Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Early Diagnosis of Epilepsy from EEG Data

Epilepsy is one of the most common neurological disorders, affecting about 1% of the population at all ages. Detecting the development of epilepsy, i.e., epileptogenesis (EPG), before any seizures occur could allow for early interventions and potentially more effective treatments. Here, we investigate if modern machine learning (ML) techniques can detect EPG from intra-cranial electroencephalography (EEG) recordings prior to the occurrence of any seizures. For this we use a rodent model of epilepsy where EPG is triggered by electrical stimulation of the brain. We propose a ML framework for EPG identification, which combines a deep convolutional neural network (CNN) with a prediction aggregation method to obtain the final classification decision. Specifically, the neural network is trained to distinguish five second segments of EEG recordings taken from either the pre-stimulation period or the post-stimulation period. Due to the gradual development of epilepsy, there is enormous overlap of the EEG patterns before and after the stimulation. Hence, a prediction aggregation process is introduced, which pools predictions over a longer period. By aggregating predictions over one hour, our approach achieves an area under the curve (AUC) of 0.99 on the EPG detection task. This demonstrates the feasibility of EPG prediction from EEG recordings.
The innervation of the spleen

The spleen has long been known to function as a blood reservoir which pIays a significant role in the regulation of blood pressure and volume flow. The gross features of its anatoniy have been adequately described, but the anatomy and physiology of the vascular bed in the red pulp area have been subjects of controversy for nearly 100 years. The recent development of a technic of transillumination has made possible a much more adequate visualization and, consequently, a better understanding of both the structural and functional details of this so-called intermediary circulation. Study of these findings emphasizes the need of more accurate knowledge of the innervation of these vascular structures which play so prominent a role in the function of the spleen. Previous accounts of the innervation of the spleen have been limited almost exclusively to the extrinsic nerves and their courses through the splenic mesentery. Recorded accounts of the intrinsic nerves afford no satisfactory description of their distribution and modes of termination. At the suggestion of Prof. Albert Kuntz, whose helpful criticism and encouragement are hereby acknowledged, the present investigation has been undertaken to obtain more complete data regarding the anatomical relationships of the splenic nerves. Particular attention has been given to their distribution within the spleen and especially to their relationships to the intermediary vascular bed, in order to provide a structural basis for the functional interpretation of the peculiar circulatory phenomena within the red pulp.
Stochastic Optimization for Performative Prediction

In performative prediction, the choice of a model influences the distribution of future data, typically through actions taken based on the model's predictions. We initiate the study of stochastic optimization for performative prediction. What sets this setting apart from traditional stochastic optimization is the difference between merely updating model parameters and deploying the new model. The latter triggers a shift in the distribution that affects future data, while the former keeps the distribution as is. Assuming smoothness and strong convexity, we prove non-asymptotic rates of convergence for both greedily deploying models after each stochastic update (greedy deploy) as well as for taking several updates before redeploying (lazy deploy). In both cases, our bounds smoothly recover the optimal $O(1/k)$ rate as the strength of performativity decreases. Furthermore, they illustrate how depending on the strength of performative effects, there exists a regime where either approach outperforms the other. We experimentally explore this trade-off on both synthetic data and a strategic classification simulator.
Learning to Play by Imitating Humans

Acquiring multiple skills has commonly involved collecting a large number of expert demonstrations per task or engineering custom reward functions. Recently it has been shown that it is possible to acquire a diverse set of skills by self-supervising control on top of human teleoperated play data. Play is rich in state space coverage and a policy trained on this data can generalize to specific tasks at test time outperforming policies trained on individual expert task demonstrations. In this work, we explore the question of whether robots can learn to play to autonomously generate play data that can ultimately enhance performance. By training a behavioral cloning policy on a relatively small quantity of human play, we autonomously generate a large quantity of cloned play data that can be used as additional training. We demonstrate that a general purpose goal-conditioned policy trained on this augmented dataset substantially outperforms one trained only with the original human data on 18 difficult user-specified manipulation tasks in a simulated robotic tabletop environment. A video example of a robot imitating human play can be seen here: this https URL
Predicting Project Health for Open Source Projects (using the DECART Hyperparameter Optimizer)

