Miracle-dz/UniFAR-FTUs huggingface dataset:
A Probabilistic Generative Grammar for Semantic Parsing

We present a generative model of natural language sentences and demonstrate its application to semantic parsing. In the generative process, a logical form sampled from a prior, and conditioned on this logical form, a grammar probabilistically generates the output sentence. Grammar induction using MCMC is applied to learn the grammar given a set of labeled sentences with corresponding logical forms. We develop a semantic parser that finds the logical form with the highest posterior probability exactly. We obtain strong results on the GeoQuery dataset and achieve state-of-the-art F1 on Jobs.
Channel Upgradation for Non-Binary Input Alphabets and MACs

Consider a single-user or multiple-access channel with a large output alphabet. A method to approximate the channel by an upgraded version having a smaller output alphabet is presented and analyzed. The original channel is not necessarily symmetric and does not necessarily have a binary input alphabet. Also, the input distribution is not necessarily uniform. The approximation method is instrumental when constructing capacity achieving polar codes for an asymmetric channel with a non-binary input alphabet. Other settings in which the method is instrumental are the wiretap setting as well as the lossy source coding setting.
Fuzzy Algebrae of the General Kähler Coset Space G / S ⊗ U ( 1 ) k

We study the fuzzy structure of the general Kähler coset space G/S⊗U(1) deformed by the Fedosov formalism. It is shown that the Killing potentials satisfy the fuzzy algebrae working in the Darboux coordinates. PACS:02.40. Gh, 04.62. +v
Invertible Extractors and Wiretap Protocols

A wiretap protocol is a pair of randomized encoding and decoding functions such that knowledge of a bounded fraction of the encoding of a message reveals essentially no information about the message, while knowledge of the entire encoding reveals the message using the decoder. In this paper, the notion of efficiently invertible extractors is studied and it is shown that a wiretap protocol can be constructed from such an extractor. Then, invertible extractors for symbol-fixing, affine, and general sources are constructed and used to create wiretap protocols with asymptotically optimal trade-offs between their rate (ratio of the length of the message versus its encoding) and resilience (ratio of the observed positions of the encoding and the length of the encoding). The results are further applied to create wiretap protocols for challenging communication problems, such as active intruders who change portions of the encoding, network coding, and intruders observing arbitrary Boolean functions of the encoding.
UNFITTED FINITE ELEMENT METHODS FOR SURFACE PDES 2139

In this paper, we define new unfitted finite element methods for numerically approximating the solution of surface partial differential equations using bulk finite elements. The key idea is that the n-dimensional hypersurface, Γ ⊂ Rn+1, is embedded in a polyhedral domain in Rn+1 consisting of a union, Th, of (n+ 1)-simplices. The unifying feature of the methodological approach is that the finite element approximating space is based on continuous piecewise linear finite element functions on the bulk triangulation Th which is independent of Γ. Our first method is a sharp interface method (SIF ) which uses the bulk finite element space in an approximating weak formulation obtained from integration on a polygonal approximation, Γh, of Γ. The full gradient is used rather than the projected tangential gradient and it is this which distinguishes SIF from the method of [M. A. Olshanskii, A. Reusken, and J. Grande, SIAM J. Numer. Anal. , 47 (2009), pp. 3339–3358]. The second method is a narrow band method (NBM ) in which the region of integration is a narrow band of width O(h). NBM is similar to the method of [K. Deckelnick et al., IMA J. Numer. Anal. , 30 (2010), pp. 351–376] but again the full gradient is used in the discrete weak formulation. The a priori error analysis in this paper shows that the methods are of optimal order in the surface L2 and H1 norms and have the advantage that the normal derivative of the discrete solution is small and converges to zero. Our third method combines bulk finite elements, discrete sharp interfaces, and narrow bands in order to give an unfitted finite element method for parabolic equations on evolving surfaces. We show that our method is conservative so that it preserves mass in the case of an advection-diffusion conservation law. Numerical results are given which illustrate the rates of convergence.
Mobile Visual Search

