Miracle-dz/UniFAR-FTUs huggingface dataset:
ECHNOLOGY AND THE F UTURE OF M AIN M EMORY

Over the past few decades, memory technology scaling has provided many benefits, including increased density and capacity and reduced cost. Scaling has provided these benefits for conventional technologies, such as DRAM and flash memory, but now scaling is in jeopardy. For continued scaling, systems might need to transition from conventional charge memory to emerging resistive memory. Charge memories require discrete amounts of charge to induce a voltage, which is detected during reads. In the nonvolatile space, flash memories must precisely control the discrete charge placed on a floating gate. In volatile main memory, DRAM must not only place charge in a storage capacitor but also mitigate subthreshold charge leakage through the access device. Capacitors must be sufficiently large to store charge for reliable sensing, and transistors must be sufficiently large to exert effective control over the channel. Given these challenges, scaling DRAM beyond 40 nanometers will be increasingly difficult. In contrast, resistive memories use electrical current to induce a change in atomic structure, which impacts the resistance detected during reads. Resistive memories are amenable to scaling because they don’t require precise charge placement and control. Programming mechanisms such as current injection scale with cell size. Phase-change memory (PCM), spin-torque transfer (STT) magnetoresistive RAM (MRAM), and ferroelectric RAM (FRAM) are examples of resistive memories. Of these, PCM is closest to realization and imminent deployment as a NOR flash competitor. In fact, various researchers and manufacturers have prototyped PCM arrays in the past decade. PCM provides a nonvolatile storage mechanism that is amenable to process scaling. During writes, an access transistor injects current into the storage material and thermally induces phase change, which is detected during reads. PCM, relying on analog current and thermal effects, doesn’t require control over discrete electrons. As technologies scale and heating contact areas shrink, programming current scales linearly. Researchers project this PCM scaling mechanism will be more robust than that of DRAM beyond 40 nm, and it has already been demonstrated in a 32-nm device prototype. As a scalable DRAM alternative, PCM could provide a clear road map for increasing main memory density and capacity. Providing a path for main-memory scaling, however, will require surmounting PCM’s disadvantages relative to DRAM. [3B2] mmi2010010131.3d 1/2/010 17:17 Page 131
Factors influencing community mangrove planting success on Manus Island, Papua NewGuinea.

This study evaluates the factors affecting community mangrove restoration at nine sites in eight different coastal villages of Manus Island, Papua New Guinea. Between June 2012 and April 2014, more than 8,400 mangrove seedlings of five species were planted on both restoration sites and sites with no history of mangroves. The timing of the plantings was uncontrolled, and some communities continued haphazard planting between the two periods. The success rate was highly variable and after 22 months the percentage of established plants ranged between 0 and 102%. My findings showed that the choice of genus planted, protection from wave action and the substrate were critical factors in reestablishment. Survival was highest for Rhizophora spp, at sites protected from wave action, and at locations with sand and gravel substrates. These results suggest that mangrove replanting success on Manus Island can be improved by preselecting sites and restricting plantings to Rhizophora spp.
Stochastic bounded consensus tracking of second-order multi-agent systems with measurement noises based on sampled-data with general sampling delay

This paper investigates the stochastic bounded consensus tracking problems of second-order multi-agent systems, where the control input of an agent can only use the information measured at the sampling instants from its neighbours or the virtual leader with a time-varying reference state, the measurements are corrupted by random noises and the signal sampling process induces the general sampling delay. First, the stochastic bounded consensus tracking protocol based on sampled-data with the general sampling delay is presented by using the delay decomposition technique. Second, the augmented matrix method, the probability limit theory and some other techniques are employed to derive the necessary and sufficient conditions guaranteeing the mean square bounded consensus tracking. The theoretical results show that the convergence of the proposed protocol simultaneously depends on the constant feedback gains, the network topology, the sampled period and the sampling delay, and that the static consensus tracking error depends on not only the above-mentioned factors, but also the noise intensity and the upper bound of the velocity and the acceleration of the virtual leader. The obtained results cover no sampling delay and the small sampling delay as two special cases. Simulations are provided to demonstrate the effectiveness of the theoretical results.
Compositional Pattern Producing Networks : A Novel Abstraction of Development

