Miracle-dz/UniFAR-FTUs huggingface dataset:
UBAR: Towards Fully End-to-End Task-Oriented Dialog Systems with GPT-2

This paper presents our task-oriented dialog system UBAR which models task-oriented dialogs on a dialog session level. Specifically, UBAR is acquired by fine-tuning the large pre-trained unidirectional language model GPT-2 on the sequence of the entire dialog session which is composed of user utterance, belief state, database result, system act, and system response of every dialog turn. Additionally, UBAR is evaluated in a more realistic setting, where its dialog context has access to user utterances and all content it generated such as belief states, system acts, and system responses. Experimental results on the MultiWOZ datasets show that UBAR achieves state-of-the-art performances in multiple settings, improving the combined score of response generation, policy optimization, and end-to-end modeling by 4.7, 3.5, and 9.4 points respectively. Thorough analyses demonstrate that the session-level training sequence formulation and the generated dialog context are essential for UBAR to operate as a fully end-to-end task-oriented dialog system in real life. We also examine the transfer ability of UBAR to new domains with limited data and provide visualization and a case study to illustrate the advantages of UBAR in modeling on a dialog session level.
Spatiotemporal tomography based on scattered multiangular signals and its application for resolving evolving clouds using moving platforms

<p>The climate is strongly affected by interaction with clouds. To reduce major errors in climate predictions, this interaction requires a much finer understanding of cloud physics than current knowledge. Current knowledge is based on empirical remote sensing data that is analyzed under the assumption that the atmosphere and clouds are made of very broad and uniform layers. To help to overcome this problem, 3D scattering computed tomography (CT) has been suggested as a way to study clouds. &#160;</p><p>CT is a powerful way to recover the inner structure of three dimensional (3D) volumetric heterogeneous objects. CT has extensive use in many research and operational domains. Aside from its common usage in medicine, CT is used for sensing geophysical terrestrial structures, atmospheric pollution and fluid dynamics. CT requires imaging from multiple directions and in nearly all CT approaches, the object is considered static during image acquisition. However, in many cases, the object changes while multi-view images are acquired sequentially. Thus, an effort has been invested to expand 3D CT to four-dimensional (4D) spatiotemporal CT. This effort has been directed at linear CT modalities. Since linear CT is computationally easier to handle, it has been a popular method for medical imaging. However, these linear CT modalities do not apply to clouds: clouds constitute a scattering medium, and therefore radiative transfer is non-linear in the clouds&#8217; content. </p><p>This work focuses on the challenge of 4D scattering CT of clouds. Scattering CT of clouds requires high-resolution multi-view images from space. There are spaceborne and high-altitude systems that may provide such data, for example AirMSPI, MAIA, HARP and AirHARP. An additional planned system is the CloudCT formation, funded by the ERC. However, these systems are costly. Deploying them in large numbers to simultaneously acquire images of the same clouds from many angles can be impractical. Therefore, the platforms are planned to move above the clouds: a sequence of images is taken, in order to span and sample a wide angular breadth. However, the clouds evolve while the angular span is sampled. </p><p>We pose conditions under which this task can be performed. These regard temporal sampling and angular breadth, in relation to the correlation time of the evolving cloud. Then, we generalize scattering CT. The generalization seeks spatiotemporal recovery of the cloud extinction field in high resolution (10m), using data taken by a small number of moving cameras. We present an optimization-based method to reach this, and then demonstrate the method both in rigorous simulations and on real data. </p>
ProMask: Probability Mask for Skeleton Detection

Detecting object skeletons in natural images presents challenging, due to varied object scales, the complexity of backgrounds and various noises. The skeleton is a highly compressing shape representation, which can bring some essential advantages but cause the difficulties of detection. This skeleton line occupies a rare proportion of an image and is overly sensitive to spatial position. Inspired by these issues, we propose the ProMask, which is a novel skeleton detection model. The ProMask includes the probability mask and vector router. The skeleton probability mask representation explicitly encodes skeletons with segmentation signals, which can provide more supervised information to learn and pay more attention to ground-truth skeleton pixels. Moreover, the vector router module possesses two sets of orthogonal basis vectors in a two-dimensional space, which can dynamically adjust the predicted skeleton position. We evaluate our method on the well-known skeleton datasets, realizing the better performance than state-of-the-art approaches. Especially, ProMask significantly outperforms the competitive DeepFlux by 6.2% on the challenging SYM-PASCAL dataset. We consider that our proposed skeleton probability mask could serve as a solid baseline for future skeleton detection, since it is very effective and it requires about 10 lines of code.
Modeling the effects of dynamic range compression on signals in noise

