Miracle-dz/UniFAR-FTUs huggingface dataset:
Constraining turbulent heat flux parameterization over a temperate maritime glacier in New Zealand

Abstract The turbulent sensible and latent heat fluxes are important components of the surface energy balance over glaciers in the Southern Alps of New Zealand, contributing over half the energy available for ablation during large melt events. To calculate these terms confidently in glacier mass-balance models it is essential to use appropriate parameterizations for surface roughness and atmospheric stability. Eddy covariance measurements at Brewster Glacier were obtained over an ice surface to help facilitate an assessment of the calculation of the turbulent heat fluxes. The roughness length for momentum was found to be 3.6 x 10−3m, while the roughness lengths for temperature and humidity were two orders of magnitude smaller, in agreement with surface renewal theory. A Monte Carlo approach was used to assess the uncertainty in turbulent heat fluxes calculated using the bulk aerodynamic method. It was found that input-data and roughness-length uncertainty could not explain underestimates of observed sensible heat fluxes during periods with low wind speed and large temperature gradients. During these periods a katabatic wind speed maximum alters the formulation of the turbulent exchange coefficient to that typically observed in a neutral atmosphere and this has implications for glacier mass-balance sensitivity.
Multi-cell processing with limited cooperation: A novel framework to timely designs and reduced feedback

In this paper, we consider two base stations (BSs) which maximize the achievable rate for two users connecting to each BS and sharing channel state information (CSI) in a multi-cell processing (MCP) framework. We propose a two way channel estimation or prediction process. Such framework has promising outcomes in terms of feedback reduction and achievable rates moving the system from one with unknown CSI at the transmitter to a system with instantaneous CSI at both sides of the communication. We provide the optimal power allocation and optimal precoding designs with respect to the estimated channel and MMSE. The designs introduced are optimal for multiple access (MAC) Gaussian coherent time-varying fading channels with general inputs and can be specialized to multiple input multiple output (MIMO) channels by decoding interference. The impact of interference on the capacity is quantified by the gradient of the mutual information with respect to the power, channel, and error and covariance of the interferer. We provide two novel distributed MCP algorithms that provide the solutions for the optimal power allocation and optimal precoding for the UL and DL with a two way channel estimation to keep track on the channel variation over blocks of data transmission. Therefore, we provide a novel solution that allows with limited cooperation: a significant reduction in the CSI feedback from the receiver to the transmitter, and timely optimal designs of the precoding and power allocation.
Quantitative Behavioural Reasoning for Higher-order Effectful Languages : Applicative Distances ( Extended Version )

Behavioural preorders and equivalences for higher-order languages have been extensively studied in the last decades, often leading to rich and satisfactory theories. However, in presence of effectful computations ordinary behavioural relations can be too discriminating, as highlighted by probabilistic higher-order languages. An elegant way to avoid such problem is to move from qualitative, boolean-valued relations to quantitative relations describing weakened notions of metrics. This paper studies the quantitative refinements of Absramsky’s applicative similarity and bisimilarity in the context of a generalisation of Fuzz, a call-by-value λ-calculus with a linear type system that can express program sensitivity, enriched with algebraic operations à la Plotkin and Power. To do so a general, abstract framework for studying behavioural relations taking values over quantales—algebraic structures representing sets of abstract quantities—is defined according to Lawvere’s analysis of generalised metric spaces. Barr’s notion of relator (or lax extension) is then extended to quantale-valued relations adapting and extending results from the field of monoidal topology. An abstract notion of quantalevalued effectful applicative similarity is then defined and proved to be a compatible generalised metric (in the sense of Lawvere). Notions of quantale-valued applicative bisimilarity and two-way similarity are also defined and proved to be compatible symmetric generalised metrics (viz. pseudometrics) under mild conditions. The resulting theory refines the existing theory of effectful applicative similarity and can be instantiated to come up with applicative distances for concrete calculi. A remarkable example is given by the probabilistic instantiation of Fuzz where a compatible probabilistic applicative distance based on the Wasserstein-Kantorovich distance is obtained. To the best of the author’s knowledge this is the first example of a (non-trivial) compatible applicative distance for a universal (i.e. Turing complete) probabilistic λ-calculus.
W J G World Journal of Gastroenterology

