Miracle-dz/UniFAR-FTUs huggingface dataset:
Fundamental Limits of Cache-Aided Interference Management

We consider a system, comprising a library of <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> files (e.g., movies) and a wireless network with a <inline-formula> <tex-math notation="LaTeX">$K_{T}$ </tex-math></inline-formula> transmitters, each equipped with a local cache of size of <inline-formula> <tex-math notation="LaTeX">$M_{T}$ </tex-math></inline-formula> files and a <inline-formula> <tex-math notation="LaTeX">$K_{R}$ </tex-math></inline-formula> receivers, each equipped with a local cache of size of <inline-formula> <tex-math notation="LaTeX">$M_{R}$ </tex-math></inline-formula> files. Each receiver will ask for one of the <inline-formula> <tex-math notation="LaTeX">$N$ </tex-math></inline-formula> files in the library, which needs to be delivered. The objective is to design the cache placement (without prior knowledge of receivers’ future requests) and the communication scheme to maximize the throughput of the delivery. In this setting, we show that the sum degrees-of-freedom (sum-DoF) of <inline-formula> <tex-math notation="LaTeX">$\min \left \{{\frac {K_{T} M_{T}+K_{R} M_{R}}{N},K_{R}}\right \}$ </tex-math></inline-formula> is achievable, and this is within a factor of 2 of the optimum, under uncoded prefetching and one-shot linear delivery schemes. This result shows that (i) the one-shot sum-DoF scales <italic>linearly</italic> with the <italic>aggregate cache size in the network</italic> (i.e., the cumulative memory available at <italic>all nodes</italic>), (ii) the transmitters’ caches and receivers’ caches contribute equally in the one-shot sum-DoF, and (iii) caching can offer a throughput gain that scales linearly with the size of the network. To prove the result, we propose an achievable scheme that exploits the redundancy of the content at transmitter’s caches to cooperatively zero-force some outgoing interference, and availability of the unintended content at the receiver’s caches to cancel (subtract) some of the incoming interference. We develop a particular pattern for cache placement that maximizes the overall gains of cache-aided transmit and receive interference cancellations. For the converse, we present an integer optimization problem which minimizes the number of communication blocks needed to deliver any set of requested files to the receivers. We then provide a lower bound on the value of this optimization problem, hence leading to an upper bound on the linear one-shot sum-DoF of the network, which is within a factor of 2 of the achievable sum-DoF.
Output : Trajectory for parts 1 Input : Object parts in point-cloud Instruction :

A robot operating in a real-world environment needs to perform reasoning with a variety of sensing modalities. However, manually designing features that allow a learning algorithm to relate these different modalities can be extremely challenging. In this work, we consider the task of manipulating novel objects and appliances. To this end, we learn to embed point-cloud, natural language, and manipulation trajectory data into a shared embedding space using a deep neural network. In order to learn semantically meaningful spaces throughout our network, we introduce a method for pre-training its lower layers for multimodal feature embedding and a method for finetuning this embedding space using a loss-based margin. We test our model on the Robobarista dataset [22], where we achieve significant improvements in both accuracy and inference time over the previous state of the art.
Spectral light absorption by yellow substance in the Kattegat-Skagerrak area

More than 1500 water samples were taken from the Kattegat, the Skagerrak and adjacent waters. The value of the absorption coefficient of yellow substance at 310 nm was found to varyfrom 0.06 to 7.4 m −1 in the open coastal waters, with a mean value of 1.3 m −1 . The corresponding wavelength-averaged value (250–450 nm) of the semilogarithmic spectral slope of the coefficient ranges from 0.008 to 0.042 nm −1 , and the mean value is 0.023 nm −1 . Closer to river discharges, as in the fjords, the values of the slope seem to be more constant at around 0.0175 ± 0.0015 nm −1 . In this area the slope must then be known in order to compare absorption at different wavelengths or to model the yellow substance absorption.
LIMIT-BERT : Linguistics Informed Multi-Task BERT

