nils-herrmann/clean_scirepeval huggingface dataset:
Generating Focused Molecule Libraries for Drug Discovery with Recurrent
Neural Networks

In de novo drug design, computational strategies are used to generate novel molecules with good affinity to the desired biological target. In this work, we show that recurrent neural networks can be trained as generative models for molecular structures, similar to statistical language models in natural language processing. We demonstrate that the properties of the generated molecules correlate very well with the properties of the molecules used to train the model. In order to enrich libraries with molecules active toward a given biological target, we propose to fine-tune the model with small sets of molecules, which are known to be active against that target. Against Staphylococcus aureus, the model reproduced 14% of 6051 hold-out test molecules that medicinal chemists designed, whereas against Plasmodium falciparum (Malaria), it reproduced 28% of 1240 test molecules. When coupled with a scoring function, our model can perform the complete de novo drug design cycle to generate large sets of novel molecules for drug discovery.
When Morality Opposes Justice : Conservatives Have Moral Intuitions that Liberals may not Recognize

Researchers in moral psychology and social justice have agreed that morality is about matters of harm, rights, and justice. On this definition of morality, conservative opposition to social justice programs appears to be immoral, and has been explained as a product of various non-moral processes such as system justification or social dominance orientation. In this article we argue that, from an anthropological perspective, the moral domain is usually much broader, encompassing many more aspects of social life and valuing institutions as much or more than individuals. We present theoretical and empirical reasons for believing that there are five psychological systems that provide the foundations for the world s many moralities. The five foundations are psychological preparations for detecting and reacting emotionally to issues related to harm/care, fairness/reciprocity, ingroup/loyalty, authority/respect, and purity/sanctity. Political liberals have moral intuitions primarily based upon the first two foundations, and therefore misunderstand the moral motivations of political conservatives, who generally rely upon all five foundations.
Can I Do That? Discovering Domain Axioms Using Declarative Programming and Relational Reinforcement Learning

Robots deployed to assist humans in complex, dynamic domains need the ability to represent, reason with, and learn from, different descriptions of incomplete domain knowledge and uncertainty. This paper presents an architecture that integrates declarative programming and relational reinforcement learning to support cumulative and interactive discovery of previously unknown axioms governing domain dynamics. Specifically, Answer Set Prolog (ASP), a declarative programming paradigm, is used to represent and reason with incomplete commonsense domain knowledge. For any given goal, unexplained failure of plans created by inference in the ASP program is taken to indicate the existence of unknown domain axioms. The task of learning these axioms is formulated as a Reinforcement Learning problem, and decision-tree regression with a relational representation is used to generalize from specific axioms identified over time. The new axioms are added to the ASP-based representation for subsequent inference. We demonstrate and evaluate the capabilities of our architecture in two simulated domains: Blocks World and Simple Mario.
Social media for software engineering

Social media has changed the way that people collaborate and share information. In this paper, we highlight its impact for enabling new ways for software teams to form and work together. Individuals will self-organize within and across organizational boundaries. Grassroots software development communities will emerge centered around new technologies, common processes and attractive target markets. Companies consisting of lone individuals will able to leverage social media to conceive of, design, develop, and deploy successful and profitable product lines. A challenge for researchers who are interested in studying, influencing, and supporting this shift in software teaming is to make sure that their research methods protect the privacy and reputation of their stakeholders.
Deriving marketing intelligence from online discussion

Weblogs and message boards provide online forums for discussion that record the voice of the public. Woven into this mass of discussion is a wide range of opinion and commentary about consumer products. This presents an opportunity for companies to understand and respond to the consumer by analyzing this unsolicited feedback. Given the volume, format and content of the data, the appropriate approach to understand this data is to use large-scale web and text data mining technologies.This paper argues that applications for mining large volumes of textual data for marketing intelligence should provide two key elements: a suite of powerful mining and visualization technologies and an interactive analysis environment which allows for rapid generation and testing of hypotheses. This paper presents such a system that gathers and annotates online discussion relating to consumer products using a wide variety of state-of-the-art techniques, including crawling, wrapping, search, text classification and computational linguistics. Marketing intelligence is derived through an interactive analysis framework uniquely configured to leverage the connectivity and content of annotated online discussion.
OpenVirteX: make your virtual SDNs programmable

