Miracle-dz/UniFAR-FTUs huggingface dataset:
LADA Algorithms: Performance Lower Bound and Cluster-based Variant

The observation that certain nonreversible chains lifted from reversible ones mix substantially faster than the original chains [1], [2] has motivated our recent work [3], where a Location-Aided Distributed Averaging (LADA) algorithm is proposed to achieve fast distributed consensus. We continue our study in this paper to gain a better understanding of distributed consensus via chain lifting and explore possible improvements for LADA. First, lower bounds for the averaging time via lifting Markov chains in a wireless network with a common transmission range r, as modeled by a geometric random graph G(n, r), are derived based on resistance, an invariant of Markov chains. It is shown that the constructed chain in LADA is optimal in the scaling law for the mixing parameter Tfill, for Markov chains with an approximately uniform stationary distribution on G(n, r). The 2-averaging time of O(r−1 log(2−1)) achieved by LADA is also close to its lower bound Ω(r−1). Finally, a cluster-based LADA (CLADA) variant is proposed to further improve on the message complexity in dense networks. Essentially, the LADA algorithm is applied to the induced graph resulted from a distributed clustering algorithm. It is shown that the number of transmitted messages per iteration is reduced from Θ(n) to Θ(r−2), which translates into significant power savings for nodes.
Challenges faced by developing countries in competition and regulation in the maritime transport sector Note by the UNCTAD secretariat Executive summary

Over 80 per cent of world merchandise trade by volume and over 70 per cent by value is carried by sea and handled by seaports worldwide. More than half of the total value of global seaborne trade involves containerized cargo transported by liner shipping carriers. Maritime transport is thus highly significant for trade and development, and the liner shipping industry plays a crucial role in international trade in manufactured goods, which contributes to economic growth.
EUROPEAN ASSOCIATION OF UROLOGY

4. Kristjansson A, Pedersen J. Management of blunt renal trauma. Br J Urol 1993; 72(5 Pt 2): 692-696. http://www.ncbi. nlm.nih. gov/entrez/query.fcgi? cmd =Retrieve&db=PubMed&list_uids=8281396&dopt=
Annex 2: An evaluation of the evidence of the impact of fishing closures around breeding colonies of African Penguins

1 Seabird Conservation Programme, BirdLife South Africa, PO Box 7119, Rogge Bay, 8012, South Africa 2 Animal Demography Unit, Department of Biological Sciences, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa 3 Environment and Sustainability Institute, University of Exeter, Penryn, Cornwall, TR10 9FE, United Kingdom 4 Percy FitzPatrick Institute, DST/NRF Centre of Excellence, University of Cape Town, Rondebosch 7701, South Africa 5 Marine Research (MA-RE) Institute University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa 6 Department of Biological Sciences, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa 7 Centre for Statistics in Ecology, Environment and Conservation, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa 8 Department of Environmental Affairs, Oceans and Coasts, PO Box 52126, Cape Town 8000, South Africa 9 South African National Biodiversity Institute, Kirstenbosch Research Centre, Private Bag X7, Claremont, Cape Town 7735, South Africa 10 Department of Statistical Sciences, University of Cape Town, Private Bag X3, Rondebosch 7701, South Africa 11 Independent Biostatistcian; Extraordinary Professor: Statistics, University of the Western Cape, Cape Town
Primary marginal zone lymphoma of the breast associated with hepatitis C virus infection: A rare case report and review of literature

Primary non-Hodgkin's lymphoma of the breast is a rare entity, comprising <0.5% of all malignant tumors of the breast. They are mainly of the diffuse large B cell lymphoma, while marginal zone B cell lymphomas (MZLs) are exceedingly rare. Links between hepatitis C virus (HCV) infection and several non-Hodgkin lymphomas have been suggested by epidemiological studies. We herein report the first case of low grade MZL of the breast associated with HCV infection showing disease free recurrence following incomplete surgery and antiviral therapy. Nevertheless, the combination of chemotherapy and radiation therapy has been proposed as the gold standard treatment for primary breast lymphoma right now.
Embracing a mechanized formalization gap

