Query: Automatic refinement of large-scale cross-domain knowledge graphs

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Multilabel Text Classification for Automated Tag Suggestion <=SEP=> The increased popularity of tagging during the last few years can be mainly attributed to its embracing by most of the recently thriving user-centric content publishing and management Web 2.0 applications. However, tagging systems have some limitations that have led researchers to develop methods that assist users in the tagging process, by automatically suggesting an appropriate set of tags. We have tried to model the automated tag suggestion problem as a multilabel text classification task in order to participate in the ECML/PKDD 2008 Discovery Challenge.

A Synopsis of Linguistic Theory 1930-1955" in Studies in Linguistic Analysis <=SEP=> 

Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction <=SEP=> We explore some of the practicalities of using random walk inference methods, such as the Path Ranking Algorithm (PRA), for the task of knowledge base completion. We show that the random walk probabilities computed (at great expense) by PRA provide no discernible benefit to performance on this task, so they can safely be dropped. This allows us to define a simpler algorithm for generating feature matrices from graphs, which we call subgraph feature extraction (SFE). In addition to being conceptually simpler than PRA, SFE is much more efficient, reducing computation by an order of magnitude, and more expressive, allowing for much richer features than paths between two nodes in a graph. We show experimentally that this technique gives substantially better performance than PRA and its variants, improving mean average precision from .432 to .528 on a knowledge base completion task using the NELL KB.

The RDF-3X engine for scalable management of RDF data <=SEP=> RDF is a data model for schema-free structured information that is gaining momentum in the context of Semantic-Web data, life sciences, and also Web 2.0 platforms. The “pay-as-you-go” nature of RDF and the flexible pattern-matching capabilities of its query language SPARQL entail efficiency and scalability challenges for complex queries including long join paths. This paper presents the RDF-3X engine, an implementation of SPARQL that achieves excellent performance by pursuing a RISC-style architecture with streamlined indexing and query processing. The physical design is identical for all RDF-3X databases regardless of their workloads, and completely eliminates the need for index tuning by exhaustive indexes for all permutations of subject-property-object triples and their binary and unary projections. These indexes are highly compressed, and the query processor can aggressively leverage fast merge joins with excellent performance of processor caches. The query optimizer is able to choose optimal join orders even for complex queries, with a cost model that includes statistical synopses for entire join paths. Although RDF-3X is optimized for queries, it also provides good support for efficient online updates by means of a staging architecture: direct updates to the main database indexes are deferred, and instead applied to compact differential indexes which are later merged into the main indexes in a batched manner. Experimental studies with several large-scale datasets with more than 50 million RDF triples and benchmark queries that include pattern matching, manyway star-joins, and long path-joins demonstrate that RDF-3X can outperform the previously best alternatives by one or two orders of magnitude.

Yago: a core of semantic knowledge <=SEP=> 

Evaluating Hive and Spark SQL with BigBench <=SEP=> Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission.

Three Models for the Description of Language <=SEP=> The grammar of a language is a device that describes the structure of that language. The grammar is comprised of a set of rules whose goal is twofold: first these rules can be used to create sentences of the associated language and only these, and second they can be used to classify whether a given sentence is an element of the language or not. The goal of a linguist is to discover grammars that are simple and yet are able to fully span the language. In [1] Chomsky describes three possible options of increasing complexity for English grammars: Finite-state, Phrase Structure and Transformational. This paper briefly present these three grammars and summarizes Chomsky’s analysis and results which state that finite-state grammars are inadequate because they fail to span all possible sentences of the English language, and phrase structure grammar is overly complex.

A MAV that flies like an airplane and hovers like a helicopter <=SEP=> Near-earth environments, such as forests, caves, tunnels, and urban structures make reconnaissance, surveillance and search-and-rescue missions difficult and dangerous to accomplish. Micro-air-vehicles (MAVs), equipped with wireless cameras, can assist in such missions by providing real-time situational awareness. This paper describes an additional flight modality enabling fixed-wing MAVs to supplement existing endurance superiority with hovering capabilities. This secondary flight mode can also be used to avoid imminent collisions by quickly transitioning from cruise to hover flight. A sensor suite which allows for autonomous hovering by regulating the aircraft's yaw. pitch and roll angles is also described

Picosecond imaging circuit analysis <=SEP=> A newly developed optical method for noninvasively measuring the switching activity of operating CMOS integrated circuit chips is described. The method, denoted as picosecond imaging circuit analysis (PICA) can be used to characterize the gate-level performance of such chips and identify the locations and nature of their operational faults. The principles underlying PICA and examples of its use are discussed.

