Reviewerly/exHarmony huggingface dataset:
Zero-Shot Learning of a Conditional Generative Adversarial Network for Data-Free Network Quantization

We propose a novel method for training a conditional generative adversarial network (CGAN) without the use of training data, called zero-shot learning of a CGAN (ZS-CGAN). Zero-shot learning of a conditional generator only needs a pre-trained discriminative (classification) model and does not need any training data. In particular, the conditional generator is trained to produce labeled synthetic samples whose characteristics mimic the original training data by using the statistics stored in the batch normalization layers of the pre-trained model. We show the usefulness of ZS-CGAN in data-free quantization of deep neural networks. We achieved the state-of-the-art data-free network quantization of the ResNet and MobileNet classification models trained on the ImageNet dataset. Data-free quantization using ZS-CGAN showed a minimal loss in accuracy compared to that obtained by conventional data-dependent quantization.
A Depolarizing Noise-aware Transpiler for Optimal Amplitude Amplification

Amplitude amplification provides a quadratic speed-up for an array of quantum algorithms when run on a quantum machine perfectly isolated from its environment. However, the advantage is substantially diminished as the NISQ-era quantum machines lack the large number of qubits necessary to provide error correction. Noise in the computation grows with the number of gate counts in the circuit with each iteration of amplitude amplification. After a certain number of amplifications, the loss in accuracy from the gate noise starts to overshadow the gain in accuracy due to amplification, forming an inflection point. Beyond this point, accuracy continues to deteriorate until the machine reaches a maximally mixed state where the result is uniformly random. Hence, quantum transpilers should take the noise parameters of the underlying quantum machine into consideration such that the circuit can be optimized to attain the maximal accuracy possible for that machine. In this work, we propose an extension to the transpiler that predicts the accuracy of the result at every amplification with high fidelity by applying pure Bayesian analysis to individual gate noise rates. Using this information, it finds the inflection point and optimizes the circuit by halting amplification at that point. The prediction is made without needing to execute the circuit either on a quantum simulator or an actual quantum machine.
Improving Adversarial Robustness via Joint Classification and Multiple Explicit Detection Classes

This work concerns the development of deep networks that are certifiably robust to adversarial attacks. Joint robust classification-detection was recently introduced as a certified defense mechanism, where adversarial examples are either correctly classified or assigned to the "abstain" class. In this work, we show that such a provable framework can benefit by extension to networks with multiple explicit abstain classes, where the adversarial examples are adaptively assigned to those. We show that naively adding multiple abstain classes can lead to "model degeneracy", then we propose a regularization approach and a training method to counter this degeneracy by promoting full use of the multiple abstain classes. Our experiments demonstrate that the proposed approach consistently achieves favorable standard vs. robust verified accuracy tradeoffs, outperforming state-of-the-art algorithms for various choices of number of abstain classes.
Modeling the Graphotactics of Low-Resource Languages Using Sequential GANs

Generative Adversarial Networks (GANs) have been shown to aid in the creation of artificial data in situations where large amounts of real data are difficult to come by. This issue is especially salient in the computational linguistics space, where researchers are often tasked with modeling the complex morphologic and grammatical processes of low-resource languages. This paper will discuss the implementation and testing of a GAN that attempts to model and reproduce the graphotactics of a language using only 100 example strings. These artificial, yet graphotactically compliant, strings are meant to aid in modeling the morphological inflection of low-resource languages.
Discourse-Aware Emotion Cause Extraction in Conversations

Emotion Cause Extraction in Conversations (ECEC) aims to extract the utterances which contain the emotional cause in conversations. Most prior research focuses on modelling conversational contexts with sequential encoding, ignoring the informative interactions between utterances and conversational-specific features for ECEC. In this paper, we investigate the importance of discourse structures in handling utterance interactions and conversationspecific features for ECEC. To this end, we propose a discourse-aware model (DAM) for this task. Concretely, we jointly model ECEC with discourse parsing using a multi-task learning (MTL) framework and explicitly encode discourse structures via gated graph neural network (gated GNN), integrating rich utterance interaction information to our model. In addition, we use gated GNN to further enhance our ECEC model with conversation-specific features. Results on the benchmark corpus show that DAM outperform the state-of-theart (SOTA) systems in the literature. This suggests that the discourse structure may contain a potential link between emotional utterances and their corresponding cause expressions. It also verifies the effectiveness of conversationalspecific features. The codes of this paper will be available on GitHub.
Convergence Rates of Oblique Regression Trees for Flexible Function Libraries