Natural DNA can encode complexity on an enormous scale. Researchers are attempting to achieve the same representational efficiency in computers by implementing developmental encodings , i.e. encodings that map the genotype to the phenotype through a process of growth from a small starting point to a mature form. A major challenge in in this effort is to find the right level of abstraction of biological development to capture its essential properties without introducing unnecessary inefficiencies. In this paper, a novel abstraction of natural development, called Compositional Pattern Producing Networks (CPPNs), is proposed. Unlike currently accepted abstractions such as iterative rewrite systems and cellular growth simulations, CPPNs map to the phenotype without local interaction, that is, each individual component of the phenotype is determined independently of every other component. Results produced with CPPNs through interactive evolution of two-dimensional images show that such an encoding can nevertheless produce structural motifs often attributed to more conventional developmental abstractions, suggesting that local interaction may not be essential to the desirable properties of natural encoding in the way that is usually assumed.
an algorithm to detect the irises of eyes using

— Eye localization is necessary for face recognition and related application areas. Most of eye localization algorithms reported so far still need to be improved about precision and computational time for successful applications. In this paper, we propose an eye location method based on multi-scale Gabor feature vectors, which is more robust with respect to initial points. The eye localization based on Gabor feature vectors first needs to constructs an Eye Model Bunch for each eye (left or right eye) which consists of n Gabor jets and average eye coordinates of each eyes obtained from n model face images, and then tries to localize eyes in an incoming face image by utilizing the fact that the true eye coordinates is most likely to be very close to the position where the Gabor jet will have the best Gabor jet similarity matching with a Gabor jet in the Eye Model Bunch. Similar ideas have been already proposed in such as EBGM (Elastic Bunch Graph Matching). However, the method used in EBGM is known to be not robust with respect to initial values and may need extensive search range for achieving the required performance, but extensive search ranges will cause much more computational burden. In this paper, we propose a multi-scale approach with a little increased computational burden where one first tries to localize eyes based on Gabor feature vectors in a coarse face image obtained from down sampling of the original face image, and then localize eyes based on Gabor feature vectors in the original resolution face image by using the eye coordinates localized in the coarse scaled image as initial points. Several experiments and comparisons with other eye localization methods reported in the other papers show the efficiency of our proposed method.
A Machine Learning Approach to Detection of Geomagnetically Induced Currents in Power Grids

Geomagnetically induced currents (GICs) in power grids are mainly caused by geomagnetic disturbances especially during solar storms. Such currents can potentially cause negative impacts on power grid equipment and even damage the power transformers resulting in a significant risk of blackouts. Therefore, monitoring GICs in power systems and developing solutions to mitigate their impacts before rising to a certain threatening level is urgently in need. Monitoring GICs is, however, quite a challenge and costly, as they usually appear in forms of DC components in the high voltage transmission lines, which are barely accessible through transformers. By examining the measured currents from the current transformers (CTs), this paper proposes a framework to detect GICs in power transmission systems through a hybrid time-frequency analysis combined with machining learning technology. Simulated results verify that the proposed approach can promisingly estimate GICs in power systems during a variety of grid operating conditions.
DeepAcr: Predicting Anti-CRISPR with Deep Learning

As an important group of proteins discovered in phages, anti-CRISPR inhibits the activity of the immune system of bacteria ( i.e. , CRISPR-Cas), showing great potential for gene editing and phage therapy. However, the prediction and discovery of anti-CRISPR are challenging for its high variability and fast evolution. Existing biological studies often depend on known CRISPR and anti-CRISPR pairs, which may not be practical considering the huge number of pairs in reality. Computational methods usually struggle with prediction performance. To tackle these issues, we propose a novel deep learning method for a nti- CR ISPR analysis ( DeepAcr ), which achieves impressive performance. On both the cross-fold and cross-dataset validation, our method outperforms the previous state-of-the-art methods signif-icantly. Impressively, DeepAcr improves the prediction performance by at least 40% regarding the F1 score for the cross-dataset test. Moreover, DeepAcr is the ﬁrst computational method to predict the detailed anti-CRISPR classes, which may help illustrate the anti-CRISPR mechanism. Taking advantage of a Transformer protein language model pre-trained on 250 million protein sequences, DeepAcr overcomes the data scarcity problem. Extensive experiments and analysis suggest that Transformer model feature, evolutionary feature, and local structure feature complement each other, which indicates the critical properties of anti-CRISPR proteins. Combined with AlphaFold prediction, further motif analysis and docking experiments demonstrate that DeepAcr captures the evolutionarily conserved pattern and the interaction between anti-CRISPR and the target implicitly. With the impressive prediction capability, DeepAcr can serve as a valuable tool for anti-CRISPR study and new anti-CRISPR discovery.
Learning patterns for group assignments: part 2