Software developed on public platforms is a source of data that can be used to make predictions about those projects. While the activity of a single developer may be random and hard to predict, when large groups of developers work together on software projects, the resulting behavior can be predicted with good accuracy. To demonstrate this, we use 78,455 months of data from 1,628 GitHub projects to make various predictions about the current status of those projects (as of April 2020). We find that traditional estimation algorithms make many mistakes. Algorithms like k-nearest neighbors (KNN), support vector regression (SVR), random forest (RFT), linear regression (LNR), and regression trees (CART) have high error rates (usually more than 50% wrong, sometimes over 130% wrong, median values). But that error rate can be greatly reduced using the DECART hyperparameter optimization. DECART is a differential evolution (DE) algorithm that tunes the CART data mining system to the particular details of a specific project. To the best of our knowledge, this is the largest study yet conducted, using the most recent data, for predicting multiple health indicators of open-source projects. Further, due to our use of hyperparameter optimization, the median predicting error of our predictions usually less than 10% which is much smaller than the errors seen in related work. Our results are a compelling argument for open-sourced development. Companies that only build in-house proprietary products may be cutting themselves off from the information needed to reason about those projects.
STONNE: A Detailed Architectural Simulator for Flexible Neural Network Accelerators

The design of specialized architectures for accelerating the inference procedure of Deep Neural Networks (DNNs) is a booming area of research nowadays. First-generation rigid proposals have been rapidly replaced by more advanced flexible accelerator architectures able to efficiently support a variety of layer types and dimensions. As the complexity of the designs grows, it is more and more appealing for researchers to have cycle-accurate simulation tools at their disposal to allow for fast and accurate design-space exploration, and rapid quantification of the efficacy of architectural enhancements during the early stages of a design. To this end, we present STONNE (Simulation TOol of Neural Network Engines), a cycle-accurate, highly-modular and highly-extensible simulation framework that enables end-to-end evaluation of flexible accelerator architectures running complete contemporary DNN models. We use STONNE to model the recently proposed MAERI architecture and show how it can closely approach the performance results of the publicly available BSV-coded MAERI implementation. Then, we conduct a comprehensive evaluation and demonstrate that the folding strategy implemented for MAERI results in very low compute unit utilization (25% on average across 5 DNN models) which in the end translates into poor performance.
Formal Verification of Access Control Model for My Health Record System

My Health Record system is the Australian Govern-ment‘s digital health record system that holds My Health Record. My Health Record is a secure online health record containing consumers' health information. The system aims to provide health care professionals with access to key health information (e.g. listing medicines, allergies and key diagnoses), radiology and pathology test results. The system (previously named Personally Controlled Electronic Health Record) enables consumers to decide how to share information with any of their health care providers who are registered and connected to the system. The My Health Record system operates under the Australian legislative framework My Health Records Act 2012. The Act establishes, inter alia, a privacy framework specifying which entities can collect, use and disclose certain information in the system and the penalties that can be imposed on improper collection, use and disclosure of this information. This paper presents the formal specification (from the legislation) and verification of the My Health Record regarding how consumers can control who access the information, and how the system adheres to such access. We rely on the correct-by-construction Event-B method to prove control and access properties of the system.
A general framework for label-efficient online evaluation with asymptotic guarantees

Achieving statistically significant evaluation with passive sampling of test data is challenging in settings such as extreme classification and record linkage, where significant class imbalance is prevalent. Adaptive importance sampling focuses labeling on informative regions of the instance space, however it breaks data independence assumptions - commonly required for asymptotic guarantees that assure estimates approximate population performance and provide practical confidence intervals. In this paper we develop an adaptive importance sampling framework for supervised evaluation that defines a sequence of proposal distributions given a user-defined discriminative model of p(y|x) and a generalized performance measure to evaluate. Under verifiable conditions on the model and performance measure, we establish strong consistency and a (martingale) central limit theorem for resulting performance estimates. We instantiate our framework with worked examples given stochastic or deterministic label oracle access. Both examples leverage Dirichlet-tree models for practical online evaluation, with the deterministic case achieving asymptotic optimality. Experiments on seven datasets demonstrate an average mean-squared error superior to state-of-the-art samplers on fixed label budgets.
Incorporating User Micro-behaviors and Item Knowledge into Multi-task Learning for Session-based Recommendation

Session-based recommendation (SR) has become an important and popular component of various e-commerce platforms, which aims to predict the next interacted item based on a given session. Most of existing SR models only focus on exploiting the consecutive items in a session interacted by a certain user, to capture the transition pattern among the items. Although some of them have been proven effective, the following two insights are often neglected. First, a user's micro-behaviors, such as the manner in which the user locates an item, the activities that the user commits on an item (e.g., reading comments, adding to cart), offer fine-grained and deep understanding of the user's preference. Second, the item attributes, also known as item knowledge, provide side information to model the transition pattern among interacted items and alleviate the data sparsity problem. These insights motivate us to propose a novel SR model MKM-SR in this paper, which incorporates user Micro-behaviors and item Knowledge into Multi-task learning for Session-based Recommendation. Specifically, a given session is modeled on micro-behavior level in MKM-SR, i.e., with a sequence of item-operation pairs rather than a sequence of items, to capture the transition pattern in the session sufficiently. Furthermore, we propose a multi-task learning paradigm to involve learning knowledge embeddings which plays a role as an auxiliary task to promote the major task of SR. It enables our model to obtain better session representations, resulting in more precise SR recommendation results. The extensive evaluations on two benchmark datasets demonstrate MKM-SR's superiority over the state-of-the-art SR models, justifying the strategy of incorporating knowledge learning.
Structural Patterns and Generative Models of Real-world Hypergraphs

