Miracle-dz/UniFAR-FTUs huggingface dataset:
Inclusion, universal design and universal design for learning in higher education: South Africa and the United States

Around the world, institutions of higher education are recognising their responsibilities to achieve the full inclusion of individuals with differing needs and/or disabilities. The frameworks of universal design (UD) and universal design for learning (UDL) offer unique ways to build inclusiveness in our systems. The role of UD and UDL to strengthen successful inclusion of persons with differing needs in higher education programmes is presented from literature, inclusive of national and international policies and resources. Examples from South African and US institutions of higher learning are shared. Discussions of online accessibility, environmental issues, professional development, barriers to inclusion and recommendations for future development in an international context provide a vision for developing inclusive learning environments in higher education.
Dynamic earthquake rupture in the lower crust

A lower crustal earthquake is initiated by dynamic rupturing with fragmentation of wall rock that precedes frictional melting. Earthquakes in the continental crust commonly occur in the upper 15 to 20 km. Recent studies demonstrate that earthquakes also occur in the lower crust of collision zones and play a key role in metamorphic processes that modify its physical properties. However, details of the failure process and sequence of events that lead to seismic slip in the lower crust remain uncertain. Here, we present observations of a fault zone from the Bergen Arcs, western Norway, which constrain the deformation processes of lower crustal earthquakes. We show that seismic slip and associated melting are preceded by fracturing, asymmetric fragmentation, and comminution of the wall rock caused by a dynamically propagating rupture. The succession of deformation processes reported here emphasize brittle failure mechanisms in a portion of the crust that until recently was assumed to be characterized by ductile deformation.
Hyaluronan Injections Show No Histologic Evidence of Adverse Tissue Effects

Background: The safety of hyaluronan intra-articular injections is mostly based on animal studies and clinical evidence rather than histologic studies from human administration. Objective: This study analyzed the histologic effects of viscosupplementation with sodium hyaluronate on the synovium and articular cartilage of human knee specimen status post total knee arthroplasty within 3 years of viscosupplementation. Methods: Twenty-four specimens from total knee arthroplasties from April 2012 to August 2016 at NYU Winthrop Hospital were selected for microscopic analysis. All cases had a diagnosis of end-stage osteoarthritis at the time of surgery. Thirteen of the cases had 3 viscosupplementation injections of the knee with Euflexxa, a hyaluronate-based viscosupplementation agent, within 3 years preceding a total knee replacement. The remaining 11 did not receive viscosupplementation and were incorporated as controls. Upon histologic review, synovium was categorized by degree of hyperplasia and inflammation and the presence or absence of foreign material and giant cell reactions. Residual articular cartilage was categorized by staining intensity, and the presence or absence of crystals, foreign material, and giant cell reactions. Results: No significant difference was found between these groups for degree of synovial hyperplasia (P = .33) or for cartilage staining density (P = .42). None of the samples displayed evidence of foreign material, crystals, or giant cell reactions. Conclusion: In this cohort of patients, we demonstrated that Euflexxa was administered without any discernible microscopic adverse tissue effects.
Shared mycorrhizae but distinct communities of other root-associated microbes on co-occurring native and invasive maples

Background Biological invasions are major drivers of environmental change that can significantly alter ecosystem function and diversity. In plants, soil microbes play an important role in plant establishment and growth; however, relatively little is known about the role they might play in biological invasions. A first step to assess whether root microbes may be playing a role in the invasion process is to find out if invasive plants host different microbes than neighbouring native plant species. Methods In this study we investigated differences in root associated microbes of native sugar maple (Acer saccharum Marsh.) and exotic Norway maple (A. platanoides L.) collected from a forested reserve in eastern Canada. We used microscopy to examine root fungi and high-throughput sequencing to characterize the bacterial, fungal and arbuscular mycorrhizal communities of both maple species over one growing season. Results We found differences in root associated bacterial and fungal communities between host species. Norway maple had a higher bacterial and fungal OTU (operational taxonomic units) richness compared to sugar maple, and the indicator species analysis revealed that nine fungal OTUs and three bacterial OTUs had a significant preference for sugar maple. The dominant bacterial phyla found on the roots of both maple species were Actinobacteria and Proteobacteria. The most common fungal orders associated with the Norway maple roots (in descending order) were Helotiales, Agaricales, Pleosporales, Hypocreales, Trechisporales while the Agaricales, Pleosporales, Helotiales, Capnodiales and Hypocreales were the dominant orders present in the sugar maple roots. Dark septate fungi colonization levels were higher in the sugar maple, but no differences in arbuscular mycorrhizal fungal communities and colonization rates were detected between maple species. Discussion Our findings show that two congeneric plant species grown in close proximity can harbor distinct root microbial communities. These findings provide further support for the importance of plant species in structuring root associated microbe communities. The high colonization levels observed in Norway maple demonstrates its compatibility with arbuscular mycorrhizal fungi in the introduced range. Plant-associated microbial communities can affect host fitness and function in many ways; therefore, the observed differences suggest a possibility that biotic interactions can influence the dynamics between native and invasive species.
AI-powered Posture Training: Application of Machine Learning in Sitting Posture Recognition Using the LifeChair Smart Cushion

