Miracle-dz/UniFAR-FTUs huggingface dataset:
Bag Query Containment and Information Theory

The query containment problem is a fundamental algorithmic problem in data management. While this problem is well understood under set semantics, it is by far less understood under bag semantics. In particular, it is a long-standing open question whether or not the conjunctive query containment problem under bag semantics is decidable. We unveil tight connections between information theory and the conjunctive query containment under bag semantics. These connections are established using information inequalities, which are considered to be the laws of information theory. Our first main result asserts that deciding the validity of a generalization of information inequalities is many-one equivalent to the restricted case of conjunctive query containment in which the containing query is acyclic; thus, either both these problems are decidable or both are undecidable. Our second main result identifies a new decidable case of the conjunctive query containment problem under bag semantics. Specifically, we give an exponential-time algorithm for conjunctive query containment under bag semantics, provided the containing query is chordal and admits a simple junction tree.
Study of CS, SiO, and SiS abundances in carbon star envelopes: Assessing their role as gas-phase precursors of dust.

Aims We aim to determine the abundances of CS, SiO, and SiS in a large sample of carbon star envelopes covering a wide range of mass loss rates to investigate the potential role that these molecules could play in the formation of dust in the surroundings of the central AGB star. Methods We surveyed a sample of 25 carbon-rich AGB stars in the λ 2 mm band, more concretely in the J = 3 - 2 line of CS and SiO, and in the J = 7 - 6 and J = 8 - 7 lines of SiS, using the IRAM 30 m telescope. We performed excitation and radiative transfer calculations based on the large velocity gradient (LVG) method to model the observed lines of the molecules and to derive their fractional abundances in the observed envelopes. We also assessed the effect of infrared pumping in the excitation of the molecules. Results We detected CS in all 25 targeted envelopes, SiO in 24 of them, and SiS in 17 sources. Remarkably, SiS is not detected in any envelope with a mass loss rate below 10-6 M⊙ yr-1 while it is detected in all envelopes with mass loss rates above that threshold. We found that CS and SiS have similar abundances in carbon star envelopes, while SiO is present with a lower abundance. We also found a strong correlation in which the denser the envelope, the less abundant are CS and SiO. The trend is however only tentatively seen for SiS in the range of high mass loss rates. Furthermore, we found a relation in which the integrated flux of the MgS dust feature at 30 μm increases as the fractional abundance of CS decreases. Conclusions The decline in the fractional abundance of CS with increasing density could be due to gas-phase chemistry in the inner envelope or to adsorption onto dust grains. The latter possibility is favored by a correlation between the CS fractional abundance and the 30 μm feature, which suggests that CS is efficiently incorporated onto MgS dust around C-rich AGB stars. In the case of SiO, the observed abundance depletion with increasing density is most likely caused by an efficient incorporation onto dust grains. We conclude that CS, SiO (very likely), and SiS (tentatively) are good candidates to act as gas-phase precursors of dust in C-rich AGB envelopes.
A reaction network scheme for hidden Markov model parameter learning

With a view towards artificial cells, molecular communication systems, molecular multiagent systems and federated learning, we propose a novel reaction network scheme (termed the Baum–Welch (BW) reaction network) that learns parameters for hidden Markov models (HMMs). All variables including inputs and outputs are encoded by separate species. Each reaction in the scheme changes only one molecule of one species to one molecule of another. The reverse change is also accessible but via a different set of enzymes, in a design reminiscent of futile cycles in biochemical pathways. We show that every positive fixed point of the BW algorithm for HMMs is a fixed point of the reaction network scheme, and vice versa. Furthermore, we prove that the ‘expectation’ step and the ‘maximization’ step of the reaction network separately converge exponentially fast and compute the same values as the E-step and the M-step of the BW algorithm. We simulate example sequences, and show that our reaction network learns the same parameters for the HMM as the BW algorithm, and that the log-likelihood increases continuously along the trajectory of the reaction network.
KP hierarchy and trigonometric Calogero–Moser hierarchy

