Miracle-dz/UniFAR-FTUs huggingface dataset:
Neural Sequence Segmentation as Determining the Leftmost Segments

Prior methods to text segmentation are mostly at token level. Despite the adequacy, this nature limits their full potential to capture the long-term dependencies among segments. In this work, we propose a novel framework that incrementally segments natural language sentences at segment level. For every step in segmentation, it recognizes the leftmost segment of the remaining sequence. Implementations involve LSTM-minus technique to construct the phrase representations and recurrent neural networks (RNN) to model the iterations of determining the leftmost segments. We have conducted extensive experiments on syntactic chunking and Chinese part-of-speech (POS) tagging across 3 datasets, demonstrating that our methods have significantly outperformed previous all baselines and achieved new state-of-the-art results. Moreover, qualitative analysis and the study on segmenting long-length sentences verify its effectiveness in modeling long-term dependencies.
IndT5: A Text-to-Text Transformer for 10 Indigenous Languages

Transformer language models have become fundamental components of NLP based pipelines. Although several Transformer have been introduced to serve many languages, there is a shortage of models pre-trained for low-resource and Indigenous languages in particular. In this work, we introduce IndT5, the first Transformer language model for Indigenous languages. To train IndT5, we build IndCorpus, a new corpus for 10 Indigenous languages and Spanish. We also present the application of IndT5 to machine translation by investigating different approaches to translate between Spanish and the Indigenous languages as part of our contribution to the AmericasNLP 2021 Shared Task on Open Machine Translation. IndT5 and IndCorpus are publicly available for research.
Self-Supervised Exploration via Latent Bayesian Surprise

Training with Reinforcement Learning requires a reward function that is used to guide the agent towards achieving its objective. However, designing smooth and well-behaved rewards is in general not trivial and requires significant human engineering efforts. Generating rewards in self-supervised way, by inspiring the agent with an intrinsic desire to learn and explore the environment, might induce more general behaviors. In this work, we propose a curiosity-based bonus as intrinsic reward for Reinforcement Learning, computed as the Bayesian surprise with respect to a latent state variable, learnt by reconstructing fixed random features. We extensively evaluate our model by measuring the agent’s performance in terms of environment exploration, for continuous tasks, and looking at the game scores achieved, for video games. Our model is computationally cheap and empirically shows state-of-the-art performance on several problems. Furthermore, experimenting on an environment with stochastic actions, our approach emerged to be the most resilient to simple stochasticity. Further visualization is available on the project webpage.1
First the Worst: Finding Better Gender Translations During Beam Search

Generating machine translations via beam search seeks the most likely output under a model. However, beam search has been shown to amplify demographic biases exhibited by a model. We aim to address this, focusing on gender bias resulting from systematic errors in grammatical gender translation. Almost all prior work on this problem adjusts the training data or the model itself. By contrast, our approach changes only the inference procedure. We constrain beam search to improve gender diversity in n-best lists, and rerank n-best lists using gender features obtained from the source sentence. Combining these strongly improves WinoMT gender translation accuracy for three language pairs without additional bilingual data or retraining. We also demonstrate our approach’s utility for consistently gendering named entities, and its flexibility to handle new gendered language beyond the binary.
Investigating the Utility of Multimodal Conversational Technology and Audiovisual Analytic Measures for the Assessment and Monitoring of Amyotrophic Lateral Sclerosis at Scale

We propose a cloud-based multimodal dialog platform for the remote assessment and monitoring of Amyotrophic Lateral Sclerosis (ALS) at scale. This paper presents our vision, technology setup, and an initial investigation of the efficacy of the various acoustic and visual speech metrics automatically extracted by the platform. 82 healthy controls and 54 people with ALS (pALS) were instructed to interact with the platform and completed a battery of speaking tasks designed to probe the acoustic, articulatory, phonatory, and respiratory aspects of their speech. We find that multiple acoustic (rate, duration, voicing) and visual (higher order statistics of the jaw and lip) speech metrics show statistically significant differences between controls, bulbar symptomatic and bulbar pre-symptomatic patients. We report on the sensitivity and specificity of these metrics using five-fold cross-validation. We further conducted a LASSO-LARS regression analysis to uncover the relative contributions of various acoustic and visual features in predicting the severity of patients' ALS (as measured by their self-reported ALSFRS-R scores). Our results provide encouraging evidence of the utility of automatically extracted audiovisual analytics for scalable remote patient assessment and monitoring in ALS.
Visible light driven photocatalytic activity of Fe-doped ZnO nanocrystals

