Miracle-dz/UniFAR-FTUs huggingface dataset:
Lower Bounds on Time-Space Trade-Offs for Approximate Near Neighbors

We show tight lower bounds for the entire trade-off between space and query time for the Approximate Near Neighbor search problem. Our lower bounds hold in a restricted model of computation, which captures all hashing-based approaches. In articular, our lower bound matches the upper bound recently shown in [Laarhoven 2015] for the random instance on a Euclidean sphere (which we show in fact extends to the entire space $\mathbb{R}^d$ using the techniques from [Andoni, Razenshteyn 2015]). We also show tight, unconditional cell-probe lower bounds for one and two probes, improving upon the best known bounds from [Panigrahy, Talwar, Wieder 2010]. In particular, this is the first space lower bound (for any static data structure) for two probes which is not polynomially smaller than for one probe. To show the result for two probes, we establish and exploit a connection to locally-decodable codes.
Fast and Robust Graphic Character Verification System for TV Sets

In this paper, we present a flexible system to verify any kind of graphic TV character or symbol in television charts. The proposed approach is very robust towards translations, rotations, changes in scale and illumination variations of the graphic characters. Another important attribute of the proposed system is its speed. The system should be very fast since the characters have to be verified in a real production line of TV sets. The proposed system is composed of fast and accurate preprocessing modules in order to normalize the characters, and a PCA- based verification method. Results show a very high performance with verification rates over the 99.8%.
Perceptual Expertise Effects Are Not All or None: Spatially Limited Perceptual Expertise for Faces in a Case of Prosopagnosia

We document a seemingly unique case of severe prosopagnosia, L. R. , who suffered damage to his anterior and inferior right temporal lobe as a result of a motor vehicle accident. We systematically investigated each of three factors associated with expert face recognition: fine-level discrimination, holistic processing, and configural processing (Experiments 1-3). Surprisingly, L. R. shows preservation of all three of these processes; that is, his performance in these experiments is comparable to that of normal controls. However, L. R. is only able to apply these processes over a limited spatial extent to the fine-level detail within faces. Thus, when the location of a given change is unpredictable (Experiment 3), L. R. exhibits normal detection of features and spatial configurations only for the lower half of each face. Similarly, when required to divide his attention over multiple face features, L. R. is able to determine the identity of only a single feature (Experiment 4). We discuss these results in the context of forming a better understanding of prosopagnosia and the mechanisms used in face recognition and visual expertise. We conclude that these mechanisms are not all-or-none, but rather can be impaired incrementally, such that they may remain functional over a restricted spatial area. This conclusion is consistent with previous research suggesting that perceptual expertise is acquired in a spatially incremental manner [Gauthier, I. , & Tarr, M. J. Unraveling mechanisms for expert object recognition: Bridging brain activity and behavior. Journal of Experimental Psychology: Human Perception & Performance, 28, 431-446, 2002].
Towards Task Recommendation in Micro-Task Markets

As researchers embrace micro-task markets for eliciting human input, the nature of the posted tasks moves from those requiring simple mechanical labor to requiring specific cognitive skills. On the other hand, increase is seen in the number of such tasks and the user population in micro-task market places requiring better search interfaces for productive user participation. In this paper we posit that understanding user skill sets and presenting them with suitable tasks not only maximizes the over quality of the output, but also attempts to maximize the benefit to the user in terms of more successfully completed tasks. We also implement a recommendation engine for suggesting tasks to users based on implicit modeling of skills and interests.We present results from a preliminary evaluation of our system using publicly available data gathered from a variety of human computation experiments recently conducted on Amazon's Mechanical Turk.
Isolation and partial characterization of a 110-kD dimer actin-binding protein

