nils-herrmann/clean_scirepeval huggingface dataset:
Detecting Local Community Structures in Social Networks Using Concept Interestingness

One key challenge in Social Network Analysis is to design an efficient and accurate community detection procedure as a means to discover intrinsic structures and extract relevant information. In this paper, we introduce a novel strategy called (COIN), which exploits COncept INterestingness measures to detect communities based on the concept lattice construction of the network. Thus, unlike off-the-shelf community detection algorithms, COIN leverages relevant conceptual characteristics inherited from Formal Concept Analysis to discover substantial local structures. On the first stage of COIN, we extract the formal concepts that capture all the cliques and bridges in the social network. On the second stage, we use the stability index to remove noisy bridges between communities and then percolate relevant adjacent cliques. Our experiments on several real-world social networks show that COIN can quickly detect communities more accurately than existing prominent algorithms such as Edge betweenness, Fast greedy modularity, and Infomap.
High-performance stock index trading: making effective use of a deep LSTM neural network

We present a deep long short-term memory (LSTM)-based neural network for predicting asset prices, together with a successful trading strategy for generating profits based on the model's predictions. Our work is motivated by the fact that the effectiveness of any prediction model is inherently coupled to the trading strategy it is used with, and vise versa. This highlights the difficulty in developing models and strategies which are jointly optimal, but also points to avenues of investigation which are broader than prevailing approaches. Our LSTM model is structurally simple and generates predictions based on price observations over a modest number of past trading days. The model's architecture is tuned to promote profitability, as opposed to accuracy, under a strategy that does not trade simply based on whether the price is predicted to rise or fall, but rather takes advantage of the distribution of predicted returns, and the fact that a prediction's position within that distribution carries useful information about the expected profitability of a trade. The proposed model and trading strategy were tested on the S&P 500, Dow Jones Industrial Average (DJIA), NASDAQ and Russel 2000 stock indices, and achieved cumulative returns of 340%, 185%, 371% and 360%, respectively, over 2010-2018, far outperforming the benchmark buy-and-hold strategy as well as other recent efforts.
Automating Interpretability: Discovering and Testing Visual Concepts Learned by Neural Networks

Interpretability has become an important topic of research as more machine learning (ML) models are deployed and widely used to make important decisions. Due to it's complexity, i For high-stakes domains such as medical, providing intuitive explanations that can be consumed by domain experts without ML expertise becomes crucial. To this demand, concept-based methods (e.g., TCAV) were introduced to provide explanations using user-chosen high-level concepts rather than individual input features. While these methods successfully leverage rich representations learned by the networks to reveal how human-defined concepts are related to the prediction, they require users to select concepts of their choice and collect labeled examples of those concepts. In this work, we introduce DTCAV (Discovery TCAV) a global concept-based interpretability method that can automatically discover concepts as image segments, along with each concept's estimated importance for a deep neural network's predictions. We validate that discovered concepts are as coherent to humans as hand-labeled concepts. We also show that the discovered concepts carry significant signal for prediction by analyzing a network's performance with stitched/added/deleted concepts. DTCAV results revealed a number of undesirable correlations (e.g., a basketball player's jersey was a more important concept for predicting the basketball class than the ball itself) and show the potential shallow reasoning of these networks.
Discretization Based Solutions for Secure Machine Learning Against Adversarial Attacks

Adversarial examples are perturbed inputs that are designed (from a deep learning network’s (DLN) parameter gradients) to mislead the DLN during test time. Intuitively, constraining the dimensionality of inputs or parameters of a network reduces the “space” in which adversarial examples exist. Guided by this intuition, we demonstrate that discretization greatly improves the robustness of the DLNs against adversarial attacks. Specifically, discretizing the input space (or allowed pixel levels from 256 values or 8<inline-formula> <tex-math notation="LaTeX">$bit$ </tex-math></inline-formula> to 4 values or 2<inline-formula> <tex-math notation="LaTeX">$bit$ </tex-math></inline-formula>) extensively improves the adversarial robustness of the DLNs for a substantial range of perturbations for minimal loss in test accuracy. Furthermore, we find that binary neural networks (BNNs) and related variants are intrinsically more robust than their full precision counterparts in adversarial scenarios. Combining input discretization with the BNNs furthers the robustness, even waiving the need for adversarial training for the certain magnitude of perturbation values. We evaluate the effect of discretization on MNIST, CIFAR10, CIFAR100, and ImageNet datasets. Across all datasets, we observe maximal adversarial resistance with 2<inline-formula> <tex-math notation="LaTeX">$bit$ </tex-math></inline-formula> input discretization that incurs an adversarial accuracy loss of just ~1% – 2% as compared to clean test accuracy against single-step attacks. We also show standalone discretization remains vulnerable to stronger multi-step attack scenarios necessitating the use of adversarial training with discretization as an improved defense strategy.
Speaker Diarisation Using 2D Self-attentive Combination of Embeddings