Robotic Forceps Manipulator With a Novel Bending Mechanism <=SEP=> This paper proposes a new bending technique with a screwdrive mechanism that allows for omnidirectional bending motion by rotating two linkages, each consisting of a right-handed screw, a universal joint, and a left-handed screw. The new screwdrive mechanism, termed double-screw-drive (DSD) mechanism, is utilized in a robotic forceps manipulator for laparoscopic surgery. A robotic forceps manipulator incorporating the DSD mechanism (DSD forceps) can bend without using wires. Without wires, it has high rigidity, and can bend at 90° in any arbitrary direction. In addition, the gripper of the DSD forceps can perform rotational motion, which is achieved by rotating a third linkage in the DSD mechanism. Opening and closing motions of the gripper are attained by wire actuation. Fundamental experiments examining the bending force and the accuracy of the DSD forceps were conducted, and an analysis of the accuracy was performed. Control of the DSD forceps through a teleoperation system was achieved via a joystick-type manipulator. A servo system was constructed for each linkage and the wire actuation mechanism, and tracking control experiments as well as a suturing experiment were conducted. The results of the experiments showed that the required design specifications were fulfilled. Thus, the validity of the DSD forceps was demonstrated.

SEARNN: Training RNNs with Global-Local Losses <=SEP=> We propose SEARNN, a novel training algorithm for recurrent neural networks (RNNs) inspired by the “learning to search” (L2S) approach to structured prediction. RNNs have been widely successful in structured prediction applications such as machine translation or parsing, and are commonly trained using maximum likelihood estimation (MLE). Unfortunately, this training loss is not always an appropriate surrogate for the test error: by only maximizing the ground truth probability, it fails to exploit the wealth of information offered by structured losses. Further, it introduces discrepancies between training and predicting (such as exposure bias) that may hurt test performance. Instead, SEARNN leverages test-alike search space exploration to introduce global-local losses that are closer to the test error. We demonstrate improved performance over MLE on three different tasks: OCR, spelling correction and text chunking. Finally, we propose a subsampling strategy to enable SEARNN to scale to large vocabulary sizes.

Ecology-driven stereotypes override race stereotypes. <=SEP=> Why do race stereotypes take the forms they do? Life history theory posits that features of the ecology shape individuals' behavior. Harsh and unpredictable ("desperate") ecologies induce fast strategy behaviors such as impulsivity, whereas resource-sufficient and predictable ("hopeful") ecologies induce slow strategy behaviors such as future focus. We suggest that individuals possess a lay understanding of ecology's influence on behavior, resulting in ecology-driven stereotypes. Importantly, because race is confounded with ecology in the United States, we propose that Americans' stereotypes about racial groups actually reflect stereotypes about these groups' presumed home ecologies. Study 1 demonstrates that individuals hold ecology stereotypes, stereotyping people from desperate ecologies as possessing faster life history strategies than people from hopeful ecologies. Studies 2-4 rule out alternative explanations for those findings. Study 5, which independently manipulates race and ecology information, demonstrates that when provided with information about a person's race (but not ecology), individuals' inferences about blacks track stereotypes of people from desperate ecologies, and individuals' inferences about whites track stereotypes of people from hopeful ecologies. However, when provided with information about both the race and ecology of others, individuals' inferences reflect the targets' ecology rather than their race: black and white targets from desperate ecologies are stereotyped as equally fast life history strategists, whereas black and white targets from hopeful ecologies are stereotyped as equally slow life history strategists. These findings suggest that the content of several predominant race stereotypes may not reflect race, per se, but rather inferences about how one's ecology influences behavior.