In this paper, we present Linguistics Informed Multi-Task BERT (LIMIT-BERT) for learning language representations across multiple linguistics tasks by Multi-Task Learning. LIMIT-BERT includes five key linguistics tasks: Part-Of-Speech (POS) tags, constituent and dependency syntactic parsing, span and dependency semantic role labeling (SRL). Different from recent Multi-Task Deep Neural Networks (MT-DNN), our LIMIT-BERT is fully linguistics motivated and thus is capable of adopting an improved masked training objective according to syntactic and semantic constituents. Besides, LIMIT-BERT takes a semi-supervised learning strategy to offer the same large amount of linguistics task data as that for the language model training. As a result, LIMIT-BERT not only improves linguistics tasks performance but also benefits from a regularization effect and linguistics information that leads to more general representations to help adapt to new tasks and domains. LIMIT-BERT outperforms the strong baseline Whole Word Masking BERT on both dependency and constituent syntactic/semantic parsing, GLUE benchmark, and SNLI task. Our practice on the proposed LIMIT-BERT also enables us to release a well pre-trained model for multi-purpose of natural language processing tasks once for all.
AdaNet: Adaptive Structural Learning of Artificial Neural Networks

There have been several major lines of research on the theoretical understanding of neural networks. The first one deals with understanding the properties of the objective function used when training neural networks (Choromanska et al., 2014; Sagun et al., 2014; Zhang et al., 2015; Livni et al., 2014; Kawaguchi, 2016). The second involves studying the black-box optimization algorithms that are often used for training these networks (Hardt et al., 2015; Lian et al., 2015). The third analyzes the statistical and generalization properties of neural networks (Bartlett, 1998; Zhang et al., 2016; Neyshabur et al., 2015; Sun et al., 2016). The fourth adopts a generative point of view assuming that the data actually comes from a particular network, which it shows how to recover (Arora et al., 2014; 2015). The fifth investigates the expressive ability of neural networks, analyzing what types of mappings they can learn (Cohen et al., 2015; Eldan & Shamir, 2015; Telgarsky, 2016; Daniely et al., 2016). This paper is most closely related to the work on statistical and generalization properties of neural networks. However, instead of analyzing the problem of learning with a fixed architecture, we study a more general task of learning both architecture and model parameters simultaneously. On the other hand, the insights that we gain by studying this more general setting can also be directly applied to the setting with a fixed architecture.
On the optimality of treating interference as noise: General message sets

In a K-user Gaussian interference channel, it has been shown that if for each user the desired signal strength is no less than the sum of the strengths of the strongest interference from this user and the strongest interference to this user (all values in dB scale), then treating interference as noise (TIN) is optimal from the perspective of generalized degrees-of-freedom (GDoF) and achieves the entire channel capacity region to within a constant gap. In this work, we show that for such TIN-optimal interference channels, even if the message set is expanded to include an independent message from each transmitter to each receiver, operating the new channel as the original interference channel and treating interference as noise is still optimal for the sum capacity up to a constant gap.
An Analysis of Temporal-Di erence Learning with Function Approximation

We discuss the temporal-di erence learning algorithm, as applied to approximating the cost-to-go function of an in nite-horizon discounted Markov chain. The algorithm we analyze updates parameters of a linear function approximator on{line, during a single endless trajectory of an irreducible aperiodic Markov chain with a nite or in nite state space. We present a proof of convergence (with probability 1), a characterization of the limit of convergence, and a bound on the resulting approximation error. Furthermore, our analysis is based on a new line of reasoning that provides new intuition about the dynamics of temporal-di erence learning. In addition to proving new and stronger positive results than those previously available, we identify the signi cance of on-line updating and potential hazards associated with the use of nonlinear function approximators. First, we prove that divergence may occur when updates are not based on trajectories of the Markov chain. This fact reconciles positive and negative results that have been discussed in the literature, regarding the soundness of temporal-di erence learning. Second, we present an example illustrating the possibility of divergence when temporal-di erence learning is used in the presence of a nonlinear function approximator.
MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models (Extended Version)