If a code base is so big and complicated that complete mechanical verification is intractable, can we still apply and benefit from verification methods? We show that by allowing a deliberate mechanized formalization gap we can shrink and simplify the model until it is manageable, while still retaining a meaningful, declaratively documented connection to the original, unmodified source code. Concretely, we translate core parts of the Haskell compiler GHC into Coq, using hs-to-coq, and verify invariants related to the use of term variables.
K-Clique Based Community Detection using Union Find : KCUF

Community detection has gained enormous popularity in the field of network analysis. Detecting communities in a graph is a complicated graph and it is directly related to cliques. Clique is a closed group of nodes in a graph. Community in a network is also a closed group of nodes but extent of closeness in communities is lesser than that in cliques. Finding clique in a network is NP-hard problem. Finding communities have similar characteristics, therefore community detection requires exponential time. In this paper, an improved k-clique detection algorithm based on union-find structure is proposed. The time efficiency of community discovery in highly overlapped complex network is improved and communities are identified within approximately linear time. The proposed algorithm KCUF uses union-find structure to store identified communities and reduces the number of unnecessary intersection test. The efficiency and effectiveness of the algorithm is investigated on two standard datasets, COND-MAT and Amazon goods network. The experimental results establish that the proposed algorithm is correct and its time efficiency is better than other overlapping community detection algorithms.
Mise au point Anomalies de la microcirculation dans le choc septique Microcirculatory alterations in septic shock

De nombreuses études expérimentales ont montré que la microcirculation est fortement altérée dans divers modèles de choc endotoxinique ou de péritonite. Ces altérations peuvent être observées dans divers organes (peau, muscle, musculeuse et muqueuse intestinale, foie, reins.) et sont caractérisées par la présence de capillaires non perfusés ou perfusés de manière intermittente. De plus, l’hétérogénéité spatiale et temporelle du flux microvasculaire est accrue. L’étude de la microcirculation a longtemps été difficile mais le développement récent d’une nouvelle technique de vidéomicroscopie intravitale, la polarisation spectrale orthogonale (OPS), a permis la visualisation directe de la microcirculation du patient de réanimation. À l’aide d’une technique OPS nous avons démontré que des patients en sepsis sévère et choc septique présentaient des altérations sévères de la microcirculation sublinguale. Ces altérations n’étaient pas influencées par la pression artérielle ou l’usage de vasopresseurs. En réalisant des examens séquentiels chez une série de patients en choc septique, nous avons observé que ces altérations s’amélioraient rapidement chez les patients qui allaient survivre à leur épisode de choc septique tandis qu’elles restaient stationnaires chez les patients qui devaient décéder de leur choc septique, que ce soit dans le tableau d’insuffisance circulatoire ou de faillite multiviscérale après résolution du choc. Il est intéressant de constater que ces altérations peuvent être résolues après application locale d’acétylcholine voire même administration systémique de dérivés nitrés. En conséquence, les altérations microcirculatoires sont fréquemment observées au cours du sepsis sévère et peuvent avoir des implications pathophysiologiques importantes.
Monocular visual mapping with the Fast Hough Transform

This article presents a mapping algorithm for bearingonly sensors, largely based on the Fast Hough Transform. While just the specific case of a camera moving in a straight path and detecting orthogonal edges was considered, the principle should be useful for more complex scenarios too. The algorithm is intended for creating initial estimates of landmark positions to allow the application of other classic mapping methods, such as maximum likelihood bundle adjustment. Tests were conducted with a video obtained with a consumer camcorder moving through a corridor, and good initial estimates were successfully generated.
Hard cases of third readings in terms of the Standard Solution 1

Schwager (2011) and Sudo (2014) argued that there are cases of the so-called third readings of attitude reports, initially discovered by Fodor (1970), that cannot be accounted for in terms of a theory of indexed world variables (Percus, 2000), which is often referred to as the Standard Solution. More complicated alternatives to the Standard Solution have been recently formulated in the literature in a number of papers. We argue that all the seemingly problematic cases can be naturally accounted for in terms of the Standard Solution, if we take into account the existence of previously unrecognized elided material in these reports.
Accuracy of clinical tests to diagnose superior labral anterior and posterior (SLAP) lesions