A compact magnetic directional proximity sensor for spherical robots <=SEP=> Spherical robots have recently attracted significant interest due to their ability to offer high speed motion with excellent locomotion efficiency. As a result of the presence of a sealed outer shell, its obstacle avoidance strategy has been simply “hit and run”. While this is convenient due to the specific geometry of the spherical robots, it however could pose serious issues when the robots are small and light. For portable spherical robots with on-board cameras, a high speed collision with a hard surface may damage the robot or the camera. This paper proposes a novel and compact proximity sensor that utilizes passive magnetic field to detect the ferromagnetic obstacles through perturbation of the magnetic field. Compared with the existing works that utilize the Earth's weak magnetic field as a means of detection, the approach undertaken here seeks to harness the same principle but uses an intelligently designed magnetic assembly. It efficiently amplifies the perturbation and therefore improves the detection performance. The presented method is able to simultaneously determine both the distance and direction of the nearby ferromagnetic obstacles. Both simulation and experimental results are presented to validate the sensing principle and operational performance.

A Provable Time and Space Efficient Implementation of NESL <=SEP=> In this paper we prove time and space bounds for the implementation of the programming language NESL on various parallel machine models. NESL is a sugared typed &lambda;-calculus with a set of array primitives and an explicit parallel map over arrays. Our results extend previous work on provable implementation bounds for functional languages by considering space and by including arrays. For modeling the cost of NESL we augment a standard call-by-value operational semantics to return two cost measures: a DAG representing the sequential dependence in the computation, and a measure of the space taken by a sequential implementation. We show that a NESL program with w work (nodes in the DAG), d depth (levels in the DAG), and s sequential space can be implemented on a p processor butterfly network, hypercube, or CRCW PRAM using O(w/p + d log p) time and O(s + dp log p) reachable space.1 For programs with sufficient parallelism these bounds are optimal in that they give linear speedup and use space within a constant factor of the sequential space.

Modeling, parameter identification and model-based control of a lightweight robotic manipulator <=SEP=> Nowadays many robotic tasks require close and compliant tracking of desired positions, paths, or forces. To achieve these goals, model-based control schemes provide a possible solution, allowing to directly consider the nonlinear dynamics. One of the key challenges, however, is the derivation of suitable models, which allow sufficiently fast evaluation, as well as the parameterization of these models based on measurements. In this work we outline and review a structured approach for model-based controller design for robots. In a first step we derive suitable models for multi-link robots. In a second step we review how such models can be parameterized and how optimal identification experiments can be designed. Based on the model we then derive a simple model based controller to experimentally validate the results considering a lightweight robot. The single steps of the derivation and controller design are supported by a newly developed freely available model toolbox for the considered lightweight robot.

Data Analytics Using Ontologies of Management Theories: Towards Implementing "From Theory to Practice" (Application Paper) <=SEP=> We explore how computational ontologies can be impactful vis-à-vis the developing discipline of "data science." We posit an approach wherein management theories are represented as formal axioms, and then applied to draw inferences about data that reside in corporate databases. That is, management theories would be implemented as rules within a data analytics engine. We demonstrate a case study development of such an ontology by formally representing an accounting theory in First-Order Logic. Though quite preliminary, the idea that an information technology, namely ontologies, can potentially actualize the academic cliché, "From Theory to Practice," and be applicable to the burgeoning domain of data analytics is novel and exciting.

Using Part-of-Speech N-grams for Sensitive-Text Classification <=SEP=> Freedom of Information legislations in many western democracies, including the United Kingdom (UK) and the United States of America (USA), state that citizens have typically the right to access government documents. However, certain sensitive information is exempt from release into the public domain. For example, in the UK, FOIA Exemption 27 (International Relations) excludes the release of Information that might damage the interests of the UK abroad. Therefore, the process of reviewing government documents for sensitivity is essential to determine if a document must be redacted before it is archived, or closed until the information is no longer sensitive. With the increased volume of digital government documents in recent years, there is a need for new tools to assist the digital sensitivity review process. Therefore, in this paper we propose an automatic approach for identifying sensitive text in documents by measuring the amount of sensitivity in sequences of text. Using government documents reviewed by trained sensitivity reviewers, we focus on an aspect of FOIA Exemption 27 which can have a major impact on international relations, namely, information supplied in confidence. We show that our approach leads to markedly increased recall of sensitive text, while achieving a very high level of precision, when compared to a baseline that has been shown to be effective at identifying sensitive text in other domains.