Graphs have been utilized as a powerful tool to model pairwise relationships between people or objects. Such structure is a special type of a broader concept referred to as hypergraph, in which each hyperedge may consist of an arbitrary number of nodes, rather than just two. A large number of real-world datasets are of this form - for example, lists of recipients of emails sent from an organization, users participating in a discussion thread or subject labels tagged in an online question. However, due to complex representations and lack of adequate tools, little attention has been paid to exploring the underlying patterns in these interactions. In this work, we empirically study a number of real-world hypergraph datasets across various domains. In order to enable thorough investigations, we introduce the multi-level decomposition method, which represents each hypergraph by a set of pairwise graphs. Each pairwise graph, which we refer to as a k-level decomposed graph, captures the interactions between pairs of subsets of k nodes. We empirically find that at each decomposition level, the investigated hypergraphs obey five structural properties. These properties serve as criteria for evaluating how realistic a hypergraph is, and establish a foundation for the hypergraph generation problem. We also propose a hypergraph generator that is remarkably simple but capable of fulfilling these evaluation metrics, which are hardly achieved by other baseline generator models.
A Face Preprocessing Approach for Improved DeepFake Detection

Recent advancements in content generation technologies (also widely known as DeepFakes) along with the online proliferation of manipulated media and disinformation campaigns render the detection of such manipulations a task of increasing importance. There are numerous works related to DeepFake detection but there is little focus on the impact of dataset preprocessing on the detection accuracy of the models. In this paper, we focus on this aspect of the DeepFake detection task and propose a preprocessing step to improve the quality of training datasets for the problem. We also examine its effects on the DeepFake detection performance. Experimental results demonstrate that the proposed preprocessing approach leads to measurable improvements in the performance of detection models.
Growth dynamics and agronomic-economic benefits of pea–oat and pea–barley intercrops

Abstract. Pea (Pisum arvense L.) is an important legume in many areas of the world, which is used for forage and grain production and could be used in intercropping systems. Intercropping of pea with oat (Avena sativa L.) and barley (Hordeum vulgare L.), in two seeding ratios 60 : 40 and 80 : 20, was compared with pea and two cereal monocrops for two growing seasons (2008–10), at the Aristotle University of Thessaloniki, Greece. The effect of the intercropping systems was determined on growth rate, plant height, chlorophyll content, DM, and N yield. Also, several competition and economic indices were used to evaluate the intercropping systems, such as land equivalent ratio (LER), relative crowding coefficient (K), aggressivity (A), competitive ratio (CR), actual yield loss (AYL), system productivity index (SPI), monetary advantage index (MAI), and intercropping advantage (IA). Growth rate of pea and cereals was lower by an average of 39 and 64%, respectively, in the intercrops than in the monocrops. DM yield was the highest in barley monocrop (13.00 Mg ha–1) followed by P80O20 intercrop (11.73 Mg ha–1). Pea monocrop, and P80O20 and P80B20 intercrops showed the highest crude protein (CP) concentration (137, 132 and 130 g kg–1 DM, respectively), whereas P80O20 intercrop also produced the highest CP yield (1552 kg ha–1). The LER, K, and AYL values (average 1.09, 1.75 and 0.29, respectively), were greater for both pea-oat intercrops compared with the pea-barley intercrops (average 0.98, 0.92 and 0.06, respectively), indicating that in these systems there was an advantage of intercropping for exploiting the resources of the environment. The A, CR, and partial AYL values in all intercrops were greater for oat and barley than pea, which indicated that cereals were more competitive partners than pea. The highest MAI, IA, and SPI values were recorded for P80O20 followed by P60O40 intercrops indicating that these intercropping systems were the most profitable. The results from this study showed that both pea-oat intercrops were more productive with high CP yield, and also they showed the best land-use efficiency.
Andreev Reflection at Hadron/Color Superconductor Interface

