Miracle-dz/UniFAR-FTUs huggingface dataset:
SAGA: a subgraph matching tool for biological graphs

MOTIVATION With the rapid increase in the availability of biological graph datasets, there is a growing need for effective and efficient graph querying methods. Due to the noisy and incomplete characteristics of these datasets, exact graph matching methods have limited use and approximate graph matching methods are required. Unfortunately, existing graph matching methods are too restrictive as they only allow exact or near exact graph matching. This paper presents a novel approximate graph matching technique called SAGA. This technique employs a flexible model for computing graph similarity, which allows for node gaps, node mismatches and graph structural differences. SAGA employs an indexing technique that allows it to efficiently evaluate queries even against large graph datasets. RESULTS SAGA has been used to query biological pathways and literature datasets, which has revealed interesting similarities between distinct pathways that cannot be found by existing methods. These matches associate seemingly unrelated biological processes, connect studies in different sub-areas of biomedical research and thus pose hypotheses for new discoveries. SAGA is also orders of magnitude faster than existing methods. AVAILABILITY SAGA can be accessed freely via the web at http://www.eecs. umich.edu/saga. Binaries are also freely available at this website.
Applicability of Ethanol for Measuring Intrinsic Hydraulic Properties of Sand with Various Water Repellency Levels

Many soils worldwide show water repellency to some degree. Soil water repellency (SWR) is known to alter hydraulic processes. Particularly in water‐repellent soils a decreased water infiltration rate can be observed. In this case soil hydraulic properties, like the hydraulic conductivity, not only are a result of the soil's pore system but also depend on the physicochemical properties of the pore surfaces (water repellency). Ethanol as a completely wetting liquid is not influenced by the soil's water repellency. In this study we introduce the concept of intrinsic soil hydraulic properties, that is, the hydraulic properties that are only dependent on the porous system and independent of its surface properties. We used the concept of intrinsic permeability, originally developed for saturated conditions. The effect of different liquid surface tensions of water and ethanol, important under unsaturated conditions, was incorporated using a correction factor for the matric potential. Retention and saturated and unsaturated liquid conductivity of water and ethanol were systematically measured in sand and glass‐bead porous media with different wettabilites. Results showed no difference between the intrinsic hydraulic properties (measured with ethanol) and the hydraulic properties (measured with water) in fully wettable porous media. In water‐repellent porous media, intrinsic hydraulic properties deviate from measured hydraulic properties. Measurements further showed that the influence of soil water repellency on hydraulic conductivity and retention of water can be predicted as a function of the macroscopic contact angle (CA) in these model substrates. In summary, at least for well‐defined substrates such as sands, we suggest measuring the hydraulic conductivity and intrinsic liquid retention of the pore system with ethanol as a standard procedure.
Cenozoic Glaciation in the Southern Hemisphere

With the end of glaciation in Gondwana in early Permian time, a prolonged interval began when the Earth was without ice sheets, though not necessarily free of mountain glaciers. Not until sometime during the Tertiary Era did ice sheets build up once more, first in Antarctica and later in the Northern Hemisphere. The fundamental cause of the cooling that led to the formation of the Antarctic Ice Sheet and, eventually, to major glaciation in lower latitudes is generally held to have been the increasing thermal isolation of Antarctica as Australia, and later South America, separated from it, allowing zonal oceanic circulation to develop. Rifting between Australia and Antarctica began between 1 10 and 90 m.y. ago (Cande & Mutter 1982), but not until much later, probably sometime between 30 and 25 m.y. ago, had it progressed far enough for deep circulation to begin as the South Tasman Rise cleared north Victoria Land (Kennett et a1 1974, Kennett 1977, Weissel et aI 1977). However, Antarctica did not reach its present high level of thermal isolation until South America had separated from it, enabling the Antarctic Circumpolar Current to become established (Savin et aI 1975). According to Barker & Burrell (1977, 1982), coherent spreading between the Antarctic Peninsula and Tierra del Fuego started about 29 m.y. ago, and deep circulation began 23.5 ± 2.5 m.y. ago, thereafter increasing in volume. The initial ice fields and ice caps that later expanded to become the
Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks

Functional maps are efﬁcient representations of shape correspondences, that provide matching of real-valued functions between pairs of shapes. Functional maps can be modelled as elements of the Lie group SO ( n ) for nearly isometric shapes. Synchronization can subsequently be employed to enforce cycle consistency between functional maps computed on a set of shapes, hereby enhancing the accuracy of the individual maps. There is an interest in developing synchronization methods that respect the geometric structure of SO ( n ) , while introducing a probabilistic framework to quantify the uncertainty associated with the synchronization results. This paper introduces a Bayesian probabilistic inference framework on SO ( n ) for Riemannian synchronization of functional maps, performs a maximum-a-posteriori estimation of functional maps through synchronization and further deploys a Riemannian Markov-Chain Monte Carlo sampler for uncertainty quantiﬁcation. Our experiments demonstrate that constraining the synchronization on the Riemannian manifold SO ( n ) improves the estimation of the functional maps, while our Riemannian MCMC sampler provides for the ﬁrst time an uncertainty quantiﬁcation of the results.
Adaptive Obstacle Avoidance Algorithm Based on Trajectory Learning

