Miracle-dz/UniFAR-FTUs huggingface dataset:
A Survey of Response Generation of Dialogue Systems

—An essential task in the ﬁeld of artiﬁcial intelligence is to allow computers to interact with people through natural language. Therefore, researches such as virtual assistants and dialogue systems have received widespread attention from industry and academia. The response generation plays a crucial role in dialogue systems, so to push forward the research on this topic, this paper surveys various methods for response generation. We sort out these methods into three categories. First one includes ﬁnite state machine methods, framework methods, and instance methods. The second contains full-text indexing methods, ontology methods, vast knowledge base method, and some other methods. The third covers retrieval methods and generative methods. We also discuss some hybrid methods based knowledge and deep learning. We compare their disadvantages and advantages and point out in which ways these studies can be improved further. Our discussion covers some studies published in leading conferences such as IJCAI and AAAI in recent years.
Novel Radiator Based Dual-Polarized Filtering Antenna for 5 G Millimeter-wave Applications

This paper presents a novel millimeter-wave (mm-Wave) dual-polarized filtering antenna with high port isolation and cross-polarization ratio. The antenna consists of a differential fed cross-shaped driven patch with four parasitic patches stacked on it. Four shorted patches are inserted into the cross-shaped patch to generate a radiation null at the lower band. The parasitic patches are used to introduced another radiation null at the edge of upper-band. Besides, a cross-shaped stub is embedded in the slot between these four parasitic patches, which generates the third radiation null to further suppress the upper-band. The proposed antenna has an impedance bandwidth of 20% (return loss >15 dB within the 5G band 24.25−29.5 GHz). The average simulated in-band gain is 6.6 dBi. While the out-of-band gain drops below −10 dB from 22.8 GHz and 32 GHz respectively, which make the proposed antenna a good candidate to reduce the complexity of the filter circuits in the mm-Wave RF frontends.
Characterization of extracellular phosphorus in enhanced biological phosphorus removal granular sludge

The effects of carbon sources and calcium on the phosphorus accumulation performance of extracellular polymers (EPS) were investigated. Results showed that polyphosphate (poly-P) with low and high molecular weight were distinguished in EPS. Total extracellular phosphorus accounted for 68.21%~71.05% and 26.97%~36.99% of the total phosphorus content of raw sludge in the acetic acid system and propionic acid system, respectively, suggesting that the ability of phosphorus absorption is poor in the EPS of the propionic acid system. It was found that when the influent Ca2+ concentration was elevated, the proportion of extracellular calcium phosphate (Ca-phosphate) precipitation in the two sludge systems increased remarkably. The variation tendency of Prelease/ VFAuptake and glycogendegraded/VFAuptake ratios with the influent Ca2+ concentration increasing suggested that polyphosphate-accumulating organisms (PAO) were able to shift their metabolic pathways from polyphosphate-accumulating metabolism to glycogen-accumulating metabolism at high influent Ca2+. The accumulation of calcium phosphate in EPS is a process that can affect the PAO abundance and metabolic patterns.
The Krein-von Neumann extension of a regular even order quasi-differential operator

We characterize by boundary conditions the Krein-von Neumann extension of a strictly positive minimal operator corresponding to a regular even order quasi-differential expression of Shin-Zettl type. The characterization is stated in terms of a specially chosen basis for the kernel of the maximal operator and employs a description of the Friedrichs extension due to Möller and Zettl.
An implementation of ROS Autonomous Navigation on Parallax Eddie platform

This paper presents an implementation of autonomous navigation functionality based on Robot Operating System (ROS) on a wheeled differential drive mobile platform called Eddie robot. ROS is a framework that contains many reusable software stacks as well as visualization and debugging tools that provides an ideal environment for any robotic project development. The main contribution of this paper is the description of the customized hardware and software system setup of Eddie robot to work with an autonomous navigation system in ROS called Navigation Stack and to implement one application use case for autonomous navigation. For this paper, photo taking is chosen to demonstrate a use case of the mobile robot.
Dynamics of thermalization of two tunnel-coupled one-dimensional quasicondensates

We study the non-equilibrium dynamics of two tunnel-coupled one-dimensional quasicondensates following a quench of the coupling strength from zero to a ﬁxed ﬁnite value. More speciﬁcally, starting from two independent quasicondensates in thermal equilibrium, with initial temperature and chemical potential imbalance, we suddenly switch on the tunnel-coupling and analyse the post-quench equilibration in terms of particle number and energy imbalances. We ﬁnd that, in certain parameter regimes, the net energy can ﬂow from the colder quasicondensate to the hotter one and is governed by the surplus of low energy particles ﬂowing from the cold to the hot system relative to the high-energy particles ﬂowing in the reverse direction. In all cases, the approach to the new thermal equilibrium occurs through transient, damped oscillations. We also ﬁnd that for a balanced initial state the coupled quasicondensates can relax into a ﬁnal thermal equilibrium state in which they display a thermal phase coherence length that is larger than their initial phase coherence length, even though the new equilibrium temperature is higher. The increase in the phase coherence length occurs due to phase locking which manifests itself via an increased degree of correlation between the local relative phases of the quasicondensates at two arbitrary points.
Natural language processing models that automate programming will transform chemistry research and teaching