We present OpenVirteX, a network virtualization platform that enables operators to create and manage virtual Software Defined Networks (vSDNs). Tenants are free to specify the topology and addressing scheme of their vSDN, and run their own Network Operating System (NOS) to control it. Since OpenVirteX logically decouples vSDNs from the infrastructure, it also enables the introduction of features such as link and switch resiliency, and network snapshotting and migration of these tenant networks. OpenVirteX builds on the design of FlowVisor, and functions as an OpenFlow controller proxy between an operator's network and the tenants' network OSes. Our evaluations of this implementation show that i) OpenVirteX is capable of presenting tenants with configurable vSDNs while incurring a modest overhead to the control channel, and ii) that our architecture enables the introduction of features and enhancements such as link resilience to tenant networks.
Improved Estimation of Trilateration Distances for Indoor Wireless Intrusion Detection

Detecting wireless network intruders is challenging since logical addressing information may be spoofed and the attacker may be located anywhere within radio range. Accurate indoor geolocation provides a method by which the physical location of rogue wireless devices may be pinpointed whilst providing an additional option for location-based access control. Existing methods for geolocation using received signal strength (RSS) are imprecise, due to the multipath nature of indoor radio propagation and additional pathloss due to walls, and aim to minimise location estimate error. This paper presents an approach to indoor geolocation that improves measurements of RSS by averaging across multiple frequency channels and determining the occurrence of walls in the signal path. Experimental results demonstrate that the approach provides improved distance estimates for trilateration and thus aids intrusion detection for wireless networks.
Fusion of Audio-visual Features using Hierarchical Classifier Systems for the Recognition of Affective States and the State of Depression

Reliable prediction of affective states in real world scenarios is very challenging and a significant amount of ongoing research is targeted towards improvement of existing systems. Major problems include the unreliability of labels, variations of the same affective states amongst different persons and in different modalities as well as the presence of sensor noise in the signals. This work presents a framework for adaptive fusion of input modalities incorporating variable degrees of certainty on different levels. Using a strategy that starts with ensembles of weak learners, gradually, level by level, the discriminative power of the system is improved by adaptively weighting favorable decisions, while concurrently dismissing unfavorable ones. For the final decision fusion the proposed system leverages a trained Kalman filter. Besides its ability to deal with missing and uncertain values, in its nature, the Kalman filter is a time series predictor and thus a suitable choice to match input signals to a reference time series in the form of ground truth labels. In the case of affect recognition, the proposed system exhibits superior performance in comparison to competing systems on the analysed dataset.
Low Cost Dual Polarized Base Station Element for Long Term Evolution

A broadband low profile dual polarized patch antenna for fourth generation long term evolution (4G LTE) is presented in this communication. By employing two wideband feeding mechanisms for the two input ports, a dual polarized patch antenna element with the characteristics of high input port isolation and wide impedance bandwidth is successfully designed. A wide meandering probe (M-probe) and a pair of twin-L-probes are proposed to feed a patch for dual polarization. The proposed design with a wideband balun has impedance bandwidths of over 47% at the two input ports as well as over 37 dB isolation within the entire operating band. Over 8 dBi gain can be achieved at the two ports.
The Role of Asymmetric Dimethylarginine (ADMA) in Endothelial Dysfunction and Cardiovascular Disease

Endothelium plays a crucial role in the maintenance of vascular tone and structure. Endothelial dysfunction is known to precede overt coronary artery disease. A number of cardiovascular risk factors, as well as metabolic diseases and systemic or local inflammation cause endothelial dysfunction. Nitric oxide (NO) is one of the major endothelium derived vaso-active substances whose role is of prime importance in maintaining endothelial homeostasis. Low levels of NO are associated with impaired endothelial function. Asymmetric dimethylarginine (ADMA), an analogue of L-arginine, is a naturally occurring product of metabolism found in human circulation. Elevated levels of ADMA inhibit NO synthesis and therefore impair endothelial function and thus promote atherosclerosis. ADMA levels are increased in people with hypercholesterolemia, atherosclerosis, hypertension, chronic heart failure, diabetes mellitus and chronic renal failure. A number of studies have reported ADMA as a novel risk marker of cardiovascular disease. Increased levels of ADMA have been shown to be the strongest risk predictor, beyond traditional risk factors, of cardiovascular events and all-cause and cardiovascular mortality in people with coronary artery disease. Interventions such as treatment with L-arginine have been shown to improve endothelium-mediated vasodilatation in people with high ADMA levels. However the clinical utility of modifying circulating ADMA levels remains uncertain.
Representing Musical Genre: A State of the Art

Musical genre is probably the most popular music descrip-tor. In the context of large musical databases and Electronic Music Distribution, genre is therefore a crucial metadata for the description of music content. However, genre is intrinsi-cally ill-defined and attempts at defining genre precisely have a strong tendency to end up in circular, ungrounded projections of fantasies. Is genre an intrinsic attribute of music titles, as, say, tempo? Or is genre a extrinsic description of the whole piece? In this article, we discuss the various approaches in representing musical genre, and propose to classify these approaches in three main categories: manual, prescriptive and emergent approaches. We discuss the pros and cons of each approach, and illustrate our study with results of the Cuidado IST project.
Conversation specification: a new approach to design and analysis of e-service composition