Teamwork and collaboration skills form an essential part of the skill-set of every professional who's not working in isolation. Group assignments are therefore included in many curricula so that the students can build up an experience with working in teams. This experience can be made explicit by describing it as collaboration learning patterns, which will help beginning students with their group assignments and also support the teachers with their supervising activities. In this paper we present six such patterns, which are part of an ongoing larger project: Manage The Project, Mediate the Dispute, Keep Motivated, Start Immediately, Regularly Check Requirements Fulfillment, and Spread Tasks Appropriately. The goal of this larger project is to work towards a pattern language for student collaboration learning.
A Survey of Intrusion Detection Systems Leveraging Host Data

This survey focuses on intrusion detection systems (IDS) that leverage host-based data sources for detecting attacks on enterprise network. The host-based IDS (HIDS) literature is organized by the input data source, presenting targeted sub-surveys of HIDS research leveraging system logs, audit data, Windows Registry, file systems, and program analysis. While system calls are generally included in audit data, several publicly available system call datasets have spawned a flurry of IDS research on this topic, which merits a separate section. Similarly, a section surveying algorithmic developments that are applicable to HIDS but tested on network data sets is included, as this is a large and growing area of applicable literature. To accommodate current researchers, a supplementary section giving descriptions of publicly available datasets is included, outlining their characteristics and shortcomings when used for IDS evaluation. Related surveys are organized and described. All sections are accompanied by tables concisely organizing the literature and datasets discussed. Finally, challenges, trends, and broader observations are throughout the survey and in the conclusion along with future directions of IDS research.
Annotating Collections of Photos Using Hierarchical Event and Scene Models

Most image annotation systems consider a single photo at a time and label photos individually. In this work, we focus on collections of personal photos and explore the associated GPS and time information for semantic annotation. First, we employ a constrained clustering method to partition a photo collection into event-based sub-collections, considering that the GPS records may be partly missing (a practical issue). We then use conditional random field (CRF) models to exploit the correlation between photos based on (1) time-location constraints and (2) the relationship between collection-level annotation (i.e., events) and image-level annotation (i.e., scenes). With the introduction of such a multi-level annotation hierarchy, our system addresses the problem of annotating consumer photo collections that requires a more hierarchical description of the customers' activities than do the simpler image annotation tasks. The efficacy of the proposed system is validated using a geotagged customer photo collection database, which consists of over 100 folders and is labeled for 12 events and 12 scenes.
Emotion Detection Through Facial Feature Recognition

Humans share a universal and fundamental set of emotions which are exhibited through consistent facial expressions. An algorithm that performs detection, extraction, and evaluation of these facial expressions will allow for automatic recognition of human emotion in images and videos. Presented here is a hybrid feature extraction and facial expression recognition method that utilizes Viola-Jones cascade object detectors and Harris corner key-points to extract faces and facial features from images and uses principal component analysis, linear discriminant analysis, histogram-of-oriented-gradients (HOG) feature extraction, and support vector machines (SVM) to train a multi-class predictor for classifying the seven fundamental human facial expressions. The hybrid approach allows for quick initial classification via projection of a testing image onto a calculated eigenvector, of a basis that has been specifically calculated to emphasize the separation of a specific emotion from others. This initial step works well for five of the seven emotions which are easier to distinguish. If further prediction is needed, then the computationally slower HOG feature extraction is performed and a class prediction is made with a trained SVM. Reasonable accuracy is achieved with the predictor, dependent on the testing set and test emotions. Accuracy is 81% with contempt, a very difficult-to-distinguish emotion, included as a target emotion and the run-time of the hybrid approach is 20% faster than using the HOG approach exclusively.
Fail-Aware Untrusted Storage §

We consider a set of clients collaborating through an online service provider that is subject to attacks, and hence not fully trusted by the clients. We introduce the abstraction of a fail-aware untrusted service, with meaningful semantics even when the provider is faulty. In the common case, when the provider is correct, such a service guarantees consistency (linearizability) and liveness (wait-freedom) of all operations. In addition, the service always provides accurate and complete consistency and failure detection. We illustrate our new abstraction by presenting a Fail-Aware Untrusted STorage service (FAUST). Existing storage protocols in this model guarantee so-called forking semantics. We observe, however, that none of the previously suggested protocols suffice for implementing fail-aware untrusted storage with the desired liveness and consistency properties (at least wait-freedom and linearizability when the server is correct). We present a new storage protocol, which does not suffer from this limitation, and implements a new consistency notion, called weak fork-linearizability. We show how to extend this protocol to provide eventual consistency and failure awareness in FAUST.
Computational Rationality : Linking Mechanism and Behavior rough Bounded Utility Maximization

