Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Ergodic Capacity of Rank-$1$ Ricean-Fading MIMO Channels

This paper investigates the ergodic capacity of Ricean-fading multiple-input-multiple-output (MIMO) channels with rank-1 mean matrices under the assumption that the channel is unknown at the transmitter and perfectly known at the receiver. After introducing the system model and the concept of ergodic capacity of MIMO channels, we derive the explicit expressions for the expected values of the determinant and log-determinant of complex noncentral Wishart matrices. Subsequently, we obtain new upper and lower bounds on the ergodic capacity of rank-1 Ricean-fading MIMO channels at any signal-to-noise ratio (SNR). We show that our bounds are tighter than previously reported analytical bounds, and discuss the impact of spatial fading correlation and Ricean K-factor with the help of these bounds. Furthermore, we extend the analysis of ergodic capacity to frequency selective spatially correlated Ricean-fading MIMO channels. We demonstrate that the calculation of ergodic capacity of frequency selective fading MIMO channels can be converted to the calculation of the one of equivalent frequency flat-fading MIMO channels. Finally, we present numerical results that confirm the theoretical analysis
A robot exploration strategy based on Q-learning network

This paper introduces a reinforcement learning method for exploring a corridor environment with the depth information from an RGB-D sensor only. The robot controller achieves obstacle avoidance ability by pre-training of feature maps using the depth information. The system is based on the recent Deep Q-Network (DQN) framework where a convolution neural network structure was adopted in the Q-value estimation of the Q-learning method. We separate the DQN into a supervised deep learning structure and a Q-learning network. The experiments of a Turtlebot in the Gazebo simulation environment show the robustness to different kinds of corridor environments. All of the experiments use the same pre-training deep learning structure. Note that the robot is traveling in environments which are different from the pre-training environment. It is the first time that raw sensor information is used to build such an exploring strategy for robotics by reinforcement learning.
Openness and Imperfect Pass-Through: Implications for the Monetary Policy

This paper analyzes the positive and normative implications of the degree of openness of a small economy for the transmission mechanism of monetary shocks. First, we show empirical evidence on the direct relationship between openness and the degree of exchange rate pass-through. Then, we develop a general equilibrium model where countries do not fully specialize according to their comparative advantages. With this framework we show that incomplete specialization makes the pass-through from exchange rate to import prices imperfect. The less open is the country --the less specialized- the lower is the pass-through from exchange rate to import prices. Despite the fact that the pass-through is incomplete and the expenditure switching effect is diminished, the flexible price allocation can still be reached with an inward-oriented monetary policy.
Visual Segmentation-Based Data Record Extraction from Web Documents

Semi-structured data records contained in the Web pages provide useful information for shopping agents and metasearch engines. In this paper, we present a visual segmentation-based data record extraction (VSDR) method to extract data records from those Web pages. VSDR method first segments a Web page into semantic blocks using the spatial closeness and visual resemblance of data records, then neighboring and non-neighboring data records are extracted based on a compress and collapse technique. Experimental results slum that unlike the existing methods which only generate good results on their test domains, VSDR is a general data record extraction method that is able to produce quite stable and good results on a wide range of Web pages.
Squash Reuse via a Simplified Implementation of Register Integration

Register integration (or simply integration) is a mechanism for the direct reuse of previously computed results. Integration uses data-dependence relationships to test for and establish reusability. In this paper, we use integration to implement squash reuse, the salvaging of instruction results that were needlessly discarded during the course of sequential recovery from a control- or data- mis-speculation. In integration, the results of squashed instructions remain in the physical register file past mis-speculation recovery. As the processor re-traces portions of the squashed path, an auxiliary table is used to search the physical register file for the registers belonging to the corresponding squashed instances of re-traced instructions. If found, a squashed register is re-validated by a simple update of the rename table. The integrating retraced instruction completes instantly and bypasses the out-of-order core. Integration reduces contention for execution resources, collapses dependent chains of operations and accelerates branch resolution. It achieves this using only rename-table manipulations; without reading or writing the physical registers themselves. We present a simplified implementation of register integration that uses explicit states to manage physical registers and in-order pre-retirement re-execution (much simpler than general out-of-order execution) to guarantee integration correctness. We also introduce a simple mechanism that learns from past integration mistakes to avoid similar mistakes in the future. These components contribute to a design that is simple, high-performing, easy to pipeline, and which has minimal impact on the rest of the microarchitecture. Our preliminary evaluation shows that a minimal integration configuration can provide performance improvements of up to 5% when applied to next-generation microarchitectures. Integration also reduces the amount of wasteful speculation in the machine, cutting the number of instructions executed by up to 17% and the number of instructions fetched along mis-speculated paths by as much as 6%.
Automata Column: The Complexity of Reachability in Vector Addition Systems

