Miracle-dz/UniFAR-FTUs huggingface dataset:
Limits on the storage of quantum information in a volume of space

We study the fundamental limits on the reliable storage of quantum information in lattices of qubits by deriving tradeoff bounds for approximate quantum error correcting codes. We introduce a notion of local approximate correctability and code distance, and give a number of equivalent formulations thereof, generalizing various exact error-correction criteria. Our tradeoff bounds relate the number of physical qubits $n$, the number of encoded qubits $k$, the code distance $d$, the accuracy parameter $\delta$ that quantifies how well the erasure channel can be reversed, and the locality parameter $\ell$ that specifies the length scale at which the recovery operation can be done. In a regime where the recovery is successful to accuracy $\epsilon$ that is exponentially small in $\ell$, which is the case for perturbations of local commuting projector codes, our bound reads $kd^{\frac{2}{D-1}} \le O\bigl(n (\log n)^{\frac{2D}{D-1}} \bigr)$ for codes on $D$-dimensional lattices of Euclidean metric. We also find that the code distance of any local approximate code cannot exceed $O\bigl(\ell n^{(D-1)/D}\bigr)$ if $\delta \le O(\ell n^{-1/D})$. As a corollary of our formulation of correctability in terms of logical operator avoidance, we show that the code distance $d$ and the size $\tilde d$ of a minimal region that can support all approximate logical operators satisfies $\tilde d d^{\frac{1}{D-1}}\le O\bigl( n \ell^{\frac{D}{D-1}} \bigr)$, where the logical operators are accurate up to $O\bigl( ( n \delta / d )^{1/2}\bigr)$ in operator norm. Finally, we prove that for two-dimensional systems if logical operators can be approximated by operators supported on constant-width flexible strings, then the dimension of the code space must be bounded. This supports one of the assumptions of algebraic anyon theories, that there exist only finitely many anyon types.
Evidence for Thermal Absorption inside Cassiopeia A

Subarcminute resolution images of Cas A at 74 and 333 MHz are presented. Comparison with each other and with higher frequency observations indicates that the spectral index of its nonthermal emission flattens below 333 MHz and that the effect is confined toward the central 1' of the source and is maximum at its center. The source of the flattening is unknown, although we show that the data are consistent with absorption by ionized gas inside the radio shell. Such a component may be related to unshocked ejecta still freely expanding within the boundaries of the reverse shock as delineated by X-ray observations.
DSM: A Low-Overhead, High-Performance, Dynamic Stream Mapping Approach for MongoDB

For write-intensive workloads, reclaiming free blocks in flash SSDs is expensive due to data fragmentation problem that leads to performance degradation. This paper addresses that problem in MongoDB, a popular document store in the current market, by introducing a novel stream mapping scheme that exploits unique characteristics of MongoDB and multi-streamed technology. It dynamically assigns streams for corresponding writes according to their hotness values and distinguishes writes on primary index files from writes on secondary index files. The proposed method is high-performance, low-overhead, and independent of data models or workloads. Empirical results in Linkbench benchmark show that compared to the original WiredTiger our approach improves the throughput and reduces the 99th-percentile latency by up to 65% and 46.2% respectively. Compared to the best-performance in the prior research, our approach improves the throughput and reduces the 99th-percentile latency by up to 23% and 28.5% respectively. Distinguishing writes on primary index files from writes on secondary index files enhances the throughput and the 99th-percentile latency by up to 11.7% and 15.7% respectively. Moreover, by tuning the leaf page size in B+Tree of MongoDB, we can significantly improve the throughput by 1.6× – 2.1× in Linkbench.
Routage distribué et adaptatif fondé sur la théorie des jeux

