Miracle-dz/UniFAR-FTUs huggingface dataset:
UvA-DARE (Digital Academic Repository) Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation Retrospective and Prospective Mixture-of-Generators for Task-oriented Dialogue Response Generation

Dialogue response generation (DRG) is a critical com-ponent of task-oriented dialogue systems (TDSs). Its purpose is to generate proper natural language responses given some context, e.g., historical utterances, system states, etc. State-of-the-art work focuses on how to better tackle DRG in an end-to-end way. Typically, such studies assume that each token is drawn from a single distribution over the output vocabulary, which may not always be optimal. Responses vary greatly with different intents, e.g., domains, system actions. We propose a novel mixture-of-generators network (MoGNet) for DRG, where we assume that each token of a response is drawn from a mixture of distributions. MoGNet consists of a chair generator and several expert generators. Each expert is specialized for DRG w.r. t. a particular intent. The chair coordinates multiple experts and combines the output they have generated to produce more appropriate responses.Wepropose two strategies to help the chair make better deci-sions, namely, a retrospective mixture-of-generators (RMoG) and a prospective mixture-of-generators (PMoG). The former only considers the historical expert-generated responses until the current time step while the latter also considers possible expert-generated responses in the future by encouraging exploration. In order to differentiate experts, we also devise a global-and-local (GL) learning scheme that forces each expert to be specialized towards a particular intent using a local loss and trains the chair and all experts to coordinate using a global loss. We carry out extensive experiments on the MultiWOZ benchmark dataset. MoGNet signiﬁcantly outperforms state-of-the-art methods in terms of both automatic and human evaluations, demonstrating its effectiveness for DRG.
Inverse Graphics: Unsupervised Learning of 3D Shapes from Single Images

Using generative models for Inverse Graphics is an active area of research. However, most works focus on developing models for supervised and semi-supervised methods. In this paper, we study the problem of unsupervised learning of 3D geometry from single images. Our approach is to use a generative model that produces 2-D images as projections of a latent 3D voxel grid, which we train either as a variational auto-encoder or using adversarial methods. Our contributions are as follows: First, we show how to recover 3D shape and pose from general datasets such as MNIST, and MNIST Fashion in good quality. Second, we compare the shapes learned using adversarial and variational methods. Adversarial approach gives denser 3D shapes. Third, we explore the idea of modelling the pose of an object as uniform distribution to recover 3D shape from a single image. Our experiment with the CelebA dataset \cite{liu2015faceattributes} proves that we can recover complete 3D shape from a single image when the object is symmetric along one, or more axis whilst results obtained using ModelNet40 \cite{wu20153d} show the potential side-effects, in which the model learns 3D shapes such that it can render the same image from any viewpoint. Forth, we present a general end-to-end approach to learning 3D shapes from single images in a completely unsupervised fashion by modelling the factors of variation such as azimuth as independent latent variables. Our method makes no assumptions about the dataset, and can work with synthetic as well as real images (i.e. unsupervised in true sense). We present our results, by training the model using the $\mu$-VAE objective \cite{ucar2019bridging} and a dataset combining all images from MNIST, MNIST Fashion, CelebA and six categories of ModelNet40. The model is able to learn 3D shapes and the pose in qood quality and leverages information learned across all datasets.
Minimax rates of $\ell_p$-losses for high-dimensional linear regression models with additive measurement errors over $\ell_q$-balls

We study minimax rates for high-dimensional linear regression with additive errors under the $\ell_p\ (1\leq p<\infty)$-losses, where the regression parameter is of weak sparsity. Our lower and upper bounds agree up to constant factors, implying that the proposed estimator is minimax optimal.
Distributed Microphone Speech Enhancement based on Deep Learning