Algorithms for the Longest Common Subsequence Problem <=SEP=> We start by def ining conven t ions and t e rmino logy that will be used th roughou t this paper . String C = clc~ ... cp is a subsequence of string A = aja2 "'" am if there is a mapp ing F : {1, 2 . . . . , p} ~ {1, 2, ... , m} such that F(i) = k only if c~ = ak and F is a m o n o t o n e strictly increasing funct ion (i .e. F(i) = u, F(]) = v, and i < j imply that u < v). C can be fo rmed by delet ing m p (not necessari ly ad jacen t ) symbols f rom A . F o r example , " c o u r s e " is a subsequence of " c o m p u t e r sc ience . " Str ing C is a c o m m o n s ubs equence of strings A and B if C is a s u b s e q u e n c e of A and also a subsequence of B. String C is a longest c o m m o n subsequence (abbrev ia ted LCS) of string A and B if C is a c o m m o n subsequence of A and B of maximal length , i .e. there is no c o m m o n subsequence of A and B that has grea te r length. Th roughou t this paper , we assume that A and B are strings of lengths m and n , m _< n , that have an LCS C of (unknown) length p . We assume that the symbols that may appea r in these strings c o m e f rom some a lphabet of size t . A symbol can be s tored in m e m o r y by using log t bits, which we assume will fit in one word of memory . Symbols can be c o m p a r e d (a -< b?) in one t ime unit . The n u m b e r of di f ferent symbols that actual ly appear in string B is def ined to be s (which must be less than n and t). The longest c o m m o n s u b s e q u e n c e prob lem has been solved by using a recurs ion re la t ionship on the length of the solut ion [7, 12, 16, 21]. These are general ly appl icable a lgor i thms that take O ( m n ) t ime for any input strings o f lengths m and n even though the lower bound on t ime of O ( m n ) need not apply to all inputs [2]. We present a lgor i thms that , depend ing on the na ture of the Input, may not requ i re quadra t ic t ime to r ecove r an LCS. The first a lgor i thm is appl icable in the genera l case and requi res O ( p n + n log n) t ime. T h e second a lgor i thm requi res t ime b o u n d e d by O((m + 1 p )p log n). In the c o m m o n special case where p is close to m , this a lgor i thm takes t ime

Optimal and Low-Complexity Algorithms for Dynamic Spectrum Access in Centralized Cognitive Radio Networks with Fading Channels <=SEP=> In this paper, we develop a centralized spectrum sensing and Dynamic Spectrum Access (DSA) scheme for secondary users (SUs) in a Cognitive Radio (CR) network. Assuming that the primary channel occupancy follows a Markovian evolution, the channel sensing problem is modeled as a Partially Observable Markov Decision Process (POMDP). We assume that each SU can sense only one channel at a time by using energy detection, and the sensing outcomes are then reported to a central unit, called the secondary system decision center (SSDC), that determines the channel sensing/accessing policies. We derive both the optimal channel assignment policy for secondary users to sense the primary channels, and the optimal channel access rule. Our proposed optimal sensing and accessing policies alleviate many shortcomings and limitations of existing proposals: (a) ours allows fully utilizing all available primary spectrum white spaces, (b) our model, and thus the proposed solution, exploits the temporal and spatial diversity across different primary channels, and (c) is based on realistic local sensing decisions rather than complete knowledge of primary signalling structure. As an alternative to the high complexity of the optimal channel sensing policy, a suboptimal sensing policy is obtained by using the Hungarian algorithm iteratively, which reduces the complexity of the channel assignment from an exponential to a polynomial order. We also propose a heuristic algorithm that reduces the complexity of the sensing policy further to a linear order. The simulation results show that the proposed algorithms achieve a near-optimal performance with a significant reduction in computational time.

