Miracle-dz/UniFAR-FTUs huggingface dataset:
TOPIC-BASED INFLUENCE COMPUTATION IN SOCIAL NETWORKS UNDER RESOURCE CONSTRAINTS A THESIS SUBMITTED TO THE GRADUATE SCHOOL OF ENGINEERING AND SCIENCE OF BILKENT UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE IN COMPUTER ENGINEERING

TOPIC-BASED INFLUENCE COMPUTATION IN SOCIAL NETWORKS UNDER RESOURCE CONSTRAINTS Kaan Bingöl M.S. in Computer Engineering Advisor: Assoc. Prof. Dr. Hakan Ferhatosmanoğlu June, 2015 As social networks are constantly changing and evolving, methods to analyze dynamic social networks are becoming more important in understanding social trends. However, due to the restrictions imposed by the social network service providers, the resources available to fetch the entire contents of a social network are typically very limited. As a result, analysis of dynamic social network data requires maintaining an approximate copy of the social network for each time period, locally. We study the problem of dynamic network and text fetching with limited probing capacities, for identifying and maintaining influential users as the social network evolves. We propose an algorithm to probe the relationships (required for global influence computation) as well as posts (required for topic-based influence computation) of a limited number of users during each probing period, based on the influence trends and activities of the users. We infer the current network based on the newly probed user data and the recent version of the network maintained locally. Additionally, we propose to use link prediction methods to further increase accuracy of our network inference. We employ PageRank as the metric for influence computation. We illustrate how the proposed solution maintains accurate PageRank scores for computing global influence, and topic-sensitive weighted PageRank scores for topic-based influence. The latter relies on a topic-based network constructed via weights determined by semantic analysis of posts and their sharing statistics. We evaluate the effectiveness of our algorithms by comparing them with the true influence scores of the full and up-to-date version of the network, using data from the micro-blogging service Twitter. Results show that our techniques significantly outperform baseline methods (80% higher accuracy for network fetching and 77% for text fetching) and are superior to state-of-the-art techniques from the literature (21% higher accuracy).
Characterizing nonlinearities in the Chandra LETGS + HRC-S dispersion relation

The dispersion relation for the Chandra Low Energy Transmission Grating Spectrometer (LETGS) is known to better than 1 part in 1000 over the wavelength range 5-150 Å. A recent resolution of a data processing software bug that lead to a systematic error in the computation of photon wavelengths has allowed us to trace further discrepancies in the dispersion relation to the boundaries between different microchannel plate segments of the HRC-S imaging detector. However, data acquired during in-flight calibration with the HRC-S detector have always shown the presence of additional non-linear deviations in the positions of some spectral lines by as much as 0.05 Å, which is of the order of a full width half maximum (FWHM) of a line profile. These latter effects are thought to be caused by spatial non-linearities in the imaging characteristics of the HRC-S detector. Here, we present an improved dispersion relation for the LETG+HRC-S and new methods to help characterize the spatial non-linearities. We also describe an empirical approach that might be used to help improve the position determination of photon events.
Modeling and Simulation of a Moving Rigid Body in a Rarefied Gas

We present a numerical scheme to simulate a moving rigid body with arbitrary shape suspended in a rarefied gas micro flows, in view of applications to complex computations of moving structures in micro or vacuum systems. The rarefied gas is simulated by solving the Boltzmann equation using a DSMC particle method. The motion of the rigid body is governed by the Newton-Euler equations, where the force and the torque on the rigid body is computed from the momentum transfer of the gas molecules colliding with the body. The resulting motion of the rigid body affects in turn again the gas flow in the surroundings. This means that a two-way coupling has been modeled. We validate the scheme by performing various numerical experiments in 1-, 2- and 3-dimensional computational domains. We have presented 1-dimensional actuator problem, 2-dimensional cavity driven flow problem, Brownian diffusion of a spherical particle both with translational and rotational motions, and finally thermophoresis on a spherical particles. We compare the numerical results obtained from the numerical simulations with the existing theories in each test examples.
Tensor-based Hardness of the Shortest Vector Problem to within Almost Polynomial Factors

