Miracle-dz/UniFAR-FTUs huggingface dataset:
Chapter 20:Displacing Student-Teacher Equilibrium in Virtual Learning Environments

A great catalyst causing considerable change in our 21st century classrooms is the recent implementation and usage of the now ubiquitous computer; this new technological innovation in the secondary school classroom has had a tremendous over-riding effect on traditional learning environments. With digital technologies supplanting older ones, the computer has had an impact within schools. Not only are computers instrumental in the creation of virtual learning environments, but also within these new environments they also affect student‐teacher social dynamics. Consequently, what are being altered arethelearningenvironmentsthemselvesaswellasthepedagogicalandsocial relationships forged between educators and their pupils. In the new virtual classrooms, frequently educators rely on their “techsavvy” students for technical assistance while these “whiz kids” often have developed digital competence far surpassing their teachers (Black, 2002; Negroponte, 1995; Tapscott, 1998). However, it is often assumed (and hoped) that teachers in training have systematically developed a rich and extensive knowledge base in their area of technological expertise before being professionally hired to disseminate their knowledge to future students. Unfortunately, this is not the case. With the rapid change from analog to digital technologies within the last decade, with the embracement of many of these new technologies within educational institutions, and with the swift technological advancements perpetually occurring, many teachers find themselves in complex and tenuous situations. Both new and experienced educators have an extremely difficult time obtaining proper training in order to keep abreast of digital technologies in the schools. In this chapter I will discuss case study research in which I examined the relationships between teachers and students in virtual learning environments (Black, 2002). For this study, I interviewed both educators and pupils in order toobtaintheirperceptionsofthecomplexinteractionsanddynamicstheyhave forged. One of the teachers I interviewed, Mr. Ross observes that good technology teachers obtain much respect because the prevalent student attitude is that mastering technology is not only important but also “cool”. The ideal situation is for teachers to have acquired a firm foundation studying computers andtechnologyatuniversity.Theconditionforthismodelfoundationissuperb technological teacher training at a teacher’s college; one can then supplement
the Regional

Rustic development represents an essential part of the regional planning goals for any state. Paying an attention for the positional factors of development give an important opportunity for their development whether in the agricultural production, or in their infrastructure services etc. With its quantitative approach, and through the variables that this study employs, the study results that the extension of the urban units take different patterns of extensions, and these patterns are systemic, rectangular, ribbon, and linear patterns. These patterns and their place variation is closely related to the positional dimensions of the rustic settlements. It marks as a positive relationship according to the multi-link of coefficient. The study results that there are three dimensions for such distribution. These dimensions are the distance from the urban center, the distance from the small rivers, and the distance from principle agricultural fields. These dimensions have a from the distribution and explain from the reasons behind the positional variations for the distribution of the urban units of the settlements that this study concerns. The study Findls ouf that the distance between the non-temple roads and the principle rivers and the neighboring settlements have no essential relationship for such distribution, for this reason these hypotheses are neglected. This means that the positional relationship which is achieved by the multi-link of sloping (step wise) can enable any regional development schema to control the future spread pattering of settlements by depending the variations in the planning of the rustic development whether in the area of this study or another one.
Domain Invariant Model with Graph Convolutional Network for Mammogram Classification

Due to its safety-critical property, the image-based diagnosis is desired to achieve robustness on out-of-distribution (OOD) samples. A natural way towards this goal is capturing only clinically disease-related features, which is composed of macroscopic attributes (e.g., margins, shapes) and microscopic image-based features (e.g., textures) of lesion-related areas. However, such disease-related features are often interweaved with data-dependent (but disease irrelevant) biases during learning, disabling the OOD generalization. To resolve this problem, we propose a novel framework, namely Domain Invariant Model with Graph Convolutional Network (DIM-GCN), which only exploits invariant disease-related features from multiple domains. Specifically, we first propose a Bayesian network, which explicitly decomposes the latent variables into disease-related and other disease-irrelevant parts that are provable to be disentangled from each other. Guided by this, we reformulate the objective function based on Variational Auto-Encoder, in which the encoder in each domain has two branches: the domain-independent and -dependent ones, which respectively encode disease-related and -irrelevant features. To better capture the macroscopic features, we leverage the observed clinical attributes as a goal for reconstruction, via Graph Convolutional Network (GCN). Finally, we only implement the disease-related features for prediction. The effectiveness and utility of our method are demonstrated by the superior OOD generalization performance over others on mammogram benign/malignant diagnosis.
XMM-Newton observations of the Narrow-Line Seyfert 1 Galaxy IRAS 13224−3809: X-ray spectral analysis II.