Fe-doped ZnO nanocrystals at the level of 8mol% were synthesized via the co-precipitation method and then characterized by transmission electron microscopy, X-ray diffraction, diffuse reflectance spectroscopy and UV–Vis absorption spectroscopy. Strong absorptions with its peak at around 500nm were recorded in the range of 400–600nm for Fe-doped ZnO nanocrystals. Visible-light driven photocatalytic activity of Fe-doped ZnO nanocrystals was demonstrated via the photocatalytic degradation of methyl orange dye. Density functional calculations show that Fe dopant can generate a deep trap level at Ev + 1.01eV in the bandgap of ZnO, which is believed to be responsible for the visible light responsive photocatalytic activity of Fe-doped ZnO.
Reformulating Sentence Ordering as Conditional Text Generation

The task of organizing a shufﬂed set of sentences into a coherent text is important in NLP and has been used to evaluate a machine’s understanding of causal and temporal relations. We present Re order-BART (R E -B ART ), a sentence ordering framework which leverages a pre-trained transformer-based model to identify a coherent order for a given set of shuf-ﬂed sentences. We reformulate the task as a conditional text-to-marker generation setup where the input is a set of shufﬂed sentences with sentence-speciﬁc markers and output is a sequence of position markers of the ordered text. Our framework achieves the state-of-the-art performance across six datasets in Perfect Match Ratio (PMR) and Kendall’s tau ( τ ) metric. We perform evaluations in a zero-shot setting, showcasing that our model is able to generalize well across other datasets. We additionally perform a series of experiments to understand the functioning and explore the limitations of our framework. 1
Internet of quantum blockchains: security modeling and dynamic resource pricing for stable digital currency

Internet of quantum blockchains (IoB) will be the future Internet. In this paper, we make two new contributions to IoB: developing a block based quantum channel networking technology to handle its security modeling in face of the quantum supremacy and establishing IoB based FinTech platform model with dynamic pricing for stable digital currency. The interaction between our new contributions is also addressed. In doing so, we establish a generalized IoB security model by quantum channel networking in terms of both time and space quantum entanglements with quantum key distribution (QKD). Our IoB can interact with general structured things (e.g., supply chain systems) having online trading and payment capability via stable digital currency and can handle vector-valued data streams requiring synchronized services. Thus, within our designed QKD, a generalized random number generator for private and public keys is proposed by a mixed zero-sum and non-zero-sum resource-competition pricing policy. The effectiveness of this policy is justified by diffusion modeling with approximation theory and numerical implementations.
Audio-Driven Emotional Video Portraits

Despite previous success in generating audio-driven talking heads, most of the previous studies focus on the correlation between speech content and the mouth shape. Facial emotion, which is one of the most important features on natural human faces, is always neglected in their methods. In this work, we present Emotional Video Portraits (EVP), a system for synthesizing high-quality video portraits with vivid emotional dynamics driven by audios. Specifically, we propose the Cross-Reconstructed Emotion Disentanglement technique to decompose speech into two decoupled spaces, i.e., a duration-independent emotion space and a duration- dependent content space. With the disentangled features, dynamic 2D emotional facial landmarks can be deduced. Then we propose the Target-Adaptive Face Synthesis technique to generate the final high-quality video portraits, by bridging the gap between the deduced landmarks and the natural head poses of target videos. Extensive experiments demonstrate the effectiveness of our method both qualitatively and quantitatively.1
Self-Supervised Learning of Remote Sensing Scene Representations Using Contrastive Multiview Coding

In recent years self-supervised learning has emerged as a promising candidate for unsupervised representation learning. In the visual domain its applications are mostly studied in the context of images of natural scenes. However, its applicability is especially interesting in specific areas, like remote sensing and medicine, where it is hard to obtain huge amounts of labeled data. In this work, we conduct an extensive analysis of the applicability of self-supervised learning in remote sensing image classification. We analyze the influence of the number and domain of images used for self-supervised pre-training on the performance on downstream tasks. We show that, for the downstream task of remote sensing image classification, using self-supervised pre-training on remote sensing images can give better results than using supervised pre-training on images of natural scenes. Besides, we also show that self-supervised pre-training can be easily extended to multispectral images producing even better results on our downstream tasks.
TransferNet: An Effective and Transparent Framework for Multi-hop Question Answering over Relation Graph

Multi-hop Question Answering (QA) is a challenging task because it requires precise reasoning with entity relations at every step towards the answer. The relations can be represented in terms of labels in knowledge graph (e.g., spouse) or text in text corpus (e.g., they have been married for 26 years). Existing models usually infer the answer by predicting the sequential relation path or aggregating the hidden graph features. The former is hard to optimize, and the latter lacks interpretability. In this paper, we propose TransferNet, an effective and transparent model for multi-hop QA, which supports both label and text relations in a unified framework. TransferNet jumps across entities at multiple steps. At each step, it attends to different parts of the question, computes activated scores for relations, and then transfer the previous entity scores along activated relations in a differentiable way. We carry out extensive experiments on three datasets and demonstrate that TransferNet surpasses the state-of-the-art models by a large margin. In particular, on MetaQA, it achieves 100% accuracy in 2-hop and 3-hop questions. By qualitative analysis, we show that TransferNet has transparent and interpretable intermediate results.
Nanomaterials for biocatalyst immobilization – state of the art and future trends