Abstract Numerous clinical tests to diagnose superior labral anterior and posterior (SLAP) lesions of the glenoid labrum have been described. The current systematic review of the relevant literature was undertaken to ascertain the accuracy of these tests as determined by likelihood ratios. Searching of electronic databases yielded 16 papers that had reported test accuracy on a total of 16 separate clinical tests for SLAP lesions, and some combinations of these tests. Although many tests appeared to be highly accurate when initially reported, all tests that had been evaluated by authors other than those who devised the test typically showed poor levels of accuracy in subsequent studies. The results of this review suggest that no one clinical test can be recommended for use in clinical practice to diagnose accurately the presence or absence of a SLAP lesion. It is possible that further investigations into the anatomical basis of these tests may explain the variability in accuracy.
Towards a Dose of Acupuncture

Although it would be quite unacceptable to attempt most forms of treatment without defining the dose, acupuncture has so far remained without any means of quantification. Electrical and deep manual needling probably rely for their main effect on direct nerve and muscle stimulation. It is suggested that superficial needling acts by producing a cone of tissue damage, with release of inflammatory mediators which increase the sensitivity of local nerve endings so that trivial, incidental stimulation continues to induce CNS inhibitory effects for a prolonged period. The degree of local inflammatory effect can easily be recorded by measuring the area of skin flare. A simple formula is offered to determine the dose of acupuncture, based on skin flare recordings after experimental needling and theoretical considerations of tissue damage. Needles of varying shaft radius were inserted to a number of depths in the abdominal dermis and subjected to specific degrees of stimulation. The experimental data correlate well with the theoretical model. Needle response increases with depth of insertion, and with needle thickness although this is outweighed by the effects of manipulation if carried out. The formula derived for determining the dose of acupuncture which correlates with inflammatory response, as measured by skin flare, is (r + 0.1 l)K, where r is the radius of the needle shaft, l is the depth of insertion and K is 10 for unmanipulated insertions or 12.4 if the needle is manipulated.
Übersicht Einfluss von Nitrat und Nitrit aus Fleisch-erzeugnissen auf die Gesundheit des Menschen

Nitrat (NO3 -) ist ein natürlich vorkommender Stoff, der eine wichtige Stickstoffquelle für Pflanzen darstellt. In der landwirtschaftlichen Produktion wird Nitrat deshalb als Dünger verwendet, was bei einem intensiven Einsatz zu hohen Nitratkonzentrationen im Grundwasser und damit auch im Trinkwasser führen kann. Die größte Nitratquelle für den Menschen sind jedoch die Pflanzen selber, von denen einige (z. B. Blattgemüse, Rettich, Radieschen, Rote Bete) sehr viel Nitrat speichern können. Außerdem wird Nitrat in Form von Naoder K-Salpeter traditionell zur Konservierung und Umrötung von Fleischerzeugnissen eingesetzt. In dieser Funktion zählt es zusammen mit der reduzierten Form Nitrit (NO2 -) zu den Lebensmittelzusatzstoffen (Pökelstoffe). Nitrit wird ausschließlich in Form von Nitritpökelsalz zum Pökeln von Fleischerzeugnissen eingesetzt, in Pflanzen kommt Nitrit nur in vernachlässigbaren Mengen vor.
Compressed Sensing and Overparametrized Networks: Overfitting Peaks in a Model of Misparametrized Sparse Regression in the Interpolation Limit