Hearing aids use dynamic range compression (DRC), a form of automatic gain control, to make quiet sounds louder and loud sounds quieter. Compression can improve listening comfort, but it can also cause unwanted distortion in noisy environments. It has been widely reported that DRC performs poorly in noise, but there has been little mathematical analysis of these noise-induced distortion effects. This work introduces a mathematical model to study the behavior of DRC in noise. By making simplifying assumptions about the signal envelopes, we define an effective compression function that models the compression applied to one signal in the presence of another. Using the properties of concave functions, we prove results about DRC that have been previously observed experimentally: that the effective compression applied to each sound in a mixture is weaker than it would have been for the signal alone; that uncorrelated signal envelopes become negatively correlated when compressed as a mixture; and that compression can reduce the long-term signal-to-noise ratio in certain conditions. These theoretical results are supported by software experiments using recorded speech signals.
Learning Interpretable Concept-Based Models with Human Feedback

Machine learning models that first learn a representation of a domain in terms of human-understandable concepts, then use it to make predictions, have been proposed to facilitate interpretation and interaction with models trained on high-dimensional data. However these methods have important limitations: the way they define concepts are not inherently interpretable, and they assume that concept labels either exist for individual instances or can easily be acquired from users. These limitations are particularly acute for high-dimensional tabular features. We propose an approach for learning a set of transparent concept definitions in high-dimensional tabular data that relies on users labeling concept features instead of individual instances. Our method produces concepts that both align with users' intuitive sense of what a concept means, and facilitate prediction of the downstream label by a transparent machine learning model. This ensures that the full model is transparent and intuitive, and as predictive as possible given this constraint. We demonstrate with simulated user feedback on real prediction problems, including one in a clinical domain, that this kind of direct feedback is much more efficient at learning solutions that align with ground truth concept definitions than alternative transparent approaches that rely on labeling instances or other existing interaction mechanisms, while maintaining similar predictive performance.
Automated Symbolic Verification of Telegram's MTProto 2.0

MTProto 2.0 is a suite of cryptographic protocols for instant messaging at the core of the popular Telegram messenger application, which is currently used by more than 400 millions of people. In this paper we analyse MTProto 2.0 using ProVerif, a symbolic cryptographic protocol verifier based on the Dolev-Yao model. In particular, we provide a fully automated proof of the soundness of MTProto 2.0's authentication, normal chat, end-to-end encrypted chat, and re-keying mechanisms with respect to several security properties, including authentication, integrity, confidentiality and perfect forward secrecy. To prove these results we proceed in a modular way: each protocol is examined in isolation, relying only on the guarantees provided by the previous ones and the robustness of the basic cryptographic primitives. Our research proves the formal correctness of MTProto 2.0 in the symbolic model, and it can serve as a reference for implementation and analysis of clients and servers.
A Theory for Backtrack-Downweighted Walks

We develop a complete theory for the combinatorics of walk-counting on a directed graph in the case where each backtracking step is downweighted by a given factor. By deriving expressions for the associated generating functions, we also obtain linear systems for computing centrality measures in this setting. In particular, we show that backtrack-downweighted Katz-style network centrality can be computed at the same cost as standard Katz. Studying the limit of this centrality measure at its radius of convergence also leads to a new expression for backtrack-downweighted eigenvector centrality that generalizes previous work to the case where directed edges are present. The new theory allows us to combine advantages of standard and nonbacktracking cases, avoiding localization while accounting for tree-like structures. We illustrate the behaviour of the backtrack-downweighted centrality measure on both synthetic and real networks.
Deep Learning for Human Mobility: a Survey on Data and Models

The study of human mobility is crucial due to its impact on several aspects of our society, such as disease spreading, urban planning, well-being, pollution, and more. The proliferation of digital mobility data, such as phone records, GPS traces, and social media posts, combined with the outstanding predictive power of artificial intelligence, triggered the application of deep learning to human mobility. In particular, the literature is focusing on three tasks: next-location prediction, i.e., predicting an individual's future locations; crowd flow prediction, i.e., forecasting flows on a geographic region; and trajectory generation, i.e., generating realistic individual trajectories. Existing surveys focus on single tasks, data sources, mechanistic or traditional machine learning approaches, while a comprehensive description of deep learning solutions is missing. This survey provides: (i) basic notions on mobility and deep learning; (ii) a review of data sources and public datasets; (iii) a description of deep learning models and (iv) a discussion about relevant open challenges. Our survey is a guide to the leading deep learning solutions to next-location prediction, crowd flow prediction, and trajectory generation. At the same time, it helps deep learning scientists and practitioners understand the fundamental concepts and the open challenges of the study of human mobility.
A CSMT challenge dataset for the identification of computer generated melodies