Acquiring evolvability through adaptive representations <=SEP=> Adaptive representations allow evolution to explore the space of phenotypes by choosing the most suitable set of genotypic parameters. Although such an approach is believed to be efficient on complex problems, few empirical studieshave been conducted in such domains. In this paper, three neural network representations, a direct encoding, a complexifying encoding, and an implicit encoding capable of adapting the genotype-phenotype mapping are compared on Nothello, a complex game playing domain from the AAAI General Game Playing Competition. Implicit encoding makes the search more efficient and uses several times fewer parameters. Random mutation leads to highly structured phenotypic variation that is acquired during the course of evolution rather than built into the representation itself. Thus, adaptive representations learn to become evolvable, and furthermore do so in a way that makes search efficient on difficult coevolutionary problems.

Algorithm for efficient seating plan for centralized exam system <=SEP=> Exam seat allocation is one of the major concerns in quality education. With the increasing number of students, subjects, departments and rooms, exam seat management becomes complex. Maintaining a decent exam environment with the proper seating arrangement is one of the difficult jobs for authority. This research offers solution for exam seating arrangement problems that can be achieved through the sequential execution of three proposed algorithms. This research offers a solution for preventing some exam hall cheating by arranging seats for large students and it also finds out the best combination of rooms to be assigned for the exam to organize perfect seating based on the room orientation and size, number of students, differentiation of subjects. To do this research, we have collected data and methods from a university those are being used for their exam seating arrangement. By using university exam information we test our algorithms. It provides better seating plan then the manual system used by the university.

CRMA: collision-resistant multiple access <=SEP=> Efficiently sharing spectrum among multiple users is critical to wireless network performance. In this paper, we propose a novel spectrum sharing protocol called Collision-Resistant Multiple Access (CRMA) to achieve high efficiency. In CRMA, each transmitter views the OFDM physical layer as multiple orthogonal but sharable channels, and independently selects a few channels for transmission. The transmissions that share the same channel naturally add up in the air. The receiver extracts the received signals from all the channels and efficiently decodes the transmissions by solving a simple linear system. We implement our approach in the Qualnet simulator and show that it yields significant improvement over existing spectrum sharing schemes. We also demonstrate the feasibility of our approach using implementation and experiments on GNU Radios.

SybilInfer: Detecting Sybil Nodes using Social Networks <=SEP=> SybilInfer is an algorithm for labelling nodes in a social network as honest users or Sybils controlled by an adversary. At the heart of SybilInfer lies a probabilistic model of honest social networks, and an inference engine that returns potential regions of dishonest nodes. The Bayesian inference approach to Sybil detection comes with the advantage label has an assigned probability, indicating its degree of certainty. We prove through analytical results as well as experiments on simulated and real-world network topologies that, given standard constraints on the adversary, SybilInfer is secure, in that it successfully distinguishes between honest and dishonest nodes and is not susceptible to manipulation by the adversary. Furthermore, our results show that SybilInfer outperforms state of the art algorithms, both in being more widely applicable, as well as providing vastly more accurate results.

Blind detection for MIMO systems with low-resolution ADCs using supervised learning <=SEP=> This paper considers a multiple-input-multiple-output (MIMO) system with low-resolution analog-to-digital converters (ADCs). In this system, we propose a novel detection framework that performs data symbol detection without explicitly knowing channel state information at a receiver. The underlying idea of the proposed framework is to exploit supervised learning. Specifically, during channel training, the proposed approach sends a sequence of data symbols as pilots so that the receiver learns a nonlinear function that is determined by both a channel matrix and a quantization function of the ADCs. During data transmission, the receiver uses the learned nonlinear function to detect which data symbols were transmitted. In this context, we propose two blind detection methods to determine the nonlinear function from the training-data set. We also provide an analytical expression for the symbol-vector-error probability of the MIMO systems with one-bit ADCs when employing the proposed framework. Simulations demonstrate the performance improvement of the proposed framework compared to existing detection techniques.