MOBILE phones have evolved into powerful image and video processing devices, equipped with highresolution cameras, color displays, and hardware-acceler at d graphics. They are increasingly also equipped with GPS, and connected to broadband wireless networks. All this enables a new class of applications which use the camera phone to initiate search queries about objects in visual proximity t o the user (Fig 1). Such applications can be used, e.g., for identifying products, comparison shopping, finding informatio n about movies, CDs, real estate, print media or artworks. Fir t deployments of such systems include Google Goggles [1], Nokia Point and Find [2], Kooaba [3], Ricoh iCandy [4], [5], [6] and Amazon Snaptell [7]. Mobile image retrieval applications pose a unique set of challenges. What part of the processing should be performed on the mobile client, and what part is better carried out at th e server? On the one hand, transmitting a JPEG image could take tens of seconds over a slow wireless link. On the other hand, extraction of salient image features is now possible o n mobile devices in seconds or less. There are several possibl e client-server architectures: • The mobile client transmits a query image to the server. The image retrieval algorithms run entirely on the server, including an analysis of the query image. • The mobile client processes the query image, extracts features and transmits feature data. The image retrieval algorithms run on the server using the feature data as query. • The mobile client downloads data from the server, and all image matching is performed on the device. One could also imagine a hybrid of the approaches mentioned above. When the database is small, it can stored on the phone and image retrieval algorithms can be run locally [8]. When the database is large, it has to be placed on a remote server and the retrieval algorithms are run remotely. In each case, the retrieval framework has to work within stringent memory, computation, power and bandwidth constraints of the mobile device. The size of the data transmitt ed over the network needs to be as small as possible to reduce network latency and improve user experience. The server latency has to be low as we scale to large databases. Further, the retrieval system needs to be robust to low quality camera phone images. This paper reviews recent advances in content based image retrieval with a focus on mobile applications.
Study of Multi-Step Knowledge-Aided Iterative ESPRIT for Direction Finding

In this work, we propose a subspace-based algorithm for DOA estimation which iteratively reduces the disturbance factors of the estimated data covariance matrix and incorporates prior knowledge which is gradually obtained on line. An analysis of the MSE of the reshaped data covariance matrix is carried out along with comparisons between computational complexities of the proposed and existing algorithms. Simulations focusing on closely-spaced sources, where they are uncorrelated and correlated, illustrate the improvements achieved.
The isomorphism problem on classes of automatic structures with transitive relations

Automatic structures are ﬁnitely presented structures where the universe and all relations can be recognized by ﬁnite automata. It is known that the isomorphism problem for automatic structures is complete for Σ 11 , the ﬁrst existential level of the analytical hierarchy. Positive results on ordinals and on Boolean algebras raised hope that the isomorphism problem is simpler for transitive relations. We prove that this is not the case. More precisely, this paper shows:
Do-calculus enables estimation of causal effects in partially observed biomolecular pathways

Estimating causal queries, such as changes in protein abundance in response to a perturbation, is a fundamental task in the analysis of biomolecular pathways. The estimation requires experimental measurements on the pathway components. However, in practice many pathway components are left unobserved (latent) because they are either unknown, or difficult to measure. Latent variable models (LVMs) are well-suited for such estimation. Unfortunately, LVM-based estimation of causal queries can be inaccurate when parameters of the latent variables are not uniquely identified, or when the number of latent variables is misspecified. This has limited the use of LVMs for causal inference in biomolecular pathways. In this manuscript, we propose a general and practical approach for LVM-based estimation of causal queries. We prove that, despite the challenges above, LVM-based estimators of causal queries are accurate if the queries are identifiable according to Pearl's do-calculus, and describe an algorithm for its estimation. We illustrate the breadth and the practical utility of this approach for estimating causal queries in four synthetic and two experimental case studies, where structures of biomolecular pathways challenge the existing methods for causal query estimation. The code and the data documenting all the case studies are available at \url{https://github.com/srtaheri/LVMwithDoCalculus}
Cross-Scale Cost Aggregation for Stereo Matching

This paper proposes a generic framework that enables a multiscale interaction in the cost aggregation step of stereo matching algorithms. Inspired by the formulation of image filters, we first reformulate cost aggregation from a weighted least-squares (WLS) optimization perspective and show that different cost aggregation methods essentially differ in the choices of similarity kernels. Our key motivation is that while the human stereo vision system processes information at both coarse and fine scales interactively for the correspondence search, state-of-the-art approaches aggregate costs at the finest scale of the input stereo images only, ignoring inter-consistency across multiple scales. This motivation leads us to introduce an inter-scale regularizer into the WLS optimization objective to enforce the consistency of the cost volume among the neighboring scales. The new optimization objective with the inter-scale regularization is convex, and thus, it is easily and analytically solved. Minimizing this new objective leads to the proposed framework. Since the regularization term is independent of the similarity kernel, various cost aggregation approaches, including discrete and continuous parameterization methods, can be easily integrated into the proposed framework. We show that the cross-scale framework is important as it effectively and efficiently expands state-of-the-art cost aggregation methods and leads to significant improvements, when evaluated on Middlebury, Middlebury Third, KITTI, and New Tsukuba data sets.
A Checkpoint Storage System for Desktop Grid Computing

A system (10) for chilling warm animal carcasses (30) more quickly and without substantial tissue shrinkage from dehydration includes an enclosure (12) defining a first subchamber (24) and a second subchamber (26). The carcasses (30) are conveyed into the first subchamber (24) for liquid immersion into a tank (52) of cryogenic fluid to crust-freeze the exterior carcass portions. Following immersion, the semi-chilled carcasses (30) are held in the second chamber (26) while cryogenically refrigerated, sub-freezing gas is circulated around the carcasses to complete the chilling process.
Asymptotic normality of maximum likelihood and its variational approximation for stochastic blockmodels