Current practice in machine learning is to employ deep nets in an overparametrized limit, with the nominal number of parameters typically exceeding the number of measurements. This resembles the situation in compressed sensing, or in sparse regression with l1 penalty terms, and provides a theoretical avenue for understanding phenomena that arise in the context of deep nets. One such phenonemon is the success of deep nets in providing good generalization in an interpolating regime with zero training error. Traditional statistical practice calls for regularization or smoothing to prevent "overfitting" (poor generalization performance). However, recent work shows that there exist data interpolation procedures which are statistically consistent and provide good generalization performance[4] ("perfect fitting"). In this context, it has been suggested that "classical" and "modern" regimes for machine learning are separated by a peak in the generalization error ("risk") curve, a phenomenon dubbed "double descent"[3]. While such overfitting peaks do exist and arise from ill-conditioned design matrices, here we challenge the interpretation of the overfitting peak as demarcating the regime where good generalization occurs under overparametrization. We propose a model of Misparamatrized Sparse Regression (MiSpaR) and analytically compute the GE curves for l2 and l1 penalties. We show that the overfitting peak arising in the interpolation limit is dissociated from the regime of good generalization. The analytical expressions are obtained in the so called "thermodynamic" limit. We find an additional interesting phenomenon: increasing overparametrization in the fitting model increases sparsity, which should intuitively improve performance of l1 penalized regression. However, at the same time, the relative number of measurements decrease compared to the number of fitting parameters, and eventually overparametrization does lead to poor generalization. Nevertheless, l1 penalized regression can show good generalization performance under conditions of data interpolation even with a large amount of overparametrization. These results provide a theoretical avenue into studying inverse problems in the interpolating regime using overparametrized fitting functions such as deep nets.
An overview of nursing in Saudi Arabia

Achieving and maintaining a stable nursing workforce is an important issue for the well-being of the rapidly growing population of Saudi Arabia. However, high turnover of expatriate staff and low recruitment of Saudi nationals have led to a serious staff shortage in the professions, particularly of well-qualified and experienced nurses. Nursing leaders need to work to improve the image of nurses and facilitate the recruitment of women into the nursing profession. Reduced working hours and part-time contracts with increased salaries and benefits could attract more young women to the profession, as might the provision of facilities such as private transportation and on-site childcare. Furthermore, establishing a national association for nurses would advance the nursing profession and help to ensure that all nurses undertake fully comprehensive training before entering the workforce.
Comment on “Causal inference using invariant prediction”

We congratulate the authors on this thought-provoking paper. Statistical inference of causality has been thoroughly studied in randomized experiments or observational studies, but is seldom considered when data from both observational and interventional settings are available. Peters et al. made an important contribution by tackling this problem with their notion of invariant causal prediction (ICP). At first look, ICP is a corollary of structural equation models, but we think its value might be much more substantial. Dawid [2000] noticed that causal researchers are predominately Laplacian determinists, for who “nothing short of a functional model relating outputs to inputs will do as a description of nature”. Peters et al. provide an alternative approach that defines causality by predictability instead of determinism, two different concepts that are not logically connected [Hoefer, 2016]. In light of Breiman [2001]’s two cultures of statistics, determinism roughly corresponds to the data modeling culture and predictability is the spirit of Breiman’s algorithmic modeling culture. Bearing this difference in mind, Peters et al. do not take a downright predictability approach in this paper. Rather, they consider two types of assumptions: invariant prediction in order to define causality and deterministic modeling assumptions such as linearity. This hybrid perspective becomes clear when comparing the assumptions in Equation (4) to (24), (28) or (31). As a consequence, ICP is able to make causal discovery only when the modeling assumptions are correct. The authors take this as a robustness property, but in our view it also limits the applicability in practice. We did not find in the paper a summary of the robustness of ICP, so we tried to outline in Table 1 the behavior of linear ICP when some of its assumptions are not met. We would welcome the authors’ comments on this summary. To test the empirical performance of ICP, we use the authors’ software on a protein signaling network dataset. Sachs et al. [2005] collected a combination of observational and 9 interventional datasets to infer the causal structure of 11 ∗Correspondence address: qyzhao@stanford.edu †Contributed equally.
CHAPTER-7 Effect of salt and acid stress on Triticum aestivum L . Var . inoculated with Glomus fasciculatum