Nanotechnology is an area that has been growing over the years, being possible nowadays to find numerous materials constructed at nanoscale. In addition, many applications have been attributed to these “new” materials. In this review is presented a brief overview of nanoparticles used for the immobilization of enzymes. Considering the extensive universe of immobilization in nanoparticles, some were chosen to be exposed here, such as chitosan, graphene, silica, polymers, magnetic, nanoflowers, among others. Advantages, disadvantages and limitations of nanoimmobilization also be discussed. Some applications of nanoimmobilized enzymes are presented, like as biodiesel, flavor synthesis ester and biosensors. The purpose of this paper is to provide an overview of what is being studied in relation to nanoparticles for enzymes immobilization, and some discussions about them, aimed at assisting researchers in future studies and reviews.
Effect of micelle breaking rate and wall slip on unsteady motion past a sphere translating steadily in wormlike micellar solutions

Many prior experimental studies have found the existence of an unsteady or fluctuating flow field around a solid sphere when falling in wormlike micellar solutions. Based on the two-species Vasquez–Cook–McKinley constitutive model for micelles, a recent numerical study shows that the breakage of long micelles downstream of the translating sphere causes this unsteady motion [C. Sasmal, “Unsteady motion past a sphere translating steadily in wormlike micellar solutions: A numerical analysis,” J. Fluid Mech. 912, A52, (2021)]. This numerical study further shows that the micelle breakage rate and wall slip can strongly influence this phenomenon. In particular, we find that the onset of this unsteady motion is delayed to higher values of the Weissenberg number as the micelle breakage rate decreases, or in other words, micelles become hard to break. Additionally, we observe that at some values of the micelle breakage rate, again, a transition in the flow field from unsteady to steady occurs at high Weissenberg numbers. Therefore, there is a window of the Weissenberg number present to observe this unsteady motion past the translating sphere. On the other hand, we show that the presence of wall slip on the sphere surface suppresses this unsteady motion past the translating sphere, and a probable explanation is also provided for the same.
Look at Me When I Talk to You: A Video Dataset to Enable Voice Assistants to Recognize Errors

People interacting with voice assistants are often frustrated by voice assistants' frequent errors and inability to respond to backchannel cues. We introduce an open-source video dataset of 21 participants' interactions with a voice assistant, and explore the possibility of using this dataset to enable automatic error recognition to inform self-repair. The dataset includes clipped and labeled videos of participants' faces during free-form interactions with the voice assistant from the smart speaker's perspective. To validate our dataset, we emulated a machine learning classifier by asking crowdsourced workers to recognize voice assistant errors from watching soundless video clips of participants' reactions. We found trends suggesting it is possible to determine the voice assistant's performance from a participant's facial reaction alone. This work posits elicited datasets of interactive responses as a key step towards improving error recognition for repair for voice assistants in a wide variety of applications.
Ensemble of MRR and NDCG models for Visual Dialog

Assessing an AI agent that can converse in human language and understand visual content is challenging. Generation metrics, such as BLEU scores favor correct syntax over semantics. Hence a discriminative approach is often used, where an agent ranks a set of candidate options. The mean reciprocal rank (MRR) metric evaluates the model performance by taking into account the rank of a single human-derived answer. This approach, however, raises a new challenge: the ambiguity and synonymy of answers, for instance, semantic equivalence (e.g., ‘yeah’ and ‘yes’). To address this, the normalized discounted cumulative gain (NDCG) metric has been used to capture the relevance of all the correct answers via dense annotations. However, the NDCG metric favors the usually applicable uncertain answers such as ‘I don’t know. ’ Crafting a model that excels on both MRR and NDCG metrics is challenging. Ideally, an AI agent should answer a human-like reply and validate the correctness of any answer. To address this issue, we describe a two-step non-parametric ranking approach that can merge strong MRR and NDCG models. Using our approach, we manage to keep most MRR state-of-the-art performance (70.41% vs. 71.24%) and the NDCG state-of-the-art performance (72.16% vs. 75.35%). Moreover, our approach won the recent Visual Dialog 2020 challenge. Source code is available at https://github.com/idansc/mrr-ndcg.
ContactOpt: Optimizing Contact to Improve Grasps