Variational methods for parameter estimation are an active research area, potentially offering computationally tractable heuristics with theoretical performance bounds. We build on recent work that applies such methods to network data, and establish asymptotic normality rates for parameter estimates of stochastic blockmodel data, by either maximum likelihood or variational estimation. The result also applies to various sub-models of the stochastic blockmodel found in the literature. 1. Introduction. The analysis of network data is an open statistical problem, with many potential applications in the social sciences [Lazer et al. (2009)] and in biology [Proulx, Promislow and Phillips (2005)]. In such applications, the models tend to pose both computational and statistical challenges, in that neither their fitting method nor their large sample properties are well understood. However, some results are becoming known for a model known as the stochastic blockmodel, which assumes that the network connections are explainable by a latent discrete class variable associated with each node. For this model, consistency has been shown for profile likelihood maximization [ varying assumptions on the sparsity of the network and the number of classes. These results suggest that the model has reasonable statistical properties, and empirical experiments suggest that efficient approximate methods may suffice to find the parameter estimates. However, formally there is no satisfactory inference theory for the behavior of classical procedures such as maximum likelihood under the model, nor for any procedure which is computationally not potentially NP under worst-case analysis.
Fully Discrete Positivity-Preserving and Energy-Decaying Schemes for Aggregation-Diffusion Equations with a Gradient Flow Structure

We propose fully discrete, implicit-in-time finite volume schemes for general nonlinear nonlocal Fokker-Planck type equations with a gradient flow structure, usually referred to as aggregation-diffusion equations, in any dimension. The schemes enjoy the positivity-preserving and energy-decaying properties, essential for their practical use. The first order in time and space scheme unconditionally verifies these properties for general nonlinear diffusion and interaction potentials while the second order scheme does so provided a CFL condition holds. Dimensional splitting allows for the construction of these schemes with the same properties and a reduced computational cost in higher dimensions. Numerical experiments validate the schemes and show their ability to handle complicated phenomena in aggregation-diffusion equations such as free boundaries, metastability, merging and phase transitions.
Efficiently Searching for Frustrated Cycles in MAP Inference ( Supplementary Material )

subject to: ∑ xj μij(xi, xj) = μi(xi), ∀ij ∈ E, xi ∑ xi μij(xi, xj) = μj(xj), ∀ij ∈ E, xj ∑ ij∈C\F ∑ xi,xj : πi(xi) 6=πj(xj) μij(xi, xj) + ∑ ij∈F ∑ xi,xj : πi(xi)=πj(xj) μij(xi, xj) ≥ 1, ∀cycles C,F ⊆ C, |F | odd, π ∑ xi,xj μij(xi, xj) = 1, ∀ij ∈ E ∑ xi μi(xi) = 1, ∀i ∈ V We first introduce the Lagrange multipliers δji(xi) an δij(xj) for the first two constraints. We also introduce Lagrange multipliers λC,F,π for each k-ary cycle inequality. Leaving the last two equality constraints and the non-negativity constraints explicit, we obtain the following equivalent optimization problem:
Defining Community: Understanding the Meaning of 'the Community' in Coastal Zone Management

A reoccurring question in coastal zone management towards sustainability concerns how to engage ‘the wider community’. With appropriate human behaviour being the key to sustainability, if scientists and managers could just involve and influence ‘the masses’, it seems that many coastal management issues would be solved. But the very framing of the problem, using terms such as ‘the wider community’, belies the truism that the wider community does not exist. Instead, multiple communities exist, overlap and are constantly changing. With a multiplicity of community types described in the literature, two overarching types are commonly used to understand social processes in coastal areas - communities of place and communities of interest. Whilst these conceptualisations may adequately describe relationships between people and what various social groupings gain from the coastal zone, they are ambiguous with regard to impacts upon these areas. In the context of coastal sustainability, we argue that communities also need to be defined in terms of their impact on sustainability. The challenge for coastal scientists and managers is to understand the impacts of various groups on the coastal zone and how these are influenced and change across space and time. This will illuminate key interventions as well as diverse entry points for effective engagement and learning with multiple communities. This paper synthesises and builds on current research focused on characteristics of coastal communities and strategies for successful engagement to both enhance coastal science impact and the adoption of coastal management initiatives. ; ;
Displacement Calculus

In this work, we establish a theory of Calculus based on the new concept of displacement. We develop all the concepts and results necessary to go from the definition to differential equations, starting with topology and measure and moving on to differentiation and integration. We find interesting notions on the way, such as the integral with respect to a path of measures or the displacement derivative. We relate both of these two concepts by a Fundamental Theorem of Calculus. Finally, we develop the necessary framework in order to study displacement equations by relating them to Stieltjes differential equations.
Codes in permutations and error correction for rank modulation