We show that unless NP ⊆ RTIME(2 ), for any ε > 0 there is no polynomial-time algorithm approximating the Shortest Vector Problem (SVP) on n-dimensional lattices in the `p norm (1 ≤ p < ∞) to within a factor of 2 n) 1−ε . This improves the previous best factor of 2 n) 1/2−ε under the same complexity assumption due to Khot [18]. Under the stronger assumption NP * RSUBEXP, we obtain a hardness factor of n log log n for some c > 0. Our proof starts with SVP instances from [18] that are hard to approximate to within some constant. To boost the hardness factor we simply apply the standard tensor product of lattices. The main novel part is in the analysis, where we show that the lattices of [18] behave nicely under tensorization. At the heart of the analysis is a certain matrix inequality which was first used in the context of lattices by de Shalit and Parzanchevski [12].
HeadlineCause: A Dataset of News Headlines for Detecting Casualties

Detecting implicit causal relations in texts is a task that requires both common sense and world knowledge. Existing datasets are focused either on commonsense causal reasoning or explicit causal relations. In this work, we present HeadlineCause, a dataset for detecting implicit causal relations between pairs of news headlines. The dataset includes over 5000 headline pairs from English news and over 9000 headline pairs from Russian news labeled through crowdsourcing. The pairs vary from totally unrelated or belonging to the same general topic to the ones including causation and refutation relations. We also present a set of models and experiments that demonstrates the dataset validity, including a multilingual XLM-RoBERTa based model for causality detection and a GPT-2 based model for possible effects prediction.
Feedback Capacity of Stationary Gaussian Channels

The feedback capacity of additive stationary Gaussian noise channels is characterized as the solution to a variational problem in the noise power spectral density. When specialized to the first-order autoregressive moving-average noise spectrum, this variational characterization yields a closed-form expression for the feedback capacity. In particular, this result shows that the celebrated Schalkwijk-Kailath coding achieves the feedback capacity for the first-order autoregressive moving-average Gaussian channel, positively answering a long-standing open problem studied by Butman, Tiernan-Schalkwijk, Wolfowitz, Ozarow, Ordentlich, Yang-Kavc¿ic¿-Tatikonda, and others. More generally, it is shown that a k-dimensional generalization of the Schalkwijk-Kailath coding achieves the feedback capacity for any autoregressive moving-average noise spectrum of order k. Simply put, the optimal transmitter iteratively refines the receiver's knowledge of the intended message. This development reveals intriguing connections between estimation, control, and feedback communication.
Earthshine : Photometry, modeling, and spectral observations

EARTHSHINE: PHOTOMETRY, MODELING, AND SPECTRAL OBSERVATIONS by Jeffrey Patrick Hickey The Earthshine group has been making sustained observations of the Earthshine from Big Bear Solar Observatory in California since late 1998. There have also been intermittent observations from 1994-5. High and low resolution Earthshine spectral observations have also been under taken at Palomar Observatory since 1999. The group has re-invigorated and modernized a nearly forgotten way of measuring the Earth's albedo, and hence its energy balance, previously studied by Danjon (and his followers) for about twenty-five years early in the last century, using their observations of the Earthshine from France. This is an overview paper covering observations, reductions, simulations, and analysis, of the Earth's reflectance from photometric and spectral observations of the moon. The Earthshine group developed a modern method of measuring, instantaneously, the large scale reflectance of the Earth. From California an observer sees the moon reflecting sunlight from the third of the Earth to the west in the evening (before midnight) which is during the moon's rising phase and from the third of the Earth to the east in the morning (after midnight) which is during the moon's declining phase. The group has precisely measured the scattering from the moon, as a function of lunar phase, which enables the measurement, in a typical night's observations, the Earth's reflectance to an accuracy of 2.0% (equivalent to measuring the Earth's emission temperature to —0.8 K). The group identified the lunar phase function as the major source of discrepancy between Danjon's estimates of the albedo and more recent measurements. The albedo variation is due to the interplay of cloud cover and the different landscapes. EARTHSHINE: PHOTOMETRY, MODELING, AND SPECTRAL OBSERVATIONS by Jeffrey Patrick Hickey A Thesis Submitted to the Faculty of New Jersey Institute of Technology in Partial Fulfillment of the Requirements for the Degree of Master of Science in Applied Physics Federated Physics Department August 2006 APPROVAL PAGE EARTHSHINE: PHOTOMETRY, MODELING, AND SPECTRAL OBSERVATIONS Jeffrey Patrick Hickey Dr. Philip R. Goode, Dissertation Advisor Date Distinguished Professor of Physics, NJIT Dr. Haimin Wang, Committee Member Date Distinguished Professor of Physics, NJIT Dr. Carsten J. Denker, Committee Member Date Assistant Professor of Physics, NJIT
Panopticon: a parallel video overview system

