Miracle-dz/UniFAR-FTUs huggingface dataset:
Performance and structural coverage of the latest, in-development AlphaFold model

The introduction of AlphaFold 2 (Jumper et al., 2021) has spurred a revolution in modelling the structure of proteins and their interactions, enabling a huge range of applications in protein modelling and design (Kreitz et al., 2023; Lim et al., 2023; Mosalaganti et al., 2022). In this note, we report on our progress on a new iteration of AlphaFold modelling that greatly expands the range of applicability of the method and is capable of joint structure prediction of complexes including proteins, nucleic acids, small molecules, ions, and modified residues. The new AlphaFold model demonstrates greatly improved accuracy over previous specialist tools in the majority of cases: far greater accuracy on protein-ligand interactions than state of the art docking tools, much higher accuracy on protein-nucleic acid interactions than specialist predictors like RoseTTA2FoldNA (Baek et al., 2022), and significantly higher antibody-antigen prediction accuracy than AlphaFold-Multimer (Evans et al., 2021). In this results-only progress report, we show quantitative benchmarks and highlight a number of specific high-accuracy predictions on recently solved structures. We believe that these results ultimately point to the achievability of atomically-accurate structure prediction for the full range of biomolecular interactions across the PDB within an AlphaFold framework.
Discrimination of Depositional Environment Using Statistical Parameters of Conglomeratic Outcrop Sections, Obubra Southeastern Nigeria

Statistical analyses reveal information of the hydrodynamics and environments of deposition of sedimentary rocks. The sediments of the outcrop sections from Uwakande 1, Obubra southeastern Nigeria were studied for their textural variations. Grain size analysis, and pebble morphogenesis carried out shows that the sediments are coarse to very coarse in size, poorly sorted, and very positively skewed. This in conjunction with the various bivariate plots employed, aid in discriminating between beach and river depositional environments. Pebble morphometric analysis of the conglomerates showed that the mean values of the various morphometric parameters range as follows: flatness ratio (S/L = 0.397 to 0.514), elongation ratio (I/L = 0.657 to 0.784), maximum projection sphericity (ΨP = 0.605 to 0.899), Oblate-Prolate index (ŌP = -1.738 to 1.594), coefficient of flatness (39.70 to 51.37), and roundness (-0.367 to 0.722). This study is significant in providing evidence of fluvial conditions during the deposition of the Eze-Aku Formation in the Turonian, and reveals the type of transporting medium; the mode of sediment deposition; as well as the environment into which the sediments encountered in the study area were deposited. Original Research Article Onu et al.; BJAST, 6(1): 24-33, 2015; Article no.BJAST. 2015.064 25
A strategy for selecting optimal vergence pose and calibration patterns for stereo-vision calibration

The usage of stereo-vision in Automobile, Automation, machine-vision, Infotainment etc. is growing these days enormously day by day. To design a stereo-vision camera, we need to know optimal vergence pose between two cameras as well as number of stereo-pairs required for calibration so that stereo-vision can yield dense disparity map, precise 3D reconstruction, higher stereo-calibration accuracy as well as lower calibration time. This paper addresses these problems of stereo-vision prior to calibration by selecting optimal vergence pose between two cameras and optimal number of stereo-calibration pattern pairs for calibration. To estimate optimal vergence pose, a set of virtual stereo-calibrations patterns are generated by varying Euler angles and it is observed that stereo-reprojection error decreases as Roll angle reduces and exhibits mirror image property and is less sensitive to the variations in Yaw and Pitch. Also to estimate the optimal number of stereo-calibration pattern pairs, an epipolar constraint error was used in eliminating redundant pairs thereby reducing stereo-calibration time and lower stereo-reprojection error.
1 Forecasting House Prices for the Four Census Regions and the Aggregate US Economy in a Data-Rich Environment

