nils-herrmann/clean_scirepeval huggingface dataset:
3 D Indoor Object Recognition by Holistic Scene Understanding

In this project, we jointly solve the problem of indoor object recognition and scene classification. The joint problem is modeled by a Conditional Random Field (CRF). Specifically, we implement unary potential on scene appearance, unary potential on object appearance, and binary potential on geometry between objects. The two appearance potentials are obtained by training two neural networks respectively and extracting features from them. Binary potential on object-object geometry relationship is inferred by projecting one object onto the other one’s surface and perceiving the overlap. Other potentials include object cuboid geometry, binary potential on scene-object concurrence, and binary potential on object-object concurrence. Experiments on the challenging NYU-RGBD v2 dataset show that the approach of jointly solving object detection and scene classification problems by integrating several feature representations achieves better performance than vanilla classification. Meanwhile, improving the potentials on scene appearance and object-object geometry relationship achieve fairly satisfactory improvements compared with the performance in . We achieve object recognition precision of 0.3859 and scene classification accuracy of 0.6208 among 21 classes of objects and 13 classes of scenes. Both improve obviously comparing to 0.3829 and 0.6086 in baseline [12]. This result is also comparable to the state-of-art method implemented on the identical dataset [18].
Joint adaptive loss and l2/l0-norm minimization for unsupervised feature selection

Unsupervised feature selection is a useful tool for reducing the complexity and improving the generalization performance of data mining tasks. In this paper, we propose an Adaptive Unsupervised Feature Selection (AUFS) algorithm with explicit l2/l0-norm minimization. We use a joint adaptive loss for data fitting and a l2/l0 minimization for feature selection. We solve the optimization problem with an efficient iterative algorithm and prove that all the expected properties of unsupervised feature selection can be preserved. We also show that the computational complexity and memory use is only linear to the number of instances and square to the number of clusters. Experiments show that our algorithm outperforms the state-of-the-arts on seven different benchmark data sets.
Tracking Time Evolving Data Streams for Short-Term Traffic Forecasting

Data streams have arisen as a relevant topic during the last few years as an efficient method for extracting knowledge from big data. In the robust layered ensemble model (RLEM) proposed in this paper for short-term traffic flow forecasting, incoming traffic flow data of all connected road links are organized in chunks corresponding to an optimal time lag. The RLEM model is composed of two layers. In the first layer, we cluster the chunks by using the Graded Possibilistic c-Means method. The second layer is made up by an ensemble of forecasters, each of them trained for short-term traffic flow forecasting on the chunks belonging to a specific cluster. In the operational phase, as a new chunk of traffic flow data presented as input to the RLEM, its memberships to all clusters are evaluated, and if it is not recognized as an outlier, the outputs of all forecasters are combined in an ensemble, obtaining in this a way a forecasting of traffic flow for a short-term time horizon. The proposed RLEM model is evaluated on a synthetic data set, on a traffic flow data simulator and on two real-world traffic flow data sets. The model gives an accurate forecasting of the traffic flow rates with outlier detection and shows a good adaptation to non-stationary traffic regimes. Given its characteristics of outlier detection, accuracy, and robustness, RLEM can be fruitfully integrated in traffic flow management systems.
Frontal fibrosing alopecia: a clinical review of 36 patients.

Frontal fibrosing alopecia (FFA) is a primary lymphocytic cicatricial alopecia with a distinctive clinical pattern of progressive frontotemporal hairline recession. Currently, there are no evidence-based studies to guide treatment for patients with FFA; thus, treatment options vary among clinicians.



We report clinical findings and treatment outcomes of 36 patients with FFA, the largest cohort to date. Further, we report the first evidence-based study of the efficacy of hydroxychloroquine in FFA using a quantitative clinical score, the Lichen Planopilaris Activity Index (LPPAI).



A retrospective case note review was performed of 36 adult patients with FFA. Data were collected on demographics and clinical findings. Treatment responses to hydroxychloroquine, doxycycline and mycophenolate mofetil were assessed using the LPPAI. Adverse events were monitored.



Most patients in our cohort were female (97%), white (92%) and postmenopausal (83%). Apart from hairline recession, 75% also reported eyebrow loss. Scalp pruritus (67%) and perifollicular erythema (86%) were the most common presenting symptom and sign, respectively. A statistically significant reduction in signs and symptoms in subjects treated with hydroxychloroquine (P < 0·05) was found at both 6- and 12-month follow up.



In FFA, hairline recession, scalp pruritus, perifollicular erythema and eyebrow loss are common at presentation. Despite the limitations of a retrospective review, our data reveal that hydroxychloroquine is significantly effective in reducing signs and symptoms of FFA after both 6 and 12 months of treatment. However, the lack of a significant reduction in signs and symptoms between 6 and 12 months indicates that the maximal benefits of hydroxychloroquine are evident within the first 6 months of use.
Molecular phylogenetic evidence for the reorganization of the Hyperiid amphipods, a diverse group of pelagic crustaceans.

