserving a much comparable generalization ability. These findings from the paper are validated in several NNs, using Gaussian synthetic data and real data sets (MNIST and CIFAR10).
Cooperative Intramolecular Dynamics Control the Chain-Length-Dependent Glass Transition in Polymers

The glass transition is a long-standing unsolved problem in materials science. For polymers, our understanding of glass-formation is particularly poor due to the added complexity of chain connectivity and flexibility; structural relaxation of polymers thus involves a complex interplay between intra- and inter-molecular cooperativity. Here we study how the glass transition temperature Tg varies with molecular weight M for different polymer chemistries and chain flexibilities. We find that Tg(M) is controlled by the average mass (or volume) per conformational degree of freedom, and that a `local' molecular relaxation (involving a few conformers) controls the larger-scale cooperative alpha relaxation responsible for Tg. We propose that dynamic facilitation where a `local' relaxation facilitates adjacent relaxations, leading to hierarchical dynamics, can explain our observations including logarithmic Tg(M) dependences. Our study provides a new understanding of molecular relaxations and the glass transition in polymers, which paves the way for predictive design of polymers based on monomer-scale metrics.
Symplectic forms on Banach spaces

We extend and generalize the result of Kalton and Swanson ( Z 2 is a symplectic Banach space with no Lagrangian subspace) by showing that all higher order Rochgberg spaces R ( n ) are symplectic Banach spaces with no Lagrangian subspaces. The nontrivial symplectic structure on even spaces is the one induced by the natural duality; while the nontrivial symplectic structure on odd spaces requires perturbation with a complex structure. We will also study symplectic structures on general Banach spaces and, motivated by the unexpected appearance of complex structures, we introduce and study almost symplectic structures.
Electromagnetic scattering analysis and radar cross section (RCS) computation of circular-like impedance objects using the method of auxiliary sources (MAS) in conjunction with the standard impedance boundary condition (SIBC)

A combination of the Method of Auxiliary Sources (MAS) and the Standard Impedance Boundary Condition (SIBC) is employed in the analysis of transverse magnetic (TM) plane wave scattering from various infinite, impedance cylindrical objects. Various cross sections, which can be considered as perturbed duplicates of the circle, are investigated. The computational error related to the numerical solution of the MAS linear system, as well as the condition number of the pertinent impedance matrix, are plotted and the results are discussed. A parametric analysis is conducted in order to probe the effect that, the degree of perturbation from the circle and the number of the auxiliary sources used, have upon the numerical error and the stability of the ensuing system. Furthermore, the monostatic RCS for the location of the auxiliary surface with optimum trade-off between error and condition number is presented. The aim of this study is the extraction of useful conclusions on the optimum location of the auxiliary sources for arbitrarily shaped impedance scatterers.
FaceSigns: Semi-Fragile Neural Watermarks for Media Authentication and Countering Deepfakes

Deepfakes and manipulated media are becoming a prominent threat due to the recent advances in realistic image and video synthesis techniques. There have been several attempts at combating Deepfakes using machine learning classifiers. However, such classifiers do not generalize well to black-box image synthesis techniques and have been shown to be vulnerable to adversarial examples. To address these challenges, we introduce a deep learning based semi-fragile watermarking technique that allows media authentication by verifying an invisible secret message embedded in the image pixels. Instead of identifying and detecting fake media using visual artifacts, we propose to proactively embed a semi-fragile watermark into a real image so that we can prove its authenticity when needed. Our watermarking framework is designed to be fragile to facial manipulations or tampering while being robust to benign image-processing operations such as image compression, scaling, saturation, contrast adjustments etc. This allows images shared over the internet to retain the verifiable watermark as long as face-swapping or any other Deepfake modification technique is not applied. We demonstrate that FaceSigns can embed a 128 bit secret as an imperceptible image watermark that can be recovered with a high bit recovery accuracy at several compression levels, while being non-recoverable when unseen Deepfake manipulations are applied. For a set of unseen benign and Deepfake manipulations studied in our work, FaceSigns can reliably detect manipulated content with an AUC score of 0.996 which is significantly higher than prior image watermarking and steganography techniques.
Kinship Is a Network Tracking Social Technology, Not an Evolutionary Phenomenon

