Miracle-dz/UniFAR-FTUs huggingface dataset:
How Should My Chatbot Interact? A Survey on Social Characteristics in Human–Chatbot Interaction Design

ABSTRACT Chatbots’ growing popularity has brought new challenges to HCI, having changed the patterns of human interactions with computers. The increasing need to approximate conversational interaction styles raises expectations for chatbots to present social behaviors that are habitual in human–human communication. In this survey, we argue that chatbots should be enriched with social characteristics that cohere with users’ expectations, ultimately avoiding frustration and dissatisfaction. We bring together the literature on disembodied, text-based chatbots to derive a conceptual model of social characteristics for chatbots. We analyzed 56 papers from various domains to understand how social characteristics can benefit human–chatbot interactions and identify the challenges and strategies to designing them. Additionally, we discussed how characteristics may influence one another. Our results provide relevant opportunities to both researchers and designers to advance human–chatbot interactions.
Unsupervised training of neural mask-based beamforming

We present an unsupervised training approach for a neural network-based mask estimator in an acoustic beamforming application. The network is trained to maximize a likelihood criterion derived from a spatial mixture model of the observations. It is trained from scratch without requiring any parallel data consisting of degraded input and clean training targets. Thus, training can be carried out on real recordings of noisy speech rather than simulated ones. In contrast to previous work on unsupervised training of neural mask estimators, our approach avoids the need for a possibly pre-trained teacher model entirely. We demonstrate the effectiveness of our approach by speech recognition experiments on two different datasets: one mainly deteriorated by noise (CHiME 4) and one by reverberation (REVERB). The results show that the performance of the proposed system is on par with a supervised system using oracle target masks for training and with a system trained using a model-based teacher.
Open strings in integrable deformations of $\sigma$-models

This contribution is based on a talk given by the author at the "Dualities and Generalized Geometries" session of the Corfu Summer Institute 2018 workshops. We overview the results of [1], focusing our attention on integrable $\lambda$-deformations of WZW models with boundaries.
Unsupervised Domain Adaptation of Contextualized Embeddings: A Case Study in Early Modern English

Contextualized word embeddings such as ELMo and BERT provide a foundation for strong performance across a range of natural language processing tasks, in part by pretraining on a large and topically-diverse corpus. However, the applicability of this approach is unknown when the target domain varies substantially from the text used during pretraining. Specifically, we are interested the scenario in which labeled data is available in only a canonical source domain such as newstext, and the target domain is distinct from both the labeled corpus and the pretraining data. To address this scenario, we propose domain-adaptive fine-tuning, in which the contextualized embeddings are adapted by masked language modeling on the target domain. We test this approach on the challenging domain of Early Modern English, which differs substantially from existing pretraining corpora. Domain-adaptive fine-tuning yields an improvement of 4\% in part-of-speech tagging accuracy over a BERT baseline, substantially improving on prior work on this task.
A Generalization Bound for Online Variational Inference

Bayesian inference provides an attractive online-learning framework to analyze sequential data, and offers generalization guarantees which hold even with model mismatch and adversaries. Unfortunately, exact Bayesian inference is rarely feasible in practice and approximation methods are usually employed, but do such methods preserve the generalization properties of Bayesian inference ? In this paper, we show that this is indeed the case for some variational inference (VI) algorithms. We consider a few existing online, tempered VI algorithms, as well as a new algorithm, and derive their generalization bounds. Our theoretical result relies on the convexity of the variational objective, but we argue that the result should hold more generally and present empirical evidence in support of this. Our work in this paper presents theoretical justifications in favor of online algorithms relying on approximate Bayesian methods.
Characterization of parameters with a mixed bias property

We study a class of parameters with the so-called mixed bias property. For parameters with this property, the bias of the semiparametric efficient one-step estimator is equal to the mean of the product of the estimation errors of two nuisance functions. In nonparametric models, parameters with the mixed bias property admit so-called rate doubly robust estimators, i.e., estimators that are consistent and asymptotically normal when one succeeds in estimating both nuisance functions at sufficiently fast rates, with the possibility of trading off slower rates of convergence for the estimator of one of the nuisance functions against faster rates for the estimator of the other nuisance function. We show that the class of parameters with the mixed bias property strictly includes two recently studied classes of parameters which, in turn, include many parameters of interest in causal inference. We characterize the form of parameters with the mixed bias property and of their influence functions. Furthermore, we derive two functional loss functions, each being minimized at one of the two nuisance functions. These loss functions can be used to derive loss-based penalized estimators of the nuisance functions.
The Impact of Pair-instability Mass Loss on the Binary Black Hole Mass Distribution