Two Triton-insoluble fractions were isolated from Acanthamoeba castellanii. The major non-membrane proteins in both fractions were actin (30-40%), myosin II (4-9%), myosin I (1-5%), and a 55-kD polypeptide (10%). The 55-kD polypeptide did not react with antibodies against tubulins from turkey brain, paramecium, or yeast. All of these proteins were much more concentrated in the Triton-insoluble fractions than in the whole homogenate or soluble supernatant. The 55-kD polypeptide was extracted with 0.3 M NaCl, fractionated by ammonium sulfate, and purified to near homogeneity by DEAE-cellulose and hydroxyapatite chromatography. The purified protein had a molecular mass of 110 kD and appeared to be a homodimer by isoelectric focusing. The 110-kD dimer bound to F-actin with a maximal binding stoichiometry of 0.5 mol/mol of actin (1 mol of 55-kD subunit/mol of actin). Although the 110-kD protein enhanced the sedimentation of F-actin, it did not affect the low shear viscosity of F-actin solutions nor was bundling of F-actin observed by electron microscopy. The 110-kD dimer protein inhibited the actin-activated Mg2+-ATPase activities of Acanthamoeba myosin I and myosin II in a concentration-dependent manner. By indirect immunofluorescence, the 110-kD protein was found to be localized in the peripheral cytoplasm near the plasma membrane which is also enriched in F-actin filaments and myosin I.
Automatic Open Knowledge Acquisition via Long Short-Term Memory Networks with Feedback Negative Sampling

Previous studies in Open Information Extraction (Open IE) are mainly based on extraction patterns. They manually define patterns or automatically learn them from a large corpus. However, these approaches are limited when grasping the context of a sentence, and they fail to capture implicit relations. In this paper, we address this problem with the following methods. First, we exploit long short-term memory (LSTM) networks to extract higher-level features along the shortest dependency paths, connecting headwords of relations and arguments. The path-level features from LSTM networks provide useful clues regarding contextual information and the validity of arguments. Second, we constructed samples to train LSTM networks without the need for manual labeling. In particular, feedback negative sampling picks highly negative samples among non-positive samples through a model trained with positive samples. The experimental results show that our approach produces more precise and abundant extractions than state-of-the-art open IE systems. To the best of our knowledge, this is the first work to apply deep learning to Open IE.
StatSnowball: a statistical approach to extracting entity relationships

Traditional relation extraction methods require pre-specified relations and relation-specific human-tagged examples. Bootstrapping systems significantly reduce the number of training examples, but they usually apply heuristic-based methods to combine a set of strict hard rules, which limit the ability to generalize and thus generate a low recall. Furthermore, existing bootstrapping methods do not perform open information extraction (Open IE), which can identify various types of relations without requiring pre-specifications. In this paper, we propose a statistical extraction framework called Statistical Snowball (StatSnowball), which is a bootstrapping system and can perform both traditional relation extraction and Open IE. StatSnowball uses the discriminative Markov logic networks (MLNs) and softens hard rules by learning their weights in a maximum likelihood estimate sense. MLN is a general model, and can be configured to perform different levels of relation extraction. In StatSnwoball, pattern selection is performed by solving an l1-norm penalized maximum likelihood estimation, which enjoys well-founded theories and efficient solvers. We extensively evaluate the performance of StatSnowball in different configurations on both a small but fully labeled data set and large-scale Web data. Empirical results show that StatSnowball can achieve a significantly higher recall without sacrificing the high precision during iterations with a small number of seeds, and the joint inference of MLN can improve the performance. Finally, StatSnowball is efficient and we have developed a working entity relation search engine called Renlifang based on it.
Direct position determination of narrowband radio frequency transmitters

The most common methods for location of communications or radar transmitters are based on measuring a specified parameter such as signal angle of arrival (AOA) or time of arrival (TOA). The measured parameters are then used to estimate the transmitter location. Since the AOA/TOA measurements are done at each base station separately, without using the constraint that all measurements must correspond to the same transmitter, they are suboptimal. We propose a technique that uses exactly the same data as the common methods, except that the estimation of location is based on exact maximum likelihood, and the location determination is direct. Although there are many stray parameters, including the attenuation coefficients and the signal waveform, the method requires only a two-dimensional search. Monte Carlo simulations indicate that the accuracy is equivalent to AOA, TOA, and their combination for high SNR, while for low SNR, the accuracy of the proposed method is superior.
Path replanning and controller design for trajectory tracking of automated guided vehicles