—Most obstacle avoidance algorithms are only effective in speciﬁc environments, and they have low adaptability to some new environments. In this paper, we propose a trajectory learning (TL)-based obstacle avoidance algorithm, which can learn implicit obstacle avoidance mechanism from trajectories generated by general obstacle avoidance algorithms and achieves better adaptability. Speciﬁcally, we deﬁne a general data structure to describe the obstacle avoidance mechanism. Based on this structure, we transform the learning of the obstacle avoidance algorithm into a multiclass classiﬁcation problem about the direction selection. Then, we design an artiﬁcial neural network (ANN) to ﬁt multiclass classiﬁcation function through supervised learning and ﬁnally obtain the obstacle avoidance mechanism that generates the observed trajectories. Our algorithm can obtain the obstacle avoidance mechanism similar to that demonstrated in the trajectories, and are adaptable to unseen environments. The automatic learning mechanism simpliﬁes modiﬁcation and debugging of obstacle avoidance algorithms in applications. Simulation results demonstrate that the proposed algorithm can learn obstacle avoidance strategy from trajectories and achieve better adaptability.
Robust Salient Object Detection via Fusing Foreground and Background Priors

Automatic salient object detection without any supervised labor tends to greatly enhance many computer vision tasks. This paper proposes a novel bottom-up salient object detection framework which considers both foreground and background priors in detecting process. First, a series of foreground seeds are extracted from an image based on surroundedness cue. Then, a foreground-corresponding saliency map is generated via ranking algorithm according to these seeds. In a similar way a series of background seeds are extracted and used for generating a background-corresponding saliency map. Finally, the two saliency maps are fused into one, and subsequently enhanced by geodesic refinement to derive the final saliency map. Extensive experimental evaluation demonstrates the effectiveness of our proposed framework against other outstanding methods.
Differentially Private Obfuscation Mechanisms for Hiding Probability Distributions

We introduce a formal model for the information leakage of probability distributions and define a notion called distribution privacy as the local differential privacy for probability distributions. Roughly, the distribution privacy of a local obfuscation mechanism means that the attacker cannot significantly gain any information on the distribution of the mechanism's input by observing its output. Then we show that existing local mechanisms can hide input distributions in terms of distribution privacy, while deteriorating the utility by adding too much noise. For example, we prove that the Laplace mechanism needs to add a large amount of noise proportionally to the infinite Wasserstein distance between the two distributions we want to make indistinguishable. To improve the tradeoff between distribution privacy and utility, we introduce a local obfuscation mechanism, called a tupling mechanism, that adds random dummy data to the output. Then we apply this mechanism to the protection of user attributes in location based services. By experiments, we demonstrate that the tupling mechanism outperforms popular local mechanisms in terms of attribute obfuscation and service quality.
A new approach for random key pre-distribution in large-scale wireless sensor networks

In a wireless sensor network (WSN), pre-distribution of secret keys is possibly the most practical approach to protect network communications. To meet the stringent resource constraints of the sensor nodes, key pre-distribution schemes should be highly efficient, require as little storage space as possible, and at the same time, maintain a strong security strength, that is, high resilience against node capture. In this paper, a new approach for random key pre-distribution is proposed to achieve both efficiency and security goals. The novelty of this approach lies in that, instead of using a key pool consisting of random keys, a key generation technique is carefully designed such that a large number of random keys can be represented by a small number of key-generation keys. Then, instead of storing a big number of random keys, each sensor node stores a small number of key-generation keys while computing the shared secret keys during the bootstrapping phase on the fly using the computationally efficient hash function. The proposed scheme outperforms the previous random key pre-distribution schemes in that it reduces the storage requirement significantly while holding the comparable security strength, as shown by our thorough analysis and simulation. Copyright © 2006 John Wiley & Sons, Ltd.
Measuring the Effect of the Change in Climate Condition on Input Use inAgriculture in Konya, Turkey