This paper introduces a framework for modeling and specifying the global behavior of e-service compositions. Under this framework, peers (individual e-services) communicate through asynchronous messages and each peer maintains a queue for incoming messages. A global "watcher" keeps track of messages as they occur. We propose and study a central notion of a "conversation", which is a sequence of (classes of) messages observed by the watcher. We consider the case where the peers are represented by Mealy machines (finite state machines with input and output). The sets of conversations exhibit unexpected behaviors. For example, there exists a composite e-service based on Mealy peers whose set of conversations is not context free (and not regular). (The set of conversations is always context sensitive.) One cause for this is the queuing of messages; we introduce an operator "prepone" that simulates queue delays from a global perspective and show that the set of conversations of each Mealy e-service is closed under prepone. We illustrate that the global prepone fails to completely capture the queue delay effects and refine prepone to a "local" version on conversations seen by individual peers. On the other hand, Mealy implementations of a composite e-service will always generate conversations whose "projections" are consistent with individual e-services. We use projection-join to reflect such situations. However, there are still Mealy peers whose set of conversations is not the local prepone and projection-join closure of any regular language. Therefore, we propose conversation specifications as a formalism to define the conversations allowed by an e-service composition. We give two technical results concerning the interplay between the local behaviors of Mealy peers and the global behaviors of their compositions. One result shows that for each regular language, its local prepone and projection-join closure corresponds to the set of conversations by some Mealy peers effectively constructed from . The second result gives a condition on the shape of a composition which guarantees that the set of conversations that can be realized is the local prepone and projection-join closure of a regular language.
Kinematics of Robot Fingers with Circular Rolling Contact Joints

This paper examines the kinematics of robot fingers that use RR subassemblies coupled by rolling contact to provide versatile one degree-of-freedom mechanical joints. A three degree-of-freedom robot finger constructed with these joints is a 6R planar chain with the R-joints coupled together in pairs. The focus is on coupling based on pure rolling of circular cylinders which can be realized by friction contact, gearing, or cable tendons. We derive the forward, inverse and rate kinematics for this 3(RR) open chain. We then focus on the two degree-of-freedom 2(RR) case to illustrate the geometry of the system. An example design of a robot finger that incorporates these joints in order to provide compact movement is provided.
Watching social interactions produces dorsomedial prefrontal and medial parietal BOLD fMRI signal increases compared to a resting baseline

Some human brain areas are tonically active in a resting state when subjects are not engaged in any overt task. The activity of these areas decreases when subjects are engaged in a wide variety of laboratory tasks designed to study cognitive operations. It has been suggested that these areas, among them the medial parietal (precyneus) and the dorsomedial prefrontal cortices, may support a "default state" of the human brain. Passive visual observation of laboratory stimuli typically yields no change in activity in these default areas compared to rest. Here we report functional magnetic resonance imaging (fMRI) data on normal subjects watching realistic movie clips depicting everyday social interactions. In contrast with previous findings on default state brain areas, the observation of the relational segment of the movie clip, during which two persons interact, yielded increased activity in the medial parietal (precuneus) and dorsomedial prefrontal cortices, compared to rest and to observation of the segment of the movie clip depicting a single individual engaged in everyday activities. To the best of our knowledge, this is the first report of joint increased activity in medial parietal and dorsomedial prefrontal cortices. We suggest that the default state areas may participate in the processing of social relations in concert with regions previously identified as critical for social cognition that were also activated by our stimuli, including the inferior frontal cortex, the superior temporal cortex, and the fusiform gyrus.
Witness encryption and its applications

We put forth the concept of witness encryption. A witness encryption scheme is defined for an NP language L (with corresponding witness relation R). In such a scheme, a user can encrypt a message M to a particular problem instance x to produce a ciphertext. A recipient of a ciphertext is able to decrypt the message if x is in the language and the recipient knows a witness w where R(x,w) holds. However, if x is not in the language, then no polynomial-time attacker can distinguish between encryptions of any two equal length messages. We emphasize that the encrypter himself may have no idea whether $x$ is actually in the language. Our contributions in this paper are threefold. First, we introduce and formally define witness encryption. Second, we show how to build several cryptographic primitives from witness encryption. Finally, we give a candidate construction based on the NP-complete Exact Cover problem and Garg, Gentry, and Halevi's recent construction of "approximate" multilinear maps.
 Our method for witness encryption also yields the first candidate construction for an open problem posed by Rudich in 1989: constructing computational secret sharing schemes for an NP-complete access structure.
A language modeling framework for resource selection and results merging