Natural language processing models have emerged that can generate useable software and automate a number of programming tasks with high fidelity. These tools have yet to have an impact on the chemistry community. Yet, our initial testing demonstrates that this form of artificial intelligence is poised to transform chemistry and chemical engineering research. Here, we review developments that brought us to this point, examine applications in chemistry, and give our perspective on how this may fundamentally alter research and teaching.
3DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variations

We propose a method to create plausible geometric and texture style variations of 3D objects in the quest to democratize 3D content creation. Given a pair of textured source and target objects, our method predicts a part-aware affine transformation field that naturally warps the source shape to imitate the overall geometric style of the target. In addition, the texture style of the target is transferred to the warped source object with the help of a multi-view differentiable renderer. Our model, 3DSTYLENET, is composed of two sub-networks trained in two stages. First, the geometric style network is trained on a large set of untextured 3D shapes. Second, we jointly optimize our geometric style network and a pre-trained image style transfer network with losses defined over both the geometry and the rendering of the result. Given a small set of high-quality textured objects, our method can create many novel stylized shapes, resulting in effortless 3D content creation and style-ware data augmentation. We showcase our approach qualitatively on 3D content stylization, and provide user studies to validate the quality of our results. In addition, our method can serve as a valuable tool to create 3D data augmentations for computer vision tasks. Extensive quantitative analysis shows that 3DSTYLENET outperforms alternative data augmentation techniques for the downstream task of single-image 3D reconstruction.
ML-based IoT Malware Detection Under Adversarial Settings: A Systematic Evaluation

The rapid growth of the Internet of Things (IoT) devices is paralleled by them being on the front-line of malicious attacks. This has led to an explosion in the number of IoT malware, with continued mutations, evolution, and sophistication. These malicious software are detected using machine learning (ML) algorithms alongside the traditional signature-based methods. Although ML-based detectors improve the detection performance, they are susceptible to malware evolution and sophistication, making them limited to the patterns that they have been trained upon. This continuous trend motivates the large body of literature on malware analysis and detection research, with many systems emerging constantly, and outperforming their predecessors. In this work, we systematically examine the state-of-the-art malware detection approaches, that utilize various representation and learning techniques, under a range of adversarial settings. Our analyses highlight the instability of the proposed detectors in learning patterns that distinguish the benign from the malicious software. The results exhibit that software mutations with functionality-preserving operations, such as stripping and padding, significantly deteriorate the accuracy of such detectors. Additionally, our analysis of the industry-standard malware detectors shows their instability to the malware mutations.
E-Commerce Promotions Personalization via Online Multiple-Choice Knapsack with Uplift Modeling

Promotions and discounts are essential components of modern e-commerce platforms, where they are often used to incentivize customers towards purchase completion. Promotions also affect revenue and may incur a monetary loss that is often limited by a dedicated promotional budget. We propose an Online Constrained Multiple-Choice Promotions Personalization framework, driven by causal incremental estimations achieved by uplift modeling. Our work formalizes the problem as an Online Multiple-Choice Knapsack Problem and extends the existent literature by addressing cases with negative weights and values as a result from causal estimations. Our real-time adaptive method guarantees budget constraints compliance achieving above 99.7% of the potential optimal impact on various datasets. It was deployed in a large-scale experimental study at Booking.com - one of the leading online travel platforms in the world. The application resulted in 162% improvement in sales while complying a zero-budget constraint, enabling long-term self-sponsored promotional campaigns.
StackGAN: Facial Image Generation Optimizations

Current state-of-the-art photorealistic generators are computationally expensive, involve unstable training processes, and have real and synthetic distributions that are dissimilar in higher-dimensional spaces. To solve these issues, we propose a variant of the StackGAN architecture. The new architecture incorporates conditional generators to construct an image in many stages. In our model, we generate grayscale facial images in two different stages: noise to edges (stage one) and edges to grayscale (stage two). Our model is trained with the CelebA facial image dataset and achieved a Fréchet Inception Distance (FID) score of 73 for edge images and a score of 59 for grayscale images generated using the synthetic edge images. Although our model achieved subpar results in relation to state-of-the-art models, dropout layers could reduce the overfitting in our conditional mapping. Additionally, since most images can be broken down into important features, improvements to our model can generalize to other datasets. Therefore, our model can potentially serve as a superior alternative to traditional means of generating photorealistic images.
Inverse magnetorotational catalysis and the phase diagram of a rotating hot and magnetized quark matter