This study was conducted to determine effect of climate change (temperature and rainfall) on the amount of input uses such as fertilizer, pesticide, animal manure, family labour, paid labour and machine by selected farmer’s.The minumum usable sample of farm enterprises were determined as 124 based on stratified sampling technique. The data were collected from six villages in Cumra and Sarayonu districts in Konya. Input applications as farmer preference under the increasing temperature and rainfall were compared with its under the decreasing temperature and rainfall situations. The binary logistic regression was applied to determine the influence of each selected agricultural practise on the probability that the change of temperature and rainfall conditions. The results showed that when the temperature rises, the percantage of farmers who decrease the amount of chemicals (fertilizer and pesticide), the amount of paid labour increase. When the rainfall rises, the percantage of farmers who increase the amount of chemicals and the amount of family labour decrease. The other factors weren’t significantly important at the level of probability or beter as 0.05.
Reconstruction-Based Membership Inference Attacks are Easier on Difficult Problems

Membership inference attacks (MIA) try to detect if data samples were used to train a neural network model, e.g. to detect copyright abuses. We show that models with higher dimensional input and output are more vulnerable to MIA, and address in more detail models for image translation and semantic segmentation. We show that reconstruction-errors can lead to very effective MIA attacks as they are indicative of memorization. Unfortunately, reconstruction error alone is less effective at discriminating between non-predictable images used in training and easy to predict images that were never seen before. To overcome this, we propose using a novel predictability score that can be computed for each sample, and its computation does not require a training set. Our membership error, obtained by subtracting the predictability score from the reconstruction error, is shown to achieve high MIA accuracy on an extensive number of benchmarks.
Insiders Behaving Badly

Often, we worry about outsiders attacking our systems and networks, breaking through the perimeter defenses we've established to keep bad actors out. However, we must also worry about "insider threats": people with legitimate access who behave in ways that put our data, systems, organizations, and even our businesses' viability at risk.
Worst-Case Polynomial-Time Exact MAP Inference on Discrete Models with Global Dependencies

Considering the worst-case scenario, junction tree algorithm remains the most efficient and general solution for exact MAP inference on discrete graphical models. Unfortunately, its main tractability assumption requires the treewidth of a corresponding MRF to be bounded strongly limiting the range of admissible applications. In fact, many practical problems in the area of structured prediction require modelling of global dependencies by either directly introducing global factors or enforcing global constraints on the prediction variables. This, however, always results in a fully-connected graph making exact inference by means of this algorithm intractable. Nevertheless, depending on the structure of the global factors, we can further relax the conditions for an efficient inference. In this paper we reformulate the work in [1] and present a better way to establish the theory also extending the set of handleable problem instances for free - since it requires only a simple modification of the originally presented algorithm. To demonstrate that this extension is not of a purely theoretical interest we identify one further use case in the context of generalisation bounds for structured learning which cannot be handled by the previous formulation. Finally, we accordingly adjust the theoretical guarantees that the modified algorithm always finds an optimal solution in polynomial time.
Comments on the eruption of basaltic magma at Tor Zawar, Balochistan, Pakistan on 27 January 2010, with a discussion of the geochemical and petrological constraints on its petrogenesis

Abstract An article by A. C. Kerr and coworkers published in 2010 in Mineralogical Magazine (74, 1027-1036), reviewed an event that was interpreted as the eruption of a basaltic flow on 27th January 2010, at Tor Zawar, Ziarat, Pakistan. The regional and local geology, volcanological aspects, petrography and major-element and trace-element analyses of two samples were presented. On the basis of these data it was proposed that this was a magmatic event and that the lava was derived from the mantle. On the basis of our extensive field work in the this area, and observations at the site of another similar incident that happened almost a year later (in January 2011) ∼300 m north of the first locality, we wish to clarify the geological context and propose an alternative origin. Our field observations suggest that both events were the result of localized surface melting at the base of metal electricity pylons, and their associated steel cable supports. The metal pylons and cable supports provided a path to earth for lightning discharges into the volcanic conglomerate of the Late Cretaceous Bibai Formation. We propose that this transmitted sufficient energy to melt the outcrop. We disagree with the proposal that the ‘lava flows’ at Tor Zawar were magmatic events and that the rocks formed are derived from the mantle. Alternatively, we propose that these 'lavas' were produced in a manner similar to fulgurites.
UltraProp: Principled and Explainable Propagation on Large Graphs

Given a large graph with few node labels, how can we (a) identify the mixed network-effect of the graph and (b) predict the unknown labels accurately and efficiently? This work proposes Network Effect Analysis (NEA) and U LTRA P ROP , which are based on two insights: (a) the network-effect (NE) insight: a graph can exhibit not only one of homophily and heterophily, but also both or none in a label-wise manner, and (b) the neighbor-differentiation (ND) insight: neighbors have different degrees of influence on the target node based on the strength of connections. NEA provides a statistical test to check whether a graph exhibits network-effect or not, and surprisingly discovers the absence of NE in many real-world graphs known to have heterophily. U LTRA P ROP solves the node classification problem with notable advantages: (a) Accurate , thanks to the network-effect (NE) and neighbor-differentiation (ND) insights; (b) Explainable , precisely estimating the compatibility matrix; (c) Scalable , being linear with the input size and handling graphs with millions of nodes; and (d) Principled , with closed-form formula and theoretical guarantee. Applied on eight real-world graph datasets, U LTRA P ROP outperforms top competitors in terms of accuracy and run time, requiring only stock CPU servers. On a large real-world graph with 1 . 6 M nodes and 22 . 3 M edges , U LTRA P ROP achieves ≥ 9 × speedup (12 minutes vs. 2 hours) compared to most competitors.
Reconstructing the Dynamic Sea Surface from Tide Gauge Records Using Optimal Data-Dependent Triangulations