Panopticon is a video surrogate system that displays multiple sub-sequences in parallel to present a rapid overview of the entire sequence to the user. A novel, precisely animated arrangement slides thumbnails to provide a consistent spatiotemporal layout while allowing any sub-sequence of the original video to be watched without interruption. Furthermore, this output can be generated offline as a highly efficient repeated animation loop, making it suitable for resource-constrained environments, such as web-based interaction. Two versions of Panopticon were evaluated using three different types of video footage with the aim of determining the usability of the proposed system. Results demonstrated an advantage over another surrogate with surveillance footage in terms of search times and this advantage was further improved with Panopticon 2. Eye tracking data suggests that Panopticon's advantage stems from the animated timeline that users heavily rely on.
MITIGATION OF FREQUENCY OSCILLATIONS IN A SYNCHRONOUS MACHINE INFINITE BUS SYSTEM WITH LOW MOMENT OF INERTIA

La integracion de fuentes renovables de energia a la red electrica ha desatado nuevos retos para la operacion y estabilidad de los sistemas electricos de potencia. Debido a que fuentes de energia como la solar o la eolica proporcionan poco o nada de inercia rotacional a la red, los sistemas de potencia actuales quedan mayormente propensos a variaciones de potencia, variaciones de carga o fallas electricas. El objetivo de esta investigacion es caracterizar las oscilaciones en la frecuencia en un sistema maquina-bus infinito sujeto a pequenos disturbios y proponer un metodo para mitigarlas utilizando tecnicas de la teoria de control optimo.
Rich feature hierarchies for accurate object detection and semantic segmentation Tech report

Can a large convolutional neural network trained for whole-image classiﬁcation on ImageNet be coaxed into detecting objects in PASCAL? We show that the answer is yes, and that the resulting system is simple, scalable, and boosts mean average precision, relative to the venerable deformable part model, by more than 40% (achieving a ﬁ-nal mAP of 48% on VOC 2007). Our framework combines powerful computer vision techniques for generating bottom-up region proposals with recent advances in learning high-capacity convolutional neural networks. We call the resulting system R-CNN : Regions with CNN features. The same framework is also competitive with state-of-the-art semantic segmentation methods, demonstrating its ﬂexibility. Beyond these results, we execute a battery of experiments that provide insight into what the network learns to represent, revealing a rich hierarchy of discriminative and often semantically meaningful features.
Trastuzumab emtansine in the treatment of HeR2-positive metastatic breast cancer in Japanese patients

: Anti-HER2 agents, such as trastuzumab, lapatinib, trastuzumab emtansine (T-DM1), and pertuzumab, are standard agents in the treatment of breast cancer overexpressing the human epidermal growth factor receptor 2 (HER2). Trastuzumab is the first approved HER2-targeted agent. Subsequent developments include agents with different mechanisms. In this paper, we review the results of clinical trials of T-DM1, a new anti-HER2 agent, with a focus on Japanese patients with breast cancer. On the basis of results from a Phase I study (JO22591), the maximum tolerated dose was determined to be 3.6 mg/kg every 3 weeks for both Japanese and western patients. In a Phase II study (JO22997), the overall response rate was 38.4% (90% confidence interval 28.8–48.6). T-DM1 was well tolerated in Japanese patients; however, the incidence of grade 3 or 4 thrombocytopenia was higher than that observed in earlier western studies, but was not associated with clinically important symptoms. Pharmacokinetic parameters for T-DM1 and its metabolites were consistent with those reported previously from a Phase I or II study in non-Japanese patients, and the data obtained showed no suggestion of ethnic differences. Several Phase III studies of T-DM1 are ongoing throughout the world, including in Japanese patients with breast cancer. Study of the Efficacy and Safety of Trastuzumab Emtansine vs. Capecitabine + Lapatinib in Patients with HeR2-positive Locally Advanced or Metastatic Breast Cancer who Have Received Prior Trastuzumab-based Therapy.
Self-intersections of curves on a surface and Bernoulli numbers