Speech-related applications deliver inferior performance in complex noise environments. Therefore, this study primarily addresses this problem by introducing speech-enhancement (SE) systems based on deep neural networks (DNNs) applied to a distributed microphone architecture, and then investigates the effectiveness of three different DNN-model structures. The first system constructs a DNN model for each microphone to enhance the recorded noisy speech signal, and the second system combines all the noisy recordings into a large feature structure that is then enhanced through a DNN model. As for the third system, a channel-dependent DNN is first used to enhance the corresponding noisy input, and all the channel-wise enhanced outputs are fed into a DNN fusion model to construct a nearly clean signal. All the three DNN SE systems are operated in the acoustic frequency domain of speech signals in a diffuse-noise field environment. Evaluation experiments were conducted on the Taiwan Mandarin Hearing in Noise Test (TMHINT) database, and the results indicate that all the three DNN-based SE systems provide the original noise-corrupted signals with improved speech quality and intelligibility, whereas the third system delivers the highest signal-to-noise ratio (SNR) improvement and optimal speech intelligibility.
vqSGD: Vector Quantized Stochastic Gradient Descent

In this work, we present a family of vector quantization schemes <italic>vqSGD</italic> (Vector-Quantized Stochastic Gradient Descent) that provide an asymptotic reduction in the communication cost with convergence guarantees in first-order distributed optimization. In the process we derive the following fundamental information theoretic fact: <inline-formula> <tex-math notation="LaTeX">$\Theta \left({\frac {d}{R^{2}}}\right)$ </tex-math></inline-formula> bits are necessary and sufficient (up to an additive <inline-formula> <tex-math notation="LaTeX">$O(\log d)$ </tex-math></inline-formula> term) to describe an unbiased estimator <inline-formula> <tex-math notation="LaTeX">$\hat{\boldsymbol {g}}(\boldsymbol {g})$ </tex-math></inline-formula> for any <inline-formula> <tex-math notation="LaTeX">$\boldsymbol {g}$ </tex-math></inline-formula> in the <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-dimensional unit sphere, under the constraint that <inline-formula> <tex-math notation="LaTeX">$\| \hat{\boldsymbol {g}}(\boldsymbol {g})\|_{2}\le R$ </tex-math></inline-formula> almost surely, <inline-formula> <tex-math notation="LaTeX">$R > 1$ </tex-math></inline-formula>. In particular, we consider a randomized scheme based on the convex hull of a point set, that returns an unbiased estimator of a <inline-formula> <tex-math notation="LaTeX">$d$ </tex-math></inline-formula>-dimensional gradient vector with almost surely bounded norm. We provide multiple efficient instances of our scheme, that are near optimal, and require <inline-formula> <tex-math notation="LaTeX">$o(d)$ </tex-math></inline-formula> bits of communication at the expense of tolerable increase in error. The instances of our quantization scheme are obtained using well-known families of binary error-correcting codes and provide a smooth tradeoff between the communication and the estimation error of quantization. Furthermore, we show that <italic>vqSGD</italic> also offers automatic privacy guarantees.
Differentiating Features for Scene Segmentation Based on Dedicated Attention Mechanisms

Semantic segmentation is a challenge in scene parsing. It requires both context information and rich spatial information. In this paper, we differentiate features for scene segmentation based on dedicated attention mechanisms (DF-DAM), and two attention modules are proposed to optimize the high-level and low-level features in the encoder, respectively. Specifically, we use the high-level and low-level features of ResNet as the source of context information and spatial information, respectively, and optimize them with attention fusion module and 2D position attention module, respectively. For attention fusion module, we adopt dual channel weight to selectively adjust the channel map for the highest two stage features of ResNet, and fuse them to get context information. For 2D position attention module, we use the context information obtained by attention fusion module to assist the selection of the lowest-stage features of ResNet as supplementary spatial information. Finally, the two sets of information obtained by the two modules are simply fused to obtain the prediction. We evaluate our approach on Cityscapes and PASCAL VOC 2012 datasets. In particular, there aren't complicated and redundant processing modules in our architecture, which greatly reduces the complexity, and we achieving 82.3% Mean IoU on PASCAL VOC 2012 test dataset without pre-training on MS-COCO dataset.
Obliquity Evolution of Circumstellar Planets in Sun-like Stellar Binaries