A population of binary black hole mergers has now been observed in gravitational waves by Advanced LIGO and Virgo. The masses of these black holes appear to show evidence for a pileup between 30 and 45 M⊙ and a cutoff above ∼45 M⊙. One possible explanation for such a pileup and subsequent cutoff are pulsational pair-instability supernovae (PPISNe) and pair-instability supernovae (PISNe) in massive stars. We investigate the plausibility of this explanation in the context of isolated massive binaries. We study a population of massive binaries using the rapid population synthesis software COMPAS, incorporating models for PPISNe and PISNe. Our models predict a maximum black hole mass of 40 M⊙. We expect ∼10% of all binary black hole mergers at redshift z = 0 will include at least one component that went through a PPISN (with mass 30–40 M⊙), constituting ∼20%–50% of binary black hole mergers observed during the first two observing runs of Advanced LIGO and Virgo. Empirical models based on fitting the gravitational-wave mass measurements to a combination of a power law and a Gaussian find a fraction too large to be associated with PPISNe in our models. The rates of PPISNe and PISNe track the low metallicity star formation rate, increasing out to redshift z = 2. These predictions may be tested both with future gravitational-wave observations and with observations of superluminous supernovae.
Data-driven discovery of coordinates and governing equations

Significance Governing equations are essential to the study of physical systems, providing models that can generalize to predict previously unseen behaviors. There are many systems of interest across disciplines where large quantities of data have been collected, but the underlying governing equations remain unknown. This work introduces an approach to discover governing models from data. The proposed method addresses a key limitation of prior approaches by simultaneously discovering coordinates that admit a parsimonious dynamical model. Developing parsimonious and interpretable governing models has the potential to transform our understanding of complex systems, including in neuroscience, biology, and climate science. The discovery of governing equations from scientific data has the potential to transform data-rich fields that lack well-characterized quantitative descriptions. Advances in sparse regression are currently enabling the tractable identification of both the structure and parameters of a nonlinear dynamical system from data. The resulting models have the fewest terms necessary to describe the dynamics, balancing model complexity with descriptive ability, and thus promoting interpretability and generalizability. This provides an algorithmic approach to Occam’s razor for model discovery. However, this approach fundamentally relies on an effective coordinate system in which the dynamics have a simple representation. In this work, we design a custom deep autoencoder network to discover a coordinate transformation into a reduced space where the dynamics may be sparsely represented. Thus, we simultaneously learn the governing equations and the associated coordinate system. We demonstrate this approach on several example high-dimensional systems with low-dimensional behavior. The resulting modeling framework combines the strengths of deep neural networks for flexible representation and sparse identification of nonlinear dynamics (SINDy) for parsimonious models. This method places the discovery of coordinates and models on an equal footing.
Instance-Level Meta Normalization

This paper presents a normalization mechanism called Instance-Level Meta Normalization (ILM~Norm) to address a learning-to-normalize problem. ILM~Norm learns to predict the normalization parameters via both the feature feed-forward and the gradient back-propagation paths. ILM~Norm provides a meta normalization mechanism and has several good properties. It can be easily plugged into existing instance-level normalization schemes such as Instance Normalization, Layer Normalization, or Group Normalization. ILM~Norm normalizes each instance individually and therefore maintains high performance even when small mini-batch is used. The experimental results show that ILM~Norm well adapts to different network architectures and tasks, and it consistently improves the performance of the original models.
Distinguishing Clinical Sentiment: The Importance of Domain Adaptation in Psychiatric Patient Health Records

Recently natural language processing (NLP) tools have been developed to identify and extract salient risk indicators in electronic health records (EHRs). Sentiment analysis, although widely used in non-medical areas for improving decision making, has been studied minimally in the clinical setting. In this study, we undertook, to our knowledge, the first domain adaptation of sentiment analysis to psychiatric EHRs by defining psychiatric clinical sentiment, performing an annotation project, and evaluating multiple sentence-level sentiment machine learning (ML) models. Results indicate that off-the-shelf sentiment analysis tools fail in identifying clinically positive or negative polarity, and that the definition of clinical sentiment that we provide is learnable with relatively small amounts of training data. This project is an initial step towards further refining sentiment analysis methods for clinical use. Our long-term objective is to incorporate the results of this project as part of a machine learning model that predicts inpatient readmission risk. We hope that this work will initiate a discussion concerning domain adaptation of sentiment analysis to the clinical setting.
Milky Way and Andromeda past-encounters in different gravity models: the impact on the estimated Local Group mass