Artificial intelligence (AI) using deep-learning (DL) has emerged as a breakthrough computer technology. By the era of big data, the accumulation of an enormous number of digital images and medical records drove the need for the utilization of AI to efficiently deal with these data, which have become fundamental resources for a machine to learn by itself. Among several DL models, the convolutional neural network showed outstanding performance in image analysis. In the field of gastroenterology, physicians handle large amounts of clinical data and various kinds of image devices such as endoscopy and ultrasound. AI has been applied in gastroenterology in terms of diagnosis, prognosis, and image analysis. However, potential inherent selection bias cannot be excluded in the form of retrospective study. Because overfitting and spectrum bias (class imbalance) have the possibility of overestimating the accuracy, external validation using unused datasets for model development, collected in a way that minimizes the spectrum bias, is mandatory. For robust verification, prospective studies with adequate inclusion/exclusion criteria, which represent the target populations, are needed. DL has its own lack of interpretability. Because interpretability is important in that it can provide safety measures, help to detect bias, and create social acceptance, further investigations should be performed.
Deep Learning for Embodied Vision Navigation: A Survey

"Embodied visual navigation"problem requires an agent to navigate in a 3D environment mainly rely on its first-person observation. This problem has attracted rising attention in recent years due to its wide application in autonomous driving, vacuum cleaner, and rescue robot. A navigation agent is supposed to have various intelligent skills, such as visual perceiving, mapping, planning, exploring and reasoning, etc. Building such an agent that observes, thinks, and acts is a key to real intelligence. The remarkable learning ability of deep learning methods empowered the agents to accomplish embodied visual navigation tasks. Despite this, embodied visual navigation is still in its infancy since a lot of advanced skills are required, including perceiving partially observed visual input, exploring unseen areas, memorizing and modeling seen scenarios, understanding cross-modal instructions, and adapting to a new environment, etc. Recently, embodied visual navigation has attracted rising attention of the community, and numerous works has been proposed to learn these skills. This paper attempts to establish an outline of the current works in the field of embodied visual navigation by providing a comprehensive literature survey. We summarize the benchmarks and metrics, review different methods, analysis the challenges, and highlight the state-of-the-art methods. Finally, we discuss unresolved challenges in the field of embodied visual navigation and give promising directions in pursuing future research.
Quantum lower bounds for the collision and the element distinctness problems

Given a function <i>f</i> as an oracle, the collision problem is to find two distinct indexes <i>i</i> and <i>j</i> such that <i>f</i>(<i>i</i>) = <i>f</i>(<i>j</i>), under the promise that such indexes exist. Since the security of many fundamental cryptographic primitives depends on the hardness of finding collisions, our lower bounds provide evidence for the existence of cryptographic primitives that are immune to quantum cryptanalysis. We prove that any quantum algorithm for finding a collision in an <i>r</i>-to-one function must evaluate the function Ω((<i>n</i>/<i>r</i>)<sup>1/3</sup>) times, where <i>n</i> is the size of the domain and <i>r</i>|<i>n</i>. This matches an upper bound of Brassard, Høyer, and Tapp. No lower bound better than constant was previously known. Our result also implies a quantum lower bound of Ω(<i>n</i><sup>2/3</sup>) queries for the element distinctness problem, which is to determine whether <i>n</i> integers are all distinct. The best previous lower bound was Ω(&sqrt;<i>n</i>) queries.
Polynomial Degree and Lower Bounds in Quantum Complexity : Collision and Element Distinctness with Small Range

We give a general method for proving quantum lower bounds for problems with small range. Namely, we show that, for any symmetric problem defined on functions f : {1, . ,N} → {1, . ,M}, its polynomial degree is the same for all M ≥ N. Therefore, if we have a quantum query lower bound for some (possibly quite large) range M which is shown using the polynomials method, we immediately get the same lower bound for all rangesM ≥ N. In particular, we get Ω(N1/3) andΩ(N2/3) quantum lower bounds for collision and element distinctness with small range, respectively. As a corollary, we obtain a better lower bound on the polynomial degree of the two-level AND–OR tree. ACM Classification: F.1. 2 AMS Classification: 81P68, 68Q17
Conjunctive Query Answering for Description Logics with Transitive Roles Birte Glimm