Changes in planetary obliquity, or axial tilt, influence the climates on Earth-like planets. In the solar system, the Earth’s obliquity is stabilized by interactions with our moon, and the resulting small amplitude variations (∼2.°4) are beneficial for advanced life. Most Sun-like stars have at least one stellar companion, and the habitability of circumstellar exoplanets is shaped by their stellar companion. We show that a stellar companion can dramatically change whether Earth-like obliquity stability is possible through planetary orbital precession relative to the binary orbit or resonant pumping of the obliquity through spin–orbit interactions. We present a new formalism for the planetary spin precession that accounts for orbital misalignments between the planet and binary. Using numerical modeling in α Centauri AB, we show the following: there is a stark contrast between the planetary obliquity variations depending on the host star, planetary neighbors limit the possible spin states for Earth-like obliquity stability, and the presence of a moon can destabilize the obliquity, defying our Earth-based expectations. An Earth-like rotator orbiting the primary star would experience small obliquity variations for 87%, 74%, or 54% of solar-type binaries, depending on the mass of the primary (0.8, 1.0, or 1.2 M⊙, respectively). Thus, Earth-like planets likely experience much larger obliquity variations, with more extreme climates, unless they are in specific states, such as orbiting nearly planar with the binary and rotating retrograde (backward) like Venus.
Cichlid: Explicit physical memory management for large machines

In this paper, we rethink how an OS supports virtual memory. Classical VM is an opaque abstraction of RAM, backed by demand paging. However, most systems today (from phones to data-centers) do not page, and indeed may require the performance benefits of non-paged physical memory, precise NUMA allocation, etc. Moreover, MMU hardware is now useful for other purposes, such as detecting page access or providing large page translation. Accordingly, the venerable VM abstraction in OSes like Windows and Linux has acquired a plethora of extra APIs to poke at the policy behind the illusion of a virtual address space. Instead, we present Cichlid, a memory system which inverts this model. Applications explicitly manage their physical RAM of different types, and directly (though safely) program the translation hardware. Cichlid is implemented in Barrelfish, requires no virtualization support, and outperforms VMM-based approaches for all but the smallest working sets. We show that Cichlid enables use-cases for virtual memory not possible in Linux today, and other use-cases are simple to program and significantly faster.
Adaptive Greedy versus Non-adaptive Greedy for Influence Maximization

We consider the adaptive influence maximization problem: given a network and a budget k, iteratively select k seeds in the network to maximize the expected number of adopters. In the full-adoption feedback model, after selecting each seed, the seed-picker observes all the resulting adoptions. In the myopic feedback model, the seed-picker only observes whether each neighbor of the chosen seed adopts. Motivated by the extreme success of greedy-based algorithms/heuristics for influence maximization, we propose the concept of greedy adaptivity gap, which compares the performance of the adaptive greedy algorithm to its non-adaptive counterpart. Our first result shows that, for submodular influence maximization, the adaptive greedy algorithm can perform up to a (1-1/e)-fraction worse than the non-adaptive greedy algorithm, and that this ratio is tight. More specifically, on one side we provide examples where the performance of the adaptive greedy algorithm is only a (1-1/e) fraction of the performance of the non-adaptive greedy algorithm in four settings: for both feedback models and both the independent cascade model and the linear threshold model. On the other side, we prove that in any submodular cascade, the adaptive greedy algorithm always outputs a (1-1/e)-approximation to the expected number of adoptions in the optimal non-adaptive seed choice. Our second result shows that, for the general submodular cascade model with full-adoption feedback, the adaptive greedy algorithm can outperform the non-adaptive greedy algorithm by an unbounded factor. Finally, we propose a risk-free variant of the adaptive greedy algorithm that always performs no worse than the non-adaptive greedy algorithm.
Effective equations for reaction coordinates in polymer transport