As Android has become increasingly popular, so has malware targeting it, thus pushing the research community to propose different detection techniques. However, the constant evolution of the Android ecosystem, and of malware itself, makes it hard to design robust tools that can operate for long periods of time without the need for modifications or costly re-training. Aiming to address this issue, we set to detect malware from a behavioral point of view, modeled as the sequence of abstracted API calls. We introduce MaMaDroid, a static-analysis based system that abstracts the API calls performed by an app to their class, package, or family, and builds a model from their sequences obtained from the call graph of an app as Markov chains. This ensures that the model is more resilient to API changes and the features set is of manageable size. We evaluate MaMaDroid using a dataset of 8.5K benign and 35.5K malicious apps collected over a period of six years, showing that it effectively detects malware (with up to 0.99 F-measure) and keeps its detection capabilities for long periods of time (up to 0.87 F-measure two years after training). We also show that MaMaDroid remarkably outperforms DroidAPIMiner, a state-of-the-art detection system that relies on the frequency of (raw) API calls. Aiming to assess whether MaMaDroid's effectiveness mainly stems from the API abstraction or from the sequencing modeling, we also evaluate a variant of it that uses frequency (instead of sequences), of abstracted API calls. We find that it is not as accurate, failing to capture maliciousness when trained on malware samples that include API calls that are equally or more frequently used by benign apps.
N A ] 2 6 A ug 2 00 7 UNIFORM UNCERTAINTY PRINCIPLE AND SIGNAL RECOVERY VIA REGULARIZED ORTHOGONAL MATCHING PURSUIT

This paper seeks to bridge the two major algorithmic approaches to sparse signal recovery from an incomplete set of linear measurements – L 1-minimization methods and iterative methods (Matching Pursuits). We find a simple regularized version of the Orthogonal Matching Pursuit (ROMP) which has advantages of both approaches: the speed and transparency of OMP and the strong uniform guarantees of the L 1-minimization. Our algorithm ROMP reconstructs a sparse signal in a number of iterations linear in the sparsity (in practice even logarithmic), and the reconstruction is exact provided the linear measurements satisfy the Uniform Uncertainty Principle.
Katyusha : The First Direct Acceleration of Stochastic Gradient Methods ( version 6 )

Nesterov’s momentum trick is famously known for accelerating gradient descent, and has been proven useful in building fast iterative algorithms. However, in the stochastic setting, counterexamples exist and prevent Nesterov’s momentum from providing similar acceleration, even if the underlying problem is convex and finite-sum. We introduce Katyusha, a direct, primal-only stochastic gradient method to fix this issue. In convex finite-sum stochastic optimization, Katyusha has an optimal accelerated convergence rate, and enjoys an optimal parallel linear speedup in the mini-batch setting. The main ingredient is Katyusha momentum, a novel “negative momentum” on top of Nesterov’s momentum. It can be incorporated into a variance-reduction based algorithm and speed it up, both in terms of sequential and parallel performance. Since variance reduction has been successfully applied to a growing list of practical problems, our paper suggests that in each of such cases, one could potentially try to give Katyusha a hug. ∗We would like to specially thank Shai Shalev-Shwartz for useful feedbacks and suggestions on this paper, thank Blake Woodworth and Nati Srebro for pointer to their paper [56], thank Guanghui Lan for correcting our citation of [21], thank Weston Jackson, Xu Chen and Zhe Li for verifying the proofs and correcting typos, thank Hongzhou Lin for discussing the experiments, thank Robert Hannah and Lin Xiao for discussing lower bounds and parallel speedup, and thank anonymous reviewers for a number of writing suggestions. This paper is partially supported by an NSF Grant, no. CCF-1412958, and a Microsoft Research Grant, no. 0518584. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of NSF or Microsoft. †V1 of this paper appeared on this date. V2 includes experiments, V3/V4 polish writing, and V5/V6 is the journal revision and includes missing proofs for some extended settings. This paper has appeared in Symposium on Theory of Computing (STOC 2017) and Journal of Machine Learning Research (JMLR 2018). ar X iv :1 60 3. 05 95 3v 6 [ m at h. O C ] 2 4 Se p 20 18
A Causal Bandit Approach to Learning Good Atomic Interventions in Presence of Unobserved Confounders (Supplementary material)