Humans spend on average more than half of their day sitting down. The ill-effects of poor sitting posture and prolonged sitting on physical and mental health have been extensively studied, and solutions for curbing this sedentary epidemic have received special attention in recent years. With the recent advances in sensing technologies and Artificial Intelligence (AI), sitting posture monitoring and correction is one of the key problems to address for enhancing human well-being using AI. We present the application of a sitting posture training smart cushion called LifeChair that combines a novel pressure sensing technology, a smartphone app interface and machine learning (ML) for real-time sitting posture recognition and seated stretching guidance. We present our experimental design for sitting posture and stretch pose data collection using our posture training system. We achieved an accuracy of 98.93% in detecting more than 13 different sitting postures using a fast and robust supervised learning algorithm. We also establish the importance of taking into account the divergence in user body mass index in posture monitoring. Additionally, we present the first ML-based human stretch pose recognition system for pressure sensor data and show its performance in classifying six common chair-bound stretches.
Decentralized Optimization with Edge Sampling

In this paper, we propose a decentralized distributed algorithm with stochastic communication among nodes, building on a sampling method called "edge sampling''. Such a sampling algorithm allows us to avoid the heavy peer-to-peer communication cost when combining neighboring weights on dense networks while still maintains a comparable convergence rate. In particular, we quantitatively analyze its theoretical convergence properties, as well as the optimal sampling rate over the underlying network. When compared with previous methods, our solution is shown to be unbiased, communication-efficient and suffers from lower sampling variances. These theoretical findings are validated by both numerical experiments on the mixing rates of Markov Chains and distributed machine learning problems.
CLVSA: A Convolutional LSTM Based Variational Sequence-to-Sequence Model with Attention for Predicting Trends of Financial Markets

Financial markets are a complex dynamical system. The complexity comes from the interaction between a market and its participants, in other words, the integrated outcome of activities of the entire participants determines the markets trend, while the markets trend affects activities of participants. These interwoven interactions make financial markets keep evolving. Inspired by stochastic recurrent models that successfully capture variability observed in natural sequential data such as speech and video, we propose CLVSA, a hybrid model that consists of stochastic recurrent networks, the sequence-to-sequence architecture, the self- and inter-attention mechanism, and convolutional LSTM units to capture variationally underlying features in raw financial trading data. Our model outperforms basic models, such as convolutional neural network, vanilla LSTM network, and sequence-to-sequence model with attention, based on backtesting results of six futures from January 2010 to December 2017. Our experimental results show that, by introducing an approximate posterior, CLVSA takes advantage of an extra regularizer based on the Kullback-Leibler divergence to prevent itself from overfitting traps.
Mask and Infill: Applying Masked Language Model for Sentiment Transfer

This paper focuses on the task of sentiment transfer on non-parallel text, which modifies sentiment attributes (e.g., positive or negative) of sentences while preserving their attribute-independent contents. Existing methods adopt RNN encoder-decoder structure to generate a new sentence of a target sentiment word by word, which is trained on a particular dataset from scratch and have limited ability to produce satisfactory sentences. When people convert the sentiment attribute of a given sentence, a simple but effective approach is to only replace the sentiment tokens of the sentence with other expressions indicative of the target sentiment, instead of building a new sentence from scratch. Such a process is very similar to the task of Text Infilling or Cloze. With this intuition, we propose a two steps approach: Mask and Infill. In the \emph{mask} step, we identify and mask the sentiment tokens of a given sentence. In the \emph{infill} step, we utilize a pre-trained Masked Language Model (MLM) to infill the masked positions by predicting words or phrases conditioned on the context\footnote{In this paper, \emph{content} and \emph{context} are equivalent, \emph{style}, \emph{attribute} and \emph{label} are equivalent. }and target sentiment. We evaluate our model on two review datasets \emph{Yelp} and \emph{Amazon} by quantitative, qualitative, and human evaluations. Experimental results demonstrate that our model achieve state-of-the-art performance on both accuracy and BLEU scores.
Semi-supervised Three-dimensional Reconstruction Framework with GAN