Previously, we modelled the X-ray spectra of the narrow-line Seyfert 1 galaxy IRAS 13224−3809 using a disc reflection model with a fixed electron density of 1015 cm−3. An additional blackbody component was required to fit the soft X-ray excess below 2 keV. In this work, we analyse simultaneously five flux-resolved XMM-Newton spectra of this source comprising data collected over 2 Ms. A disc reflection model with an electron density of ne ≈ 1020 cm−3 and an iron abundance of ZFe = 3.2 ± 0.5 Z⊙ is used to fit the broad-band spectra of this source. No additional component is required to fit the soft excess. Our best-fit model provides consistent measurements of black hole spin and disc inclination angle as previous models where a low disc density was assumed. In the end, we calculate the average illumination distance between the corona and the reflection region in the disc of IRAS 13224−3809 based on best-fit density and ionisation parameters, which changes from 0.43$\sqrt{f_{\rm AD}/f_{\rm INF}}$ rg in the lowest flux state to 1.71$\sqrt{f_{\rm AD}/f_{\rm INF}}$ rg in the highest flux state assuming a black hole mass of 2 × 106 M⊙. fAD/fINF is the ratio between the flux of the coronal emission that reaches the accretion disc and infinity. This ratio depends on the geometry of the coronal region in IRAS 13224−3809. So we only discuss its value based on the simple ‘lamp-post’ model, although detailed modelling of the disc emissivity profile of IRAS 13224−3809 is required in future to reveal the exact geometry of the corona.
Referring Expression Comprehension via Cross-Level Multi-Modal Fusion

As an important and challenging problem in vision-language tasks, referring expression comprehension (REC) aims to localize the target object specified by a given referring expression. Recently, most of the state-of-the-art REC methods mainly focus on multi-modal fusion while overlooking the inherent hierarchical information contained in visual and language encoders. Considering that REC requires visual and textual hierarchical information for accurate target localization, and encoders inherently extract features in a hierarchical fashion, we propose to effectively utilize the rich hierarchical information contained in different layers of visual and language encoders. To this end, we design a Cross-level Multi-modal Fusion (CMF) framework, which gradually integrates visual and textual features of multi-layer through intra- and inter-modal. Experimental results on RefCOCO, RefCOCO+, RefCOCOg, and ReferItGame datasets demonstrate the proposed framework achieves significant performance improvements over state-of-the-art methods.
DiffCSE: Difference-based Contrastive Learning for Sentence Embeddings

We propose DiffCSE, an unsupervised contrastive learning framework for learning sentence embeddings. DiffCSE learns sentence embeddings that are sensitive to the difference between the original sentence and an edited sentence, where the edited sentence is obtained by stochastically masking out the original sentence and then sampling from a masked language model. We show that DiffSCE is an instance of equivariant contrastive learning, which generalizes contrastive learning and learns representations that are insensitive to certain types of augmentations and sensitive to other “harmful” types of augmentations. Our experiments show that DiffCSE achieves state-of-the-art results among unsupervised sentence representation learning methods, outperforming unsupervised SimCSE by 2.3 absolute points on semantic textual similarity tasks.
Standing on the Shoulders of Giant Frozen Language Models

Huge pretrained language models (LMs) have demonstrated surprisingly good zero-shot capabilities on a wide variety of tasks. This gives rise to the appealing vision of a single, versatile model with a wide range of functionalities across disparate applications. However, current leading techniques for leveraging a"frozen"LM -- i.e., leaving its weights untouched -- still often underperform fine-tuning approaches which modify these weights in a task-dependent way. Those, in turn, suffer forgetfulness and compromise versatility, suggesting a tradeoff between performance and versatility. The main message of this paper is that current frozen-model techniques such as prompt tuning are only the tip of the iceberg, and more powerful methods for leveraging frozen LMs can do just as well as fine tuning in challenging domains without sacrificing the underlying model's versatility. To demonstrate this, we introduce three novel methods for leveraging frozen models: input-dependent prompt tuning, frozen readers, and recursive LMs, each of which vastly improves on current frozen-model approaches. Indeed, some of our methods even outperform fine-tuning approaches in domains currently dominated by the latter. The computational cost of each method is higher than that of existing frozen model methods, but still negligible relative to a single pass through a huge frozen LM. Each of these methods constitutes a meaningful contribution in its own right, but by presenting these contributions together we aim to convince the reader of a broader message that goes beyond the details of any given method: that frozen models have untapped potential and that fine-tuning is often unnecessary.
Revisiting initial sets in abstract argumentation