We study an operation which measures self-intersections of curves on an oriented surface. It turns out that a certain computation on this topological operation is related to the Bernoulli numbers Bm, and our study yields a family of explicit formulas for Bm. As a special case, this family contains the celebrated formula for Bm due to Kronecker.
BciNet: A Biased Contest-Based Crowdsourcing Incentive Mechanism Through Exploiting Social Networks

Crowdsourcing has proved to be a splendid tool to aggregate the knowledge from a pool of individuals in order to perform abundant microtasks efficiently. Recently, with the explosive growth of online social network, Word of Mouth (WoM)-based crowdsourcing systems have emerged, in which besides conducting the tasks by themselves, participants simultaneously recruit other individuals through exploiting their social networks to help solve crowdsourced tasks. This crowdsourcing paradigm can greatly facilitate to grow the pool of crowdworkers. However, there exist two conflicting challenges in designing an effective WoM-based incentive mechanism: 1) sybil attack and 2) heterogeneous effect of participants. That is, intuitively, incentivizing (usually compensating for) common-ability individuals will inevitably stimulate the behavior of sybil attack (i.e., some individuals create multiple sybils, and split the total efforts into those sybils to expect more compensation). This paper proposes a novel biased contest-based crowdsourcing incentive mechanism through exploiting social networks (BciNet), aiming to balance those two conflicting objectives. BciNet is composed of two phases. First, based on spreading activation model, an enhanced geometric virtual point dissemination mechanism is able to provide sybil-proof property and accommodate the realistic social network structure. Second, based on participants’ virtual points, a biased contest gives more reward to less able participants. Through carefully calibrating the bias factor, simulation results based on the real dataset show that BciNet can greatly improve the amount of participants’ effort levels, and actually be robust against the sybil attack. In brief, for a practical incentive mechanism, the methodology to address conflicting goals is to put rational individuals into dilemma: to be sybil or not to be, it is the problem, i.e., the potential gain from the sybils in the second phase may be offset by the loss in the first phase.
Using ground based data as a precursor for Gaia in getting proper motions of satellites

Abstract We present our effort to measure the proper motions of satellites in the halo of the Milky Way with mainly ground based telescopes as a precursor on what is possible with Gaia. For our first study, we used wide field optical data from the LBT combined with a first epoch of SDSS observations, on the globular cluster Palomar 5 (Pal 5). Since Pal 5 is associated with a tidal stream it is very useful to constrain the shape of the potential of the Milky Way. The motion and other properties of the Pal 5 system constrain the inner halo of the Milky Way to be rather spherical. Further, we combined adaptive optics and HST to get an absolute proper motion of the globular cluster Pyxis. Using the proper motion and the line-of-sight velocity we find that the orbit of Pyxis is rather eccentric with its apocenter at more than 100 kpc and its pericenter at about 30 kpc. The dynamics excludes an association with the ATLAS stream, the Magellanic clouds, and all satellites of the Milky Way at least down to the mass of Leo II. However, the properties of Pyxis, like metallicity and age, point to an origin from a dwarf of at least the mass of Leo II. We therefore propose that Pyxis originated from an unknown relatively massive dwarf galaxy, which is likely today fully disrupted. Assuming that Pyxis is bound to the Milky Way we derive a 68% lower limit on the mass of the Milky Way of 9.5 × 1011 M⊙.
Limitations of Post-Hoc Feature Alignment for Robustness

Recent work has found that models become increasingly less calibrated under distribution shift [6]. Motivated by this problem, we also test whether AdaBN helps with calibration error on the target distribution. We use a simple and popular measure of calibration: the Expected Calibration Error (ECE) [3]. In Table 1 we show the ECE for AdaBN on each dataset. We find that AdaBN substantially reduces calibration error on the corruption benchmarks and Stylized ImageNet, even cutting it in half in most cases.
Ownership Structure, Voting and Risk