A common challenge in current and future communication networks is to exploit path diversity to increase performance and reliability. This problem can be modeled as a multi-commodity flow problem [1]. Given a number of concurrent source-destination flows, the problem is to assign these flows to network paths, while respecting capacity constraints and optimizing a performance metric. Our goal is to provide a distributed solution to this problem, that requires neither cooperation between sites nor knowledge of the network topology and performance parameters. Adaptive routing algorithms base the routing decisions on dynamic properties of the network (such as real time link load, end-to-end latency, or packet loss). Despite years of research, adaptive routing techniques did not see wide adoption. The main reason is the presence of potential instabilities and routing oscillations, which could make the cure worse than the disease. Indeed, early experiments with delay-based routing in the ARPANET resulted in severe stability issues under high load, rendering the network close to unusable [2].
MAGNETIC RESONANCE BRAIN IMAGE CLASSIFICATION BY AN IMPROVED ARTIFICIAL BEE COLONY ALGORITHM

Automated and accurate classiflcation of magnetic resonance (MR) brain images is a hot topic in the fleld of neuroimaging. Recently many difierent and innovative methods have been proposed to improve upon this technology. In this study, we presented a hybrid method based on forward neural network (FNN) to classify an MR brain image as normal or abnormal. The method flrst employed a discrete wavelet transform to extract features from images, and then applied the technique of principle component analysis (PCA) to reduce the size of the features. The reduced features were sent to an FNN, of which the parameters were optimized via an improved artiflcial bee colony (ABC) algorithm based on both fltness scaling and chaotic theory. We referred to the improved algorithm as scaled chaotic artiflcial bee colony (SCABC). Moreover, the K-fold stratifled cross validation was employed to avoid overfltting. In the experiment, we applied the proposed method on the data set of T2-weighted MRI images consisting of 66 brain images (18 normal and 48 abnormal). The proposed SCABC was compared with traditional training methods such as BP, momentum BP, genetic algorithm, elite genetic algorithm with migration, simulated annealing, and ABC. Each algorithm was run 20times to reduce randomness. The results show that our SCABC can obtain the least mean MSE and 100% classiflcation accuracy.
The Illusion of Wealth and Its Reversal

Research on choice architecture is shaping policy around the world, touching on areas ranging from retirement economics to environmental issues. Recently, researchers and policy makers have begun paying more attention not just to choice architecture but also to information architecture, or the format in which information is presented to people. In this article, the authors investigate information architecture as it applies to consumption in retirement. Specifically, in three experiments, they examine how people react to lump sums versus equivalent streams of monthly income. Their primary question of interest is whether people exhibit more or less sensitivity to changes in retirement wealth expressed as lump sums (e.g., $100,000) or monthly equivalents (e.g., $500 per month for life). They also test whether people exhibit an “illusion of wealth,” by which lump sums seem more adequate than monthly amounts in certain conditions, as well as the opposite effect, in which lump sums seem less adequate. They conclude by discussing how format-dependent perceptions of wealth can affect policy and consumers’ financial decision making.
Temporal Motifs in Heterogeneous Information Networks

Network motifs are crucial building blocks of understanding and modeling complex networks for their capacity in characterizing higher-order interactions. Meanwhile, heterogeneous information networks (HINs) are ubiquitous in real-world applications, which oen come with rich temporal information. We are hence motivated to study temporal motifs in the context of heterogeneous information networks. With examples from real-world datasets, we demonstrate HIN motifs can be armed with substantially more discriminability by incorporating temporal information. Furthermore, counting temporal HIN motif instances in large-scale networks is time consuming. We therefore develop ecient counting algorithm for the HIN motifs that are of the most interests in the literature. Empirical observations in the experiment have shown that interesting motif instances can be identied from large-scale HINs thanks to the improved discriminability of temporal HIN motifs, and the proposed ecient counting algorithm enjoys linear complexity that is multiple orders of magnitude faster than the baseline method in three real-world HINs.
AN AUTOMATIC GUIDANCE SYSTEM FOR A SMALL WORK-CLASS ROV