INTRODUCTION Arbuscular mycorrhizal fungi (AMF) are known to facilitate water and mineral uptake by the host plants under normal and stress conditions (Gupta and Krishnamurthy, 1996; Morte et al., 2000). Several studies have demonstrated that AMF symbiosis in plants can improve resistance to various abiotic stresses (Azcon and El-Atrash, 2000). AM fungi help to overcome salt stresses by increasing the water and nutrient uptake from the soil. Salt and acid tolerance in plants is a complex phenomenon that involves physiological, biochemical and molecular changes (Porcel et al., 2003). Reduction in growth and yield are the most important physiological response of plants to the excess salts in the media. Salt resistance was improved by AMF colonization in Maize (Feng et al., 2002), Mung bean and Clover with the AM fungal effect correlated with improved osmoregulation or proline accumulation. AMF colonization improves NaCl resistance in tomato, with extent of improvement related to salt sensitivity of the cultivar (Al-Karaki et a/.,2001). There is a considerable evidence to suggest that AM fungi are able to increase the host plant’s tolerance to water stress (Davies et al, 1992; Smith and Read, 1997), including that caused by high salinity (Al-Karaki et al,2001;Feng et al, 2002). Soil salinity affects crop plants in three major ways, osmotic stress results in decreasing water availability, ionic stress and changes in the cellular ionic
Priming of Abelmoschus esculentus (L.) Moench (okra) seeds with liquid phosphobacterium: An approach to mitigate drought stress

The present investigation aimed to evaluate the effect of priming of Abelmoschus esculentus (okra) seeds with liquid phosphobaterium (LPB) on water stress. The phosphobacterium is known for its use as inoculants to increase the uptake of soil phosphorus as well as crop yield. The availability of rain fall is getting decreased every year causing severe drought and that may adversely affect the agricultural crops in the state of Kerala. So the aim of the study is to provide a helping hand to the farming community to fight against drought stress. In the present study the seeds of okra cv. Arka anamika were subjected to priming treatment with 5% and 10% liquid phosphobaterium, and the parameters like biomass, relative water content, chlorophyll content, total protein and yield were studied. Priming with liquid phosphobaterium showed considerable variation in both the physiological and biochemical parameters. Among the concentrations of liquid phosphobaterium tested seeds primed with 10% liquid phosphobaterium were found to effective in mitigating the effect of water stress, stimulating early flowering and also increase in yield.
Weed Management in Secondary Nursery of Tectona grandis and Pterocarpus santalinus

The out-planting of seedlings in tree nurseries is the principal method for establishing woodlands in India where the use of natural regeneration is not viable. This is partly because the problems of seed predation and weed competition are more easily addressed in an intensively managed nursery site (Willoughby et al., 2004a). In nursery production, weeds can compete with tree seedlings for light, moisture and nutrients, which can kill small, recently emerged seedlings.
Analysts’ Influence on Firms’ Voluntary Disclosures

We provide evidence that analysts influence managers’ disclosure decisions. Analysts’ questions during earnings conference calls prompt guidance in subsequent quarters’ conference calls and earnings announcements. However, analysts’ ability to mitigate that managers from small, low earnings, and high earnings volatility companies are less willing to provide guidance is limited to guidance about capital expenditures and taxes.
How Are You Doing ? How Are You Doing ? a

Spoken Language Understanding (SLU) typically comprises of an automatic speech recognition (ASR) followed by a natural language understanding (NLU) module. The two modules process signals in a blocking sequential fashion, i.e., the NLU often has to wait for the ASR to finish processing on an utterance basis, potentially leading to high latencies that render the spoken interaction less natural. In this paper, we propose recurrent neural network (RNN) based incremental processing towards the SLU task of intent detection. The proposed methodology offers lower latencies than a typical SLU system, without any significant reduction in system accuracy. We introduce and analyze different recurrent neural network architectures for incremental and online processing of the ASR transcripts and compare it to the existing offline systems. A lexical End-of-Sentence (EOS) detector is proposed for segmenting the stream of transcript into sentences for intent classification. Intent detection experiments are conducted on benchmark ATIS dataset modified to emulate a continuous incremental stream of words with no utterance demarcation. We also analyze the prospects of early intent detection, before EOS, with our proposed system.