Within the crustaceans, the Amphipoda rank as one of the most speciose extant orders. Amphipods have successfully invaded and become major constituents of a variety of ecosystems. The hyperiid amphipods are classically defined as an exclusively pelagic group broadly inhabiting oceanic midwater environments and often having close associations with gelatinous zooplankton. As with other amphipod groups they have largely been classified based on appendage structures, however evidence suggests that at least some of these characters are the product of convergent evolution. Here we present the first multi-locus molecular phylogenetic assessment of relationships among the hyperiid amphipods. We sampled 51 species belonging to 16 of the 23 recognized hyperiidian families for three nuclear loci (18S, 28S, and H3) and mitochondrial COI. We performed both Bayesian Inference and Maximum Likelihood analyses of concatenated sequences. In addition, we also explored the utility of species-tree methods for reconstructing deep evolutionary histories using the Minimize Deep Coalescence (MDC) approach. Our results are compared with previous molecular analyses and traditional systematic groupings. We discuss these results within the context of adaptations correlated with the pelagic life history of hyperiid amphipods. Within the infraorder Physocephalata (Bowman and Gruner, 1973) we inferred support for three reciprocally monophyletic clades; the Platysceloidea, Vibilioidea, and Phronimoidea. Our results also place the enigmatic Cystisomatidae and Paraphronimidae at the base of the infraorder Physosomata (Bowman and Gruner, 1973) suggesting that Physosomata as traditionally recognized is paraphyletic. Based on our multilocus phylogeny, major rearrangements to existing taxonomic groupings of hyperiid amphipods are warranted.
A software-radio test-bed for measuring the performance of DVB-S2 receiver circuits

This paper presents a flexible and versatile DVB-S2 test-bed that combines a set of automated signal generation and analysis instruments along with a powerful software radio processing unit for executing performance analysis experiments of DVB-S2 receiver circuits using realistic scenarios. The test-bed provides a rich set of forward satellite channel impairments and also supports real DVB-S2 signals. All test-bed subunits are interconnected over a LAN and are controlled through a central processing unit, allowing fully automated data acquisition and processing of measurements collected from the various receiver circuits.
Software engineering for security: a roadmap

doesn't need to be secure? Almost every softwarecontrolled system faces threats from potential adversaries, from Internet-aware client applications running on PCs, to complex telecommunications and power systems accessible over the Internet, to commodity software with copy protection mechanisms. Software engineers must be cognizant of these threats and engineer systems with credible defenses, while still delivering value to customers. In this paper, we present our perspectives on the research issues that arise in the interactions between software engineering and security.
Regulation of autophagy by cytoplasmic p53

Multiple cellular stressors, including activation of the tumour suppressor p53, can stimulate autophagy. Here we show that deletion, depletion or inhibition of p53 can induce autophagy in human, mouse and nematode cells subjected to knockout, knockdown or pharmacological inhibition of p53. Enhanced autophagy improved the survival of p53-deficient cancer cells under conditions of hypoxia and nutrient depletion, allowing them to maintain high ATP levels. Inhibition of p53 led to autophagy in enucleated cells, and cytoplasmic, not nuclear, p53 was able to repress the enhanced autophagy of p53−/− cells. Many different inducers of autophagy (for example, starvation, rapamycin and toxins affecting the endoplasmic reticulum) stimulated proteasome-mediated degradation of p53 through a pathway relying on the E3 ubiquitin ligase HDM2. Inhibition of p53 degradation prevented the activation of autophagy in several cell lines, in response to several distinct stimuli. These results provide evidence of a key signalling pathway that links autophagy to the cancer-associated dysregulation of p53.
Effect of occlusal plane on smile attractiveness.

To determine the influence of the occlusal plane angle on smile attractiveness as perceived by a group of adult orthodontic patients and dentists.



The first stage utilized a laboratory approach to record changes in vertical tooth position at different occlusal plane angles using a maxillary model mounted on an articulator. In the second stage, photographic manipulation was undertaken, using data from stage 1, to produce a computerized prediction of the appearance of the smile at differing occlusal plane angles (0, 5, 10, 15, and 20 degrees). Finally, the five developed photographs were assessed by participants.



Alterations in the occlusal plane angle did affect relative smile attractiveness for both patients (n  =  66) and dentists (n  =  66). For patients, the 10 degree smile was rated better than the 0 and 20 degree smiles (P < .01); for dentists, the 15 degree smile was rated better than the 0 and 20 degree smiles (P < .01). The 5, 10, and 15 degree smiles were indistinguishable for patients, and the 10 and 15 degree smiles were indistinguishable for dentists.