Because of the intrinsic complexity in computation, three-dimensional (3D) reconstruction is an essential and challenging topic in computer vision research and applications. The existing methods for 3D reconstruction often produce holes, distortions and obscure parts in the reconstructed 3D models, or can only reconstruct voxelized 3D models for simple isolated objects. So they are not adequate for real usage. From 2014, the Generative Adversarial Network (GAN) is widely used in generating unreal dataset and semi-supervised learning. So the focus of this paper is to achieve high quality 3D reconstruction performance by adopting GAN principle. We propose a novel semi-supervised 3D reconstruction framework, namely SS-3D-GAN, which can iteratively improve any raw 3D reconstruction models by training the GAN models to converge. This new model only takes real-time 2D observation images as the weak supervision, and doesn't rely on prior knowledge of shape models or any referenced observations. Finally, through the qualitative and quantitative experiments & analysis, this new method shows compelling advantages over the current state-of-the-art methods on Tanks & Temples reconstruction benchmark dataset.
Automatic Grassland Degradation Estimation Using Deep Learning

Grassland degradation estimation is essential to prevent global land desertification and sandstorms. Typically, the key to such estimation is to measure the coverage of indicator plants. However, traditional methods of estimation rely heavily on human eyes and manual labor, thus inevitably leading to subjective results and high labor costs. In contrast, deep learning-based image segmentation algorithms are potentially capable of automatic assessment of the coverage of indicator plants. Nevertheless, a suitable image dataset comprising grassland images is not publicly available. To this end, we build an original Automatic Grassland Degradation Estimation Dataset (AGDE-Dataset), with a large number of grassland images captured from the wild. Based on AGDE-Dataset, we are able to propose a brand new scheme to automatically estimate grassland degradation, which mainly consists of two components. 1) Semantic segmentation: we design a deep neural network with an improved encoder-decoder structure to implement semantic segmentation of grassland images. In addition, we propose a novel Focal-Hinge Loss to alleviate the class imbalance of semantics in the training stage. 2) Degradation estimation: we provide the estimation of grassland degradation based on the results of semantic segmentation. Experimental results show that the proposed method achieves satisfactory accuracy in grassland degradation estimation.
Some Things are Easier for the Dumb and the Bright Ones (Beware the Average!)

Model checking strategic abilities in multi-agent systems is hard, especially for agents with partial observability of the state of the system. In that case, it ranges from NP-complete to undecidable, depending on the precise syntax and the semantic variant. That, however, is the worst case complexity, and the problem might as well be easier when restricted to particular subclasses of inputs. In this paper, we look at the verification of models with "extreme" epistemic structure, and identify several special cases for which model checking is easier than in general. We also prove that, in the other cases, no gain is possible even if the agents have almost full (or almost nil) observability. To prove the latter kind of results, we develop generic techniques that may be useful also outside of this study.
A Survey on Hierarchical Planning - One Abstract Idea, Many Concrete Realizations

Hierarchical planning has attracted renewed interest in the last couple of years, which led to numerous novel formalisms, problem classes, and theoretical investigations. Yet it is important to differentiate between the various formalisms and problem classes, since they show -- sometimes fundamental -- differences with regard to their expressivity and computational complexity: Some of them can be regarded equivalent to non-hierarchical formalisms while others are clearly more expressive. We survey the most important hierarchical problem classes and explain their differences and similarities. We furthermore give pointers to some of the best-known planning systems capable of solving the respective problem classes.
Stratified Evidence Logics

Evidence logics model agents' belief revision process as they incorporate and aggregate information obtained from multiple sources. This information is captured using neighbourhood structures, where individual neighbourhoods represent pieces of evidence. In this paper we propose an extended framework which allows one to explicitly quantify either the number of evidence sets, or effort, needed to justify a given proposition, provide a complete deductive calculus and a proof of decidability, and show how existing frameworks can be embedded into ours.
Election with Bribe-Effect Uncertainty: A Dichotomy Result

We consider the electoral bribery problem in computational social choice. In this context, extensive studies have been carried out to analyze the computational vulnerability of various voting (or election) rules. However, essentially all prior studies assume a deterministic model where each voter has an associated threshold value, which is used as follows. A voter will take a bribe and vote according to the attacker's (i.e., briber's) preference when the amount of the bribe is above the threshold, and a voter will not take a bribe when the amount of the bribe is not above the threshold (in this case, the voter will vote according to its own preference, rather than the attacker's). In this paper, we initiate the study of a more realistic model where each voter is associated with a willingness function, rather than a fixed threshold value. The willingness function characterizes the likelihood a bribed voter would vote according to the attacker's preference; we call this bribe-effect uncertainty. We characterize the computational complexity of the electoral bribery problem in this new model. In particular, we discover a dichotomy result: a certain mathematical property of the willingness function dictates whether or not the computational hardness can serve as a deterrence to bribery attackers.
Dispatching Through Pricing: Modeling Ride-Sharing and Designing Dynamic Prices