In this paper, the dataset used for the data challenge organised by Conference on Sound and Music Technology (CSMT) is introduced. The CSMT data challenge requires participants to identify whether a given piece of melody is generated by computer or is composed by human. The dataset is formed by two parts: development dataset and evaluation dataset. The development dataset contains only computer generated melodies whereas the evaluation dataset contain both computer generated melodies and human composed melodies. The aim of the dataset is to examine whether it is possible to distinguish computer generated melodies by learning the feature of generated melodies.
Influence of some anti-inflammatory drugs in membrane fluidity studied by fluorescence anisotropy measurementsPresented at the 17th Conference of the European Colloid & Interface Science Society, Firenze, Italy, September 21?26, 2003

Non-steroidal anti-inflammatory drugs (NSAIDs) have an important role as anti-rheumatic drugs and their therapeutic effects may be partly due to their ability to induce modifications on physical characteristics of the membrane lipid bilayer. In this study we used fluorescence anisotropy measurements in order to assess the influence of different NSAIDS (nimesulide, tolmetin, acemetacin and indomethacin) in the membrane fluidity. Liposomes were prepared by the thin film hydration method and were used as models of the biological membranes. Membrane fluidity was estimated in large unilamellar vesicles (LUVs) by fluorescence measurements using a set of n-(9-anthroyloxy) fatty acid probes (n = 2, 6, 9 and 12). As the fluorophores of these molecules are located at a graded series of levels from the surface to the centre of the lipid bilayer, using these probes, it was possible to monitor the fluidity gradient through a bilayer leaflet by measurements of fluorescence anisotropy. The location of tolmetin on LUVs was evaluated by fluorescence quenching using the same spectroscopic probes. All the NSAIDs increased membrane fluidity (decreased the fluorescence anisotropy) in a concentration dependent manner with an effectiveness ordered as nimesulide > indomethacin ≥ acemetacin > tolmetin. Although all probes were perturbed, 2, 6 and 9-AS, which are part of the plateau region, have shown to be less susceptible to perturbation than the 12-AS probe closely located to the centre of the bilayer. Moreover, each n-AS probe was differently affected, according to the perturbers preferential location. Besides the investigation of membrane structural perturbations induced by NSAIDs, the aim of the present study was to provide a data analysis of steady-state anisotropy measurements taking into account that the probe itself strongly influences the data, as this problem is most of the times overlooked.
Graphene plasmon-phonon coupled modes at the exceptional point

Properties of graphene plasmons are greatly affected by their coupling to phonons. While such coupling has been routinely observed in both near-field and far-field graphene spectroscopy, the interplay between coupling strength and mode losses, and its exceptional point physics has not been discussed. By applying a non-Hermitian framework, we identify the transition point between strong and weak coupling as the exceptional point. Enhanced sensitivity to perturbations near the exceptional point is observed by varying the coupling strength and through gate modulation of the graphene Fermi level. Finally, we also show that the transition from strong to weak coupling is observable by changing the incident angle of radiation.
New Insights on Chemical Oxidation of Single-Wall Carbon Nanotubes: A Theoretical Study

In order to understand the morphology of oxidized positions in single-wall carbon nanotubes (SWNTs), several chemical models, resulting from the interaction of an armchair (5,5) carbon nanotube (a(5,5)) with molecular oxygen and water, have been proposed. Structural and thermodynamic properties were calculated using the density functional theory at the B3LYP/6-31G(d) level, and the results are discussed with an aim to assess the viability of the processes and structures proposed. In addition, the changes in the Raman active modes relative to the D and G absorption regions were analyzed and the characteristic frequencies assigned. In general, our results show a blue shift of about 10 cm−1 in the D band with the progress of the oxidation reaction and an opposite trend for the G band, which is red-shifted by about 5 cm−1. Although the extension of oxidation was short as reported in the present paper, taking only 1 mol of O2 and water, the main functional groups were generated: C═O, C−O−C, and C−OH. Thus, the.
A Sequence-Oblivious Generation Method for Context-Aware Hashtag Recommendation

Like search, a recommendation task accepts an input query or cue and provides desirable items, often based on a ranking function. Such a ranking approach rarely considers explicit dependency among the recommended items. In this work, we propose a generative approach to tag recommendation, where semantic tags are selected one at a time conditioned on the previously generated tags to model inter-dependency among the generated tags. We apply this tag recommendation approach to an Instagram data set where an array of context feature types (image, location, time, and text) are available for posts. To exploit the inter-dependency among the distinct types of features, we adopt a simple yet effective architecture using self-attention, making deep interactions possible. Empirical results show that our method is significantly superior to not only the usual ranking schemes but also autoregressive models for tag recommendation. They indicate that it is critical to fuse mutually supporting features at an early stage to induce extensive and comprehensive view on inter-context interaction in generating tags in a recurrent feedback loop.
Modeling Voters in Multi-Winner Approval Voting