We consider trigonometric solutions of the KP hierarchy. It is known that their poles move as particles of the Calogero-Moser model with trigonometric potential. We show that this correspondence can be extended to the level of hierarchies: the evolution of the poles with respect to the $k$-th hierarchical time of the KP hierarchy is governed by a Hamiltonian which is a linear combination of the first $k$ higher Hamiltonians of the trigonometric Calogero-Moser hierarchy.
Emotionally-Aware Chatbots: A Survey

Textual conversational agent or chatbots' development gather tremendous traction from both academia and industries in recent years. Nowadays, chatbots are widely used as an agent to communicate with a human in some services such as booking assistant, customer service, and also a personal partner. The biggest challenge in building chatbot is to build a humanizing machine to improve user engagement. Some studies show that emotion is an important aspect to humanize machine, including chatbot. In this paper, we will provide a systematic review of approaches in building an emotionally-aware chatbot (EAC). As far as our knowledge, there is still no work focusing on this area. We propose three research question regarding EAC studies. We start with the history and evolution of EAC, then several approaches to build EAC by previous studies, and some available resources in building EAC. Based on our investigation, we found that in the early development, EAC exploits a simple rule-based approach while now most of EAC use neural-based approach. We also notice that most of EAC contain emotion classifier in their architecture, which utilize several available affective resources. We also predict that the development of EAC will continue to gain more and more attention from scholars, noted by some recent studies propose new datasets for building EAC in various languages.
Encrypted DNS -> Privacy? A Traffic Analysis Perspective

Virtually every connection to an Internet service is preceded by a DNS lookup which is performed without any traffic-level protection, thus enabling manipulation, redirection, surveillance, and censorship. To address these issues, large organizations such as Google and Cloudflare are deploying recently standardized protocols that encrypt DNS traffic between end users and recursive resolvers such as DNS-over-TLS (DoT) and DNS-over-HTTPS (DoH). In this paper, we examine whether encrypting DNS traffic can protect users from traffic analysis-based monitoring and censoring. We propose a novel feature set to perform the attacks, as those used to attack HTTPS or Tor traffic are not suitable for DNS' characteristics. We show that traffic analysis enables the identification of domains with high accuracy in closed and open world settings, using 124 times less data than attacks on HTTPS flows. We find that factors such as location, resolver, platform, or client do mitigate the attacks performance but they are far from completely stopping them. Our results indicate that DNS-based censorship is still possible on encrypted DNS traffic. In fact, we demonstrate that the standardized padding schemes are not effective. Yet, Tor -- which does not effectively mitigate traffic analysis attacks on web traffic -- is a good defense against DoH traffic analysis.
Parzen Filters for Spectral Decomposition of Signals

We propose a novel family of band-pass filters for efficient spectral decomposition of signals. Previous work has already established the effectiveness of representations based on static band-pass filtering of speech signals (e.g., mel-frequency cepstral coefficients and deep scattering spectrum). A potential shortcoming of these approaches is the fact that the parameters specifying such a representation are fixed a priori and not learned using the available data. To address this limitation, we propose a family of filters defined via cosine modulations of Parzen windows, where the modulation frequency models the center of a spectral band-pass filter and the length of a Parzen window is inversely proportional to the filter width in the spectral domain. We propose to learn such a representation using stochastic variational Bayesian inference based on Gaussian dropout posteriors and sparsity inducing priors. Such a prior leads to an intractable integral defining the Kullback--Leibler divergence term for which we propose an effective approximation based on the Gauss--Hermite quadrature. Our empirical results demonstrate that the proposed approach is competitive with state-of-the-art models on speech recognition tasks.
The GMRT High-resolution Southern Sky Survey for Pulsars and Transients. II. New Discoveries, Timing, and Polarization Properties