In the framework of the problem of finding proper reaction coordinates (RCs) for complex systems and their effective evolution equations, we consider the case study of a polymer chain in an external double-well potential, experiencing thermally activated dynamics. Langevin effective equations describing the macroscopic dynamics of the system can be inferred from data by using a data-driven approach, once a suitable set of RCs is chosen. We show that, in this case, the validity of such choice depends on the stiffness of the polymer’s bonds: if they are sufficiently rigid, we can employ a reduced description based only on the coordinate of the center of mass; whereas, if the stiffness reduces, the one-variable dynamics is no more Markovian and (at least) a second reaction coordinate has to be taken into account to achieve a realistic dynamical description in terms of memoryless Langevin equations.
Stream Semantic Registers: A Lightweight RISC-V ISA Extension Achieving Full Compute Utilization in Single-Issue Cores

Single-issue processor cores are very energy efficient but suffer from the von Neumann bottleneck, in that they must explicitly fetch and issue the loads/storse necessary to feed their ALU/FPU. Each instruction spent on moving data is a cycle not spent on computation, limiting ALU/FPU utilization to 33 percent on reductions. We propose “Stream Semantic Registers” to boost utilization and increase energy efficiency. SSR is a lightweight, non-invasive RISC-V ISA extension which implicitly encodes memory accesses as register reads/writes, eliminating a large number of loads/stores. We implement the proposed extension in the RTL of an existing multi-core cluster and synthesize the design for a modern 22 nm technology. Our extension provides a significant, 2x to 5x, architectural speedup across different kernels at a small 11 percent increase in core area. Sequential code runs 3x faster on a single core, and 3x fewer cores are needed in a cluster to achieve the same performance. The utilization increase to almost 100 percent in leads to a 2x energy efficiency improvement in a multi-core cluster. The extension reduces instruction fetches by up to 3.5x and instruction cache power consumption by up to 5.6x. Compilers can automatically map loop nests to SSRs, making the changes transparent to the programmer.
A novel method for identifying the deep neural network model with the Serial Number

Deep neural network (DNN) with the state of art performance has emerged as a viable and lucrative business service. However, those impressive performances require a large number of computational resources, which comes at a high cost for the model creators. The necessity for protecting DNN models from illegal reproducing and distribution appears salient now. Recently, trigger-set watermarking, breaking the white-box restriction, relying on adversarial training pre-defined (incorrect) labels for crafted inputs, and subsequently using them to verify the model authenticity, has been the main topic of DNN ownership verification. While these methods have successfully demonstrated robustness against removal attacks, few are effective against the tampering attacks from competitors forging the fake watermarks and dogging in the manager. In this paper, we put forth a new framework of the trigger-set watermark by embedding a unique Serial Number (relatedness less original labels) to the deep neural network for model ownership identification, which is both robust to model pruning and resist to tampering attacks. Experiment results demonstrate that the DNN Serial Number only incurs slight accuracy degradation of the original performance and is valid for ownership verification.
Copy-Move Forgery Classification via Unsupervised Domain Adaptation

In the current era, image manipulation is becoming increasingly easier, yielding more natural looking images, owing to the modern tools in image processing and computer vision techniques. The task of the segregation of forged images has become very challenging. To tackle such problems, publicly available datasets are insufficient. In this paper, we propose to create a synthetic forged dataset using deep semantic image inpainting algorithm. Furthermore, we use an unsupervised domain adaptation network to detect copy-move forgery in images. Our approach can be helpful in those cases, where the classification of data is unavailable.
The impact of atmospheric dispersion in the performance of high-resolution spectrographs