We analyze the determinants of a firm's ownership structure when decisions over risk are taken by majority vote of risk-averse shareholders. We show that when a fraction of small, diversified shareholders abstains from voting, mid-sized blockholders may emerge to mitigate the conflict of interests between one large shareholder, who prefers less risky investments, and these small, non-voting shareholders. The paper offers a novel explanation for the puzzling observation that many firms have multiple blockholders. The paper develops numerous empirical implications, for example on the link between ownership structure and risk choices and on the relative size of blocks.
On the minimum degree of minimal Ramsey graphs

We investigate the minimization problem of the minimum degree of minimal Ramsey graphs, initiated by Burr et al. We determine the corresponding graph parameter for numerous bipartite graphs, including bi‐regular bipartite graphs and forests. We also make initial progress for graphs of larger chromatic number. Numerous interesting problems remain open. © 2009 Wiley Periodicals, Inc. J Graph Theory 64: 150–164, 2010
Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation Supplementary Materials

In this document, we provide supplementary materials for our main paper submission. The main paper reported our experimental results using three standard UDA evaluation protocols (EPs) where the SYNTHIA dataset [7] is used as the synthetic domain. To demonstrate our proposed method’s effectiveness on an entirely new UDA setting, in Sec. S1, we report semantic segmentation results of our method on a new EP: Virtual KITTI → KITTI. In this setup, we use synthetic Virtual KITTI [3] as the source domain and real KITTI [4] as the target domain. We show that our proposed method consistently outperforms the SOTA DADA method [9] when evaluated on this new EP with different synthetic and real domains. In Sec. S2, we present a t-SNE [8] plot comparing our method with [9]. We also share additional qualitative results on SYNTHIA → Cityscapes (16 classes). Sec. S3 details our network de-sign. To demonstrate that the proposed CTRL is not sensitive to a particular network design (in our case, the residual auxiliary block [5]), we train a standard multi-task learning network architecture (i.e., a shared encoder followed by multiple task-speciﬁc decoders without any residual auxiliary block) with CTRL and notice a similar improvement trend over the baselines. The set of experiments and the results are discussed in Sec. S4.
Unsupervised change detection in high spatial resolution remote sensing images based on a conditional random field model

Abstract In this paper, we propose a novel technique for unsupervised change detection in high spatial remote sensing images based on a conditional random field (CRF) model. The change-detection problem is formulated as a labeling issue to discriminate the changed class from the unchanged class in the difference image. CRF which employs the spatial property on both pixel's spectral data and labels have been widely used in many remote sensing applications. However, as there are a large number of model parameters to train, the CRF-based change-detection approach is time consuming and difficult to implement. The proposed method artfully uses memberships of Fuzzy C-means as unary potentials and defines pairwise potentials using a scaled squared Euclidean distance between neighboring pixels. This not only avoids training parameters but also helps improving the accuracy and the degree of automation. The experimental results obtained from three different remote sensing images demonstrate the accuracy and efficiency of our proposed method.
A System for Video-Based Navigation for Endoscopic Endonasal Skull Base Surgery

Surgeries of the skull base require accuracy to safely navigate the critical anatomy. This is particularly the case for endoscopic endonasal skull base surgery (ESBS) where the surgeons work within millimeters of neurovascular structures at the skull base. Today's navigation systems provide approximately 2 mm accuracy. Accuracy is limited by the indirect relationship of the navigation system, the image and the patient. We propose a method to directly track the position of the endoscope using video data acquired from the endoscope camera. Our method first tracks image feature points in the video and reconstructs the image feature points to produce 3D points, and then registers the reconstructed point cloud to a surface segmented from preoperative computed tomography (CT) data. After the initial registration, the system tracks image features and maintains the 2D-3D correspondence of image features and 3D locations. These data are then used to update the current camera pose. We present a method for validation of our system, which achieves submillimeter (0.70 mm mean) target registration error (TRE) results.