We have been conducting the Giant Metrewave Radio Telescope High Resolution Southern Sky (GHRSS) survey for the last four years and have discovered 18 pulsars to date. The GHRSS survey is an off-Galactic-plane survey at 322 MHz in a region of the sky (decl. range −40° to −54°) complementary to other ongoing low-frequency surveys. In this paper we report the discovery of three pulsars (PSRs), J1239−48, J1516−43, and J1726−52. We also present timing solutions for three pulsars previously discovered with the GHRSS survey: PSR J2144−5237, a millisecond pulsar with a period P = 5 ms in a 10 days orbit around a ≤0.18 M⊙ companion; PSR J1516−43, a mildly recycled P = 36 ms pulsar in a 228 days orbit with a companion of mass ∼0.4 M⊙; and the P = 320 ms PSR J0514−4408, which we show is a source of pulsed γ-ray emission. We also report radio polarimetric observations of three of the GHRSS discoveries, PSRs J0418−4154, J0514−4408, and J2144−5237.
A Practical Marine Wireless Sensor Network Monitoring System Based on LoRa and MQTT

Under the advocacy of the international community, more and more research topics have been built around the ocean. This paper proposed an implementation scheme of marine wireless sensor network monitoring system based on LoRa and MQTT. Different from the traditional network architecture, the system was constructed by combining with two network forms, and according to their respective characteristics, the overall design followed the transition from LoRa to MQTT. We first used LoRa to interconnect the sensor nodes with the gateway, and on this basis, the collected data was sent to the server visualization platform through MQTT, the backend management server would continuously refresh the monitoring page. At the same time, the client could use a browser-based web application to directly access and call data for global maritime information monitoring. In the future, we will further improve the system and optimize the algorithm, to achieve more dimensions and deeper exploration of the underwater world.
Feedback from OB stars on their parent cloud: gas exhaustion rather than gas ejection

Context. Stellar feedback from high-mass stars shapes the interstellar medium, and thereby impacts gas that will form future generations of stars. However, due to our inability to track the time evolution of individual molecular clouds, quantifying the exact role of stellar feedback on their star formation history is an observationally challenging task. Aims. In the present study, we take advantage of the unique properties of the G316.75-00.00 massive-star forming ridge to determine how stellar feedback from O-stars impacts the dynamical stability of massive filaments. The G316.75 ridge is 13.6 pc long and contains 18 900 M⊙ of H2 gas, half of which is infrared dark and half of which infrared bright. The infrared bright part has already formed four O-type stars over the past 2 Myr, while the infrared dark part is still quiescent. Therefore, by assuming the star forming properties of the infrared dark part represent the earlier evolutionary stage of the infrared bright part, we can quantify how feedback impacts these properties by contrasting the two. Methods. We used publicly available Herschel/HiGAL and molecular line data to measure the ratio of kinetic to gravitational energy per-unit-length, αvirline, across the entire ridge. By using both dense (i.e. N2H+ and NH3) and more diffuse (i.e. 13CO) gas tracers, we were able to compute αvirline for a range of gas volume densities (~1 × 102–1 × 105 cm−3). Results. This study shows that despite the presence of four embedded O-stars, the ridge remains gravitationally bound (i.e. αvirline ≤ 2) nearly everywhere, except for some small gas pockets near the high-mass stars. In fact, αvirline is almost indistinguishable for both parts of the ridge. These results are at odds with most hydrodynamical simulations in which O-star-forming clouds are completely dispersed by stellar feedback within a few cloud free-fall times. However, from simple theoretical calculations, we show that such feedback inefficiency is expected in the case of high-gas-density filamentary clouds. Conclusions. We conclude that the discrepancy between numerical simulations and the observations presented here originates from different cloud morphologies and average densities at the time when the first O-stars form. In the case of G316.75, we speculate that the ridge could arise from the aftermath of a cloud-cloud collision, and that such filamentary configuration promotes the inefficiency of stellar feedback. This does very little to the dense gas already present, but potentially prevents further gas accretion onto the ridge. These results have important implications regarding, for instance, how stellar feedback is implemented in cosmological and galaxy scale simulations.
GANalyze: Toward Visual Definitions of Cognitive Image Properties