We study the properties of a hot and magnetized quark matter in a rotating cylinder in the presence of a constant magnetic field. To do this, we solve the corresponding Dirac equation using the Ritus eigenfunction method. This leads to the energy dispersion relation, Ritus eigenfunctions, and the quantization relation for magnetized fermions. To avoid causality-violating effects, we impose a certain global boundary condition, and study its effect, in particular, on the energy eigenmodes and the quantization relations of fermions. Using the fermion propagator arising from this method, we then solve the gap equation at zero and nonzero temperatures. At zero temperature, the dynamical mass $\bar{m}$ does not depend on the angular frequency, as expected. We thus study its dependence on the distance $r$ relative to the axis of rotation and the magnetic field $B$, and explore the corresponding finite size effect for various couplings $G$. We then consider the finite temperature case. The dependence of $\bar{m}$ on the temperature $T$, magnetic field $B$, angular frequency $\Omega$, and distance $r$ for various $G$ is studied. We show that $\bar{m}$ decreases, in general, with $B$ and $\Omega$. This is the ''inverse magneto-rotational catalysis (IMRC)'' or the ''rotational magnetic inhibition'', previously discussed in the literature. To explore the evidence of this effect in the phase diagrams of our model, we examine the phase portraits of the critical temperature $T_c$ as well as the critical angular frequency $\Omega_c$ with respect to $G, B,\Omega$, and $r$ as well as $G, B, T$, and $r$, respectively. We show that $T_{c}$ and $\Omega_c$ decrease, in particular, with $B$. This is interpreted as clear evidence for IMRC.
Anytime Stochastic Task and Motion Policies

In order to solve complex, long-horizon tasks, intelligent robots need to carry out high level, abstract planning and reasoning in conjunction with motion planning. However, abstract models are typically lossy and plans or policies computed using them can be inexecutable. These problems are exacerbated in stochastic situations where the robot needs to reason about, and plan for multiple contingencies. We present a new approach for integrated task and motion planning in stochastic settings. In contrast to prior work in this direction, we show that our approach can effectively compute integrated task and motion policies whose branching structures encode agent behaviors that handle multiple execution-time contingencies. We prove that our algorithm is probabilistically complete and can compute feasible solution policies in an anytime fashion so that the probability of encountering an unresolved contingency decreases over time. Empirical results on a set of challenging problems show the utility and scope of our method.
A Metric for Tree-Like Topological Summaries

In this work we deﬁne a novel metric structure for a family of tree-like topological summaries. This family of objects is a natural combinatoric generalization of merge trees of scalar ﬁelds and hierarchical dendrograms. The metric introduced can be computed with a dynamical integer linear programming approach and we showcase its feasibility and the eﬀectiveness of the whole framework with simulated data sets. In particular we stress the versatility of these topological summaries, which prove to be very eﬀective in situation where other topological data analysis tools, like persistence diagrams, can not be meaningfully employed.
Generalized Huber Loss for Robust Learning and its Efficient Minimization for a Robust Statistics

We propose a generalized formulation of the Huber loss. We show that with a suitable function of choice, specifically the log-exp transform; we can achieve a loss function which combines the desirable properties of both the absolute and the quadratic loss. We provide an algorithm to find the minimizer of such loss functions and show that finding a centralizing metric is not that much harder than the traditional mean and median.
TweetBLM: A Hate Speech Dataset and Analysis of Black Lives Matter-related Microblogs on Twitter

In the past few years, there has been a significant rise in toxic and hateful content on various social media platforms. Recently Black Lives Matter movement came into the picture, causing an avalanche of user generated responses on the internet. In this paper, we have proposed a Black Lives Matter related tweet hate speech dataset TweetBLM. Our dataset comprises 9165 manually annotated tweets that target the Black Lives Matter movement. We annotated the tweets into two classes, i.e., HATE and NONHATE based on their content related to racism erupted from the movement for the black community. In this work, we also generated useful statistical insights on our dataset and performed a systematic analysis of various machine learning models such as Random Forest, CNN, LSTM, BiLSTM, Fasttext, BERTbase, and BERTlarge for the classification task on our dataset. Through our work, we aim at contributing to the substantial efforts of the research community for the identification and mitigation of hate speech on the internet. The dataset is publicly available.
Deep Learning Based Power Allocation Schemes in NOMA Systems: A Review

