nils-herrmann/clean_scirepeval huggingface dataset:
Affective Computing by Rosalind

Computers are beginning to acquire the ability to express and recognize affect, and may soon be given the ability to " have emotions. " The essential role of emotion in both human cognition and perception, as demonstrated by recent neurological studies, indicates that affective computers should not only provide better performance in assisting humans, but also might enhance computers' abilities to make decisions. This paper presents and discusses key issues in " affective computing, " computing that relates to, arises from, or influences emotions. Models are suggested for computer recognition of human emotion, and new applications are presented for computer-assisted learning, perceptual information retrieval, arts and entertainment, and human health and interaction. Affective computing, coupled with new wear-able computers, will also provide the ability to gather new data necessary for advances in emotion and cog-nition theory. Nothing in life is to be feared. It is only to be understood. – Marie Curie Emotions have a stigma in science; they are believed to be inherently non-scientific. Scientific principles are derived from rational thought, logical arguments, testable hypotheses, and repeatable experiments. There is room alongside science for " non-interfering " emotions such as those involved in curiosity, frustration, and the pleasure of discovery. In fact, much scientific research has been prompted by fear. Nonetheless, the role of emotions is marginalized at best. Why bring " emotion " or " affect " into any of the deliberate tools of science? Moreover, shouldn't it be completely avoided when considering properties to design into computers? After all, computers control significant parts of our lives – the phone system, the stock market, nuclear power plants, jet landings, and more. Who wants a computer to be able to " feel angry " at them? To feel contempt for any living thing? In this essay I will submit for discussion a set of ideas on what I call " affective computing, " computing that relates to, arises from, or influences emotions. This will need some further clarification which I shall attempt below. I should say up front that I am not proposing the pursuit of computerized cingulotomies 1 or even into the business of building " emotional computers ". 1 The making of small wounds in the ridge of the limbic system known as the cingulate gyrus, a surgical procedure to aid severely depressed patients. Nor will I propose answers to the difficult and intriguing questions , " …
Convergent Tree-Backup and Retrace with Function Approximation

Off-policy learning is key to scaling up reinforcement learning as it allows to learn about a target policy from the experience generated by a different behavior policy. Unfortunately, it has been challenging to combine off-policy learning with function approximation and multi-step bootstrapping in a way that leads to both stable and efficient algorithms. In this work, we show that the TREE BACKUP and RETRACE algorithms are unstable with linear function approximation, both in theory and in practice with specific examples. Based on our analysis, we then derive stable and efficient gradient-based algorithms using a quadratic convex-concave saddle-point formulation. By exploiting the problem structure proper to these algorithms, we are able to provide convergence guarantees and finite-sample bounds. The applicability of our new analysis also goes beyond TREE BACKUP and RETRACE and allows us to provide new convergence rates for the GTD and GTD2 algorithms without having recourse to projections or Polyak averaging.
Recycling of Mixed Plastic Waste – Is Separation Worthwhile ?

The automated separation of plastic waste fractions intended for mechanical recycling is associated with substantial investments. It is therefore essential to evaluate to what degree separation really brings value to waste plastics as raw materials for new products. The possibility of reducing separation requirements and broadening the range of possible applications for recycled materials through the addition of elastomers, mineral fillers or other additives, should also taken into consideration. Material from a Swedish collection system for rigid (non-film) plastic packaging waste was studied. The non-film polyolefin fraction, which dominated the collected material, consisted of 55% polyethylene (PE) and 45% polypropylene (PP). Mechanical tests for injection-moulded blends of varying composition showed that complete separation of PE and PP is favourable for yield strength, impact strength, tensile energy to break and tensile modulus. Yield strength exhibited a minimum at 80% PE whereas fracture toughness was lowest for blends with 80% PP. The PE fraction, which was dominated by blow-moulded high density polyethylene (HDPE) containers, could be made more suitable for injection-moulding by commingling with the PP fraction. Nucleating agents present in the recycled material were found to influence the microstructure by causing PP to crystallise at a higher temperature than PE in PP-rich blends but not in PE-rich blends. Studies of sheet-extruded multi-component polyolefin mixtures, containing some film plastics, showed that fracture toughness was severely disfavoured if the PE-film component was dominated by low density polyethylene (LDPE) rather than linear low density polyethylene (LLDPE). This trend was reduced when the non-film component was dominated by bottle -grade HDPE. A modifier can be added if it is desired to increase fracture toughness or if there are substantial variations in the composition of the waste-stream. A very low density polyethylene (VLDPE) was found to be a more effective modifier than poly(ethylene-co-vinyl acetate) and poly(1-butene). The addition of 20% VLDPE to multi-component polyolefin mixtures increased the tensile strength and tear propagation resistance by 30% on average, while standard deviations for mechanical properties were reduced by 50%, which would allow product quality to be kept more consistent. ABS was found to be more sensitive to contamination by small amounts of talc-filled PP than viceversa. Contamination levels over 3% of talc -filled PP in ABS gave a very brittle material whereas talcfilled PP retained a ductile behaviour in blends with up to 9% ABS. Compatibility in blends of ABS, high-impact polystyrene and talc -filled PP was poorer at high deformation rates, as opposed to blends of PE and PP from rigid packaging waste where incompatibility was lower at fast deformation. This difference was explained by a higher degree of interfacial interaction through chain entanglements in PE/PP blends.
Speech and Language Processing. Dependency Parsing