Physical contact between hands and objects plays a critical role in human grasps. We show that optimizing the pose of a hand to achieve expected contact with an object can improve hand poses inferred via image-based methods. Given a hand mesh and an object mesh, a deep model trained on ground truth contact data infers desirable contact across the surfaces of the meshes. Then, ContactOpt efficiently optimizes the pose of the hand to achieve desirable contact using a differentiable contact model. Notably, our contact model encourages mesh interpenetration to approximate deformable soft tissue in the hand. In our evaluations, our methods result in grasps that better match ground truth contact, have lower kinematic error, and are significantly preferred by human participants. Code and models are available online1.
Peacock patterns and new integer invariants in topological string theory

Topological string theory near the conifold point of a Calabi--Yau threefold gives rise to factorially divergent power series which encode the all-genus enumerative information. These series lead to infinite towers of singularities in their Borel plane (also known as ``peacock patterns"), and we conjecture that the corresponding Stokes constants are integer invariants of the Calabi--Yau threefold. We calculate these Stokes constants in some toric examples, confirming our conjecture and providing in some cases explicit generating functions for the new integer invariants, in the form of $q$-series. Our calculations in the toric case rely on the TS/ST correspondence, which promotes the asymptotic series near the conifold point to spectral traces of operators, and makes it easier to identify the Stokes data. The resulting mathematical structure turns out to be very similar to the one of complex Chern--Simons theory. In particular, spectral traces correspond to state integral invariants and factorize in holomorphic/anti-holomorphic blocks.
Flexural Wave Modulation and Mitigation in Airfoils Using Acoustic Black Holes

This study introduces a framework for the design and implementation of acoustic black holes (ABHs) in airfoils. A generalized multi-parameter damped-ABH generation function is mapped onto NACA series airfoils. Representative geometries and a uniformly distributed baseline, all with the same mass of structure and damping are fabricated using multi-material PolyJet 3D printing. Laser Doppler vibrometer measurements along the airfoil chord in response to a broadband 0.1 12 kHz excitation show a decrease in trailing edge vibrations by as much as 10 dB, a broadband 5 dB reduction across the entire chord as well as substantial spatial and temporal modulation of flexural waves by ABH-embedded foils. Finite element analysis (FEA) models are developed and validated based on the measured data. Furthermore, a parametric FEA study is performed on a set of comparable designs to elucidate the scope of modulation achievable. These findings are applicable to trailing-edge noise reduction, flow control, structural enhancement and energy harvesting for airfoils.
Partition–Mallows Model and Its Inference for Rank Aggregation

Abstract Learning how to aggregate ranking lists has been an active research area for many years and its advances have played a vital role in many applications ranging from bioinformatics to internet commerce. The problem of discerning reliability of rankers based only on the rank data is of great interest to many practitioners, but has received less attention from researchers. By dividing the ranked entities into two disjoint groups, that is, relevant and irrelevant/background ones, and incorporating the Mallows model for the relative ranking of relevant entities, we propose a framework for rank aggregation that can not only distinguish quality differences among the rankers but also provide the detailed ranking information for relevant entities. Theoretical properties of the proposed approach are established, and its advantages over existing approaches are demonstrated via simulation studies and real-data applications. Extensions of the proposed method to handle partial ranking lists and conduct covariate-assisted rank aggregation are also discussed.
Enhanced generation of nondegenerate photon pairs in nonlinear metasurfaces

We reveal a novel regime of photon-pair generation driven by the interplay of multiple bound states in the continuum resonances in nonlinear metasurfaces. This non-degenerate photon-pair generation is derived from the hyperbolic topology of the transverse phase-matching and can enable orders-of-magnitude enhancement of the photon rate and spectral brightness, as compared to the degenerate regime. We show that the entanglement of the photon-pairs can be tuned by varying the pump polarization, which can underpin future advances and applications of ultra-compact quantum light sources.
Mission Statement Effect on Research and Innovation Performance

The mission statement (MS) is the most used organizational strategic planning tool worldwide. The relationship between an MS and an organization’s financial performance has been shown to be significantly positive, albeit small. However, an MS’s relationship to the macroeconomic environment and to organizational innovation has not been investigated. We implemented a Structural Equation Modeling using the SCImago Institutional Ranking (SIR) as a global baseline sample and assessment of organizational research and innovation (RandI), an automated MS content analysis, and the Economic Complexity Index (ECI) as a comprehensive macroeconomic environment measure. We found that the median performance of organizations that do not report an MS is significantly higher than that of reporting organizations, and that a path-dependence driven by the State's long-term view and investment is a better explanatory variable for organizational RandI performance than the MS construct or the intermediate-term macroeconomic environment.