The Two-body problem of $M31$ and the Milky Way (MW) galaxies with a Cosmological Constant background is studied, with emphasis on the possibility that they experienced Past Encounters. By implementing the Timing Argument (TA), it is shown that if $M{31}$ and the MW have had more than one encounter then the deduced mass of the Local Group (LG) would be larger. Past encounters are possible only for non-zero transverse velocity, and their viability is subject to observations of the imprints of such near collisions. Using a recent $Gaia$ - based measurement of the transverse velocity we show that the presence of the Cosmological Constant requires the mass for the LG to be $35\%$ higher: $3.36^{+1.14}_{-0.70} \cdot 10^{12} M_{\odot}$ with no Cosmological Constant or $4.54^{+1.20}_{-0.75} \cdot 10^{12} M_{\odot}$ with a Cosmological Constant background. If the LG has had one past encounter, the LG mass is $9.99^{+2.22}_{-1.58}\cdot 10^{12} M_{\odot}$ with a Cosmological Constant background. Modified Newtonian Dynamics (MOND) is studied as the accelerations of the Local Group are fully in the deep-MOND regime. MOND yields the order of magnitude for the expected baryonic mass only if at least one encounter occurred. While we only consider the LG as two point masses, our calculations provide a benchmark for future work with simulations to test Dynamical Friction and other effects. This model can be also used to test screening mechanisms and alternative theories of gravity.
Effect of Adding Different Levels of Grapes (Vitis vinifera L.) Seeds and Leaf Powder or their Extracts on Some Bone Characteristics and Total Ash Content in Broiler Chickens

Background: Some natural sources of polyphenols like grape seed, leaf or extracts, have many benefits in maintaining bone health in animals. This study aimed to investigate the efficacy of grape seeds, grape leaves powder, or their extracts on some bone characteristics and total ash content in broiler chickens. Methods: Three hundred and twenty four one-day-old broilers were allocated to nine treatments with three replicates containing 12 chicks each. Chicks were fed nine experimental diets for 35 days; as a control diet without supplementation (T1), control diets supplemented with 15 and 30 g/kg grape seeds powder (GSP) (T2, T3), 15 and 30 g/kg grape leaf powder (GLP) (T4, T5), grape seed extract (GSE) at levels 2 and 4 ml/l added in drinking water (T6, T7) and with grape leaf extract (GLE ) at levels 2 and 4 ml/l (T8 and T9), respectively. Result: Significant increase (p≤0.05) in the bone length was recorded with GSP (30 g/kg), GLP (15 g/kg), GSE or GLE and calcium percent with GLE as compared with control. Furthermore, significant improvement was recorded in predictive skeletal weight of broilers fed GSE (T6 α T7) as compared to the other treatments. The study concluded that the best results were achieved at the 2 or 4 ml/l of grape seed extract in improving femur length, predicted skeletal weight and from grape leaf extract in improving calcium percentage in broiler bone ash.
Improving Domain Adaptation Translation with Domain Invariant and Specific Information

In domain adaptation for neural machine translation, translation performance can benefit from separating features into domain-specific features and common features. In this paper, we propose a method to explicitly model the two kinds of information in the encoder-decoder framework so as to exploit out-of-domain data in in-domain training. In our method, we maintain a private encoder and a private decoder for each domain which are used to model domain-specific information. In the meantime, we introduce a common encoder and a common decoder shared by all the domains which can only have domain-independent information flow through. Besides, we add a discriminator to the shared encoder and employ adversarial training for the whole model to reinforce the performance of information separation and machine translation simultaneously. Experiment results show that our method can outperform competitive baselines greatly on multiple data sets.
Black is to Criminal as Caucasian is to Police: Detecting and Removing Multiclass Bias in Word Embeddings

Online texts - across genres, registers, domains, and styles - are riddled with human stereotypes, expressed in overt or subtle ways. Word embeddings, trained on these texts, perpetuate and amplify these stereotypes, and propagate biases to machine learning models that use word embeddings as features. In this work, we propose a method to debias word embeddings in multiclass settings such as race and religion, extending the work of (Bolukbasi et al., 2016) from the binary setting, such as binary gender. Next, we propose a novel methodology for the evaluation of multiclass debiasing. We demonstrate that our multiclass debiasing is robust and maintains the efficacy in standard NLP tasks.
When is an action caused from within? Quantifying the causal chain leading to actions in simulated agents

An agent’s actions can be influenced by external factors through the inputs it receives from the environment, as well as internal factors, such as memories or intrinsic preferences. The extent to which an agent’s actions are “caused from within”, as opposed to being externally driven, should depend on its sensor capacity as well as environmental demands for memory and context-dependent behavior. Here, we test this hypothesis using simulated agents (“animats”), equipped with small adaptive Markov Brains (MB) that evolve to solve a perceptual-categorization task under conditions varied with regards to the agents’ sensor capacity and task difficulty. Using a novel formalism developed to identify and quantify the actual causes of occurrences (“what caused what? ”) in complex networks, we evaluate the direct causes of the animats’ actions. In addition, we extend this framework to trace the causal chain (“causes of causes”) leading to an animat’s actions back in time, and compare the obtained spatio-temporal causal history across task conditions. We found that measures quantifying the extent to which an animat’s actions are caused by internal factors (as opposed to being driven by the environment through its sensors) varied consistently with defining aspects of the task conditions they evolved to thrive in.
Cross-Lingual Transfer of Semantic Roles: From Raw Text to Semantic Roles