The focus of the three previous chapters has been on context-free grammars and their use in automatically generating constituent-based representations. Here we present another family of grammar formalisms called dependency grammars that Dependency grammar are quite important in contemporary speech and language processing systems. In these formalisms, phrasal constituents and phrase-structure rules do not play a direct role. Instead, the syntactic structure of a sentence is described solely in terms of the words (or lemmas) in a sentence and an associated set of directed binary grammatical relations that hold among the words. The following diagram illustrates a dependency-style analysis using the standard graphical method favored in the dependency-parsing community. (14.1) I prefer the morning flight through Denver nsubj dobj det nmod nmod case root Relations among the words are illustrated above the sentence with directed, labeled arcs from heads to dependents. We call this a typed dependency structure Typed dependency because the labels are drawn from a fixed inventory of grammatical relations. It also includes a root node that explicitly marks the root of the tree, the head of the entire structure. Figure 14.1 shows the same dependency analysis as a tree alongside its corresponding phrase-structure analysis of the kind given in Chapter 11. Note the absence of nodes corresponding to phrasal constituents or lexical categories in the dependency parse; the internal structure of the dependency parse consists solely of directed relations between lexical items in the sentence. These relationships directly encode important information that is often buried in the more complex phrase-structure parses. For example, the arguments to the verb prefer are directly linked to it in the dependency structure, while their connection to the main verb is more distant in the phrase-structure tree. Similarly, morning and Denver, modifiers of flight, are linked to it directly in the dependency structure. A major advantage of dependency grammars is their ability to deal with languages that are morphologically rich and have a relatively free word order. For Free word order example, word order in Czech can be much more flexible than in English; a grammatical object might occur before or after a location adverbial. A phrase-structure grammar would need a separate rule for each possible place in the parse tree where such an adverbial phrase could occur. A dependency-based approach would just have one link type representing this particular adverbial relation. Thus, a dependency grammar approach abstracts away from word-order information, …
Biodiversity management in the face of climate change: A review of 22 years of recommendations

Climate change creates new challenges for biodiversity conservation. Species ranges and ecological dynamics are already responding to recent climate shifts, and current reserves will not continue to support all species they were designed to protect. These problems are exacerbated by other global changes. Scholarly articles recommending measures to adapt conservation to climate change have proliferated over the last 22 years. We systematically reviewed this literature to explore what potential solutions it has identified and what consensus and direction it provides to cope with climate change. Several consistent recommendations emerge for action at diverse spatial scales, requiring leadership by diverse actors. Broadly, adaptation requires improved regional institutional coordination, expanded spatial and temporal perspective, incorporation of climate change scenarios into all planning and action, and greater effort to address multiple threats and global change drivers simultaneously in ways that are responsive to and inclusive of human communities. However, in the case of many recommendations the how, by whom, and under what conditions they can be implemented is not specified. We synthesize recommendations with respect to three likely conservation pathways: regional planning; site-scale management; and modification of existing conservation plans. We identify major gaps, including the need for (1) more specific, operational examples of adaptation principles that are consistent with unavoidable uncertainty about the future; (2) a practical adaptation planning process to guide selection and integration of recommendations into existing policies and programs; and (3) greater integration of social science into an endeavor that, although dominated by ecology, increasingly recommends extension beyond reserves and into human-occupied landscapes. ! 2008
3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions

Matching local geometric features on real-world depth images is a challenging task due to the noisy, low-resolution, and incomplete nature of 3D scan data. These difficulties limit the performance of current state-of-art methods, which are typically based on histograms over geometric properties. In this paper, we present 3DMatch, a data-driven model that learns a local volumetric patch descriptor for establishing correspondences between partial 3D data. To amass training data for our model, we propose a self-supervised feature learning method that leverages the millions of correspondence labels found in existing RGB-D reconstructions. Experiments show that our descriptor is not only able to match local geometry in new scenes for reconstruction, but also generalize to different tasks and spatial scales (e.g. instance-level object model alignment for the Amazon Picking Challenge, and mesh surface correspondence). Results show that 3DMatch consistently outperforms other state-of-the-art approaches by a significant margin. Code, data, benchmarks, and pre-trained models are available online at http://3dmatch.cs.princeton.edu.
Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

The blind application of machine learning runs the risk of amplifying biases present in data. Such a danger is facing us with word embedding, a popular framework to represent text data as vectors which has been used in many machine learning and natural language processing tasks. We show that even word embeddings trained on Google News articles exhibit female/male gender stereotypes to a disturbing extent. This raises concerns because their widespread use, as we describe, often tends to amplify these biases. Geometrically, gender bias is first shown to be captured by a direction in the word embedding. Second, gender neutral words are shown to be linearly separable from gender definition words in the word embedding. Using these properties, we provide a methodology for modifying an embedding to remove gender stereotypes, such as the association between between the words receptionist and female, while maintaining desired associations such as between the words queen and female. We define metrics to quantify both direct and indirect gender biases in embeddings, and develop algorithms to “debias” the embedding. Using crowd-worker evaluation as well as standard benchmarks, we empirically demonstrate that our algorithms significantly reduce gender bias in embeddings while preserving the its useful properties such as the ability to cluster related concepts and to solve analogy tasks. The resulting embeddings can be used in applications without amplifying gender bias.
Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets

This paper presents a model based on Deep Learning algorithms of LSTM and GRU for facilitating an anomaly detection in Large Hadron Collider superconducting magnets. We used high resolution data available in Post Mortem database to train a set of models and chose the best possible set of their hyper-parameters. Using Deep Learning approach allowed to examine a vast body of data and extract the fragments which require further experts examination and are regarded as anomalies. The presented method does not require tedious manual threshold setting and operator attention at the stage of the system setup. Instead, the automatic approach is proposed, which achieves according to our experiments accuracy of 99 %. This is reached for the largest dataset of 302 MB and the following architecture of the network: single layer LSTM, 128 cells, 20 epochs of training, look_back=16, look_ahead=128, grid=100 and optimizer Adam. All the experiments were run on GPU Nvidia Tesla K80.
STATL: An Attack Language for State-Based Intrusion Detection

STATL is an extensible state/transition-based attack desc ription language designed to support intrusion detection. The language allows one to describe computer pen trations as sequences of actions that an attacker performs to compromise a computer system. A STATL descripti on of an attack scenario can be used by an intrusion detection system to analyze a stream of events and de tect possible ongoing intrusions. Since intrusion detection is performed in different domains (i.e., the netw ork or the hosts) and in different operating environments (e.g., Linux, Solaris, or Windows NT), it is useful to h ave an extensible language that can be easily tailored to different target environments. STATL defines do main-independent features of attack scenarios and provides constructs for extending the language to describe attacks in particular domains and environments. The STATL language has been successfully used in describing both network-based and host-based attacks, and it has been tailored to very different environments, e.g ., Sun Microsystems’ Solaris and Microsoft’s Windows NT. An implementation of the runtime support for the STATL language has been developed and a toolset of intrusion detection systems based on STATL has b een implemented. The toolset was used in a recent intrusion detection evaluation effort, delivering very favorable results. This paper presents the details of the STATL syntax and its semantics. Real examples from bot h the host and network-based extensions of the language are also presented.
An Analysis of Hierarchical Text Classification Using Word Embeddings

Efficient distributed numerical word representation models (word embeddings) combined with modern machine learning algorithms have recently yielded considerable improvement on automatic document classification tasks. However, the effectiveness of such techniques has not been assessed for the hierarchical text classification (HTC) yet. This study investigates application of those models and algorithms on this specific problem by means of experimentation and analysis. We trained classification models with prominent machine learning algorithm implementations—fastText, XGBoost, SVM, and Keras’ CNN—and noticeable word embeddings generation methods—GloVe, word2vec, and fastText—with publicly available data and evaluated them with measures specifically appropriate for the hierarchical context. FastText achieved an lcaF1 of 0.893 on a single-labeled version of the RCV1 dataset. An analysis indicates that using word embeddings and its flavors is a very promising approach for HTC.
Deep Super Learner: A Deep Ensemble for Classification Problems