This paper considers the ability of large-scale (involving 145 fundamental variables) time-series models, estimated based on dynamic factor analysis and Bayesian shrinkage, to forecast real house price growth rates of the four US census regions and the aggregate US economy. Besides, the standard Minnesota prior, we also use additional priors that constrain the sum of coefficients of the VAR models. We compare oneto twenty four-months-ahead forecasts of the large-scale models over an out-of-sample horizon of 1995:1-2009:3, based on an insample of 1968:2-1994:12, relative to a random walk model, a small-scale VAR model comprising of just the five real house price growth rates, and a medium-scale VAR model containing 36 of the 145 fundamental variables besides the 5 real house price growth rates. In addition to the forecast comparison exercise across small-, mediumand largescale models, we also look at the ability of the “optimal” model (i.e., the model that produces the minimum average mean squared forecast error (MSFE)) for a specific region, in predicting ex ante real house prices (in levels) over the period of 2009:4 till 2012:2. Factor-based models (classical or Bayesian) performs the best for the North East, Mid-West, West census regions and the aggregate US economy, and equally as well to a small-scale VAR for the South region. The “optimal” factor models also tend to predict the downward trend in the data when we conduct an ex ante forecasting exercise. Our results highlight the importance of information content in large number of fundamentals in predicting house prices accurately.
Heuristic Planning for Hybrid Dynamical Systems with Constraint Logic Programming

We explore how planning for near optimal behaviors of mixed discrete-continuous systems can be done by deductive reasoning. For reasoning to be efficient, it must be properly controlled. It is surprising and mathematically non-trivial fact that this control can be achieved in a domain-independent way for a large class of planning domains with non-linear continuous processes. Moreover, in contrast to other planners for hybrid dynamical systems that use the single-model approaches and have to instantiate all actions and fluents before search starts, our planner is lifted, namely it works directly with action schemas. Following Constraint Logic Programming framework, it delegates solution of the optimization problem with respect to numerical and temporal constraints to an external numerical optimization solver. We discuss that our theorem-proving based planner for hybrid systems achieves performance competitive with the state-of-the-art approaches to temporal numerical planning on several well-known benchmarks. We argue that our deductive approach may have potential to compete with existing operation research techniques when solving large-scale optimization problems for relational hybrid systems.
Estimation of Vegetative Characteristics by Remote Sensing

Measurements of biomass, leaf area and carbon cycling rates are important components for understanding ecosystem processes. While other techniques are available to measure these components, remote sensing has the advantages of non-destructive sampling and sampling at large scales. An experiment was conducted from June through mid-October of 2006 to estimate gross canopy photosynthesis (GCP), leaf area index (LAI) and total aboveground biomass of an ungrazed tallgrass prairie with a hyperspectral radiometer and compare these estimates to flux chamber measurements. Four indices (Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), (Rnir/Rrededge)-1, and (Rnir/Rgreen)-1 were used to predict LAI and total biomass. Six indices (NDVI, SR, [(Rnir/Rrededge)-1]*photosynthetically active radiation (PAR),[(Rnir/Rgreen)-1]*PAR, Photochemical Reflectance Index (PRI) and the Water Band Index (WBI)) were used to derive an estimate of GCP. All indices had poor correlations to LAI and total aboveground biomass. However, GCP was significantly correlated to all six indices utilized in this study. While GCP measured from June-October was significantly correlated to all indices, removal of the October scans greatly increased the accuracy of all models except the PRI. This study demonstrates the strong relationship between GCP and spectral reflectance in the tallgrass prairie. These relationships may be further utilized with other methods of measuring carbon exchange to gain greater understanding of larger scale ecosystem processes.
Behavioral / Systems / Cognitive Information Processing Architecture of Functionally Defined Clusters in the Macaque Cortex

Kelly Shen,1 Gleb Bezgin,1 R. Matthew Hutchison,2,3 Joseph S. Gati,3 Ravi S. Menon,2,3 Stefan Everling,2,3 and Anthony R. McIntosh1,4 1Rotman Research Institute, Baycrest, Toronto, Ontario, M6A 2E1, Canada, 2Department of Physiology and Pharmacology and 3Robarts Research Institute, Western University, London, Ontario, N6A 5K8, Canada, and 4Department of Psychology, University of Toronto, Toronto, Ontario, M5S 3G3, Canada
Learning and disrupting invariance in visual recognition