Differential atmospheric dispersion is a wavelength-dependent effect introduced by the atmosphere. It is one of the instrumental errors that can affect the position of the target as perceived on the sky and its flux distribution. This effect will affect the results of astronomical observations if not corrected by an atmospheric dispersion corrector (ADC). In high-resolution spectrographs, in order to reach a radial velocity (RV) precision of 10 cm s−1, an ADC is expected to return residuals at only a few tens of milliarcseconds (mas). In fact, current state-of-the-art spectrograph conservatively require this level of residuals, although no work has been done to quantify the impact of atmospheric dispersion. In this work, we test the effect of atmospheric dispersion on astronomical observations in general, and in particular on RV precision degradation and flux losses. Our scientific objective was to quantify the amount of residuals needed to fulfil the requirements set on an ADC during the design phase. We found that up to a dispersion of 100 mas, the effect on the RV is negligible. However, on the flux losses, such a dispersion can create a loss of ∼2 per cent at 380 nm, a significant value when efficiency is critical. The requirements set on ADC residuals should take into consideration the atmospheric conditions where the ADC will function, and also all the aspects related with not only the RV precision requirements but also the guiding camera used, the tolerances on the flux loss, and the different melt data of the chosen glasses.
Improved Clustering Algorithms for the Bipartite Stochastic Block Model

We establish sufficient conditions of exact and almost full recovery of the node partition in Bipartite Stochastic Block Model (BSBM) using polynomial time algorithms. First, we improve upon the known conditions of almost full recovery by spectral clustering algorithms in BSBM. Next, we propose a new computationally simple and fast procedure achieving exact recovery under milder conditions than the state of the art. Namely, if the vertex sets <inline-formula> <tex-math notation="LaTeX">$V_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$V_{2}$ </tex-math></inline-formula> in BSBM have sizes <inline-formula> <tex-math notation="LaTeX">$n_{1}$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$n_{2}$ </tex-math></inline-formula>, we show that the condition <inline-formula> <tex-math notation="LaTeX">$ p = \Omega \left ({\max \left ({\sqrt {\frac {\log {n_{1}}}{n_{1}n_{2}}},\frac {\log {n_{1}}}{n_{2}}}\right )}\right )$ </tex-math></inline-formula> on the edge intensity <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula> is sufficient for exact recovery within <inline-formula> <tex-math notation="LaTeX">$V_{1}$ </tex-math></inline-formula>. This condition exhibits an elbow at <inline-formula> <tex-math notation="LaTeX">$n_{2} \asymp n_{1}\log {n_{1}}$ </tex-math></inline-formula> between the low-dimensional and high-dimensional regimes. The suggested procedure is a variant of Lloyd’s iterations initialized with a well-chosen spectral estimator leading to what we expect to be the optimal condition for exact recovery in BSBM. The optimality conjecture is supported by showing that, for a supervised oracle procedure, such a condition is necessary to achieve exact recovery. The key elements of the proof techniques are different from classical community detection tools on random graphs. Numerical studies confirm our theory, and show that the suggested algorithm is both very fast and achieves almost the same performance as the supervised oracle. Finally, using the connection between planted satisfiability problems and the BSBM, we improve upon the sufficient number of clauses to completely recover the planted assignment.
A Catalog of AGN Host Galaxies Observed with HST/ACS: Correlations between Star Formation and AGN Activity

We present the Advanced Camera for Surveys Active Galactic Nuclei (ACS-AGN) Catalog, a catalog of 2585 active galactic nucleus (AGN) host galaxies that are at redshifts 0.2 < z < 2.5 and that were imaged with the Hubble Space Telescope’s Advanced Camera for Surveys (ACS). Using the ACS General Catalog (ACS-GC) as our initial sample, we select an AGN subsample using Spitzer and Chandra data along with their respective established AGN selection criteria. We then gather further multiwavelength photometric data in order to construct spectral energy distributions (SEDs). Using these SEDs, we are able to derive multiple AGN and host galaxy properties, such as star formation rate (SFR), AGN luminosity, stellar mass, and nuclear column density. From these data, we show that AGN host galaxies tend to lie below the star-forming main sequence, with X-ray-selected AGN host galaxies being more offset than IR-selected AGN host galaxies. This suggests that there is some process, possibly negative feedback, in AGN host galaxies causing decreased star formation. We also demonstrate that there is a positive trend between the SFR and AGN luminosity in AGN host galaxies, in individual redshift bins, and across all redshift bins, and that both are correlated with the stellar mass of their galaxies. This points toward an underlying link between the stellar mass, stellar growth, and supermassive black hole growth in a galaxy.