Speaker diarisation systems often cluster audio segments using speaker embeddings such as i-vectors and d-vectors. Since different types of embeddings are often complementary, this paper proposes a generic framework to improve performance by combining them into a single embedding, referred to as a c-vector. This combination uses a 2-dimensional (2D) self-attentive structure, which extends the standard self-attentive layer by averaging not only across time but also across different types of embeddings. Two types of 2D self-attentive structure studied in this paper are simultaneous combination and consecutive combination, which adopt single and multiple self-attentive layers respectively. The penalty term in the original self-attentive layer, which is jointly minimised with the objective function to encourage diversity of annotation vectors, is also modified to obtain not only different local peaks but also the overall trends in the multiple annotation vectors. Experiments on the AMI meeting corpus show that our modified penalty term improves the d-vector relative speaker error rate (SER) by 6% and 21% for d-vector systems, and a 10% further relative SER reduction can be obtained using the c-vector from our best 2D self-attentive structure.
Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces

Bayesian optimization is known to be difficult to scale to high dimensions, because the acquisition step requires solving a non-convex optimization problem in the same search space. In order to scale the method and keep its benefits, we propose an algorithm (LineBO) that restricts the problem to a sequence of iteratively chosen one-dimensional sub-problems that can be solved efficiently. We show that our algorithm converges globally and obtains a fast local rate when the function is strongly convex. Further, if the objective has an invariant subspace, our method automatically adapts to the effective dimension without changing the algorithm. When combined with the SafeOpt algorithm to solve the sub-problems, we obtain the first safe Bayesian optimization algorithm with theoretical guarantees applicable in high-dimensional settings. We evaluate our method on multiple synthetic benchmarks, where we obtain competitive performance. Further, we deploy our algorithm to optimize the beam intensity of the Swiss Free Electron Laser with up to 40 parameters while satisfying safe operation constraints.
A 3D Probabilistic Deep Learning System for Detection and Diagnosis of Lung Cancer Using Low-Dose CT Scans

We introduce a new computer aided detection and diagnosis system for lung cancer screening with low-dose CT scans that produces meaningful probability assessments. Our system is based entirely on 3D convolutional neural networks and achieves state-of-the-art performance for both lung nodule detection and malignancy classification tasks on the publicly available LUNA16 and Kaggle Data Science Bowl challenges. While nodule detection systems are typically designed and optimized on their own, we find that it is important to consider the coupling between detection and diagnosis components. Exploiting this coupling allows us to develop an end-to-end system that has higher and more robust performance and eliminates the need for a nodule detection false positive reduction stage. Furthermore, we characterize model uncertainty in our deep learning systems, a first for lung CT analysis, and show that we can use this to provide well-calibrated classification probabilities for both nodule detection and patient malignancy diagnosis. These calibrated probabilities informed by model uncertainty can be used for subsequent risk-based decision making towards diagnostic interventions or disease treatments, as we demonstrate using a probability-based patient referral strategy to further improve our results.
Ask Not What AI Can Do, But What AI Should Do: Towards a Framework of Task Delegability

While artificial intelligence (AI) holds promise for addressing societal challenges, issues of exactly which tasks to automate and to what extent to do so remain understudied. We approach this problem of task delegability from a human-centered perspective by developing a framework on human perception of task delegation to AI. We consider four high-level factors that can contribute to a delegation decision: motivation, difficulty, risk, and trust. To obtain an empirical understanding of human preferences in different tasks, we build a dataset of 100 tasks from academic papers, popular media portrayal of AI, and everyday life, and administer a survey based on our proposed framework. We find little preference for full AI control and a strong preference for machine-in-the-loop designs, in which humans play the leading role. Among the four factors, trust is the most correlated with human preferences of optimal human-machine delegation. This framework represents a first step towards characterizing human preferences of AI automation across tasks. We hope this work encourages future efforts towards understanding such individual attitudes; our goal is to inform the public and the AI research community rather than dictating any direction in technology development.
Positivity of Amplitudes, Weak Gravity Conjecture, and Modified Gravity.