We introduce a framework that uses Generative Adversarial Networks (GANs) to study cognitive properties like memorability. These attributes are of interest because we do not have a concrete visual definition of what they entail. What does it look like for a dog to be more memorable? GANs allow us to generate a manifold of natural-looking images with fine-grained differences in their visual attributes. By navigating this manifold in directions that increase memorability, we can visualize what it looks like for a particular generated image to become more memorable. The resulting ``visual definitions" surface image properties (like ``object size") that may underlie memorability. Through behavioral experiments, we verify that our method indeed discovers image manipulations that causally affect human memory performance. We further demonstrate that the same framework can be used to analyze image aesthetics and emotional valence. ganalyze.csail. mit.edu.
Large deviations of the empirical measure of diffusions in fine topologies with applications

We consider large deviations of the empirical measure of diffusion processes. In a first part, we present conditions to obtain a large deviations principle (LDP) in a Wasserstein topology. In particular, we derive a precise class of unbounded functions for which the LDP holds. This provides an analogue to the standard Cramer condition in the context of diffusion processes, which turns out to be related to a spectral gap condition for a Witten-Schrodinger operator. Secondly, we study more precisely the properties of the Donsker-Varadhan rate functional associated to the LDP. We revisit and generalize some standard duality results as well as a more original decomposition of the rate functional with respect to the symmetric and antisymmetric parts of the dynamics. Finally, we apply our results to overdamped and underdamped Langevin dynamics, showing the applicability of our framework in both unbounded and degenerate situations.
Optimal Solution Predictions for Mixed Integer Programs

Mixed Integer Programming (MIP) is one of the most widely used modeling techniques to deal with combinatorial optimization problems. In many applications, a similar MIP model is solved on a regular basis, maintaining remarkable similarities in model structures and solution appearances but differing in formulation coefficients. This offers the opportunity for machine learning method to explore the correlations between model structures and the resulting solution values. To address this issue, we propose to represent an MIP instance using a tripartite graph, based on which a Graph Convolutional Network (GCN) is constructed to predict solution values for binary variables. The predicted solutions are used to generate a local branching cut to the model which accelerate the solution process for MIP. Computational evaluations on 8 distinct types of MIP problems show that the proposed framework improves the performance of a state-of-the-art open source MIP solver significantly in terms of running time and solution quality.
Adversarial Multimodal Network for Movie Question Answering

Visual question answering by using information from multiple modalities has attracted more and more attention in recent years. However, it is a very challenging task, as the visual content and natural language have quite different statistical properties. In this work, we present a method called Adversarial Multimodal Network (AMN) to better understand video stories for question answering. In AMN, as inspired by generative adversarial networks, we propose to learn multimodal feature representations by finding a more coherent subspace for video clips and the corresponding texts (e.g., subtitles and questions). Moreover, we introduce a self-attention mechanism to enforce the so-called consistency constraints in order to preserve the self-correlation of visual cues of the original video clips in the learned multimodal representations. Extensive experiments on the MovieQA dataset show the effectiveness of our proposed AMN over other published state-of-the-art methods.
Keyword Spotting for Hearing Assistive Devices Robust to External Speakers

Keyword spotting (KWS) is experiencing an upswing due to the pervasiveness of small electronic devices that allow interaction with them via speech. Often, KWS systems are speaker-independent, which means that any person --user or not-- might trigger them. For applications like KWS for hearing assistive devices this is unacceptable, as only the user must be allowed to handle them. In this paper we propose KWS for hearing assistive devices that is robust to external speakers. A state-of-the-art deep residual network for small-footprint KWS is regarded as a basis to build upon. By following a multi-task learning scheme, this system is extended to jointly perform KWS and users' own-voice/external speaker detection with a negligible increase in the number of parameters. For experiments, we generate from the Google Speech Commands Dataset a speech corpus emulating hearing aids as a capturing device. Our results show that this multi-task deep residual network is able to achieve a KWS accuracy relative improvement of around 32% with respect to a system that does not deal with external speakers.
Mumford–Shah functionals on graphs and their asymptotics