Abstract In this paper, the design of the overall architecture and the development of specific components of a supervised, automatic system for guidance of an ROV are described. Basically, automation is achieved by inserting a PC, equipped with A/D-D/A boards, in the operator/ROV loop. In this way, the original configuration of the ROV manual guidance system is substituted by a more versatile, user-friendly and powerful PC-based configuration. The paper describes the basic features of the realized system, that, by allowing both on-line processing of sensory data and implementation of automatic control procedures, enhances the performances of the ROV as a robotic tools.
Critical Success Factors for Information Technology Infrastructure Library Implementation in Public Service Organizations : An Exploratory Study

In recent years, market competitions and internal efficiency requirements derived many Information Technology (IT) functions to shift their paradigms from IT asset management to IT service management (ITSM). Consequently, a growing number of public and private organizations are implementing the ITIL (IT Infrastructure Library) “best practice” as a framework for improving IT service management processes. This paper presents an exploratory in-depth case study of two public service organizations in the kingdom of Saudi Arabia deemed successfully implemented ITIL V3 processes. The case studies identify several critical success factors (CSF) associated with ITIL implementation success. These CSF are then compared with factors identified in the literature to shed light on success factors and challenges to offer a learning experience for organizations currently undergoing or planning ITIL implementation.
Indexing in Database

- Databases can store large amounts of data. each stored entity is a complex structure, called a record. Records are indexed based on the values of certain fields. A techniques for indexing data is provide.a method for compressed an index to obtain a compressed index that is easily stored and transmitted. The invention also provides for the decompression of such a compressed index. The main aim of this paper is to provide to the different techniques of indexing in database. Keyboards:- types of indexing , techniques and methods.
Modelling and Simulation Based Matlab/Simulink of a Strap- Down Inertial Navigation System’ Errors due to the Inertial Sensors

Inertial navigation is a dead reckoning positioning method based on the measurement and 10 mathematical processing of the vehicle absolute acceleration and angular speed in order to 11 estimate its attitude, speed and position related to different reference. Due to the specific 12 operation principle, the positioning errors for this method result from the imperfection of the 13 initial conditions knowledge, from the errors due to the numerical calculation in the inertial 14 system, and from the accelerometers and gyros errors. Therefore, the inertial sensors perform‐ 15 ances play a main role in the establishment of the navigation system precision, and should be 16 considered in its design phase frames (Bekir, 2007; Farrell, 2008; Grewal et al., 2013; Grigorie, 17 2007; Salychev, 1998; Titterton and Weston, 2004). 18
Satellite monitoring of ammonia: A case study of the San Joaquin Valley

[1] Atmospheric ammonia (NH3) has recently been observed with infrared sounders from space. Here we present 1 year of detailed bidaily satellite retrievals with the Infrared Atmospheric Sounding Interferometer and some retrievals of the Tropospheric Emission Spectrometer over the San Joaquin Valley, California, a highly polluted agricultural production region. Several sensitivity issues are discussed related to the sounding of ammonia, in terms of degrees of freedom, averaging kernels, and altitude of maximum sensitivity and in relation to thermal contrast and concentration. We also discuss their seasonal dependence and sources of errors. We demonstrate boundary layer sensitivity of infrared sounders when there is a large thermal contrast between the surface and the bottom of the atmosphere. For the San Joaquin Valley, large thermal contrast is the case for daytime measurements in spring, summer, and autumn and for nighttime measurements in autumn and winter when a large negative thermal contrast is amplified by temperature inversion.
Grammar Based Pre-Processing for PPM

In this paper, we apply grammar-based pre-processing prior to using the Prediction by Partial Matching (PPM) compression algorithm. This achieves significantly better compression for different natural language texts compared to other well-known compression methods. Our method first generates a grammar based on the most common two-character sequences (bigraphs) or three-character sequences (trigraphs) in the text being compressed and then substitutes these sequences using the respective non-terminal symbols defined by the grammar in a pre-processing phase prior to the compression. This leads to significantly improved results in compression for various natural languages (a 5% improvement for American English, 10% for British English, 29% for Welsh, 10% for Arabic, 3% for Persian and 35% for Chinese). We describe further improvements using a two pass scheme where the grammar-based pre-processing is applied again in a second pass through the text. We then apply the algorithms to the files in the Calgary Corpus and also achieve significantly improved results in compression, between 11% and 20%, when compared with other compression algorithms, including a grammar-based approach, the Sequitur algorithm.
Indigenous Healing Practices in Australia