We derive new positivity bounds for scattering amplitudes in theories with a massless graviton in the spectrum in four spacetime dimensions, of relevance for the weak gravity conjecture and modified gravity theories. The bounds imply that extremal black holes are self-repulsive, M/|Q|<1 in suitable units, and that they are unstable to decay to smaller extremal black holes, providing an S-matrix proof of the weak gravity conjecture. We also present other applications of our bounds to the effective field theory of weakly broken Galileons, axions, and P(X) theories.
Invariant-equivariant representation learning for multi-class data

Representations learnt through deep neural networks tend to be highly informative, but opaque in terms of what information they learn to encode. We introduce an approach to probabilistic modelling that learns to represent data with two separate deep representations: an invariant representation that encodes the information of the class from which the data belongs, and an equivariant representation that encodes the symmetry transformation defining the particular data point within the class manifold (equivariant in the sense that the representation varies naturally with symmetry transformations). This approach is based primarily on the strategic routing of data through the two latent variables, and thus is conceptually transparent, easy to implement, and in-principle generally applicable to any data comprised of discrete classes of continuous distributions (e.g. objects in images, topics in language, individuals in behavioural data). We demonstrate qualitatively compelling representation learning and competitive quantitative performance, in both supervised and semi-supervised settings, versus comparable modelling approaches in the literature with little fine tuning.
Topological Control of Synchronization Patterns: Trading Symmetry for Stability.

Symmetries are ubiquitous in network systems and have profound impacts on the observable dynamics. At the most fundamental level, many synchronization patterns are induced by underlying network symmetry, and a high degree of symmetry is believed to enhance the stability of identical synchronization. Yet, here we show that the synchronizability of almost any symmetry cluster in a network of identical nodes can be enhanced precisely by breaking its structural symmetry. This counterintuitive effect holds for generic node dynamics and arbitrary network structure and is, moreover, robust against noise and imperfections typical of real systems, which we demonstrate by implementing a state-of-the-art optoelectronic experiment. These results lead to new possibilities for the topological control of synchronization patterns, which we substantiate by presenting an algorithm that optimizes the structure of individual clusters under various constraints.
FSNet: Compression of Deep Convolutional Neural Networks by Filter Summary

We present a novel method of compression of deep Convolutional Neural Networks (CNNs) by weight sharing through a new representation of convolutional filters. The proposed method reduces the number of parameters of each convolutional layer by learning a 1D vector termed Filter Summary (FS). The convolutional filters are located in FS as overlapping 1D segments, and nearby filters in FS share weights in their overlapping regions in a natural way. The resultant neural network based on such weight sharing scheme, termed Filter  CNNs or FSNet, has a FS in each convolution layer instead of a set of independent filters in the conventional convolution layer. FSNet has the same architecture as that of the baseline CNN to be compressed, and each convolution layer of FSNet has the same number of filters from FS as that of the basline CNN in the forward process. With compelling computational acceleration ratio, the parameter space of FSNet is much smaller than that of the baseline CNN. In addition, FSNet is quantization friendly. FSNet with weight quantization leads to even higher compression ratio without noticeable performance loss. We further propose Differentiable FSNet where the way filters share weights is learned in a differentiable and end-to-end manner. Experiments demonstrate the effectiveness of FSNet in compression of CNNs for computer vision tasks including image classification and object detection, and the effectiveness of DFSNet is evidenced by the task of Neural Architecture Search.
Milky Way Halo Vibrations and Incommensurate Stream Velocities

Collisionless dark matter galactic halos are expected to exhibit damped oscillations as a result of ongoing late time accretion. An n-body model of the cosmological assembly of a Milky-Way-like halo is used to quantify the time dependence of its gravitational field. The simulation contains stellar streams, with velocities transverse to the path of the stream comparable to those reported for the Orphan stream. The fluctuations in the quadrupole moment of the dark matter halo are measured to be about 1% of the radial component of the gravity field, but are sufficient to largely explain the perpendicular velocities. The largest late time sub-halo within 100 kpc is a 2% mass at about 70 kpc from the galactic center. Simulations of self-interacting dark matter halos have reduced potential fluctuations as the collisions create a viscosity. If velocity measurements of a larger sample of Milky Way streams finds (or does not find) the expected distribution of transverse velocities, it will lead to limits on the cross section of self-interacting dark matter.
A Light-Weight Authentication Scheme for Air Force Internet of Things