Codes for rank modulation have been recently proposed as a means of protecting flash memory devices from errors. We study basic coding theoretic problems for such codes, representing them as subsets of the set of permutations of n elements equipped with the Kendall tau distance. We derive several lower and upper bounds on the size of codes. These bounds enable us to establish the exact scaling of the size of optimal codes for large values of n. We also show the existence of codes whose size is within a constant factor of the sphere packing bound for any fixed number of errors.
LOW-RANK SUBSPACE LEARNING FROM INCOMPLETE DATA USING RANK REVEALING l 2 / l 1 REGULARIZATION

Massive amounts of data (also called big data) generated by a wealth of sources such as social networks, satellite sensors etc., necessitate the deployment of efficient processing tools. In this context, online subspace learning algorithms that aim at retrieving low-rank representations of data constitute a mainstay in many applications. Working with incomplete (partially observed) data has recently become commonplace. Moreover, the knowledge of the real rank of the sought subspace is rarely at our disposal a priori. Herein, a novel low-rank subspace learning algorithm from incomplete data is presented. Its main premise is the online processing of incomplete data along with the imposition of low-rankness on the sought subspace via a sophisticated utilization of the group sparsity inducing l2/l1 norm. As is experimentally shown, the resulting scheme is efficient in accurately learning the subspace as well as in unveiling its real rank.
A prior-based integrative framework for functional transcriptional regulatory network inference

Transcriptional regulatory networks specify regulatory proteins controlling the context-speciﬁc expression levels of genes. Inference of genome-wide regulatory networks is central to understanding gene regulation, but remains an open challenge. Expression-based network inference is among the most popular methods to infer regulatory networks, however, networks inferred from such methods have low overlap with experimentally derived (e.g. ChIP-chip and transcription factor (TF) knockouts) networks. Currently we have a limited understanding of this discrepancy. To address this gap, we ﬁrst develop a regulatory network inference algorithm, based on probabilistic graphical models, to integrate expression with auxiliary datasets supporting a regulatory edge. Second, we comprehensively analyze our and other state-of-the-art methods on different expression perturbation datasets. Networks inferred by integrating sequence-speciﬁc motifs with expression have substantially greater agreement with experimentally derived networks, while remaining more predictive of expression than motif-based networks. Our analysis suggests natural genetic variation as the most informative perturbation for network inference, and, identi-ﬁes core TFs whose targets are
UK Government Web Continuity: Persisting Access through Aligning Infrastructures

Normal 0 Government's use of the Web in the UK is prolific and a wide range of services are now available though this channel. The government set out to address the problem that links from Hansard (the transcripts of Parliamentary debates) were not maintained over time and that therefore there was need for some long-term storage and stewardship of information, including maintaining access. Further investigation revealed that linking was key, not only in maintaining access to information, but also to the discovery of information. This resulted in a project that affects the entire government Web estate, with a solution leveraging the basic building blocks of the Internet (DNS) and the Web (HTTP and URIs) in a pragmatic way, to ensure that an infrastructure is in place to provide access to important information both now and in the future.
Feature Importance Guided Attack: A Model Agnostic Adversarial Attack

Machine learning models are susceptible to adversarial attacks which dramatically reduce their performance. Reliable defenses to these attacks are an unsolved challenge[11]. In this work we present a novel evasion attack: the ‘Feature Importance Guided Attack’ (FIGA) which generates adversarial evasion samples. FIGA is model agnostic, it assumes no prior knowledge of the defending model’s learning algo-rithm, but does assume knowledge of the feature representation. FIGA leverages feature importance rankings; it perturbs the most important features of the input in the direction of the target class we wish to mimic. We demonstrate FIGA against eight phishing detection models. We keep the attack realistic by perturbing phishing website features that an adversary would have control over. Using FIGA we are able to cause a a reduction in the F1-score of a phishing detection model from 0.96 to 0.41 on average. Finally, we implement adversarial training as a defense against FIGA and show that while it is sometimes eﬀective, it can be evaded by changing the parameters of FIGA.
Positive Algorithmic Bias Cannot Stop Fragmentation in Homophilic Social Networks

Fragmentation, echo chambers, and their amelioration in social networks have been a growing concern in the academic and non-academic world. This paper shows how, under the assumption of homophily, echo chambers and fragmentation are system-immanent phenomena of highly flexible social networks, even under ideal conditions for heterogeneity. We achieve this by finding an analytical, network-based solution to the Schelling model and by proving that weak ties do not hinder the process. Furthermore, we derive the minimal algorithmic bias needed for the mitigation of such fragmentation. However, we show that the effect of such algorithmic bias on stopping fragmentation is limited.