On one hand, kinship is a universal human phenomenon that tends to align with biological relatedness, which might suggest evolutionary foundations. On the other hand, kinship has exceptional variation across the human populations, which points to cultural foundations. Furthermore, even if its foundation was biological, kinship is often too imprecise to track genetic relatedness efficiently, while inclusive fitness theory would suggest focusing only on the closest relatives, which is not the case in most human cultures. It was the parallel validity of these contradicting arguments that led to decades of fierce debate about the definition and measurement of the phenomenon. This paper offers a new approach to kinship. First, the model demonstrates that it is possible to generate kinship networks (a) derived from the kind of basic kin connections that our species shares with other apes, but (b) driven by network rather than biological logic beyond the immediate family. Second the model demonstrates that kinship as a network heuristic works efficiently only in high fertility societies, and gives way to similarity-based friendship with demographic transition. The results explain (i) why kinship labelling is unique to our species, (ii) why kinship is universal among human cultures, (iii) why kinship terminology systems are varied across cultures, (iv) why linguistic kin assignment is imprecise, and (v) why kinship is replaced by homophily when relatives are scarce. The model offers a unifying framework to the debate between social and evolutionary anthropology concerning the concept of human kinship.
Non-Euclidean Monotone Operator Theory with Applications to Recurrent Neural Networks

We provide a novel transcription of monotone operator theory to the non-Euclidean finite-dimensional spaces ℓ1 and ℓ∞. We first establish properties of mappings which are monotone with respect to the non-Euclidean norms ℓ1 or ℓ∞. In analogy with their Euclidean counterparts, mappings which are monotone with respect to a non-Euclidean norm are amenable to numerous algorithms for computing their zeros. We demonstrate that several classic iterative methods for computing zeros of monotone operators are directly applicable in the non-Euclidean framework. We present a case-study in the equilibrium computation of recurrent neural networks and demonstrate that casting the computation as a suitable operator splitting problem improves convergence rates.
Improving Human-AI Partnerships in Child Welfare: Understanding Worker Practices, Challenges, and Desires for Algorithmic Decision Support

AI-based decision support tools (ADS) are increasingly used to augment human decision-making in high-stakes, social contexts. As public sector agencies begin to adopt ADS, it is critical that we understand workers’ experiences with these systems in practice. In this paper, we present findings from a series of interviews and contextual inquiries at a child welfare agency, to understand how they currently make AI-assisted child maltreatment screening decisions. Overall, we observe how workers’ reliance upon the ADS is guided by (1) their knowledge of rich, contextual information beyond what the AI model captures, (2) their beliefs about the ADS’s capabilities and limitations relative to their own, (3) organizational pressures and incentives around the use of the ADS, and (4) awareness of misalignments between algorithmic predictions and their own decision-making objectives. Drawing upon these findings, we discuss design implications towards supporting more effective human-AI decision-making.
Protecting Celebrities from DeepFake with Identity Consistency Transformer

In this work we propose Identity Consistency Transformer, a novel face forgery detection method that focuses on high-level semantics, specifically identity information, and detecting a suspect face by finding identity inconsistency in inner and outer face regions. The Identity Consistency Transformer incorporates a consistency loss for identity consistency determination. We show that Identity Consistency Transformer exhibits superior generalization ability not only across different datasets but also across various types of image degradation forms found in real-world applications including deepfake videos. The Identity Consistency Transformer can be easily enhanced with additional identity information when such information is available, and for this reason it is especially well-suited for detecting face forgeries involving celebrities.11Code will be released at https://github.com/LightDXY/ICT_DeepFake
Preference Robust Optimization with Quasi-Concave Choice Functions for Multi-Attribute Prospects