We revisit the notion of initial sets by Xu and Cayrol (In Proceedings of the 1st Chinese Conference on Logic and Argumentation (CLAR’16) 2016), i. e. , non-empty minimal admissible sets in abstract argumentation frameworks. Initial sets are a simple concept for analysing conflicts in an abstract argumentation framework and to explain why certain arguments can be accepted. We contribute with new insights on the structure of initial sets and devise a simple non-deterministic construction principle for any admissible set, based on iterative selection of initial sets of the original framework and its induced reducts. In particular, we characterise many existing admissibility-based semantics via this construction principle, thus providing a constructive explanation on the structure of extensions. We also investigate certain problems related to initial sets with respect to their computational complexity.
Learning to Revise References for Faithful Summarization

In real-world scenarios with naturally occurring datasets, reference summaries are noisy and may contain information that cannot be inferred from the source text. On large news corpora, removing low quality samples has been shown to reduce model hallucinations. Yet, for smaller, and/or noisier corpora, filtering is detrimental to performance. To improve reference quality while retaining all data, we propose a new approach: to selectively re-write unsupported reference sentences to better reflect source data. We automatically generate a synthetic dataset of positive and negative revisions by corrupting supported sentences and learn to revise reference sentences with contrastive learning. The intensity of revisions is treated as a controllable attribute so that, at inference, diverse candidates can be over-generated-then-rescored to balance faithfulness and abstraction. To test our methods, we extract noisy references from publicly available MIMIC-III discharge summaries for the task of hospital-course summarization, and vary the data on which models are trained. According to metrics and human evaluation, models trained on revised clinical references are much more faithful, informative, and fluent than models trained on original or filtered data.
Multiply-and-Fire (MNF): An Event-driven Sparse Neural Network Accelerator

Machine learning, particularly deep neural network inference, has become a vital workload for many computing systems, from data centers and HPC systems to edge-based computing. As advances in sparsity have helped improve the efficiency of AI acceleration, there is a continued need for improved system efficiency for both high-performance and system-level acceleration. This work takes a unique look at sparsity with an event (or activation-driven) approach to ANN acceleration that aims to minimize useless work, improve utilization, and increase performance and energy efficiency. Our analytical and experimental results show that this event-driven solution presents a new direction to enable highly efficient AI inference for both CNN and MLP workloads. This work demonstrates state-of-the-art energy efficiency and performance centring on activation-based sparsity and a highly-parallel dataflow method that improves the overall functional unit utilization (at 30 fps). This work enhances energy efficiency over a state-of-the-art solution by 1.46$\times$. Taken together, this methodology presents a novel, new direction to achieve high-efficiency, high-performance designs for next-generation AI acceleration platforms.
Intact-VAE: Estimating Treatment Effects under Unobserved Confounding

NOTE: This preprint has a flawed theoretical formulation. Please avoid it and refer to the ICLR22 publication https://openreview.net/forum? id=q7n2RngwOM. Also, arXiv:2109.15062 contains some new ideas on unobserved Confounding. As an important problem of causal inference, we discuss the identification and estimation of treatment effects under unobserved confounding. Representing the confounder as a latent variable, we propose Intact-VAE, a new variant of variational autoencoder (VAE), motivated by the prognostic score that is sufficient for identifying treatment effects. We theoretically show that, under certain settings, treatment effects are identified by our model, and further, based on the identifiability of our model (i.e., determinacy of representation), our VAE is a consistent estimator with representation balanced for treatment groups. Experiments on (semi-)synthetic datasets show state-of-the-art performance under diverse settings.
Self-Supervised Audio-Visual Representation Learning with Relaxed Cross-Modal Synchronicity

We present CrissCross, a self-supervised framework for learning audio-visual representations. A novel notion is introduced in our framework whereby in addition to learning the intra-modal and standard 'synchronous' cross-modal relations, CrissCross also learns 'asynchronous' cross-modal relationships. We perform in-depth studies showing that by relaxing the temporal synchronicity between the audio and visual modalities, the network learns strong generalized representations useful for a variety of downstream tasks. To pretrain our proposed solution, we use 3 different datasets with varying sizes, Kinetics-Sound, Kinetics400, and AudioSet. The learned representations are evaluated on a number of downstream tasks namely action recognition, sound classification, and action retrieval. Our experiments show that CrissCross either outperforms or achieves performances on par with the current state-of-the-art self-supervised methods on action recognition and action retrieval with UCF101 and HMDB51, as well as sound classification with ESC50 and DCASE. Moreover, CrissCross outperforms fully-supervised pretraining while pretrained on Kinetics-Sound.
On the Kawaguchi--Silverman Conjecture for birational automorphisms of irregular varieties

We study the main open parts of the Kawaguchi–Silverman Conjecture, as-serting that for a birational self-map f of a smooth projective variety X deﬁned over Q , the arithmetic degree α f ( x ) exists and coincides with the ﬁrst dynamical degree δ f for any Q -point x of X with a Zariski dense orbit. Among other results, we show that this holds when X has Kodaira dimension zero and irregularity q ( X ) ≥ dim X − 1 or X is an irregular threefold (modulo one possible exception). We also study the existence of Zariski dense orbits, with explicit examples.
Magnetic Field Strength from Turbulence Theory. I. Using Differential Measure Approach