Existing Description Logic (DL) reasoners provide automated reasoning support for checking concepts for satisfiability and subsumption, and also for answering queries that retrieve instances of concepts and roles. The development of a decision procedure for conjunctive query answering in expressive DLs is, however, still an open question. Grounded conjunctive queries for SHIQ are supported by KAON2, Pellet, and Racer’s query language nRQL. However, the semantics of grounded queries is different from the usually assumed open-world semantics in DLs, since existentially quantified variables are always replaced with individual names. None of the existing conjunctive query answering techniques [11, 9, 3, 8] is able to handle transitive roles or nominals in the query body. In this paper, we present an extension of SHQ with a restricted form of the binder operator (↓) and state variables known from Hybrid Logics [2], which allows to extend the rolling-up technique [11] to transitive roles. Further on, we adapt the SHQ tableaux algorithm [5] in order to decide conjunctive query entailment with this extended logic. We also highlight why the extension with either nominals or inverse roles makes the design of such a decision procedure much harder. Query answering for SHOQ, i.e., SHQ plus nominals, can, however, be realised by a suitable guessing technique that we introduce in Section 4.
Conjunctive Query Answering for the Description Logic SHIQ Knowledge Representation / Reasoning : : description logics Knowledge Representation / Reasoning : : ontologies

Conjunctive queries play an important role as an expressive query language for Description Logics (DLs). However, although modern DLs usually provide for transitive roles, conjunctive query answering over DL knowledge bases is only poorly understood if transitive roles are admitted in the query. In this paper, we consider conjunctive queries over knowledge bases formulated in the popular DL SHIQ and allow transitive roles in both the query and the knowledge base. We show decidability of query answering in this setup and establish two complexity bounds: regarding combined complexity, we prove that there is a deterministic algorithm for query answering that needs time single exponential in the size of the KB and double exponential in the size of the query. Regarding data complexity, we prove co-NP-completeness.
Drinfeld Twists and Algebraic Bethe Ansatz of the Supersymmetric tJ Model

We construct the Drinfeld twists (factorizing F -matrices) for the supersymmetric t-J model. Working in the basis provided by the F -matrix (i.e. the so-called F -basis), we obtain completely symmetric representations of the monodromy matrix and the pseudo-parasite creation operators of the model. These enable us to resolve the hierarchy of the nested Bethe vectors for the gl(2|1) invariant t-J model.
Drinfeld Twists and Algebraic Bethe Ansatz of the Supersymmetric tJ Model

We construct the Drinfeld twists (factorizing F -matrices) for the supersymmetric t-J model. Working in the basis provided by the F -matrix (i.e. the so-called F -basis), we obtain completely symmetric representations of the monodromy matrix and the pseudo-particle creation operators of the model. These enable us to resolve the hierarchy of the nested Bethe vectors for the gl(2|1) invariant t-J model.
Bilingual Word Embeddings Softm ax Chn 这里服务很好。 F ( x ) avg P ( x ) Q ( x ) Sentiment Language Very

In recent years deep neural networks have achieved great success in sentiment classification for English, thanks in part to the availability of copious annotated resources. Unfortunately, most other languages do not enjoy such an abundance of annotated data for sentiment analysis. To tackle this problem, we propose an Adversarial Deep Averaging Network (ADAN) to transfer sentiment knowledge learned from labeled English data to lowresource languages where only unlabeled data exists. ADAN is a “Y-shaped” network with two discriminative branches: a sentiment classifier and an adversarial language identification scorer. Both branches take input from a shared feature extractor that aims to learn hidden representations that capture the underlying sentiment of the text and are invariant across languages. Experiments on Chinese and Arabic sentiment classification demonstrate that ADAN significantly outperforms several baselines, including a strong pipeline approach that relies on state-of-the-art Machine Translation.
Characterization and observation of ( transmission control protocol ) TCP-Vegas performance with different parameters over ( Long term evolution ) LTE networks