Preference robust choice models concern decision-making problems where the decision maker’s (DM) utility/risk preferences are ambiguous and the evaluation is based on the worst-case utility function/risk measure from a set of plausible utility functions/risk measures. The current preference robust choice models are mostly built upon von Neumann-Morgenstern expected utility theory, the theory of convex risk measures, or Yaari’s dual theory of choice, all of which assume the DM’s preferences satisfy some speciﬁed axioms. In this paper, we extend the preference robust approach to a broader class of choice functions which satisfy monotonicity and quasi-concavity in the space of multi-attribute random prospects. While our new model is non-parametric and signiﬁcantly extends the coverage of decision-making problems, it also brings new computational challenges due to the non-convexity of the optimization formulations, which arises from the non-concavity of the class of quasi-concave choice functions. To tackle these challenges, we develop a sorting-based algorithm that eﬃciently evaluates the robust choice function (RCF) by solving a sequence of linear programming problems. Then, we show how to solve preference robust optimization (PRO) problems by solving a sequence of convex optimization problems. We test our robust choice model and computational scheme on a single-attribute portfolio optimization problem and a multi-attribute capital allocation problem.
Magnetization statics and dynamics in (Ir/Co/Pt)6 multilayers with Dzyaloshinskii–Moriya interaction

Magnetic multilayers of (Ir/Co/Pt)6 with interfacial Dzyaloshinskii-Moriya interaction (IDMI) were deposited by magnetron sputtering with Co thickness d=1.8 nm. Exploiting magneto-optical Kerr effect in longitudinal mode microscopy, magnetic force microscopy, and vibrating sample magnetometry, the magnetic field-driven evolution of domain structures and magnetization hysteresis loops have been studied. The existence of weak stripe domains structure was deduced – tens micrometers size domains with in-plane “core” magnetization modulated by hundred of nanometers domains with out-of-plane magnetization. Micromagnetic simulations interpreted such magnetization distribution. Quantitative evaluation of IDMI was carried out using Brillouin light scattering (BLS) spectroscopy as the difference between Stokes and anti-Stokes peak frequencies Δ f. Due to the additive nature of IDMI, the asymmetric combination of Ir and Pt covers led to large values of effective IDMI energy density Deff. It was found that Stokes and anti-Stokes frequencies as well as Δ f, measured as a function of in-plane applied magnetic field, show hysteresis. These results are explained under the consideration of the influence of IDMI on the dynamics of the in-plane magnetized “core” with weak stripe domains.
The impacts of armed conflict on child health: Evidence from 56 developing countries

This article evaluates the extent to which armed conflicts influence early childhood health for 56 developing countries over nearly 30 years. Exploiting both spatial and temporal variations in conflict exposure within a difference-in-differences framework, the article uncovers detrimental ramifications of armed conflicts on the health outcomes of children under five years old. Particularly, children exposed to armed conflicts have lower height-for-age, weight-for-height, and weight-for-age z-scores by 0.08, 0.05, and 0.10 standard deviations, respectively. Taking the average corresponding anthropometric z-scores of unexposed children as the benchmarks, exposure to armed conflicts makes children 6.6% shorter for their age, 11% thinner for their height, and 9% thinner for their age. Exposure to armed conflicts further makes children 2.2, 0.8, and 2.6 percentage points more likely to be stunted, wasted, and underweight, respectively (i.e. 7.3%, 7.9%, and 10.2% increases compared to the benchmark averages of those unexposed to armed conflicts). In addition, the heterogeneity analyses suggest that children born to low-education mothers, children from relatively poor households, and children living in rural areas tend to bear the larger health setbacks. Given the long-lasting impacts of poor health in early childhood on adult outcomes, delivering nutrition interventions to children in conflict zones is of utmost importance, and extra attention should be given to children of disadvantaged backgrounds. JEL codes: I10, I15, J13, O15
Single Particle Electrochemical Oxidation of Polyvinylpyrrolidone-Capped Silver Nanospheres, Nanocubes, and Nanoplates in Potassium Nitrate and Potassium Hydroxide