This paper proposes D* Lite algorithm and controller design based on backstepping method for automated guided vehicles. The D* Lite algorithm is to replan a path for automated guided vehicle to avoid obstacles in the work environment such as objects that are stationary, moving, known and unknown. The designed controller is for the automated guided vehicle to track the replanned path. To do this task, the followings are done: system modeling, path replanning development and controller design. Simulations and experiments are conducted for verification of the proposed algorithm and controller. The simulation and experimental results show that the proposed algorithm and controller work well by enabling the automated guided vehicle to replan the path that passes unknown obstacle and track the planned trajectory with very small errors.
New tricks of an old enemy: isolates of F usarium graminearum produce a type A trichothecene mycotoxin

Summary The ubiquitous filamentous fungus F usarium graminearum causes the important disease Fusarium head blight on various species of cereals, leading to contamination of grains with mycotoxins. In a survey of F . graminearum (sensu stricto) on wheat in North America several novel strains were isolated, which produced none of the known trichothecene mycotoxins despite causing normal disease symptoms. In rice cultures, a new trichothecene mycotoxin (named NX‐2) was characterized by liquid chromatography‐tandem mass spectrometry. Nuclear magnetic resonance measurements identified NX‐2 as 3α‐acetoxy‐7α,15‐dihydroxy‐12,13‐epoxytrichothec‐9‐ene. Compared with the well‐known 3‐acetyl‐deoxynivalenol (3‐ADON), it lacks the keto group at C‐8 and hence is a type A trichothecene. Wheat ears inoculated with the isolated strains revealed a 10‐fold higher contamination with its deacetylated form, named NX‐3, (up to 540 mg kg−1) compared with NX‐2. The toxicities of the novel mycotoxins were evaluated utilizing two in vitro translation assays and the alga C hlamydomonas reinhardtii. NX‐3 inhibits protein biosynthesis to almost the same extent as the prominent mycotoxin deoxynivalenol, while NX‐2 is far less toxic, similar to 3‐ADON. Genetic analysis revealed a different TRI 1 allele in the N‐isolates, which was verified to be responsible for the difference in hydroxylation at C‐8.
Silicon Physical Random Functions (cid:3)

We introduce the notion of a Physical Random Function (PUF). We argue that a complex integrated circuit can be viewed as a silicon PUF and describe a technique to identify and authenticate individual integrated circuits (ICs). We describe several possible circuit realizations of di(cid:11)er-ent PUFs. These circuits have been implemented in commodity Field Programmable Gate Arrays (FPGAs). We present experiments which indicate that reliable authentication of individual FPGAs can be performed even in the presence of signi(cid:12)cant environmental variations. We describe how secure smart cards can be built, and also brie(cid:13)y describe how PUFs can be applied to licensing and certi(cid:12)cation applications.
Partitioning of Intermediary Carbon Metabolism in Vesicular-Arbuscular Mycorrhizal Leek

Vesicular-arbuscular mycorrhizal fungi are symbionts for a large variety of crop plants; however, the form in which they take up carbon from the host is not established. To trace the course of carbon metabolism, we have used nuclear magnetic resonance spectroscopy with [13C]glucose labeling in vivo and in extracts to examine leek (Allium porrum) roots colonized by Glomus etunicatum (and uncolonized controls) as well as germinating spores. These studies implicate glucose as a likely substrate for vesicular-arbuscular mycorrhizal fungi in the symbiotic state. Root feeding of 0.6 mM 1-[13C]glucose labeled only the fungal metabolites trehalose and glycogen. The time course of this labeling was dependent on the status of the host. Incubation with 50 mM 1-[13C]glucose caused labeling of sucrose (in addition to fungal metabolites) with twice as much labeling in uncolonized plants. There was no detectable scrambling of the label from C1 glucose to the C6 position of glucose moieties in trehalose or glycogen. Labeling of mannitol C1,6 in the colonized root tissue was much less than in axenically germinating spores. Thus, carbohydrate metabolism of host and fungus are significantly altered in the symbiotic state.
A Unified Approach to Unwarping of Omni-Images into Panoramic and Perspective-View Images Using Pano-Mapping Tables