Changing the occlusal plane angle does affect relative smile attractiveness. However, patients were more tolerant of these changes than dentists. This suggests that large changes in the occlusal plane angle would affect relative smile attractiveness, and small changes are unlikely to affect smile attractiveness.
Inclusive yet Selective: Supervised Distributional Hypernymy Detection

We test the Distributional Inclusion Hypothesis, which states that hypernyms tend to occur in a superset of contexts in which their hyponyms are found. We find that this hypothesis only holds when it is applied to relevant dimensions. We propose a robust supervised approach that achieves accuracies of .84 and .85 on two existing datasets and that can be interpreted as selecting the dimensions that are relevant for distributional inclusion.
Understanding Network Centric Warfare

Network centric warfare (NCW) is a new theory for war in the information age. NCW advocates that networking battlefield entities will produce shared information, shared knowledge and shared understanding which produce information superiority. In turn information superiority dramatically increases the power of combat. Since it is new, there do exist proponents and opponents. This paper reviews the theory of NCW, and some techniques and combat simulation systems which may help people gain more understanding of NCW.
Security Against Hardware Trojan Attacks Using Key-Based Design Obfuscation

Malicious modification of hardware in untrusted fabrication facilities, referred to as hardware Trojan, has emerged as a major security concern. Comprehensive detection of these Trojans during postmanufacturing test has been shown to be extremely difficult. Hence, it is important to develop design techniques that provide effective countermeasures against hardware Trojans by either preventing Trojan attacks or facilitating detection during test. Obfuscation is a technique that is conventionally employed to prevent piracy of software and hardware intellectual property (IP). In this work, we propose a novel application of key-based circuit structure and functionality obfuscation to achieve protection against hardware Trojans triggered by rare internal circuit conditions. The proposed obfuscation scheme is based on judicious modification of the state transition function, which creates two distinct functional modes: normal and Responsible Editor: S. T. Chakradhar A preliminary version of this work has been published in the International Conference on Computer Aided Design (ICCAD), 2009. R. S. Chakraborty (B) Department of Computer Science and Engineering, Indian Institute of Technology, Kharagpur, West Bengal 721302, India e-mail: rschakraborty@cse.iitkgp.ernet.in S. Bhunia Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA e-mail: skb21@case.edu obfuscated. A circuit transitions from the obfuscated to the normal mode only upon application of a specific input sequence, which defines the key. We show that it provides security against Trojan attacks in two ways: (1) it makes some inserted Trojans benign, i.e. they become effective only in the obfuscated mode; and (2) it prevents an adversary from exploiting the true rare events in a circuit to insert hard-to-detect Trojans. The proposed design methodology can thus achieve simultaneous protection from hardware Trojans and hardware IP piracy. Besides protecting ICs against Trojan attacks in foundry, we show that it can also protect against malicious modifications by untrusted computeraided design (CAD) tools in both SoC and FPGA design flows. Simulation results for a set of benchmark circuits show that the scheme is capable of achieving high levels of security against Trojan attacks at modest area, power and delay overhead.
Nearest neighbor pattern classification

The case of n unity-variance random variables x1, XZ,. * *, x, governed by the joint probability density w(xl, xz, * * * x,) is considered, where the density depends on the (normalized) cross-covariances pii = E[(xi jzi)(xi li)]. It is shown that the condition holds for an “arbitrary” function f(xl, x2, * * * , x,) of n variables if and only if the underlying density w(xl, XZ, * * * , x,) is the usual n-dimensional Gaussian density for correlated random variables. This result establishes a generalized form of Price’s theorem in which: 1) the relevant condition (*) subsumes Price’s original condition; 2) the proof is accomplished without appeal to Laplace integral expansions; and 3) conditions referring to derivatives with respect to diagonal terms pii are avoided, so that the unity variance assumption can be retained. Manuscript received February 10, 1966; revised May 2, 1966. The author is with the Ordnance Research Laboratory, Pennsylvania State University, State College, Pa. RICE’S THEOREM and its various extensions ([l]-[4]) have had great utility in the determination of output correlations between zero-memory nonlinearities subjected to jointly Gaussian inputs. In its original form, the theorem considered n jointly normal random variables, x1, x2, . . . x,, with respective means 21, LE2, . . . ) Z, and nth-order joint probability density, P(z 1, .x2, . 1 . , :r,,) = (27p ply,, y . exp { -a F F ;;;, ~ (2,. 2,)(:r, 5,) I , (1) where IM,l is the determinant’ of J1,, = [p,,], Pr-. = E[(sr 5$.)(x, ,2J] = xvx, &IL:, is the correlation coefficient of x, and x,, and Ail,, is the cofactor of p.? in ilf,. From [l], the theorem statement is as follows: “Let there be n zero-memory nonlinear devices specified by the input-output relationship f<(x), i = 1, 2, . 1 . , n. Let each xi be the single input to a corresponding fi(x) Authorized licensed use limited to: West Virginia University. Downloaded on February 26, 2009 at 13:41 from IEEE Xplore. Restrictions apply.
Exploration in Interactive Personalized Music Recommendation: A Reinforcement Learning Approach