We develop a theoretical framework for the analysis of oblique decision trees, where the splits at each decision node occur at linear combinations of the covariates (as opposed to conventional tree constructions that force axis-aligned splits involving only a single covariate). While this methodology has garnered significant attention from the computer science and optimization communities since the mid-80s, the advantages they offer over their axis-aligned counterparts remain only empirically justified, and explanations for their success are largely based on heuristics. Filling this long-standing gap between theory and practice, we show that oblique regression trees (constructed by recursively minimizing squared error) satisfy a type of oracle inequality and can adapt to a rich library of regression models consisting of linear combinations of ridge functions and their limit points. This provides a quantitative baseline to compare and contrast decision trees with other less interpretable methods, such as projection pursuit regression and neural networks, which target similar model forms. Contrary to popular belief, one need not always trade-off interpretability with accuracy. Specifically, we show that, under suitable conditions, oblique decision trees achieve similar predictive accuracy as neural networks for the same library of regression models. To address the combinatorial complexity of finding the optimal splitting hyperplane at each decision node, our proposed theoretical framework can accommodate many existing computational tools in the literature. Our results rely on (arguably surprising) connections between recursive adaptive partitioning and sequential greedy approximation algorithms for convex optimization problems (e.g., orthogonal greedy algorithms), which may be of independent theoretical interest. Using our theory and methods, we also study oblique random forests.
The NPU-ASLP System for The ISCSLP 2022 Magichub Code-Swiching ASR Challenge

This paper describes our NPU-ASLP system submitted to the ISCSLP 2022 Magichub Code-Switching ASR Challenge. In this challenge, we first explore several popular end-to-end ASR architectures and training strategies, including bi-encoder, language-aware encoder (LAE) and mixture of experts (MoE). To improve our system's language modeling ability, we further attempt the internal language model as well as the long context language model. Given the limited training data in the challenge, we further investigate the effects of data augmentation, including speed perturbation, pitch shifting, speech codec, SpecAugment and synthetic data from text-to-speech (TTS). Finally, we explore ROVER-based score fusion to make full use of complementary hypotheses from different models. Our submitted system achieves 16.87% on mix error rate (MER) on the test set and comes to the 2nd place in the challenge ranking.
Eeny, meeny, miny, moe. How to choose data for morphological inflection

Data scarcity is a widespread problem in numerous natural language processing (NLP) tasks for low-resource languages. Within morphology, the labour-intensive work of tagging/glossing data is a serious bottleneck for both NLP and language documentation. Active learning (AL) aims to reduce the cost of data annotation by selecting data that is most informative for improving the model. In this paper, we explore four sampling strategies for the task of morphological inflection using a Transformer model: a pair of oracle experiments where data is chosen based on whether the model already can or cannot inflect the test forms correctly, as well as strategies based on high/low model confidence, entropy, as well as random selection. We investigate the robustness of each strategy across 30 typologically diverse languages. We also perform a more in-depth case study of Nat\"ugu. Our results show a clear benefit to selecting data based on model confidence and entropy. Unsurprisingly, the oracle experiment, where only incorrectly handled forms are chosen for further training, which is presented as a proxy for linguist/language consultant feedback, shows the most improvement. This is followed closely by choosing low-confidence and high-entropy predictions. We also show that despite the conventional wisdom of larger data sets yielding better accuracy, introducing more instances of high-confidence or low-entropy forms, or forms that the model can already inflect correctly, can reduce model performance.
Ensemble Projection Pursuit for General Nonparametric Regression

The projection pursuit regression (PPR) has played an important role in the development of statistics and machine learning. However, when compared to other established methods like random forests (RF) and support vector machines (SVM), PPR has yet to showcase a similar level of accuracy as a statistical learning technique. In this paper, we revisit the estimation of PPR and propose an \textit{optimal} greedy algorithm and an ensemble approach via "feature bagging", hereafter referred to as ePPR, aiming to improve the efficacy. Compared to RF, ePPR has two main advantages. Firstly, its theoretical consistency can be proved for more general regression functions as long as they are $L^2$ integrable, and higher consistency rates can be achieved. Secondly, ePPR does not split the samples, and thus each term of PPR is estimated using the whole data, making the minimization more efficient and guaranteeing the smoothness of the estimator. Extensive comparisons based on real data sets show that ePPR is more efficient in regression and classification than RF and other competitors. The efficacy of ePPR, a variant of Artificial Neural Networks (ANN), demonstrates that with suitable statistical tuning, ANN can equal or even exceed RF in dealing with small to medium-sized datasets. This revelation challenges the widespread belief that ANN's superiority over RF is limited to processing extensive sample sizes.
Constructions of entanglement-assisted quantum MDS from generalized Reed-Solomon codes