ABSTRACT Indigenous Australian women are among the most disadvantaged women in the world. Over two centuries of colonization have had a damaging impact on perceptions of their gender roles and status as well as many other consequential oppressions. These experiences have affected the social and emotional wellbeing of Indigenous women of all ages, resulting in socio-economic ghettoization, higher suicide rates, psychological distress, illness, and poverty. Generations of women have experienced the forced removal of their children, resulting in complex forms of historical trauma. Despite this, Indigenous women have also maintained strong leadership roles and have kept families and communities intact. In the last few decades, the Australian Indigenous mental health movement has emerged within the context of a broader self-determination movement, restoring and strengthening women’s traditional therapeutic practices. This article offers an overview of the social and emotional wellbeing of Indigenous women within neocolonial Australia and explores women’s relationship to traditional therapeutic practices. Future directions and key issues for the capacity building of Indigenous women’s healing are explored.
Synthesis and Characterization of Tungsten Oxide Sponge like Sub-Microstructures in the Presence Of Novel Surfactants

Tungsten trioxide (WO3) nanostructures were synthesized by precipitation method using sodium tungstate dihydrate (Na2WO4-2H2O) in the presence of surfactant CTAB and PEG-6000. X-ray diffraction, scanning electron microscope, UV-Vis spectroscopy and thermo-gravimetric and differential thermal analysis (TG-DTA) were used to characterize the final product. XRD analysis revealed the presence of an amorphous structure for as-prepared WO3 nanoparticles. On the other hand, XRD showed formation of a crystalline WO3 structure with annealing at higher temperature. SEM results depicted the presence of sponge-like sub-microstructure. UV-Vis spectroscopy shows the average reflectance of about 61% in visible region and about 51% in nearinfrared region. The calculated optical band gap value is 2.98 eV. The process of decomposition of this system is investigated by TGDTA.
Reps, Jenna M. and Garibaldi, Jonathan M. and Aickelin, Uwe and Soria, Daniele and Gibson, Jack E. and Hubbard, Richard B. (2014) Signalling paediatric side effects using an ensemble of simple study

Background: Children are frequently prescribed medication ‘off-label’, meaning there has not been sufficient testing of the medication to determine its safety or effectiveness. The main reason this safety knowledge is lacking is due to ethical restrictions that prevent children from being included in the majority of clinical trials. Methods: Multiple measures of association are calculated for each drug and medical event pair and these are used as features that are fed into a classifier to determine the likelihood of the drug and medical event pair corresponding to an adverse drug reaction. The classifier is trained using known adverse drug reactions or known non-adverse drug reaction relationships. Results: The novel ensemble framework obtained a false positive rate of 0.149, a sensitivity of 0.547 and a specificity of 0.851 when implemented on a reference set of drug and medical event pairs. The novel framework consistently outperformed each individual simple study design. Conclusion: This research shows that it is possible to exploit the mechanism of causality and presents a framework for signalling adverse drug reactions effectively.
Neuropsychological evaluation and cognitive evolution of a bilingual Alzheimer patient

We present the case of a university-educated bilingual (Italian-Spanish) Alzheimer patient aged 61. After a neuropsychological assessment carried out at the Applied Psychology Service of the Faculty of Psychology at the Universidad Nacional de Educacion a Distancia (UNED), an individualized integral cognitive stimulation program was started that lasted three years. The interest of this case lies in the fact that the progress of this patient in the two languages was followed through a series of cognitive tasks (mainly linguistic and categorization). The results show that, as the illness develops, the patient cannot separate both languages and interferences appear. His first language remains intact for a longer period of time, as studies of this type have shown.
Nearly Balanced Heterogeneous Algorithms for Multiplying Scale-Free Sparse Matrices