Despite the larger performance and higher throughput compared to transmission control protocol (TCP) Reno, TCP Vegas still has a few obstacles to be deployed in new networks, such as 4G LTE systems (4th Generation, Long term evolution). One of these obstacles is the Vegas congestion control that is not used in all available bandwidth capacity of the network path and which causes minimization in packet quantity transferred from source to destination. However, many researches have shown unfair treatment of TCP Vegas connections when they compete with Reno connections. So, Vegas need more developments and more modifications to be efficient over bidirectional links with unbalanced traffic, and on wireless links. TCP-Vegas is a congestion control algorithm that reduces queuing and packet loss, and thus reduces latency and increases overall throughput, by carefully matching the sending rate to the rate at which packets are successfully being drained by the network. This paper presents results from a series of simulation experiments designed to study TCP Vegas performance in LTE network model using NS-2 network simulator. The characterization and analysis of Vegas behavior performed using the main two parameters, alpha and beta, to configure the congestion window (cwnd) phases. After used multiple values, the configuration results show that TCP Vegas perform better than TCP Reno.
Multi-Tier Diversified Service Architecture for Internet 3 . 0 : The Next Generation Internet

ion” and a detailed discussion of the various design aspects of the architecture, including the design of the “Context Router-” the key architectural element that powers an evolutionary deployment plan for the clean slate design ideas of Internet 3.0. This technical report is available at Washington University Open Scholarship: https://openscholarship.wustl. edu/cse_research/45 Department of Computer Science & Engineering
Why the Decision Theoretic Perspective Misrepresents Frequentist Inference: 'Nuts and Bolts' vs. Learning from Data

The primary objective of this paper is to revisit and make a case for the merits of R.A. Fisher's objections to the decision-theoretic framing of frequentist inference. It is argued that this framing is congruent with the Bayesian but incongruent with the frequentist inference. It provides the Bayesian approach with a theory of optimal inference, but it misrepresents the theory of optimal frequentist inference by framing inferences solely in terms of the universal quantifier `for all values of theta in the parameter space'. This framing is at odds with the primary objective of model-based frequentist inference, which is to learn from data about the true value of theta (unknown parameter(s)); the one that gave rise to the particular data. The frequentist approach relies on factual (estimation, prediction), as well as hypothetical (testing) reasoning whose primary aim is to learn from data about the true theta. The paper calls into question the appropriateness of admissibility and reassesses Stein's paradox as it relates to the capacity of frequentist estimators to pinpoint the true theta. The paper also compares and contrasts loss-based errors with traditional frequentist errors, such as coverage, type I and II; the former are attached to {\theta}, but the latter to the inference procedure itself.
Organization mechanism and counting algorithm on vertex-cover solutions

Counting the solution number of combinational optimization problems is an important topic in the study of computational complexity, which is concerned with Vertex-Cover in this paper. First, we investigate organizations of Vertex-Cover solution spaces by the underlying connectivity of unfrozen vertices and provide facts on the global and local environment. Then, a Vertex-Cover Solution Number Counting Algorithm is proposed and its complexity analysis is provided, the results of which fit very well with the simulations and have a better performance than those by 1-RSB in the neighborhood of c = e for random graphs. Based on the algorithm, variation and fluctuation on the solution number the statistics are studied to reveal the evolution mechanism of the solution numbers. Furthermore, the marginal probability distributions on the solution space are investigated on both the random graph and scale-free graph to illustrate the different evolution characteristics of their solution spaces. Thus, doing solution number counting based on the graph expression of the solution space should be an alternative and meaningful way to study the hardness of NP-complete and #P-complete problems and the appropriate algorithm design can help to achieve better approximations of solving combinational optimization problems and the corresponding counting problems.
KnightCap: A Chess Programm That Learns by Combining TD(lambda) with Game-Tree Search