In many real world situations, collective decisions are made using voting and, in scenarios such as committee or board elections, employing voting rules that return multiple winners. In multi-winner approval voting (AV), an agent submits a ballot consisting of approvals for as many candidates as they wish, and winners are chosen by tallying up the votes and choosing the top-k candidates receiving the most approvals. In many scenarios, an agent may manipulate the ballot they submit in order to achieve a better outcome by voting in a way that does not reflect their true preferences. In complex and uncertain situations, agents may use heuristics instead of incurring the additional effort required to compute the manipulation which most favors them. In this paper, we examine voting behavior in single-winner and multi-winner approval voting scenarios with varying degrees of uncertainty using behavioral data obtained from Mechanical Turk. We find that people generally manipulate their vote to obtain a better outcome, but often do not identify the optimal manipulation. There are a number of predictive models of agent behavior in the social choice and psychology literature that are based on cognitively plausible heuristic strategies. We show that the existing approaches do not adequately model our real-world data. We propose a novel model that takes into account the size of the winning set and human cognitive constraints; and demonstrate that this model is more effective at capturing real-world behaviors in multi-winner approval voting scenarios.
The Fictitious Domain Method Based on Navier Slip Boundary Condition for Simulation of Flow-Particle Interaction

In this article, we develop a least--squares/fictitious domain method for direct simulation of fluid particle motion with Navier slip boundary condition at the fluid--particle interface. Let $\Omega$ and $B$ be two bounded domains of $\mathbb{R}^{d}$ such that $\overline{B} \subset \Omega$. The motion of solid particle $B$ is governed by Newton's equations. Our goal here is to develop a fictitious domain method where one solves a variant of the original problem on the full $\Omega$, followed by a well--chosen correction over $B$ and corrections related to translation velocity and angular velocity of the particle. This method is of the virtual control type and relies on a least--squares formulation making the problem solvable by a conjugate gradient algorithm operating in a well chosen control space. Since the fully explicit scheme to update the particle motion using Newton's equation is unstable, we propose and implement an explicit--implicit scheme in which, at each time step, the position of the particle is updated explicitly, and the solution of Navier-Stokes equations and particle velocities are solved by the the least--squares/fictitious domain method implicitly. Numerical results are given to verify our numerical method.
Efficient Kernel based Matched Filter Approach for Segmentation of Retinal Blood Vessels

Retinal blood vessels structure contains information about diseases like obesity, diabetes, hypertension and glaucoma. This information is very useful in identification and treatment of these fatal diseases. To obtain this information, there is need to segment these retinal vessels. Many kernel based methods have been given for segmentation of retinal vessels but their kernels are not appropriate to vessel profile cause poor performance. To overcome this, a new and efficient kernel based matched filter approach has been proposed. The new matched filter is used to generate the matched filter response (MFR) image. We have applied Otsu thresholding method on obtained MFR image to extract the vessels. We have conducted extensive experiments to choose best value of parameters for the proposed matched filter kernel. The proposed approach has examined and validated on two online available DRIVE and STARE datasets. The proposed approach has specificity 98.50%, 98.23% and accuracy 95.77 %, 95.13% for DRIVE and STARE dataset respectively. Obtained results confirm that the proposed method has better performance than others. The reason behind increased performance is due to appropriate proposed kernel which matches retinal blood vessel profile more accurately.
Pre-training Protein Language Models with Label-Agnostic Binding Pairs Enhances Performance in Downstream Tasks

Less than 1% of protein sequences are structurally and functionally annotated. Natural Language Processing (NLP) community has recently embraced self-supervised learning as a powerful approach to learn representations from unlabeled text, in large part due to the attention-based context-aware Transformer models. In this work we present a modification to the RoBERTa model by inputting during pre-training a mixture of binding and non-binding protein sequences (from STRING database). However, the sequence pairs have no label to indicate their binding status, as the model relies solely on Masked Language Modeling (MLM) objective during pre-training. After fine-tuning, such approach surpasses models trained on single protein sequences for protein-protein binding prediction, TCR-epitope binding prediction, cellular-localization and remote homology classification tasks. We suggest that the Transformer's attention mechanism contributes to protein binding site discovery. Furthermore, we compress protein sequences by 64% with the Byte Pair Encoding (BPE) vocabulary consisting of 10K subwords, each around 3-4 amino acids long. Finally, to expand the model input space to even larger proteins and multi-protein assemblies, we pre-train Longformer models that support 2,048 tokens. Further work in token-level classification for secondary structure prediction is needed. Code available at: this https URL