Consider a CBN C = (G,P) with N intervenable nodes and in-degree at most k− 1, and let k be such that 2 ≪ N . Further, let P be such that for at most 2 nodes, chosen in the reverse topological order, the conditional probability of a node being 1 given its parents is Bernoulli with parameter 1/2, and for the remaining nodes the conditional probability of a node being 1 given its parents is Bernoulli with parameter 1/2. Now, using the definition of m(C) provided in Section 3, it is easy to see that m(C) ≤ 2 ≪ N .
On Optimal Scaling of Additive Transformation Based Markov Chain Monte Carlo

Study of diffusion limits of the Metropolis-Hastings algorithm in high dimensions yields useful quantificaton of the scaling of the underlying proposal distribution in terms of the dimensionality. Here we consider the recently introduced Transformation-based Markov Chain Monte Carlo (TMCMC) (Dutta and Bhattacharya (2013a)), a methodology that is designed to update all the parameters simultaneously using some simple deterministic transformation of a one-dimensional random variable drawn from some arbitrary distribution on a relevant support. The additive transformation based TMCMC is similar in spirit to random walk Metropolis, except the fact that unlike the latter, additive TMCMC uses a single draw from a one-dimensional proposal distribution to update the high-dimensional parameter. In this paper, we study the diffusion limits of additive TMCMC under various set-ups ranging from the product structure of the target density to the case where the target is absolutely continuous with respect to a Gaussian measure; we also consider the additive TMCMC within Gibbs approach for all the above set-ups. These investigations lead to appropriate scaling of the one-dimensional proposal density. We also show that the optimal acceptance rate of additive TMCMC is 0.439 under all the aforementioned set-ups, in contrast with the well-established 0.234 acceptance rate associated with optimal random walk Metropolis algorithms under the same set-ups. We also elucidate the ramifications of our results and the advantages of additive TMCMC over random walk Metropolis with ample simulation studies. It was found that in all set ups we considered- namely iid component set up, independent scaling components, a wide range of dependent set ups as well as real spatial data application- TMCMC performed much better and converged to the target density much faster than the RWMH algorithm.
Domain Generalization using Causal Matching

Dataset The synthetic slab dataset (Section 3.2) consists of a binary label y and 2-dimensional features; one feature has a linear relationship with y while the other has a more complex “slab” relationship with y. The features vary in their simplicity, a measure of the simplicity of the feature is given by the number of linear pieces in the optimal classification/decision curve (Figure 1, (Shah et al., 2020)). Hence, the linear features are simpler as they only have 1 linear piece in the optimal decision boundary, as opposed to the slab features that have k linear pieces in the piecewise linear optimal decision boundary.
Neural Wavelet-domain Diffusion for 3D Shape Generation, Inversion, and Manipulation

This paper presents a new approach for 3D shape generation, inversion, and manipulation, through a direct generative modeling on a continuous implicit representation in wavelet domain. Specifically, we propose a compact wavelet representation with a pair of coarse and detail coefficient volumes to implicitly represent 3D shapes via truncated signed distance functions and multi-scale biorthogonal wavelets. Then, we design a pair of neural networks: a diffusion-based generator to produce diverse shapes in the form of the coarse coefficient volumes and a detail predictor to produce compatible detail coefficient volumes for introducing fine structures and details. Further, we may jointly train an encoder network to learn a latent space for inverting shapes, allowing us to enable a rich variety of whole-shape and region-aware shape manipulations. Both quantitative and qualitative experimental results manifest the compelling shape generation, inversion, and manipulation capabilities of our approach over the state-of-the-art methods.
Bayesian Graphical Entity Resolution Using Exchangeable Random Partition Priors