Achieving significant performance gains both in terms of system throughput and massive connectivity, non-orthogonal multiple access (NOMA) has been considered as very promising candidate for the future wireless communications technologies. It has already received serious consideration for implementation in the fifth generation (5G) and beyond wireless communication systems. This is mainly due to NOMA allowing more than one user to utilise one transmission resource simultaneously at the transmitter side and successive interference cancellation (SIC) at the receiver side. However, in order to take advantage of the benefits NOMA provides in an optimal manner, power allocation needs to be considered to maximise the system throughput.This problem is non-deterministic polynomial-time (NP)-hard which is mainly why the use of deep learning techniques for power allocation is required. In this paper, a state-of-the-art review on cutting edge solutions to the power allocation optimisation problem using deep learning is provided. It is shown that the use of deep learning techniques to obtain effective solutions to the power allocation problem in NOMA is paramount for the future of NOMA based wireless communication systems. Furthermore, several possible research directions based on the the use of deep learning in NOMA systems are presented.
M17 MIR: A Massive Protostar with Multiple Accretion Outbursts

We report the discovery of a massive protostar M17 MIR embedded in a hot molecular core in M17. The multiwavelength data obtained during 1993–2019 show significant mid-IR (MIR) variations, which can be split into three stages: the decreasing phase during 1993.03–mid-2004, the quiescent phase from mid-2004 to mid-2010, and the rebrightening phase from mid-2010 until now. The variation of the 22 GHz H2O maser emission, together with the MIR variation, indicates an enhanced disk accretion rate onto M17 MIR during the decreasing and rebrightening phases. Radiative transfer modeling of the spectral energy distributions of M17 MIR in the 2005 epoch (quiescent) and 2017 epoch (accretion outburst) constrains the basic stellar parameters of M17 MIR, which is an intermediate-mass protostar (M * ∼ 5.4 M ⊙) with Ṁacc∼1.1×10−5M⊙yr−1 in the 2005 epoch and Ṁacc∼1.7×10−3M⊙yr−1 in the 2017 epoch. The enhanced Ṁacc during outburst induces the luminosity outburst ΔL ≈ 7600 L ⊙. In the accretion outburst, a larger stellar radius is required to produce Ṁacc consistent with the value estimated from the kinematics of H2O masers. M17 MIR shows two accretion outbursts (Δt ∼ 9–20 yr) with outburst magnitudes of about 2 mag, separated by a 6 yr quiescent phase. The accretion outburst occupies 83% of the time over 26 yr. The accretion rate in outburst is variable with amplitude much lower than the contrast between quiescent and outburst phases. The extreme youth of M17 MIR suggests that minor accretion bursts are frequent in the earliest stages of massive star formation.
A DFT+U Study of the Ground State and Meta-Stable States of UO2: The SMC Method

It is well-known that the total energy of the system in the DFT+U method is a multi-minima function of inputs and one has to single out the ground state out of the couples of minimum-energy states. Researchers had already introduced methods to determine these states and it seems their methods were not able to predict all meta-stable states. In this work, we introduce a new simple and straight-forward method of SMC to find the GS as well as the meta-stable states of 1k-order antiferromagnetic configuration for UO2. Depending on the way we apply the SMC method, we obtain different numbers of meta-stable states, but the same ground states. The energetic properties, geometric properties, the electronic density distributions, and the electronic polarization density distributions of the ground state and the meta-stable states are shown to be different from each other. These properties also are shown to be sensitive to the initial magnetizations of U1 and U2 atoms in the 1k-order anti-ferromagnetic configuration. Using the PBEsol-GGA approximation for the exchange-correlation we obtain the ground-state properties in excellent agreement with experiments.
Concentration inequalities for some negatively dependent binary random variables

We investigate concentration properties of functions of random vectors with values in the discrete cube, satisfying the stochastic covering property (SCP) or the strong Rayleigh property (SRP). Our result for SCP measures include subgaussian inequalities of bounded-difference type extending classical results by Pemantle and Peres and their counterparts for matrix-valued setting strengthening recent estimates by Aoun, Banna and Youssef. Under a stronger assumption of the SRP we obtain Bernstein-type inequalities for matrix-valued functions, generalizing recent bounds for linear combinations of positive definite matrices due to Kyng and Song. We also treat in detail the special case of independent Bernoulli random variables conditioned on their sum for which we obtain strengthened estimates, deriving in particular modified log-Sobolev inequalities, Talagrand's convex distance inequality and, as corollaries, concentration results for convex functions and polynomials, as well as improved estimates for matrix-valued functions. These results generalize inequalities for the uniform measure on slices of the discrete cube, studied extensively by many authors. Our approach is based on recent results by Hermon and Salez and a general framework involving modified log-Sobolev inequalities on the discrete cube, which is of independent interest.