By generalizing the stabilizer quantum error-correcting codes, entanglement-assisted quantum error-correcting (EAQEC) codes were introduced, which could be derived from any classical linear codes via the relaxation of self-orthogonality conditions with the aid of pre-shared entanglement between the sender and the receiver. In this paper, three classes of entanglement-assisted quantum error-correcting maximum-distance-separable (EAQMDS) codes are constructed through generalized Reed-Solomon codes. Under our constructions, the minimum distances of our EAQMDS codes are much larger than those of the known EAQMDS codes of the same lengths that consume the same number of ebits. Furthermore, some of the lengths of the EAQMDS codes are not divisors of $q^2-1$, which are completely new and unlike all those known lengths existed before.
Is MultiWOZ a Solved Task? An Interactive TOD Evaluation Framework with User Simulator

Task-Oriented Dialogue (TOD) systems are drawing more and more attention in recent studies. Current methods focus on constructing pre-trained models or fine-tuning strategies while the evaluation of TOD is limited by a policy mismatch problem. That is, during evaluation, the user utterances are from the annotated dataset while these utterances should interact with previous responses which can have many alternatives besides annotated texts. Therefore, in this work, we propose an interactive evaluation framework for TOD. We first build a goal-oriented user simulator based on pre-trained models and then use the user simulator to interact with the dialogue system to generate dialogues. Besides, we introduce a sentence-level and a session-level score to measure the sentence fluency and session coherence in the interactive evaluation. Experimental results show that RL-based TOD systems trained by our proposed user simulator can achieve nearly 98% inform and success rates in the interactive evaluation of MultiWOZ dataset and the proposed scores measure the response quality besides the inform and success rates. We are hoping that our work will encourage simulator-based interactive evaluations in the TOD task.
Look to the Right: Mitigating Relative Position Bias in Extractive Question Answering

Extractive question answering (QA) models tend to exploit spurious correlations to make predictions when a training set has unintended biases. This tendency results in models not being generalizable to examples where the correlations do not hold. Determining the spurious correlations QA models can exploit is crucial in building generalizable QA models in real-world applications; moreover, a method needs to be developed that prevents these models from learning the spurious correlations even when a training set is biased. In this study, we discovered that the relative position of an answer, which is defined as the relative distance from an answer span to the closest question-context overlap word, can be exploited by QA models as superficial cues for making predictions. Specifically, we find that when the relative positions in a training set are biased, the performance on examples with relative positions unseen during training is significantly degraded. To mitigate the performance degradation for unseen relative positions, we propose an ensemble-based debiasing method that does not require prior knowledge about the distribution of relative positions. We demonstrate that the proposed method mitigates the models' reliance on relative positions using the biased and full SQuAD dataset. We hope that this study can help enhance the generalization ability of QA models in real-world applications.
Towards Practical Few-Shot Query Sets: Transductive Minimum Description Length Inference

Standard few-shot benchmarks are often built upon simplifying assumptions on the query sets, which may not always hold in practice. In particular, for each task at testing time, the classes effectively present in the unlabeled query set are known a priori, and correspond exactly to the set of classes represented in the labeled support set. We relax these assumptions and extend current benchmarks, so that the query-set classes of a given task are unknown, but just belong to a much larger set of possible classes. Our setting could be viewed as an instance of the challenging yet practical problem of extremely imbalanced K-way classification, K being much larger than the values typically used in standard benchmarks, and with potentially irrelevant supervision from the support set. Expectedly, our setting incurs drops in the performances of state-of-the-art methods. Motivated by these observations, we introduce a PrimAl Dual Minimum Description LEngth (PADDLE) formulation, which balances data-fitting accuracy and model complexity for a given few-shot task, under supervision constraints from the support set. Our constrained MDL-like objective promotes competition among a large set of possible classes, preserving only effective classes that befit better the data of a few-shot task. It is hyperparameter free, and could be applied on top of any base-class training. Furthermore, we derive a fast block coordinate descent algorithm for optimizing our objective, with convergence guarantee, and a linear computational complexity at each iteration. Comprehensive experiments over the standard few-shot datasets and the more realistic and challenging i-Nat dataset show highly competitive performances of our method, more so when the numbers of possible classes in the tasks increase. Our code is publicly available at https://github.com/SegoleneMartin/PADDLE.
Hybrid Modality in Statistical Learning: Effects on Attitudes and Skills