We consider adaptations of the Mumford–Shah functional to graphs. These are based on discretizations of nonlocal approximations to the Mumford–Shah functional. Motivated by applications in machine learning we study the random geometric graphs associated to random samples of a measure. We establish the conditions on the graph constructions under which the minimizers of graph Mumford–Shah functionals converge to a minimizer of a continuum Mumford–Shah functional. Furthermore we explicitly identify the limiting functional. Moreover we describe an efficient algorithm for computing the approximate minimizers of the graph Mumford–Shah functional.
Neuro-fuzzy hybrid control system of nonlinear process in petroleum plant

This paper proposes a practical control method using neural networks and fuzzy control techniques. Neural networks effectively simulate a well-experienced operator's procedure to control the tank level with estimation of a rough control target. Fuzzy control techniques compensate the estimated rough control target using operator's knowledge. The control system was applied to a reflux tank level control of a hydro-desulfurizing plant in the feed oil switching and it controlled the level effectively.
ISTA-Net: Iterative Shrinkage-Thresholding Algorithm Inspired Deep Network for Image Compressive Sensing

Traditional methods for image compressive sensing (CS) reconstruction solve a well-defined inverse problem that is based on a predefined CS model, which defines the underlying structure of the problem and is generally solved by employing convergent iterative solvers. These optimization-based CS methods face the challenge of choosing optimal transforms and tuning parameters in their solvers, while also suffering from high computational complexity in most cases. Recently, some deep network based CS algorithms have been proposed to improve CS reconstruction performance, while dramatically reducing time complexity as compared to optimization-based methods. Despite their impressive results, the proposed networks (either with fully-connected or repetitive convolutional layers) lack any structural diversity and they are trained as a black box, void of any insights from the CS domain. In this paper, we combine the merits of both types of CS methods: the structure insights of optimization-based method and the performance/speed of network-based ones. We propose a novel structured deep network, dubbed ISTA-Net, which is inspired by the Iterative Shrinkage-Thresholding Algorithm (ISTA) for optimizing a general $l_1$ norm CS reconstruction model. ISTA-Net essentially implements a truncated form of ISTA, where all ISTA-Net parameters are learned end-to-end to minimize a reconstruction error in training. Borrowing more insights from the optimization realm, we propose an accelerated version of ISTA-Net, dubbed FISTA-Net, which is inspired by the fast iterative shrinkage-thresholding algorithm (FISTA). Interestingly, this acceleration naturally leads to skip connections in the underlying network design. Extensive CS experiments demonstrate that the proposed ISTA-Net and FISTA-Net outperform existing optimization-based and network-based CS methods by large margins, while maintaining a fast runtime.
An Unsupervised Learning Method for Early Event Detection in Smart Grid with Big Data

Early Event Detection (EED) is becoming increasingly complicated in smart grids, due to the exploration of data with features of volume, velocity, variety, and veracity (i.e. 4Vs data). This paper develops a data-driven unsupervised learning method based on random matrix theory (RMT) to handle this challenge problem. Compared to model-based methods, datadriven ones conduct data analysis requiring no knowledge of the system model/topology based on assumptions or simplifications. On the other hand, compared to supervised learning methods, unsupervised ones extract analysis directly from the raw data without any label. The proposed method using to processing all the raw data rather than only the labeled ones, calculates the mean spectral energy radius (MSR) as high-dimensional statistic and visualize it to form 3D power-map for EED. This method is easier in logic, faster in speed, and more universal and objective. Moreover, it is veracious and robust to traditional EED challenges such as error accumulations, spurious correlations, incidental correlations, and even bad data existence in the core area. Case studies with both simulated data and realistic data validate the effectiveness and higher performance of the proposed method for EED execution in smart grids.