The program of the 30th Symposium on Logic in Computer Science held in 2015 in Kyoto included two contributions on the computational complexity of the reachability problem for vector addition systems: Blondin, Finkel, Goller, Haase, and McKenzie [2015] attacked the problem by providing the first tight complexity bounds in the case of dimension 2 systems with states, while Leroux and Schmitz [2015] proved the first complexity upper bound in the general case. The purpose of this column is to present the main ideas behind these two results, and more generally survey the current state of affairs.
Methylenetetrahydrofolate reductase C677T mutation and risk of retinal vein thrombosis

Background: Elevated plasma homocysteine (Hcy) level has been established as a significant risk factor for venous thrombosis and cardiovascular disease. Homozygosity for the methylenetetrahydrofolate reductase (MTHFR) C677T mutation has been associated with elevated plasma Hcy concentration and may contribute to retinal vein thrombosis (RVT) development. The aim of the present study was to investigate whether the hyperhomocysteinemia and/or homozygosity for the MTHFR C677T mutation are associated with an increased risk for RVT. Materials and Methods: Our study population consisted of 73 consecutive patients (50-78 years old) with RVT and 73 control subjects (51-80 years old), matched for age and sex. Genotyping for the MTHFR C677T mutation was performed by polymerase chain reaction-restriction fragment length polymorphism technique and Hcy level was determined by an enzyme immunoassay kit. Results: The prevalence of 677TT genotype was higher in patients than control subjects, but the difference in frequency didn't reach a significant value (P = 0.07). The frequency of the 677T allele was 26% and 21.2% in patients and controls, respectively and did not differ significantly between the two groups (odds ratio = 1.3, 95% confidence interval (0.75-2.24), P = 0.33). Fasting plasma total Hcy level was significantly higher in patients than controls (P = 0.001). Conclusion: Our study demonstrated that hyperhomocysteinemia, but not the MTHFR C677T mutation, is associated with RVT.
The Architecture of Markets

In measuring the fluid content of soil by circulating a fluid within a borehole and measuring the amount of soil fluid it entrains, the borehole is formed by drilling-in a telescoping auger that has extendable internal conduits and is arranged to form the borehole and equip it for the fluid circulation without allowing atmospheric contamination.
Pan vs. Selective Computed Tomography Scans in Management of Multiple Trauma Patients; a Brief Report

Introduction: Using pan or selective computed tomography (CT) scan in management of multiple trauma patient is a matter of debate. Therefore, the present study was designed aiming to compare the findings of pan and selective CT scans in management of multiple trauma patients. Method: This is a prospective cross-sectional study, on patients presented to the emergency department (ED) of Shohadaye Haftome Tir Hospital, Tehran, Iran, following blunt multiple trauma over a 1-year period, from March 2014 to March 2015. Findings regarding presence or absence of injury in head, face, neck, chest, abdomen and hip were compared between patients that underwent pan and selective CT using SPSS 21. Results: 443 patients with the mean age of 34.54 ± 17.88 years were evaluated (78% male). 248 (56%) patients underwent selective CT scan and 195 (44%) underwent pan CT scan. The 2 groups were similar regarding vital signs and mean age. Mean hospital length of stay was 21.05 ± 24.64 days for selective CT scan group and 18.18 ± 22.75 days for the other one (p = 0.209). A significant difference was only seen regarding findings of chest injury between the 2 groups (p < 0.001). In other cases a proper overlap was seen between findings of the 2 groups. Conclusion: Based on the results of the present study, it seems that doing selective CT scan yields results similar to pan CT in detection of head and face, neck and abdomen and hip injuries in multiple trauma patients. However, using pan CT in these patients led to 16% increase in detection and diagnosis of traumatic intra-thoracic injuries.
uence of temperature on the zoeal development and elemental composition of the cancrid crab , Cancer setosus Molina , 1782 from Paci fi c South America