Learning by temporal association rules such as Foldiak’s trace rule [1] is an attractive hypothesis that explains the development of invariance in visual recognition. Consistent with these rules, several recent experiments have shown that invariance can be broken by appropriately altering the visual environment but found puzzling differences in the effects at the psychophysical [2, 3] versus single cell [4, 5] level. We show a) that associative learning provides appropriate invariance in models of object recognition inspired by Hubel and Wiesel [6], b) that we can replicate the ”invariance disruption” experiments using these models with a temporal association learning rule to develop and maintain invariance, and c) that we can thereby explain the apparent discrepancies between psychophysics and singe cells effects. We argue that these models account for the stability of perceptual invariance despite the underlying plasticity of the system, the variability of the visual world and expected noise in the biological mechanisms.
2017) The power of monitoring: optimizing survey designs to detect occupancy changes in a rare amphibian population.

Biodiversity conservation requires reliable species assessments and rigorously designed surveys. However, determining the survey effort required to reliably detect population change can be challenging for rare, cryptic and elusive species. We used a tropical bromeliad-dwelling frog as a model system to explore a cost-effective sampling design that optimizes the chances of detecting a population decline. Relatively few sampling visits were needed to estimate occupancy and detectability with good precision, and to detect a 30% change in occupancy with 80% power. Detectability was influenced by observer expertise, which therefore also had an effect on the sampling design – less experienced observers require more sampling visits to detect the species. Even when the sampling design provides precise parameter estimates, only moderate to large changes in occupancy will be detected with reliable power. Detecting a population change of 15% or less requires a large number of sites to be surveyed, which might be unachievable for range-restricted species occurring at relatively few sites. Unless there is high initial occupancy, rare and cryptic species will be particularly challenging when it comes to detecting small population changes. This may be a particular issue for long-term monitoring of amphibians which often display low detectability and wide natural fluctuations. The global biodiversity crisis has driven the development of increasingly sophisticated databases, such as the Living Planet Index 1 and the IUCN Red List for Threatened Species 2 , which require reliable baseline information on species, habitats and population trends. Although monitoring data are of increasing value to conservation managers, population and status assessments are currently limited by the lack of data 3 , resulting in poor evidence for conservation practitioners. Monitoring programmes must inform decision-making through the application of reliable survey design and statistical analysis – otherwise they will be an ineffective use of resources. Conservationists must therefore develop projects with clear objectives 4 and provide appropriate sampling designs 5,6 with sufficient statistical power to reliably describe population trends
ACM/IEEE 17th International Conference on Model Driven Engineering Languages and Systems

Since the launch of the Agile Manifesto there has been numerous propositions in how to combine agile practices with Model-driven Development. Ideally the combination would give the benefits of agile – e.g. rapid response to changes and shorter lead times – with the promises of Model-driven development – such as high-level designs and automation. A commonality among the proposals is that they lack in empirical evaluation. Our contribution is a systematic literature review to find out to what experiences there are of Model-driven Agile Development, MAD, from an empirical context. Among our conslusions is that MAD is sitll an immature research area and that more experience reports from industry are needed before we can claim to have understood the possibilities and drawbacks of MAD.
Delta Oscillation Coupled Propagating Fast Ripples Precede Delta Oscillation Coupled Propagating Fast Ripples Precede Epileptiform Discharges in Patients With Focal Epilepsy Epileptiform Discharges in Patients With Focal Epilepsy