Reconstructions of sea level prior to the satellite altimeter era are usually derived from tide gauge records; however most algorithms for this assume that modes of sea level variability are stationary which is not true over several decades. Here we suggest a method that is based on optimized data-dependent triangulations of the network of gauge stations. Data-dependent triangulations are triangulations of point sets that rely not only on 2D point positions but also on additional data (e.g. elevation, anomalies). In this article, we show how data-dependent triangulations with min-error criteria can be used to reconstruct 2D maps of the sea surface anomaly over a longer time period, assuming that height anomalies are continuously monitored at a sparse set of stations and, in addition, observations of a reference surface is provided over a shorter time period. At the heart of our method is the idea to learn a min-error triangulation based on the available reference data, and to use the learned triangulation subsequently to compute piece-wise linear surface models for epochs in which only observations from monitoring stations are given. We combine our approach of min-error triangulation with $k$-order Delaunay triangulation to stabilize the triangles geometrically. We show that this approach is advantageous for the reconstruction of the sea surface by combining tide gauge measurements with data of modern satellite altimetry. We show how to learn a min-error triangulation and a min-error $k$-order Delaunay triangulation using integer linear programming. We confront our reconstructions against the Delaunay triangulation. With real data for the North Sea we show that the min-error triangulation outperforms the Delaunay method significantly for reconstructions back in time up to 18 years, and the $k$-order Delaunay min-error triangulation even up to 21 years for $k=2$.
Relating spectral shape to cyanobacterial blooms in the Laurentian Great Lakes

A change in the spectral shape at 681 nm is used to distinguish blooms of cyanobacteria from blooms of other phytoplankton via MERIS satellite sensor imagery. During large cyanobacterial blooms, the spectral shape around 681 nm is not a positive quantity as scattering due to cyanobacteria overwhelms the fluorescence signal, thus creating a negative spectral shape. This relationship is consistent in both remotely sensed and in situ data.
Automated 3D solid reconstruction from 2D CAD using OpenCV

This work demonstrates the application of OpenCV towards feature extraction from 2D engineering drawings. The extracted features are used in the reconstruction of 3D CAD models in SCAD format and generation of 3D point cloud data that is equivalent to LIDAR scan data. Several legacy designs in mechanical and aerospace engineering are available as engineering drawings rather than software generated CAD models. Even in civil engineering, many of the earlier plans are available as drawings rather than BIM/CAD models. The methods proposed in this work can help convert such drawings easily into 3D CAD and BIM models using camera capture or scanned drawing data. The method demonstrates the applicability for simple shapes and does not yet account for the presence of hidden lines in CAD drawings. The developed codes are made available as python Jupyter notebooks through Github.
An Exponential Lower Bound to the Size of Bounded Depth Frege Proofs of the Pigeonhole Principle

We prove lower bounds of the form exp(ne d), ed > 0, on the length of proofs of an explicit sequence of tautologies, based on the Pigeonhole Principle, in proof systems using formulas of depth d, for any constant d. This is the largest lower bound for the strongest proof system, for which any superpolynomial lower bounds are known.
A time-multiplexed FPGA overlay with linear interconnect

Coarse-grained overlays improve FPGA design productivity by providing fast compilation and software like programmability. Soft processor based overlays with well-defined ISAs are attractive to application developers due to their ease of use. However, these overlays have significant FPGA resource overheads. Time multiplexed (TM) CGRA-like overlays represent an interesting alternative as they are able to change their behavior on a cycle by cycle basis while the compute kernel executes. This reduces the FPGA resource needed, but at the cost of a higher initiation interval (II) and hence reduced throughput. The fully flexible routing network of current CGRA-like overlays results in high FPGA resource usage. However, many application kernels are acyclic and can be implemented using a much simpler linear feed-forward routing network. This paper examines a DSP block based TM overlay with linear interconnect where the overlay architecture takes account of the application kernels' characteristics and the underlying FPGA architecture, so as to minimize the II and the FPGA resource usage. We examine a number of architectural extensions to the DSP block based functional unit to improve the II, throughput and latency. The results show an average 70% reduction in II, with corresponding improvements in throughput and latency.