We consider the phenomenon of the Andreev reflection of "hadrons" at the interface between hadronic and color superconducting phases, which are expected to appear in the neutron star interior. Here, hadrons are defined as a superposition of constituent quarks, each of which is Andreev-reflected. We study what kind of reflections are possible to come out of incident mesons and baryons in the hadronic phase, attached to different color superconducting phases. Then, some peculiar patterns of the reflections are obtained.
Resource dependency and survivability in complex networks

Components in many real-world complex systems depend on each other for the resources required for survival and may die of a shortage. These patterns of dependencies often take the form of a complex network whose structure potentially affects how the resources produced in the system are efficiently shared among its components, which in turn decides a network's survivability. Here we present a simple threshold model that provides insight into this relationship between the network structure and survivability. We show that, as a combined effect of local sharing and finite lifetime of resources, many components in a complex system may die of lack of resources even when a sufficient amount is available in the system. We also obtain a surprising result that although the scale-free networks exhibit a significantly higher survivability compared to their homogeneous counterparts, a vertex in the latter survives longer on average. Finally, we demonstrate that the system's survivability can be substantially improved by changing the way vertices distribute resources among the neighbors. Our work is a step towards understanding the relationship between intricate resource dependencies present in many real-world complex systems and their survivability.
Floristic analyses of British mires and mire communities.

(1) Floristic data were collected from forty quadrats (1 x 1 m) at each of fifty-six mire sites in Great Britain. These data were used to derive ordination scatter diagrams (using reciprocal averaging) and classification hierarchies (using indicator-species analysis) at both site and community levels of differentiation. (2) Ordination of the sites showed a major line of variation from ombrotrophic mires with acid waters and peats to geotrophic sites with circumneutral waters and peats. In floristic terms, these two divisions were reflected in the types of plant comprising the majority of species recorded: in ombrotrophic mires bryophytes were more numerous, and in geotrophic sites vascular plants predominated. A second line of variation, shown particularly among ombrotrophic sites, was from wet sites with extensive pool systems to those in which pools were absent and surface peats were drier. (3) Different floristic groupings corresponded with different mire types as defined on the basis of hydrological, morphological and chemical criteria: this was shown particularly in the correspondence of floristic classification units and hydromorphological m,ire types. (4) Classification of individual quadrats to produce floristically similar units (noda) again revealed a major line of variation, from those noda found in the most acid peats to those of more basic, mineral-enriched locations. The change from one extreme to the other was, for the most part, continuous, with a number of noda containing common species, the proportions of which varied from one nodum to another. (5) By combining site and nodum data, a clearer picture emerged of the changes taking place between the extremes. In the most acid and the most basic sites there were high proportions of quadrats falling into a few noda, but in the intermediate sites a large number of noda were represented, each by few quadrats. (7) A series was produced which linked floristic classifications on the basis of speciescontent of sites and the proportions of different communities (noda) within sites, and related these to hydromorphological mire types and overall chemical conditions, as indicated by pH. By the use of the classification keys produced by indicator-species analysis new sites may be fitted into this graded series without recourse to re-analysis of the full data-set for all sites. The assigning of a new site to indicate its relationships with other British mires may be by use of species-content of the site as a whole, or on the basis of the proportions of different noda present within that site.
DEVELOPMENTAL GENETICS IN BARLEY: A MUTANT FOR STOMATAL DEVELOPMENT

A B S T R A C T The barley mutants eceriferum-g, which have wax deficiencies in several organs, show an abnormal stomatal development as a pleiotropic effect. The main abnormalities are double and triple stomatal complexes. Minor alterations are present as extra cells that alter the normal sequence of the stomatal row and abnormal subsidiary cells that are either larger, smaller, or displaced from their normal position. These characteristics are present in the 12 independently isolated mutants of the g locus, but could not be found in any of the other eceriferum mutants studied. The morphogenetic origin and a developmental sequence for the abnormalities are postulated. THE UNDERSTANDING of gene action in higher plants has been hindered by the formidable number of steps that separate the final expression of a character from the primary action of genes and enzymes. The study of morphological mutants
Deep Transfer Learning with Ridge Regression

The large amount of online data and vast array of computing resources enable current researchers in both industry and academia to employ the power of deep learning with neural networks. While deep models trained with massive amounts of data demonstrate promising generalisation ability on unseen data from relevant domains, the computational cost of finetuning gradually becomes a bottleneck in transfering the learning to new domains. We address this issue by leveraging the low-rank property of learnt feature vectors produced from deep neural networks (DNNs) with the closed-form solution provided in kernel ridge regression (KRR). This frees transfer learning from finetuning and replaces it with an ensemble of linear systems with many fewer hyperparameters. Our method is successful on supervised and semi-supervised transfer learning tasks.