Sparse matrix computations such as multiplying a sparse matrix with a sparse matrix, denoted spmm, and multiplying a sparse matrix with a dense matrix, denoted csrmm, are fundamental operations in linear algebra that have several applications. Hence, efficient and scalable implementations of spmm and csrmm have been a topic of immense research. Efforts are aimed at implementations on FPGAs, multi-core architectures, GPUs and such emerging computational platforms. The architectural trend towards heterogeneity has pushed heterogeneous computing to the fore of parallel computing research. Owing to the highly irregular nature of spmm and csrmm it is observed that CPUs and GPUs can offer comparable performance (Lee et al. [26]) and hence implementations on CPU+GPU systems are widely researched. In this thesis, we propose Sorted-Workqueue, a CPU+GPU heterogeneous algorithm for spmm and csrmm based upon Workqueue framework proposed by Kothapalli et al. [25]. We perform experiments on general sparse matrices from standard datasets and study the performance of spmm and csrmm. Another trend towards customizing algorithms according to characteristics of the input are gaining research interest in recent times. In this thesis, we consider real-world sparse matrices that exhibit scale-free nature and exploit their characteristics to propose better algorithms for spmm and csrmm. Focusing on such matrices, we propose novel algorithms HH-CPU for spmm and HD-CPU for csrmm on a CPU+GPU heterogeneous platform. We perform experiments on a wide variety of real-world scale-free matrices from standard datasets and show performance improvement over the state-of-art algorithms on CPU+GPU heterogeneous platform. A majority of the heterogeneous algorithms follow a work partitioning approach where the input is divided into appropriate sized pieces so that individual computing units can process the “right” pieces of the input. Such a division is done by means of some parameters, usually thresholds which vary for each input instance. However, identifying the right value of the threshold is usually non-trivial and may require an extensive empirical search. Such an extensive empirical search may potentially offset any gains accrued out of heterogeneous algorithms. We also show that one of our proposed method, HH-CPU, requires some thresholds to be computed in order to identify the right work that can be assigned to the right processor. For this, we propose a simple, yet effective technique based on sampling which can adapt to the algorithm used, the input instance and the heterogeneous computing units in the platform. Our technique is generic in its applicability as
Bioinformatic Survey of SLayer Proteins in Bifidobacteria

Surface layer (S-layer) proteins are one of the most commonly observed cell envelope components in both Archaea and Bacteria. It has versatile functions and holds considerable application potential in biotechnology. Bifidobacteria are representative probiotics conferring health promoting properties. However, there is little study of S-layer in bifidobacteria yet. The distribution and characteristics of S-layer in bifidobacteria are unknown. In this study, search for S-layer protein in the identical protein groups in NCBI yielded 49 hits belonging to bifidobacteria. These proteins were annotated as either “S-layer (domain) protein” or “putative S-layer (y) domain protein” that distributed among 26 species of Bifidobacterium genus. Multiple alignments suggest S-layer proteins are relatively conservative. Phylogenetic analysis of 24 S-layer (domain) protein sequences groups them into three distinct clusters, with the majority species in Cluster-2. S-layer (domain) protein has a universe motif DUF4381, though its function is unknown. Meanwhile, two other motifs CARDB and EphA2_TM involved in cell adhesion and cell signaling respectively, presented in most S-layer (domain) protein in bifidobacteria. All S-layer proteins have a typical N-terminal Sec-dependent signal peptide and a C-terminal trans-membrane region. Homological modeling of representative S-layer proteins from each cluster revealed a few unique structural features. All representative S-layer proteins have a plenty of β-meander motif that exclusively composed by β-barrel structural architectures linked together by hairpin loops.