In this paper we present TDLeaf(lambda), a variation on the TD(lambda) algorithm that enables it to be used in conjunction with game-tree search. We present some experiments in which our chess program ``KnightCap'' used TDLeaf(lambda) to learn its evaluation function while playing on the Free Internet Chess Server (FICS, fics.onenet.net). The main success we report is that KnightCap improved from a 1650 rating to a 2150 rating in just 308 games and 3 days of play. As a reference, a rating of 1650 corresponds to about level B human play (on a scale from E (1000) to A (1800)), while 2150 is human master level. We discuss some of the reasons for this success, principle among them being the use of on-line, rather than self-play.
DoD Information Assurance and Agile : Challenges and Recommendations Gathered Through Interviews with Agile Program Managers and DoD Accreditation Reviewers

ix 1 Overview and Scope 1 2 Research Approach 3 3 Overview of Highlights from Brief Literature Search 4 4 Summary of Recommendations from Interviews 7 5 Interview Findings: Introduction 15 6 Interview Findings: Challenges from the Agile Program Manager Perspective 17 7 Interview Findings: Challenges from the Accrediting Authority Perspective 22 8 Implications of Existing Information Assurance Processes on Agile Principles 25 9 Looking Ahead: What to Expect Five to 10 Years from Now 27 9.1 DoD Concern for Operational Security Grows 27 9.2 DoD Information Assurance Controls Strengthen 27 9.3 Agile Approaches are Well Positioned to Address the Future 28 10 Closing Thoughts 30 References/Bibliography 32
Megaminería y desposesión en el Sur: un análisis comparativo* Large-scale Mining and Dispossession in the South: A Comparative Analysis Mega-mineração e expropriação no Sul: uma análise comparativa

In the present article, I evaluate the relevance of the concept of accumulation by dispossession in the case of large-scale mining in Africa and Latin America. I inventory and classify various types of dispossession processes associated with the implementation of large-scale mining activities in both regions. I evidence the relevance of this category in these two contexts, and its theoretical power, by showing how it captures essential elements of the dynamics of capitalism at the global level. In both regions, the processes constitutive of accumulation by dispossession are very similar. Social struggles against large-scale mining on both continents share common global determinants. �is evidences the possibility of south-south alliances between these struggles.
HANDWRITTEN CHARACTER RECOGNITION USING NEURAL NETWORKS

The aim of this work is to assess the possibility of having on-line handwriting recognition abilities on a small portable computer. We studied the recognitions rates dependence on the target character set and the incremental training made possible by new neural network techniques. We worked on cross-cultural data gathered from French and Dutch people containing about 70000 characters from 200 peoples, both segmented and unsegmented.
A generalization of multiple choice balls-into-bins

In the multiple choice balls into bins problem, each ball is placed into the least loaded one out of <i>d</i> bins chosen independently and uniformly at random (<i>i.u.r.}</i>). It is known that the maximum load after <i>n</i> balls are placed into <i>n</i> bins is ln ln <i>n</i>/ln <i>d</i> + <i>O</i>(1). In this paper, we consider a variation of the standard multiple choice process. For <i>k</i><<i>d</i>, we place <i>k</i> balls at a time into <i>k</i> least loaded bins among <i>d</i> possible locations chosen <i>i.u. r. </i> We provide the maximum load in terms of <i>k</i>, <i>d</i> and <i>n</i>. The maximum load in the standard multiple choice problem can be derived from our general formulation as a special case with <i>k</i>=1. More interestingly, our result indicates that, for any <i>d</i> ≤ (ln <i>n</i>)<sup>Θ(1)</sup> and <i>k</i> < <i>d</i>, the maximum load is still <i>O</i>(ln ln <i>n</i>). Our allocation scheme can be employed as optimal file replication and data partition policies in distributed file systems and databases. When a new file is created, <i>k</i> copies/fragments of the file are stored into <i>k</i> least loaded among <i>d</i> randomly chosen servers, where <i>k</i> is a tunable parameter that may depend on the level of load balance, file availability, fault tolerance, and popularity or size of a file.