Current music recommender systems typically act in a greedy manner by recommending songs with the highest user ratings. Greedy recommendation, however, is suboptimal over the long term: it does not actively gather information on user preferences and fails to recommend novel songs that are potentially interesting. A successful recommender system must balance the needs to explore user preferences and to exploit this information for recommendation. This article presents a new approach to music recommendation by formulating this exploration-exploitation trade-off as a reinforcement learning task. To learn user preferences, it uses a Bayesian model that accounts for both audio content and the novelty of recommendations. A piecewise-linear approximation to the model and a variational inference algorithm help to speed up Bayesian inference. One additional benefit of our approach is a single unified model for both music recommendation and playlist generation. We demonstrate the strong potential of the proposed approach with simulation results and a user study.
Understanding deep learning requires rethinking generalization

Despite their massivesize, successful deep artificial neural networkscan exhibit a remarkably small differencebetween training and test performance. Conventional wisdom attributessmall generalization error either to propertiesof themodel family, or to the regularization techniquesused during training. Through extensive systematic experiments, we show how these traditional approaches fail to explain why large neural networks generalize well in practice. Specifically, our experimentsestablish that state-of-the-art convolutional networks for image classification trained with stochastic gradient methods easily fit a random labeling of the training data. This phenomenon is qualitatively unaffected by explicit regularization, and occurs even if we replace the true images by completely unstructured random noise. We corroborate these experimental findings with a theoretical construction showing that simpledepth two neural networksalready haveperfect finitesampleexpressivity assoon as thenumber of parameters exceeds thenumber of datapointsas it usually does in practice. We interpret our experimental findingsby comparison with traditional models.
Stochastic policy gradient reinforcement learning on a simple 3D biped

We present a learning system which is able to quickly and reliably acquire a robust feedback control policy for 3D dynamic walking from a blank-slate using only trials implemented on our physical robot. The robot begins walking within a minute and learning converges in approximately 20 minutes. This success can be attributed to the mechanics of our robot, which are modeled after a passive dynamic walker, and to a dramatic reduction in the dimensionality of the learning problem. We reduce the dimensionality by designing a robot with only 6 internal degrees of freedom and 4 actuators, by decomposing the control system in the frontal and sagittal planes, and by formulating the learning problem on the discrete return map dynamics. We apply a stochastic policy gradient algorithm to this reduced problem and decrease the variance of the update using a state-based estimate of the expected cost. This optimized learning system works quickly enough that the robot is able to continually adapt to the terrain as it walks.
Statistical Comparisons of Classifiers over Multiple Data Sets

While methods for comparing two learning algorithms on a single data set have been scrutinized for quite some time already, the issue of statistical tests for comparisons of more algorithms on multiple data sets, which is even more essential to typical machine learning studies, has been all but ignored. This article reviews the current practice and then theoretically and empirically examines several suitable tests. Based on that, we recommend a set of simple, yet safe and robust non-parametric tests for statistical comparisons of classifiers: the Wilcoxon signed ranks test for comparison of two classifiers and the Friedman test with the corresponding post-hoc tests for comparison of more classifiers over multiple data sets. Results of the latter can also be neatly presented with the newly introduced CD (critical difference) diagrams.
Cochrane handbook for systematic reviews of interventions

.................................................................................. 3 1.1 Cochrane ................................................................................ 3 1.1.1 What is Cochrane? ......................................................................... 4 1.1.2 A brief history of Cochrane .................................................................... 4 1.1.3 Cochrane organization and structure .............................................................. 5 1.2 Systematic reviews .......................................................................... 5 1.2.1 The need for systematic reviews ................................................................. 5 1.2.2 What is a systematic review? ................................................................... 5 1.3 Cochrane Reviews .......................................................................... 6 1.3.1 Reviews of the effects of interventions ............................................................. 6 1.3.2 Reviews of diagnostic test accuracy .............................................................. 6 1.3.3 Overviews of Reviews ....................................................................... 6 1.3.4 Reviews of methodology ...................................................................... 7 1.4 Publication of Cochrane Reviews ................................................................. 7 1.4.1 The Cochrane Library ....................................................................... 7 1.5 Handbook structure .......................................................................... 8 1.6 Chapter information .......................................................................... 8 1.7 References ............................................................................... 8 Chapter 3: Reporting the review ..................................................................... 9 3.