We describe a transfer method based on annotation projection to develop a dependency-based semantic role labeling system for languages for which no supervised linguistic information other than parallel data is available. Unlike previous work that presumes the availability of supervised features such as lemmas, part-of-speech tags, and dependency parse trees, we only make use of word and character features. Our deep model considers using character-based representations as well as unsupervised stem embeddings to alleviate the need for supervised features. Our experiments outperform a state-of-the-art method that uses supervised lexico-syntactic features on 6 out of 7 languages in the Universal Proposition Bank.
Unsupervised Embedding Learning via Invariant and Spreading Instance Feature

This paper studies the unsupervised embedding learning problem, which requires an effective similarity measurement between samples in low-dimensional embedding space. Motivated by the positive concentrated and negative separated properties observed from category-wise supervised learning, we propose to utilize the instance-wise supervision to approximate these properties, which aims at learning data augmentation invariant and instance spread-out features. To achieve this goal, we propose a novel instance based softmax embedding method, which directly optimizes the `real' instance features on top of the softmax function. It achieves significantly faster learning speed and higher accuracy than all existing methods. The proposed method performs well for both seen and unseen testing categories with cosine similarity. It also achieves competitive performance even without pre-trained network over samples from fine-grained categories.
Trapped-ion quantum computing: Progress and challenges

Trapped ions are among the most promising systems for practical quantum computing (QC). The basic requirements for universal QC have all been demonstrated with ions, and quantum algorithms using few-ion-qubit systems have been implemented. We review the state of the field, covering the basics of how trapped ions are used for QC and their strengths and limitations as qubits. In addition, we discuss what is being done, and what may be required, to increase the scale of trapped ion quantum computers while mitigating decoherence and control errors. Finally, we explore the outlook for trapped-ion QC. In particular, we discuss near-term applications, considerations impacting the design of future systems of trapped ions, and experiments and demonstrations that may further inform these considerations.Trapped ions are among the most promising systems for practical quantum computing (QC). The basic requirements for universal QC have all been demonstrated with ions, and quantum algorithms using few-ion-qubit systems have been implemented. We review the state of the field, covering the basics of how trapped ions are used for QC and their strengths and limitations as qubits. In addition, we discuss what is being done, and what may be required, to increase the scale of trapped ion quantum computers while mitigating decoherence and control errors. Finally, we explore the outlook for trapped-ion QC. In particular, we discuss near-term applications, considerations impacting the design of future systems of trapped ions, and experiments and demonstrations that may further inform these considerations.
Is 'Unsupervised Learning' a Misconceived Term?

Is all of machine learning supervised to some degree? The field of machine learning has traditionally been categorized pedagogically into $supervised~vs~unsupervised~learning$; where supervised learning has typically referred to learning from labeled data, while unsupervised learning has typically referred to learning from unlabeled data. In this paper, we assert that all machine learning is in fact supervised to some degree, and that the scope of supervision is necessarily commensurate to the scope of learning potential. In particular, we argue that clustering algorithms such as k-means, and dimensionality reduction algorithms such as principal component analysis, variational autoencoders, and deep belief networks are each internally supervised by the data itself to learn their respective representations of its features. Furthermore, these algorithms are not capable of external inference until their respective outputs (clusters, principal components, or representation codes) have been identified and externally labeled in effect. As such, they do not suffice as examples of unsupervised learning. We propose that the categorization `supervised vs unsupervised learning' be dispensed with, and instead, learning algorithms be categorized as either $internally~or~externally~supervised$ (or both). We believe this change in perspective will yield new fundamental insights into the structure and character of data and of learning algorithms.
The Art, Science, and Engineering of Fuzzing: A Survey

Among the many software testing techniques available today, fuzzing has remained highly popular due to its conceptual simplicity, its low barrier to deployment, and its vast amount of empirical evidence in discovering real-world software vulnerabilities. At a high level, fuzzing refers to a process of repeatedly running a program with generated inputs that may be syntactically or semantically malformed. While researchers and practitioners alike have invested a large and diverse effort towards improving fuzzing in recent years, this surge of work has also made it difficult to gain a comprehensive and coherent view of fuzzing. To help preserve and bring coherence to the vast literature of fuzzing, this paper presents a unified, general-purpose model of fuzzing together with a taxonomy of the current fuzzing literature. We methodically explore the design decisions at every stage of our model fuzzer by surveying the related literature and innovations in the art, science, and engineering that make modern-day fuzzers effective.