Statistical language models have been proposed recently for several information retrieval tasks, including the resource selection task in distributed information retrieval. This paper extends the language modeling approach to integrate resource selection, ad-hoc searching, and merging of results from different text databases into a single probabilistic retrieval model. This new approach is designed primarily for Intranet environments, where it is reasonable to assume that resource providers are relatively homogeneous and can adopt the same kind of search engine. Experiments demonstrate that this new, integrated approach is at least as effective as the prior state-of-the-art in distributed IR.
Decomposing a scene into geometric and semantically consistent regions

High-level, or holistic, scene understanding involves reasoning about objects, regions, and the 3D relationships between them. This requires a representation above the level of pixels that can be endowed with high-level attributes such as class of object/region, its orientation, and (rough 3D) location within the scene. Towards this goal, we propose a region-based model which combines appearance and scene geometry to automatically decompose a scene into semantically meaningful regions. Our model is defined in terms of a unified energy function over scene appearance and structure. We show how this energy function can be learned from data and present an efficient inference technique that makes use of multiple over-segmentations of the image to propose moves in the energy-space. We show, experimentally, that our method achieves state-of-the-art performance on the tasks of both multi-class image segmentation and geometric reasoning. Finally, by understanding region classes and geometry, we show how our model can be used as the basis for 3D reconstruction of the scene.
Inflammation and cancer: how hot is the link?

Although inflammation has long been known as a localized protective reaction of tissue to irritation, injury, or infection, characterized by pain, redness, swelling, and sometimes loss of function, there has been a new realization about its role in a wide variety of diseases, including cancer. While acute inflammation is a part of the defense response, chronic inflammation can lead to cancer, diabetes, cardiovascular, pulmonary, and neurological diseases. Several pro-inflammatory gene products have been identified that mediate a critical role in suppression of apoptosis, proliferation, angiogenesis, invasion, and metastasis. Among these gene products are TNF and members of its superfamily, IL-1alpha, IL-1beta, IL-6, IL-8, IL-18, chemokines, MMP-9, VEGF, COX-2, and 5-LOX. The expression of all these genes are mainly regulated by the transcription factor NF-kappaB, which is constitutively active in most tumors and is induced by carcinogens (such as cigarette smoke), tumor promoters, carcinogenic viral proteins (HIV-tat, HIV-nef, HIV-vpr, KHSV, EBV-LMP1, HTLV1-tax, HPV, HCV, and HBV), chemotherapeutic agents, and gamma-irradiation. These observations imply that anti-inflammatory agents that suppress NF-kappaB or NF-kappaB-regulated products should have a potential in both the prevention and treatment of cancer. The current review describes in detail the critical link between inflammation and cancer.
VisComplete: Automating Suggestions for Visualization Pipelines

Building visualization and analysis pipelines is a large hurdle in the adoption of visualization and workflow systems by domain scientists. In this paper, we propose techniques to help users construct pipelines by consensus-automatically suggesting completions based on a database of previously created pipelines. In particular, we compute correspondences between existing pipeline subgraphs from the database, and use these to predict sets of likely pipeline additions to a given partial pipeline. By presenting these predictions in a carefully designed interface, users can create visualizations and other data products more efficiently because they can augment their normal work patterns with the suggested completions. We present an implementation of our technique in a publicly-available, open-source scientific workflow system and demonstrate efficiency gains in real-world situations.
Open Logo Detection Challenge

Existing logo detection benchmarks consider artificial deployment scenarios by assuming that large training data with fine-grained bounding box annotations for each class are available for model training. Such assumptions are often invalid in realistic logo detection scenarios where new logo classes come progressively and require to be detected with little or none budget for exhaustively labelling fine-grained training data for every new class. Existing benchmarks are thus unable to evaluate the true performance of a logo detection method in realistic and open deployments. In this work, we introduce a more realistic and challenging logo detection setting, called Open Logo Detection. Specifically, this new setting assumes fine-grained labelling only on a small proportion of logo classes whilst the remaining classes have no labelled training data to simulate the open deployment. We further create an open logo detection benchmark, called QMUL-OpenLogo, to promote the investigation of this new challenge. QMUL-OpenLogo contains 27,083 images from 352 logo classes, built by aggregating/refining 7 existing datasets and establishing an open logo detection evaluation protocol. To address this challenge, we propose a Context Adversarial Learning (CAL) approach to synthesising training data with coherent logo instance appearance against diverse background context for enabling more effective optimisation of contemporary deep learning detection models. Experiments show the performance advantage of CAL over existing state-of-the-art alternative methods on the more realistic and challenging open logo benchmark. The QMUL-OpenLogo benchmark is publicly available at