Over the past few years, ride-sharing has emerged as an effective way to relieve traffic congestion. A key problem for the ride-sharing platforms is to come up with a revenue-optimal (or GMV-optimal) pricing scheme and a vehicle dispatching policy that incorporate geographic and temporal information. In this paper, we aim to tackle this problem via an economic approach. Modeled naively, the underlying optimization problem may be non-convex and thus hard to solve. To this end, we use a so-called ``ironing'' technique to convert the problem into an equivalent convex optimization one via a clean Markov decision process (MDP) formulation, where the states are the driver distributions and the decision variables are the prices for each pair of locations. Our main finding is an efficient algorithm that computes the exact revenue-optimal (or GMV-optimal) randomized pricing scheme, which naturally induces the accompany vehicle dispatching policy. We also conduct empirical evaluations of our solution through real data of a major ride-sharing platform and show its advantages over fixed pricing schemes as well as several prevalent surge-based pricing schemes.
Fair and Explainable Dynamic Engagement of Crowd Workers

Years of rural-urban migration has resulted in a significant population in China seeking ad-hoc work in large urban centres. At the same time, many businesses face large fluctuations in demand for manpower and require more efficient ways to satisfy such demands. This paper outlines AlgoCrowd, an artificial intelligence (AI)-empowered algorithmic crowdsourcing platform. Equipped with an efficient explainable task-worker matching optimization approach designed to focus on fair treatment of workers while maximizing collective utility, the platform provides explainable task recommendations to workers' personal work management mobile apps which are becoming popular, with the aim to address the above societal challenge.
From Statistical Transportability to Estimating the Effect of Stochastic Interventions

Learning systems often face a critical challenge when applied to settings that differ from those under which they were initially trained. In particular, the assumption that both the source/training and the target/deployment domains follow the same causal mechanisms and observed distributions is commonly violated. This implies that the robustness and convergence guarantees usually expected from these methods are no longer attainable. In this paper, we study these violations through causal lens using the formalism of statistical transportability [Pearl and Bareinboim, 2011] (PB, for short). We start by proving sufficient and necessary graphical conditions under which a probability distribution observed in the source domain can be extrapolated to the target one, where strictly less data is available. We develop the first sound and complete procedure for statistical transportability, which formally closes the problem introduced by PB. Further, we tackle the general challenge of identification of stochastic interventions from observational data [Sec. ~4.4, Pearl, 2000]. This problem has been solved in the context of atomic interventions using Pearl's do-calculus, which lacks complete treatment in the stochastic case. We prove completeness of stochastic identification by constructing a reduction of any instance of this problem to an instance of statistical transportability, closing the problem.
MEGAN: A Generative Adversarial Network for Multi-View Network Embedding

Data from many real-world applications can be naturally represented by multi-view networks where the different views encode different types of relationships (e.g., friendship, shared interests in music, etc.) between real-world individuals or entities. There is an urgent need for methods to obtain low-dimensional, information preserving and typically nonlinear embeddings of such multi-view networks. However, most of the work on multi-view learning focuses on data that lack a network structure, and most of the work on network embeddings has focused primarily on single-view networks. Against this background, we consider the multi-view network representation learning problem, i.e., the problem of constructing low-dimensional information preserving embeddings of multi-view networks. Specifically, we investigate a novel Generative Adversarial Network (GAN) framework for Multi-View Network Embedding, namely MEGAN, aimed at preserving the information from the individual network views, while accounting for connectivity across (and hence complementarity of and correlations between) different views. The results of our experiments on two real-world multi-view data sets show that the embeddings obtained using MEGAN outperform the state-of-the-art methods on node classification, link prediction and visualization tasks.
Accelerated Incremental Gradient Descent using Momentum Acceleration with Scaling Factor

Recently, research on variance reduced incremental gradient descent methods (e.g., SAGA) has made exciting progress (e.g., linear convergence for strongly convex (SC) problems). However, existing accelerated methods (e.g., point-SAGA) suffer from drawbacks such as inflexibility. In this paper, we design a novel and simple momentum to accelerate the classical SAGA algorithm, and propose a direct accelerated incremental gradient descent algorithm. In particular, our theoretical result shows that our algorithm attains a best known oracle complexity for strongly convex problems and an improved convergence rate for the case of n>=L/\mu. We also give experimental results justifying our theoretical results and showing the effectiveness of our algorithm.