We propose a framework for including information processing bounds in rational analyses. It is an application of bounded optimality (Russell & Subramanian, 1995) to the challenges of developing theories of mechanism and behavior. e framework is based on the idea that behaviors are generated by cognitive mechanisms that are adapted to the structure of not only the environment, but also the mind and brain itself. We call the framework computational rationality to emphasize the incorporation of computational mechanism into the de nition of rational action. eories are speci ed as optimal program problems, de ned by an adaptation environment, a bounded machine, and a utility function. Such theories yield different classes of explanation, depending on the extent to which they emphasize adaptation to bounds, and adaptation to some ecology that differs from the immediate local environment. We illustrate this variation with examples from three domains: visual attention in a linguistic task, manual response ordering, and reasoning. We explore the relation of this framework to existing “levels” approaches to explanation, and to other optimality-based modeling approaches.
CATA: a comprehensive chromatin accessibility database for cancer

Abstract Accessible chromatin refers to the active regions of a chromosome that are bound by many transcription factors (TFs). Changes in chromatin accessibility play a critical role in tumorigenesis. With the emergence of novel methods like Assay for Transposase-accessible Chromatin Sequencing, a sequencing method that maps chromatin-accessible regions (CARs) and enables the computational analysis of TF binding at chromatin-accessible sites, the regulatory landscape in cancer can be dissected. Herein, we developed a comprehensive cancer chromatin accessibility database named CATA, which aims to provide available resources of cancer CARs and to annotate their potential roles in the regulation of genes in a cancer type-specific manner. In this version, CATA stores 2 991 163 CARs from 23 cancer types, binding information of 1398 TFs within the CARs, and provides multiple annotations about these regions, including common single nucleotide polymorphisms (SNPs), risk SNPs, copy number variation, somatic mutations, motif changes, expression quantitative trait loci, methylation and CRISPR/Cas9 target loci. Moreover, CATA supports cancer survival analysis of the CAR-associated genes and provides detailed clinical information of the tumor samples. Database URL: CATA is available at http://www.xiejjlab. bio/cata/.
Seismic imaging and thermal modeling of active continental rifting processes in the Salton Trough, Southern California

Continental rifting ultimately creates a deep accommodation space for sediment. When a major river flows into a late-stage rift, thick deltaic sediment can change the thermal regime and alter the mechanisms of extension and continental breakup. The Salton Trough, the northernmost rift segment of the Gulf of California plate boundary, has experienced the same extension as the rest of the Gulf, but is filled to sea level by sediment from the Colorado River. Unlike the southern Gulf, seafloor spreading has not initiated. Instead, seismicity, high heat flow, and minor volcanoes attest to ongoing rifting of thin, transitional crust. Recently acquired controlled-source seismic refraction and wide-angle reflection data in the Salton Trough provide constraints upon crustal architecture and active rift processes. The crust in the central Salton Trough is only 17-18 km thick, with a strongly layered but relatively one-dimensional structure for ~100 km in the direction of plate motion. The upper crust includes 2-3 km of Colorado River sediment. The basement below the sediment is interpreted to be similar sediment metamorphosed by the high heat flow and geothermal activity. Metasedimentary rock extends to at least 7-8 km depth. A 4-5 km thick layer in the middle crust is either additional meta-sedimentary rock or stretched pre-existing continental crust. The lowermost 4-5 km of the crust is rift-related mafic magmatic material underplated from partial melting in the hot upper mantle. North American lithosphere in the Salton Trough has been almost or completely rifted apart. The gap has been filled by ~100 km of new transitional crust created by magmatism from below and sedimentation from above. These processes create strong lithologic, thermal, and rheologic layering. Brittle extension occurs within new meta-sedimentary rock. The lower crust, in comparison, stretches by ductile flow and magmatism is not localized. This seismic interpretation is also supported by 1D thermal and rheological modeling. In this passive rift driven by far-field extensional stresses, rapid sedimentation keeps the crust thick and ductile, which delays final breakup of the crust and the initiation of seafloor spreading.
Unbounded-Time Analysis of Guarded LTI Systems with Inputs by Abstract Acceleration

Reachability analysis of continuous and discrete time systems is a hard problem that has seen much progress in the last decades. In many cases the problem has been reduced to bisimulations with a number of limitations in the nature of the dynamics, soundness, or time horizon. In this article we focus on sound safety verification of UnboundedTime Linear Time-Invariant (LTI) systems with inputs using reachability analysis. We achieve this by using Abstract Acceleration, which over-approximates the reach tube of a system over unbounded time by using abstraction . The technique is applied to a number of models and the results show good performance when compared to state-of-the-art tools.