A unified approach to unwarping of omni-images into panoramic or perspective-view images is proposed. The approach does not adopt the conventional technique of calibrating the related parameters of an omni-camera. Instead, it is based on a new concept of pano-mapping table, which is created once forever by a simple learning process for an omni-camera of any kind as a summary of the information conveyed by all the camera parameters. The learning process takes as input a set of landmark point pairs in the world space and in a given image. With the help of the pano-mapping table, any panoramic or perspective-view image can be created from an input omni-image taken by the omni-camera according to an analytic computation process proposed in this study. Experimental results show the feasibility of the proposed approach.
Deep Linguistic Analysis for the Accurate Identification of Predicate-Argument Relations

This paper evaluates the accuracy of HPSG parsing in terms of the identification of predicate-argument relations. We could directly compare the output of HPSG parsing with PropBank annotations, by assuming a unique mapping from HPSG semantic representation into PropBank annotation. Even though PropBank was not used for the training of a disambiguation model, an HPSG parser achieved the accuracy competitive with existing studies on the task of identifying PropBank annotations.
Quantitative Analysis of Pulmonary Emphysema Using Local Binary Patterns

We aim at improving quantitative measures of emphysema in computed tomography (CT) images of the lungs. Current standard measures, such as the relative area of emphysema (RA), rely on a single intensity threshold on individual pixels, thus ignoring any interrelations between pixels. Texture analysis allows for a much richer representation that also takes the local structure around pixels into account. This paper presents a texture classification-based system for emphysema quantification in CT images. Measures of emphysema severity are obtained by fusing pixel posterior probabilities output by a classifier. Local binary patterns (LBP) are used as texture features, and joint LBP and intensity histograms are used for characterizing regions of interest (ROIs). Classification is then performed using a k nearest neighbor classifier with a histogram dissimilarity measure as distance. A 95.2% classification accuracy was achieved on a set of 168 manually annotated ROIs, comprising the three classes: normal tissue, centrilobular emphysema, and paraseptal emphysema. The measured emphysema severity was in good agreement with a pulmonary function test (PFT) achieving correlation coefficients of up to |r| = 0.79 in 39 subjects. The results were compared to RA and to a Gaussian filter bank, and the texture-based measures correlated significantly better with PFT than did RA.
Compound splitting and lexical unit recombination for improved performance of a speech recognition system for German parliamentary speeches

This paper proposes a novel combined compound splitting and phrase recombination method that optimizes the composition of the speech recognition lexicon for a given domain. Data-driven compound word splitting is followed by iterative recombination of high frequency combinations. Language model perplexity and size are the criteria used to identify a balance between compound decomposition, which reduces OOV, and lexical unit recombination, which packs additional context into a fixed-size vocabulary. The method provides a basis for lexicon design for a LVCSR system on the domain of German parliamentary speeches that is to be used as the foundation of a spoken document information retrieval system. The approach achieves a 35% reduction in OOV without a prohibitively large sacrifice in recognition performance.
Porting concepts from DNNs back to GMMs

Deep neural networks (DNNs) have been shown to outperform Gaussian Mixture Models (GMM) on a variety of speech recognition benchmarks. In this paper we analyze the differences between the DNN and GMM modeling techniques and port the best ideas from the DNN-based modeling to a GMM-based system. By going both deep (multiple layers) and wide (multiple parallel sub-models) and by sharing model parameters, we are able to close the gap between the two modeling techniques on the TIMIT database. Since the `deep' GMMs retain the maximum-likelihood trained Gaussians as first layer, advanced techniques such as speaker adaptation and model-based noise robustness can be readily incorporated. Regardless of their similarities, the DNNs and the deep GMMs still show a sufficient amount of complementarity to allow effective system combination.
Protocol Coding for Two-Way Communications with Half-Duplex Constraints