Epileptiform spikes are used to localize epileptogenic brain tissue. The mechanisms that spontaneously trigger epileptiform discharges are not yet elucidated. Pathological fast ripple (FR, 200 – 600 Hz) are biomarkers of epileptogenic brain, and we postulated that FR network interactions are involved in generating epileptiform spikes. Using macroelectrode stereo intracranial EEG (iEEG) recordings from a cohort of 46 patients we found that, in the seizure onset zone (SOZ), propagating FR were more often followed by an epileptiform spike, as compared with non-propagating FR ( p < 0.05). Propagating FR had a distinct frequency and larger power ( p < 1e-10) and were more strongly phase coupled to the peak of iEEG delta oscillation, which likely correspond with the DOWN states during non-REM sleep (p < 1e-8), than non-propagating FR. While FR propagation was rare, all FR occurred with the highest probability within + / (cid:0) 400 msec of epileptiform spikes with superimposed high-frequency oscillations ( p < 0.05). Thus, a sub-population of epileptiform spikes in the SOZ, are preceded by propagating FR that are coordinated by the DOWN state during non-REM sleep.
Assessing SARAL/AltiKa Data in the Coastal Zone: Comparisons with HF Radar Observations

We present an initial assessment of SARAL/AltiKa data in the coastal band. The study focuses on the Ibiza Channel where the north-south water exchanges play a key role in controlling the circulation variability in the western Mediterranean. In this area, the track 16 of SARAL/AltiKa intercepts the domain covered by a coastal high-frequency (HF) radar system, which provides surface currents with a range up to 60 km. We evaluate the performance of the SARAL/AltiKa Ssalto/Duacs delayed-time along-track products compared to the HF radar surface velocity fields. SARAL/AltiKa data are retrieved at a distance of only 7 km from the coast, putting in evidence the emerging capabilities of the new altimeter. The derived velocities resolved the general features of the seasonal mesoscale variability with reasonable agreement with HF radar fields (significant correlations of 0.54). However, some discrepancies appear, which might be caused by instrumental hardware radar errors, ageostrophic velocities as well as inaccurate corrections and editing in the altimeter data. Root mean square (rms) differences between the estimated SARAL/AltiKa and the HF radar velocities are about 13 cm/s. These results are consistent with recent studies in other parts of the ocean applying similar approaches to Topex/Poseidon and Jason-1 missions and using coastal altimeter corrections.
Chapter 1 Morphology and Origin of the Osbourn Trough

The Osbourn Trough is a 900 km long, east-west trending gravity low, visible in satellite altimetry data, which intersects the Tonga Trench at 2530’S. A recent survey collected gravity, magnetic, echo sounder, and swath bathymetry data on three adjacent, north-south trending ship tracks centered on the trough. The linear gravity low is 20–30 mGal less than the regional value and is accompanied by a flat-lying, 200–500 m deep sediment-filled valley. Swath bathymetry images reveal several parallel, east-west trending linear ridges and valleys on either side of the main trough as well as other morphologic features characteristic of relict spreading centers, including a prominent inside corner high and possible pseudo-fault trace. The presence of magnetic anomalies (possibly anomalies 33 and 32) suggests that the seafloor here was formed after the end of the Cretaceous Normal Superchron (anomaly 34). These data support the conclusion that this trough is a spreading center, which stopped spreading in Late Cretaceous time. The existence of this feature has important implications for tectonic reconstructions in this region. The Osbourn Trough could be part of the fossil spreading center between the Pacific Plate and a fragment of the Phoenix Plate, the Bellingshausen Plate. This would require the seafloor between the Osbourn Trough and the Chatham Rise to the south to be a remnant fragment of the Bellingshausen Plate. This remnant may have joined to the Pacific Plate when the Hikurangi Plateau entered the Gondwana subduction zone at the Chatham Rise possibly causing the cessation of spreading on the Osbourn Trough.
The Composite Control Method for the GDI Engine Idle Speed Control