a r t i c l e i n f o Temperature changes during ENSO cause mass mortalities of adult Cancer setosus, but the effects on early life stages are unknown. The influence of temperature on survival, development and biochemical composition was studied in larvae of the hairy crab, C. setosus, from a population off the northern Chilean coast. In rearing experiments conducted at four different temperatures (12, 16, 20, 22 °C), zoeal development was only completed at 16 and 20 °C, after 78 and 36 days, respectively. Instar duration was negatively correlated with temperature. A multiple linear model relating larval body mass (in carbon) to temperature and developmental time suggests that successful larval development is possible within a narrow temperature range only. The biochemical composition, measured as carbon, hydrogen, and nitrogen (C, H, N) content, show in general the typical oscillating changes during the moult cycle of brachyuran crab larvae. However, at high (22 °C) and low (16 °C) temperatures, CHN values show deviations from the typical pattern, indicating threshold temperatures for larval activity and survival. These findings indicate that the larval development of C. setosus is compromised under conditions of El Niño, with temperatures exceeding the upper thermal temperature tolerance threshold of larvae. Effects of El Niño on early life history stages and recruitment rates should be increasingly taken into account in fisheries management strategies. Most cancrid crabs are found in cold temperate and boreal waters (MacKay, 1943) and provide a huge proportion of crustacean fisheries in those regions (Bennett, 1995; Johnson and Shanks, 2002). Due to their commercial importance population structure and recruitment of cancrid crabs have been the subject of several studies (e. g. Successful recruitment is based on larval development. Larval hatching normally occurs during spring when food availability (plankton bloom) (Park et al., 2007) and temperatures are favourable for larval growth. This seasonal dependency is not valid for larvae in the highly productive upwelling region of the Humboldt Current, which provides a huge amount of plankton year-round during upwelling periods. The persistent stable temperate conditions in a broad range of the Humboldt Current allow larvae to develop all year round (for review see Fischer and Thatje, 2008). As one of the world's most productive ecosystems, the Humboldt Current supports one of the world's largest fisheries (Bertrand et al., 2004) which is of high economical importance for the adjacent countries The availability of resources is …
Robust and perfect tracking of discrete time systems

We study here the problem of robust and perfect tracking for discrete-time linear systems. By robust and perfect tracking we mean the ability of a controller to track a given reference signal with arbitrarily small settling time in the face of external disturbances and initial conditions. A set of necessary and sufficient conditions under which the proposed problem is solvable axe obtained and, under these conditions, constructive algorithms are given that yield required solutions. As is general to discrete-time systems, the solvability conditions to the above problem are quite restrictive. To relax these conditions, we propose an almost perfect tracking scheme, which is capable of tracking references precisely after certain initial steps.
LE ROLE DE L'ETAT DANS LA GOUVERNANCE D'ENTREPRISE

The topic of corporate governance has become increasingly prominent in recent years in many different academic areas in recent years. In this article we address various approaches to corporate governance, with a particular emphasis on the historical role of the State. It seems that certain approaches to corporate governance avoid discussing the role of the State, focusing instead on interactions between corporate managers and shareholders. The changes in corporate governance which have followed various financial crises show that the role of the State and the desires of the larger society are key factors in gaining a better understanding of corporate governance.
Metalloporphyrins and Functional Analogues as MRI Contrast Agents

Following the footprint of porphyrin-mediated photodynamic therapy (PDT), paramagnetic metalloporphyrins were originated as tumor seeking contrast agents (CAs) for magnetic resonance imaging (MRI). However, serial research has disproved their tumor selectivity, identified nonviable tissues as their real targets, and eventually elicited new applica- tions in myocardial infarction delineation, tissue viability evaluation, ablation therapy assessment, as well as first pass or dynamic perfusion MRI, multi-organ contrast enhancement (CE), atherosclerotic plaque imaging and stem cell labeling or tracking. Furthermore, nonporphyrin analogues have been developed to reduce porphyrin related toxicities. These porphy- rin and nonporphyrin compounds have been termed as necrosis-avid contrast agents (NACAs) to denote their major dis- covered affinity. The present review aims to document the evolving research in this particular field, to discuss possible mechanisms, to promote further preclinical and clinical development of this unique and promising class of MRI CAs, and to implicate a novel stroma targeting strategy for diagnosis and treatment of malignant and benign disorders.
Commentary: methods of policy accommodation at the interest-rate lower bound

If Michael Woodford says it is daunting to present his paper in front of this distinguished audience, you can imagine how daunting it is to be the discussant of his paper, which is probably the longest paper at this conference—but a paper, typically for Mike, dense with insight. I am grateful to have this opportunity, not just to discuss this paper, but to be a participant at this year’s Federal Reserve Bank of Kansas City conference.
Using Metrics of Curation to Evaluate Information-Based Ideation