Due to the poor preparation with which students arrive, we have used a text analysis strategy, underlining statistical analysis, encountering resistance from students, presumably by a negative attitude towards statistics. The objective was to test the modification of attitudes in addition to the acquisition of skills. Participants were 34 psychology students. With a pretest posttest design, an attitudes scale and an examination were applied to them. In addition to participating in a virtual course, a video showed the logic of statistical analysis, and they were asked for the strategic analysis of seven articles with a discussion forum. Significant differences were found. It is shown that the teaching of statistics will only make sense within the context of the research and intervention process. Debido a la mala preparación con que llegan los estudiantes, hemos utilizado una estrategia de análisis de textos, subrayando el análisis estadístico, encontrando resistencia de los alumnos, presumiblemente por una actitud negativa hacia la estadística. El objetivo fue probar la modificación de actitudes además de la adquisición de habilidades. Participaron 34 estudiantes de Psicología Presencial. Con un diseño pretest postest, se les aplicó una escala de actitudes y un examen, además de participar en un curso virtual, un video mostró la lógica del análisis estadístico, y se les pidió el análisis estratégico de siete artículos, con un foro de discusión. Se encontraron diferencias significativas. Se demuestra que la enseñanza de la estadística solo tendrá sentido dentro del contexto del proceso de investigación e intervención.
Grouping and the Power of Hierarchical Data

This paper explores what can happen if we organize data hierarchically instead of in a flat rectangular structure. We will see that, given a data analysis platform in which this is easy and natural, a hierarchical structure lets us perform many “data moves” (Erickson et al., 2019) dynamically and transparently. This is not limited to datasets that exhibit what we might call “natural hierarchy.” With many datasets, we can use hierarchy to group and regroup, create summaries, filter data, and even recode categorical values. We conjecture that students who work with hierarchical data will better understand underlying data moves, such as grouping, and be better equipped as advanced students and as informed citizens.
NFCStack: Identifiable Physical Building Blocks that Support Concurrent Construction and Frictionless Interaction

In this paper, we propose NFCStack, which is a physical building block system that supports stacking and frictionless interaction and is based on near-field communication (NFC). This system consists of a portable station that can support and resolve the order of three types of passive identifiable stackable: bricks, boxes, and adapters. The bricks support stable and sturdy physical construction, whereas the boxes support frictionless tangible interactions. The adapters provide an interface between the aforementioned two types of stackable and convert the top of a stack into a terminal for detecting interactions between NFC-tagged objects. In contrast to existing systems based on NFC or radio-frequency identification technologies, NFCStack is portable, supports simultaneous interactions, and resolves stacking and interaction events responsively, even when objects are not strictly aligned. Evaluation results indicate that the proposed system effectively supports 12 layers of rich-ID stacking with the three types of building block, even if every box is stacked with a 6-mm offset. The results also indicate possible generalized applications of the proposed system, including 2.5-dimensional construction. The interaction styles are described using several educational application examples, and the design implications of this research are explained.
KineCAM: An Instant Camera for Animated Photographs

The kinegram is a classic animation technique that involves sliding a striped overlay across an interlaced image to create the effect of frame-by-frame motion. While there are known tools for generating kinegrams from pre-existing videos and images, there exists no system for capturing and fabricating kinegrams in situ. To bridge this gap, we created KineCAM, an open source1 instant camera that captures and prints animated photographs in the form of kinegrams. KineCAM combines the form factor of instant cameras with the expressiveness of animated photographs to explore and extend creative applications for instant photography.
Challenges Faced by University Professors While Teaching Statistics Online During the COVID-19 Pandemic

This study explores the numerous challenges that university professors have faced in teaching statistics courses online during the COVID-19 pandemic. It is part of a broader research project that explores characteristics of statistics teaching in university education. For this research, we designed a questionnaire that was answered by 750 statistics professors from 420 Mexican universities. The challenges they faced involve: (a) the use of information and communications technology; (b) technological tools for teaching statistics; (c) data; (d) the online learning environment; (e) students' knowledge; (f) statistical projects; (g) curricula; (h) attitudes; (i) the application of statistics; (j) the professors’ pedagogical content; (k) assessment; and (l) financial resources. Results provide us with a broad picture of the many challenges involved in teaching statistics online.