Single particle electrochemical oxidation of poly(vinylpyrrolidone)-capped silver nanoparticles at a microdisk electrode was investigated as a function of particle shape (spheres, cubes, and plates) in potassium nitrate and potassium hydroxide solutions. In potassium nitrate, extreme anodic potentials (≥1500 mV vs. Ag/AgCl (3 M KCl)) were necessary to achieve oxidation, while lower anodic potentials were required in potassium hydroxide (≥900 mV vs. Ag/AgCl (saturated KCl)). Upon oxidation, silver oxide is formed, readily catalyzing water oxidation, producing a spike-step current response. The spike duration for each particle was used to probe effects of particle shape on the oxidation mechanism, and is substantially shorter in nitrate at the large overpotentials than in hydroxide solution. The integration of current spikes indicate initial oxidation to Ag(I) in a mixed-valance complex. In both electrolytes, the rate of silver oxidation strongly depends on silver content of the nanoparticles, rather than the shape-dependent variable–surface area. The step height, which reflects rate of water oxidation, also tracks the silver content more so than shape. Results were compared to those from less-protected citrate-capped particles and suggest that contributions of the polymer capping ligand to kinetic barriers to oxidation are negligible under these conditions.
Information driven entrepreneurship: A competitive intelligence approach

Abstract At present, there is an increasing concern about the dearth of white-collar job opportunities for many recent graduates of tertiary institutions in Nigeria, including graduates of Library and Information Science (LIS) programmes. Hence, there is a need for LIS professionals to proactively explore ways to create jobs based on their professional skills and knowledge, rather than wait to be employed. This paper identifies and discusses areas of information driven entrepreneurial opportunities available for LIS professionals in Nigeria and highlights how competitive intelligence could be applied as a strategic tool to start their own businesses by putting LIS skills into practice. It also highlights the necessary skills required for entrepreneur startup and its sustainability as well as the benefits of being a librapreneur.
White-Cell Derived Inflammatory Biomarkers in Prediction of Postoperative Delirium in Elderly Patients Undergoing Surgery for Lower Limb Fracture Under Non-General Anaesthesia

Purpose The aim of this study was to investigate whether white-cell derived biomarkers could serve as potential markers in prediction of postoperative delirium (POD) after lower limb fracture. Patients and Methods Elderly patients with surgery for lower limb fracture under non-general anaesthesia were included. Neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR) and platelet-to-white cell ratio (PWR), which were most recently measured preceding surgery and measured within 24h after surgery, were calculated. Delirium was measured with Confusion Assessment Method (CAM) once daily from preoperative day 1 to postoperative day 3 or hospital discharge. Results The incidence of POD was 32.6% (60/184). Between patients with and those without POD, there were significant differences in preoperative hematological biomarkers (neutrophil count, lymphocyte count, NLR and PWR) and postoperative hematological biomarkers (white cell count, neutrophil count, lymphocyte count, NLR, PLR and PWR). More obvious changes before and after operation for NLR, PLR and C-reactive protein (CRP) were found in patients with POD. Multivariate logistic regression showed that benzodiazepines (OR, 7.912; 95% CI, 1.884–33.230; p = 0.005), change of CRP (OR, 1.017; 95% CI, 1.007–1.027; p = 0.001) and postoperative NLR (OR, 1.358; 95% CI, 1.012–1.823; p = 0.041) were associated with POD. When the changes of NLR, PLR and PWR entered multivariate logistic regression, older age (OR, 1.073; 95% CI, 1.001–1.149; p = 0.046), benzodiazepines (OR, 6.811; 95% CI, 1.652–28.081; p = 0.008), greater change of CRP (OR, 1.015; 95% CI, 1.006–1.023; p = 0.001) and greater change of NLR (OR, 1.266; 95% CI, 1.035–1.549; p = 0.022) were associated with increased risk of POD. Postoperative NLR had high accuracy to predict POD with area under curve (AUC) of 0.790 (95% CI 0.708 to 0.872). Conclusion Age, benzodiazepines, postoperative NLR, change of NLR and change of CRP were independent predictable markers for POD in elderly patients undergoing surgery for lower limb fracture under non-general anaesthesia. Early postoperative NLR may help to recognize POD as soon as possible.