Entity resolution (record linkage or deduplication) is the process of identifying and linking duplicate records in databases. In this paper, we propose a Bayesian graphical approach for entity resolution that links records to latent entities, where the prior representation on the linkage structure is exchangeable. First, we adopt a flexible and tractable set of priors for the linkage structure, which corresponds to a special class of random partition models. Second, we propose a more realistic distortion model for categorical/discrete record attributes, which corrects a logical inconsistency with the standard hit-miss model. Third, we incorporate hyperpriors to improve flexibility. Fourth, we employ a partially collapsed Gibbs sampler for inferential speedups. Using a selection of private and nonprivate data sets, we investigate the impact of our modeling contributions and compare our model with two alternative Bayesian models. In addition, we conduct a simulation study for household survey data, where we vary distortion, duplication rates and data set size. We find that our model performs more consistently than the alternatives across a variety of scenarios and typically achieves the highest entity resolution accuracy (F1 score). Open source software is available for our proposed methodology, and we provide a discussion regarding our work and future directions.
Mineralogical controls on arsenic mobility in the Baccu Locci stream catchment (Sardinia, Italy) affected by past mining

Abstract Mineralogical-chemical techniques (XRD, SEM/EDX, WDXRF) and a sequential selective extraction procedure were applied to mine-waste materials and stream sediments from the Baccu Locci stream catchment (Sardinia, Italy) affected by serious As contamination as a consequence of past mining. Results indicate that solid-state speciation of As is mainly dominated by the presence of Fe(III) hydroxides (arsenical ferrihydrites with various Fe/As molar ratios) occurring as coatings of silicate grains, in which As is contained as sorbed or co-precipitated species. Scorodite (FeAsO4·2H2O) is common too, whereas arsenopyrite is generally subordinate but, owing to its relatively rapid oxidation, environmentally significant. Moreover, some unidentified arsenates of Ca-Fe or K-Fe were also detected. Arsenic contained in these phases is slowly, but continuously, released in relatively small amounts through three main mechanisms: (1) oxidation of residual arsenopyrite to scorodite; (2) decomposition of scorodite into a hydroxide or oxide of Fe(III); (3) desorption/release from Fe(III) hydroxides. Decomposition of the unidentified arsenates is also probable, e.g. Ca-Fe arsenate → calcite + Fe(III) hydroxide + As release. The flotation tailings are widely scattered and distributed in the middle-lower Baccu Locci stream catchment, and represent the most dangerous As-generating contamination source in the study area.
Completely stale transmitter channel state information is still very useful

Transmitter channel state information (CSIT) is crucial for the multiplexing gains offered by advanced interference management techniques such as multiuser MIMO and interference alignment. Such CSIT is usually obtained by feedback from the receivers, but the feedback is subject to delays. The usual approach is to use the fed back information to predict the current channel state and then apply a scheme designed assuming perfect CSIT. When the feedback delay is large compared to the channel coherence time, such a prediction approach completely fails to achieve any multiplexing gain. In this paper, we show that even in this case, the completely stale CSI is still very useful. More concretely, we showed that in a MIMO broadcast channel with K transmit antennas and K receivers each with 1 receive antenna, equation (> 1) degrees of freedom is achievable even when the fed back channel state is completely independent of the current channel state. Moreover, we establish that if all receivers have identically distributed channels, then this is the optimal number of degrees of freedom achievable. In the optimal scheme, the transmitter uses the fed back CSI to learn the side information that the receivers receive from previous transmissions rather than to predict the current channel state. Our result can be viewed as the first example of feedback providing a degree-of-freedom gain in memoryless channels.
Human genomic disease variants : A neutral evolutionary explanation

References http://genome.cshlp. org/content/22/8/1383.full. html#ref-list-1 This article cites 159 articles, 57 of which can be accessed free at: License Commons Creative . http://creativecommons.org/licenses/by-nc/3.0/ described at as a Creative Commons License (Attribution-NonCommercial 3.0 Unported License), ). After six months, it is available under http://genome.cshlp. org/site/misc/terms.xhtml for the first six months after the full-issue publication date (see This article is distributed exclusively by Cold Spring Harbor Laboratory Press