Evaluating creativity support environments is challenging. Some approaches address people’s experiences of creativity. The present method measures creativity, across conditions, in the products that people make. This research introduces information-based ideation (IBI), a paradigm for investigating open-ended tasks and activities in which users develop new ideas. IBI tasks span imagining, planning, and reflecting on a weekend, vacation, outfit, makeover, paper, internship, thesis, design, campaign, crisis response, career, or invention. What products do people create through engagement in IBI? Curation of digital media incorporates conceptualization, finding and choosing information objects, annotation, and synthesis. Through engagement in IBI tasks, people create curation products. This article formulates a quantitative methodology for evaluating IBI support tools, building on prior creative cognition research in engineering design to derive a battery of ideation metrics of curation. Elemental ideation metrics evaluate creativity within curated found objects. Holistic ideation metrics evaluate how a curation puts elements together. IBI support environments are characterized by their underlying medium of curation. Curation media include lists, such as listicles, and grids, such as the boards of Pinterest. An in-depth case study investigates information composition, an art-based medium representing a curation as a freeform visual semantic connected whole. We raise two creative cognition challenges for IBI. One challenge is overcoming fixation—for instance, when a person gets stuck in a counterproductive mental set. The other challenge is to bridge information visualization’s synthesis gap, by providing support for connecting findings. To address the challenges, we develop mixed-initiative information composition (MI2C), integrating human curation of information composition with automated agents of information retrieval and visualization. We hypothesize that MI2C generates provocative stimuli that help users overcome fixation to become more creative on IBI tasks. We hypothesize that MI2C’s integration of curation and visualization bridges the synthesis gap to help users become more creative. To investigate these hypotheses, we apply ideation metrics of curation to interpret results from experiments with 44 and 49 participants.
The Money Market Daily Session :: an UHF-GARCH Model Applied to the Portuguese Case Before and After the Introduction Of the Minimum Reserve System of the Single Monetary Policy

The study of the determination of the overnight interest rate in the interbank market, and the behaviour of its volatility, gained new insights with contributions from the microstructure theory. The aim of this article is to study the effect of the trade intensity over the volatility of the overnight interest rate, using intra-daily data related to the Portuguese Money Market (MMI). The analysis is focused in two different periods of time, before and after the introduction of the minimum reserve rules of the Single Monetary Policy. We find that these rules have contributed to interest rate stability.
Scalable Object Discovery: A Hash-Based Approach to Clustering Co-occurring Visual Words

We present a scalable approach to automatically discovering particular objects (as opposed to object categories) from a set of images. The basic idea is to search for local image features that consistently appear in the same images under the assumption that such co-occurring features underlie the same object. We first represent each image in the set as a set of visual words (vector quantized local image features) and construct an inverted file to memorize the set of images in which each visual word appears. Then, our object discovery method proceeds by searching the inverted file and extracting visual word sets whose elements tend to appear in the same images; such visual word sets are called co-occurring word sets. Because of unstable and polysemous visual words, a co-occurring word set typically represents only a part of an object. We observe that co-occurring word sets associated with the same object often share many visual words with one another. Hence, to obtain the object models, we further cluster highly overlapping co-occurring word sets in an agglomerative manner. Remarkably, we accelerate both extraction and clustering of co-occurring word sets by Min-Hashing. We show that the models generated by our method can effectively discriminate particular objects. We demonstrate our method on the Oxford buildings dataset. In a quantitative evaluation using a set of ground truth landmarks, our method achieved higher scores than the state-of-the-art methods.
Compact parametric models for efficient sequential decision making in high-dimensional, uncertain domains

Within artificial intelligence and robotics there is considerable interest in how a single agent can autonomously make sequential decisions in large, high-dimensional, uncertain domains. This thesis presents decision-making algorithms for maximizing the expcted sum of future rewards in two types of large, high-dimensional, uncertain situations: when the agent knows its current state but does not have a model of the world dynamics within a Markov decision process (MDP) framework, and in partially observable Markov decision processes (POMDPs), when the agent knows the dynamics and reward models, but only receives information about its state through its potentially noisy sensors. One of the key challenges in the sequential decision making field is the tradeoff between optimality and tractability. To handle high-dimensional (many variables), large (many potential values per variable) domains, an algorithm must have a computational complexity that scales gracefully with the number of dimensions. However, many prior approaches achieve such scalability through the use of heuristic methods with limited or no guarantees on how close to optimal, and under what circumstances, are the decisions made by the algorithm. Algorithms that do provide rigorous optimality bounds often do so at the expense of tractability. This thesis proposes that the use of parametric models of the world dynamics, rewards and observations can enable efficient, provably close to optimal, decision making in large, high-dimensional uncertain environments. In support of this, we present a reinforcement learning (RL) algorithm where the use of a parametric model allows the algorithm to make close to optimal decisions on all but a number of samples that scales polynomially with the dimension, a significant improvement over most prior RL provably approximately optimal algorithms. We also show that parametric models can be used to reduce the computational complexity from an exponential to polynomial dependence on the state dimension in forward search partially observable MDP planning. Under mild conditions our new forwardsearch POMDP planner maintains prior optimality guarantees on the resulting decisions. We present experimental results on a robot navigation over varying terrain RL task and a large global driving POMDP planning simulation. Thesis Supervisor: Nicholas Roy Title: Assistant Professor