The operation of communication protocols is conventionally independent of the information being transmitted. This paper puts forth the notion of protocol coding to refer to transmission strategies in which data can be encoded by modulating the protocol actions according to the information message. We focus on communication in the presence of half-duplex constraints, where the task of the protocol is to schedule the transmission/reception times of different nodes. Such a schedule is conventionally decided a priori in the form of time-sharing. While previous work has focused on protocol coding for standard relay channels, this paper tackles two-way communications aided by a relay. Since the techniques developed for standard relay channels cannot be applied to the scenario at hand, a novel simple strategy is proposed that is tailored to two-way communications. The proposed scheme is shown to significantly outperform conventional time-sharing for a deterministic two-way relay channel.
Rhizopusmycosis in a Harbor Porpoise from the Baltic Sea

A case of systemic mycosis due to a Rhizopus sp. infection is described in a dead-stranded, 10-yr-old, male harbor porpoise (Phocoena phocoena) found on the beach of Neustadt, Schleswig-Holstein on the Baltic Sea (Germany). At necropsy, granulomatous mycotic lesions in brain, lung, kidneys, testis, and draining lymph nodes were found. In addition, a focal ulcerative gastritis of the first stomach, due to a nematode infection, was present and is suspected to be the portal of entry for the fungus.
Comparison of adding tocilizumab to methotrexate with switching to tocilizumab in patients with rheumatoid arthritis with inadequate response to methotrexate: 52-week results from a prospective, randomised, controlled study (SURPRISE study)

Objective To compare the efficacy and safety between tocilizumab added to methotrexate and tocilizumab switched from methotrexate in patients with active rheumatoid arthritis (RA). Methods This is a 2-year randomised, controlled study. RA patients with moderate or high disease activity despite methotrexate were randomly assigned either to tocilizumab added to methotrexate (add-on) or tocilizumab switched from methotrexate (switch). The primary endpoint was the DAS28 remission rate at week 24. Secondary objectives included other clinical efficacy indices, radiological outcomes assessed with the van der Heijde-modified total Sharp scoring system (mTSS), and safety. Results Of 223 randomised patients, 83% completed 52 weeks. DAS28 remission rates at week 24 were 70% for add-on and 55% for switch (p=0.02), but they became comparable at week 52 (72% vs 70%, p=0.86). Structural remission rates (mTSS≤0.5) at week 52 were not different (66% vs 64%, p=0.92). However, clinically relevant radiographic progression rates (CRRP; mTSS≥3) tended to be higher with the switch than with the add-on (15% vs 7%, p=0.07). Radiographic progression in the CRRP patients was larger with the switch than with the add-on (9.0/year vs 5.0/year, p=0.04). The difference in the mean C-reactive protein of the CRRP patients was significant for the first 24 weeks (1.56 vs 0.49, p=0.001) but not for the following 28 weeks (0.10 vs 0.04, p=0.1). Overall safety was preferable in the switch group. Conclusions In RA patients with inadequate response to methotrexate, tocilizumab added to methotrexate more rapidly suppressed inflammation than tocilizumab switched from methotrexate, leading to superior clinical efficacy and prevention of joint destruction. Trial registration number NCT01120366.
Active fault detection in MIMO systems

The focus in this paper is on active fault detection (AFD) for MIMO systems with parametric faults. The problem of design of auxiliary inputs with respect to detection of parametric faults is investigated. An analysis of the design of auxiliary inputs is given based on analytic transfer functions from auxiliary input to residual outputs. The analysis is based on a singular value decomposition of these transfer functions Based on this analysis, it is possible to design auxiliary input as well as design of the associated residual vector with respect to every single parametric fault in the system such that it is possible to detect these faults.