Internet of Things (IoT) is ubiquitous because of its broad applications and the advance in communication technologies. The capabilities of IoT also enable its important role in homeland security and tactical missions, including Reconnaissance, Intelligence, Surveillance, and Target Acquisition (RISTA). IoT security becomes the most critical issue before its extensive use in military operations. While the majority of research focuses on smart IoT devices, treatments for legacy dumb network-ready devices are lacking; moreover, IoT devices deployed in a hostile environment are often required to be dumb due to the strict hardware constraints, making them highly vulnerable to cyber attacks. To mitigate the problem, we propose a light-weight authentication scheme for dumb IoT devices, in a case study of the UAV-sensor collaborative RISTA missions. Our scheme utilizes the covert channels in the physical layer for authentications and does not request conventional key deployments, key generations which may cause security risks and large overhead that a dumb sensor cannot afford. Our scheme operates on the physical layer, and thus it is highly portable and generalizable to most commercial and military communication protocols. We demonstrate the viability of our scheme by building a prototype system and conducting experiments to emulate the behaviors of UAVs and sensors in real scenarios.
Machine learning and chord based feature engineering for genre prediction in popular Brazilian music

Music genre can be hard to describe: many factors are involved, such as style, music technique, and historical context. Some genres even have overlapping characteristics. Looking for a better understanding of how music genres are related to musical harmonic structures, we gathered data about the music chords for thousands of popular Brazilian songs. Here, 'popular' does not only refer to the genre named MPB (Brazilian Popular Music) but to nine different genres that were considered particular to the Brazilian case. The main goals of the present work are to extract and engineer harmonically related features from chords data and to use it to classify popular Brazilian music genres towards establishing a connection between harmonic relationships and Brazilian genres. We also emphasize the generalization of the method for obtaining the data, allowing for the replication and direct extension of this work. Our final model is a combination of multiple classification trees, also known as the random forest model. We found that features extracted from harmonic elements can satisfactorily predict music genre for the Brazilian case, as well as features obtained from the Spotify API. The variables considered in this work also give an intuition about how they relate to the genres.
FERAtt: Facial Expression Recognition With Attention Net

We present a new end-to-end network architecture for facial expression recognition with an attention model. It focuses attention in the human face and uses a Gaussian space representation for expression recognition. We devise this architecture based on two fundamental complementary components: (1) facial image correction and attention and (2) facial expression representation and classification. The first component uses an encoder-decoder style network and a convolutional feature extractor that are pixel-wise multiplied to obtain a feature attention map. The second component is responsible for obtaining an embedded representation and classification of the facial expression. We propose a loss function that creates a Gaussian structure on the representation space. To demonstrate the proposed method, we create two larger and more comprehensive synthetic datasets using the traditional BU3DFE and CK+ facial datasets. We compared results with the PreActResNet18 baseline. Our experiments on these datasets have shown the superiority of our approach in recognizing facial expressions.
Open data to evaluate academic researchers: an experiment with the Italian Scientific Habilitation

The need for scholarly open data is ever increasing. While there are large repositories of open access articles and free publication indexes, there are still a few examples of free citation networks and their coverage is partial. One of the results is that most of the evaluation processes based on citation counts rely on commercial citation databases. Things are changing under the pressure of the Initiative for Open Citations (I4OC), whose goal is to campaign for scholarly publishers to make their citations as totally open. This paper investigates the growth of open citations with an experiment on the Italian Scientific Habilitation, the National process for University Professor qualification which instead uses data from commercial indexes. We simulated the procedure by only using open data and explored similarities and differences with the official results. The outcomes of the experiment show that the amount of open citation data currently available is not yet enough for obtaining similar results.
Power accretion in social systems.

We consider a model of power distribution in a social system where a set of agents plays a simple game on a graph: The probability of winning each round is proportional to the agent's current power, and the winner gets more power as a result. We show that when the agents are distributed on simple one-dimensional and two-dimensional networks, inequality grows naturally up to a certain stationary value characterized by a clear division between a higher and a lower class of agents. High class agents are separated by one or several lower class agents which serve as a geometrical barrier preventing further flow of power between them. Moreover, we consider the effect of redistributive mechanisms, such as proportional (nonprogressive) taxation. Sufficient taxation will induce a sharp transition towards a more equal society, and we argue that the critical taxation level is uniquely determined by the system geometry. Interestingly, we find that the roughness and Shannon entropy of the power distributions are a very useful complement to the standard measures of inequality, such as the Gini index and the Lorenz curve.