Deep learning has become very popular for tasks such as predictive modeling and pattern recognition in handling big data. Deep learning is a powerful machine learning method that extracts lower level features and feeds them forward for the next layer to identify higher level features that improve performance. However, deep neural networks have drawbacks, which include many hyper-parameters and infinite architectures, opaqueness into results, and relatively slower convergence on smaller datasets. While traditional machine learning algorithms can address these drawbacks, they are not typically capable of the performance levels achieved by deep neural networks. To improve performance, ensemble methods are used to combine multiple base learners. Super learning is an ensemble that finds the optimal combination of diverse learning algorithms. This paper proposes deep super learning as an approach which achieves log loss and accuracy results competitive to deep neural networks while employing traditional machine learning algorithms in a hierarchical structure. The deep super learner is flexible, adaptable, and easy to train with good performance across different tasks using identical hyper-parameter values. Using traditional machine learning requires fewer hyper-parameters, allows transparency into results, and has relatively fast convergence on smaller datasets. Experimental results show that the deep super learner has superior performance compared to the individual base learners, single-layer ensembles, and in some cases deep neural networks. Performance of the deep super learner may further be improved with task-specific tuning.
FOG COMPUTING-Shifting from Cloud to Fog

To support the computational demand of real-t ime latency-sensitive applications of largely geo-distributed IoT devices/sensors, a new computing paradigm named Fog computing has been in troduced. Generally, Fog computing resides closer to the IoT devices/sensors and extends the Cloud-based computing, storage and networking facilities. These devices, called fog nodes, can be deployed anywhere with a network connection: on a factory floor, on top of a power pole, alongside a railway track, in a vehicle. Any device with computing, storage, and network connectivity can be a fog node. Examples include industrial controllers, switches, routers, embedded servers, and video surveillance cameras.
PF74 Reinforces the HIV-1 Capsid To Impair Reverse Transcription-Induced Uncoating.

The RNA genome of human immunodeficiency virus type 1 (HIV-1) is enclosed in a cone-shaped capsid shell that disassembles following cell entry via a process known as uncoating. During HIV-1 infection, the capsid is important for reverse transcription and entry of the virus into the target cell nucleus. The small molecule PF74 inhibits HIV-1 infection at early stages by binding to the capsid and perturbing uncoating. However, the mechanism by which PF74 alters capsid stability and reduces viral infection is presently unknown. Here, we show, using atomic force microscopy (AFM), that binding of PF74 to recombinant capsid-like assemblies and to HIV-1 isolated cores stabilizes the capsid in a concentration-dependent manner. At a PF74 concentration of 10 μM, the mechanical stability of the core is increased to a level similar to that of the intrinsically hyperstable capsid mutant E45A. PF74 also prevented the complete disassembly of HIV-1 cores normally observed during 24 h of reverse transcription. Specifically, cores treated with PF74 only partially disassembled: the main body of the capsid remained intact and stiff, and a cap-like structure dissociated from the narrow end of the core. Moreover, the internal coiled structure that was observed to form during reverse transcription in vitro persisted throughout the duration of the measurement (∼24 h). Our results provide direct evidence that PF74 directly stabilizes the HIV-1 capsid lattice, thereby permitting reverse transcription while interfering with a late step in uncoating. The capsid-binding small molecule PF74 inhibits HIV-1 infection at early stages and perturbs uncoating. However, the mechanism by which PF74 alters capsid stability and reduces viral infection is presently unknown. We recently introduced time-lapse atomic force microscopy to study the morphology and physical properties of HIV-1 cores during the course of reverse transcription. Here, we apply this AFM methodology to show that PF74 prevented the complete disassembly of HIV-1 cores normally observed during 24 h of reverse transcription. Specifically, cores with PF74 only partially disassembled: the main body of the capsid remained intact and stiff, but a cap-like structure dissociated from the narrow end of the core HIV-1. Our result provides direct evidence that PF74 directly stabilizes the HIV-1 capsid lattice.
Graphene-based nano-patch antenna for terahertz radiation

The scattering of terahertz radiation on a graphene-based nano-patch antenna is numerically analyzed. The extinction cross section of the nano-antenna supported by silicon and silicon dioxide substrates of di erent thickness are calculated. Scattering resonances in the terahertz band are identi ed as Fabry-Perot resonances of surface plasmon polaritons supported by the graphene lm. A strong tunability of the antenna resonances via electrostatic bias is numerically demonstrated, opening perspectives to design tunable graphene-based nano-antennas. These antennas are envisaged to enable wireless communications at the nanoscale.