The mean plane-of-sky magnetic field strength is traditionally obtained from the combination of polarization and spectroscopic data using the Davis–Chandrasekhar–Fermi (DCF) technique. However, we identify the major problem of the DCF technique to be its disregard of the anisotropic character of MHD turbulence. On the basis of the modern MHD turbulence theory we introduce a new way of obtaining magnetic field strength from observations. Unlike the DCF technique, the new technique uses not the dispersion of the polarization angle and line-of-sight velocities, but increments of these quantities given by the structure functions. To address the variety of astrophysical conditions for which our technique can be applied, we consider turbulence in both media with magnetic pressure higher than the gas pressure, corresponding, e.g., to molecular clouds, and media with gas pressure higher than the magnetic pressure, corresponding to the warm neutral medium. We provide general expressions for arbitrary admixtures of Alfvén, slow, and fast modes in these media and consider in detail particular cases relevant to diffuse media and molecular clouds. We successfully test our results using synthetic observations obtained from MHD turbulence simulations. We demonstrate that our differential measure approach, unlike the DCF technique, can be used to measure the distribution of magnetic field strengths, can provide magnetic field measurements with limited data, and is much more stable in the presence of induced large-scale variations of nonturbulent nature. Furthermore, our study uncovers the deficiencies of earlier DCF research.
A Hierarchical N-Gram Framework for Zero-Shot Link Prediction

Due to the incompleteness of knowledge graphs (KGs), zero-shot link prediction (ZSLP) which aims to predict unobserved relations in KGs has attracted recent interest from researchers. A common solution is to use textual features of relations (e.g., surface name or textual descriptions) as auxiliary information to bridge the gap between seen and unseen relations. Current approaches learn an embedding for each word token in the text. These methods lack robustness as they suffer from the out-of-vocabulary (OOV) problem. Meanwhile, models built on character n-grams have the capability of generating expressive representations for OOV words. Thus, in this paper, we propose a Hierarchical N-Gram framework for Zero-Shot Link Prediction (HNZSLP), which considers the dependencies among character n-grams of the relation surface name for ZSLP. Our approach works by first constructing a hierarchical n-gram graph on the surface name to model the organizational structure of n-grams that leads to the surface name. A GramTransformer, based on the Transformer is then presented to model the hierarchical n-gram graph to construct the relation embedding for ZSLP. Experimental results show the proposed HNZSLP achieved state-of-the-art performance on two ZSLP datasets.
Supplemental Materials: Toward Fast, Flexible, and Robust Low-Light Image Enhancement

Fig. 1 showed the visual results of different settings for self-calibrated module Kθ. We can easily see that our method always generated stable outputs no matter how the number of convolutions. Thus we can actually use the lightweight enough architecture to instantiate it. More importantly, the self-calibrated module is just adopted in the training phase. This is to say, the architecture for Kθ will be not appeared during the inference.
A Model-agnostic Data Manipulation Method for Persona-based Dialogue Generation

Towards building intelligent dialogue agents, there has been a growing interest in introducing explicit personas in generation models. However, with limited persona-based dialogue data at hand, it may be difficult to train a dialogue generation model well. We point out that the data challenges of this generation task lie in two aspects: first, it is expensive to scale up current persona-based dialogue datasets; second, each data sample in this task is more complex to learn with than conventional dialogue data. To alleviate the above data issues, we propose a data manipulation method, which is model-agnostic to be packed with any persona-based dialogue generation model to improve their performance. The original training samples will first be distilled and thus expected to be fitted more easily. Next, we show various effective ways that can diversify such easier distilled data. A given base model will then be trained via the constructed data curricula, i.e. first on augmented distilled samples and then on original ones. Experiments illustrate the superiority of our method with two strong base dialogue models (Transformer encoder-decoder and GPT2).
SemEval-2022 Task 2: Multilingual Idiomaticity Detection and Sentence Embedding

This paper presents the shared task on Multilingual Idiomaticity Detection and Sentence Embedding, which consists of two subtasks: (a) a binary classification task aimed at identifying whether a sentence contains an idiomatic expression, and (b) a task based on semantic text similarity which requires the model to adequately represent potentially idiomatic expressions in context. Each subtask includes different settings regarding the amount of training data. Besides the task description, this paper introduces the datasets in English, Portuguese, and Galician and their annotation procedure, the evaluation metrics, and a summary of the participant systems and their results. The task had close to 100 registered participants organised into twenty five teams making over 650 and 150 submissions in the practice and evaluation phases respectively.