Engine idle control is related to the engine’s fuel consumption and exhaust-gas pollution level, effective idle speed control (ISC) can improve fuel economy and reduce exhaust emissions. Based on the Gasoline Direct Injection engine (GDI) as the research object, the average value of three-state kinetic model is adopted to construct four-stroke gasoline engine dynamics model. The composite controller is proposed to control the engine model, the throttle valve and the spark advance angle are selected as control variable. Fuzzy self-turning PID control mode as feedback controller is presented to control the speed error. The neural network prediction algorithm combines the advantages of predictive control and advance regulation with the advantages of neural network infinite approximation, which has a great effect on improving the following and anti-interference of the system. The simulation results show that the idle speed ripple value is less than 8rpm when the sudden variable load is add to engine, and engine idle speed fluctuation with controlling the throttle value and the spark advance angle is reduced. This control method has better performance than only by controlling the throttle value. Keywords—Gasoline engine, fuzzy self-tuning PID, idle speed, self-adaptive neural network.
1 PREDICTION OF SLUMP GENERATED TSUNAMIS : THE JULY 17 TH 1998 PAPUA NEW GUINEA EVENT

The local tsunami of July 17 1998 that struck Papua New Guinea is most probably the result of an offshore sediment slump. This conclusion is based on multibeam bathymetric data, visual observation of the seabed, and sediment piston cores. Offshore data has been utilised to interpret the tectonic framework of the area off northern PNG, the local sedimentary regime and, from this and onshore evidence of sediment dispersal a model of sediment failure has been constructed. Utilising the seabed observations, the distribution of seabed failure has been mapped and the tsunami source location accurately identified. Fluid expulsion from the sediment has been assessed from the presence of authigenic carbonates and chemosynthetic communities. It is proposed that multibeam bathymetry, sediment sampling, and visual seabed observation are critical in the identification of coastlines vulnerable to tsunami attack and, in combination with onshore studies and numerical simulations, tsunami prone areas may be identified. Ultimately, as more case studies are undertaken, underwater landslide prediction may at last become possible.
If a Tree Falls in a Forest

lightning that struck its first victim crackling lovingly through the dead of winter’s brutal chill, shrieks of an aghast newborn as it broadcasts life for the first time through tiny lungs, sighs of shade as it coos gently at the heat-stroked, chirps of birds and the howls of apes, furious clicking and scribbling on sheets that determine humankind’s future, the gasp of life inhaled in the wind, groans of industrialization and agricultural means, the first juicy crunch of sin that casted Adam and Eve to the depths of Earth.
Edinburgh Research Explorer Recovery of muscle strength and power after limb-lengthening surgery

Barker KL, Lamb SE, Simpson HRW. Recovery of muscle strength and power after limb-lengthening surgery. Arch Phys Med Rehabil 2010;91:384-8. Objective: To report muscle strength, power, and function after limb-lengthening surgery performed by using the Ilizarov technique. Design: Prospective, longitudinal observational study of a cohort of consecutive patients who underwent limb-lengthening distraction followed up for 2 years after surgery. Setting: National Health Service hospital specializing in orthopedic surgery. Participants: Patients (N (cid:1) 16) who had undergone limb-lengthening surgery performed by using the Ilizarov method (11 men, 5 women; mean age (cid:1) 27y; range, 13–56y). Interventions: None.
Edinburgh Research Explorer A Deep DUAL-PATH Network for Improved Mammogram Image Processing

We present, for the ﬁrst time, a novel deep neural network architecture called D UAL C ORE N ET with a dual-path connection between the input image and output class label for mammogram image processing. This architecture is built upon U-Net, which non-linearly maps the input data into a deep latent space. One path of the D UAL C ORE N ET , the locality preserving learner, is devoted to hierarchically extracting and exploiting intrinsic features of the input, while the other path, called the conditional graph learner, focuses on modeling the input-mask correlations. The learned mask is further used to improve classiﬁcation results, and the two learning paths complement each other. By integrating the two learners our new architecture provides a simple but effective way to jointly learn the segmentation and predict the class label. Beneﬁt-ing from the powerful expressive capacity of deep neural networks a more discriminative representation can be learned, in which both the semantics and structure are well preserved. Experimental results show that D UAL C ORE N ET achieves the best mammography segmentation and classiﬁcation simultaneously, outperforming recent state-of-the-art models.