An Ultra-Low Power 1.7-2.7 GHz Fractional-N Sub-Sampling Digital Frequency Synthesizer and Modulator for IoT Applications in 40 nm CMOS <=SEP=> This paper introduces an ultra-low power 1.7-2.7-GHz fractional-N sub-sampling digital PLL (SS-DPLL) for Internet-of-Things (IoT) applications targeting compliance with Bluetooth Low Energy (BLE) and IEEE802.15.4 standards. A snapshot time-to-digital converter (TDC) acts as a digital sub-sampler featuring an increased out-of-range gain and without any assistance from the traditional counting of DCO edges, thus further reducing power consumption. With a proposed DCO-divider phase rotation in the feedback path, the impact of the digital-to-time converter’s (DTC’s) non-linearity on the PLL is reduced and improves fractional spurs by at least 8 dB across BLE channels. Moreover, a “variable-preconditioned LMS” calibration algorithm is introduced to dynamically correct the DTC gain error with fractional frequency control word (FCW) down to 1/16384. Fabricated in 40 nm CMOS, the SS-DPLL achieves phase noise performance of −109 dBc/Hz at 1 MHz offset, while consuming a record-low power of 1.19 mW.

Point cloud labeling using 3D Convolutional Neural Network <=SEP=> In this paper, we tackle the labeling problem for 3D point clouds. We introduce a 3D point cloud labeling scheme based on 3D Convolutional Neural Network. Our approach minimizes the prior knowledge of the labeling problem and does not require a segmentation step or hand-crafted features as most previous approaches did. Particularly, we present solutions for large data handling during the training and testing process. Experiments performed on the urban point cloud dataset containing 7 categories of objects show the robustness of our approach.

Another Stemmer <=SEP=> In natural language processing, conflation is the process of merging or lumping together nonidentical words which refer to the same principal concept. This can relate both to words which are entirely different in form (e.g., "group" and "collection"), and to words which share some common root (e.g., "group", "grouping", "subgroups"). In the former case the words can only be mapped by referring to a dictionary or thesaurus, but in the latter case use can be made of the orthographic similarities between the forms. One popular approach is to remove affixes from the input words, thus reducing them to a stem; if this could be done correctly, all the variant forms of a word would be converted to the same standard form. Since the process is aimed at mapping for retrieval purposes, the stem need not be a linguistically correct lemma or root (see also Frakes 1982).

Global Ridge Orientation Modeling for Partial Fingerprint Identification <=SEP=> Identifying incomplete or partial fingerprints from a large fingerprint database remains a difficult challenge today. Existing studies on partial fingerprints focus on one-to-one matching using local ridge details. In this paper, we investigate the problem of retrieving candidate lists for matching partial fingerprints by exploiting global topological features. Specifically, we propose an analytical approach for reconstructing the global topology representation from a partial fingerprint. First, we present an inverse orientation model for describing the reconstruction problem. Then, we provide a general expression for all valid solutions to the inverse model. This allows us to preserve data fidelity in the existing segments while exploring missing structures in the unknown parts. We have further developed algorithms for estimating the missing orientation structures based on some a priori knowledge of ridge topology features. Our statistical experiments show that our proposed model-based approach can effectively reduce the number of candidates for pair-wised fingerprint matching, and thus significantly improve the system retrieval performance for partial fingerprint identification.

Exploring the therapeutic potential of Ayahuasca: acute intake increases mindfulness-related capacities <=SEP=> Ayahuasca is a psychotropic plant tea used for ritual purposes by the indigenous populations of the Amazon. In the last two decades, its use has expanded worldwide. The tea contains the psychedelic 5-HT2A receptor agonist N,N-dimethyltryptamine (DMT), plus β-carboline alkaloids with monoamine-oxidase-inhibiting properties. Acute administration induces an introspective dream-like experience characterized by visions and autobiographic and emotional memories. Studies of long-term users have suggested its therapeutic potential, reporting that its use has helped individuals abandon the consumption of addictive drugs. Furthermore, recent open-label studies in patients with treatment-resistant depression found that a single ayahuasca dose induced a rapid antidepressant effect that was maintained weeks after administration. Here, we conducted an exploratory study of the psychological mechanisms that could underlie the beneficial effects of ayahuasca. We assessed a group of 25 individuals before and 24 h after an ayahuasca session using two instruments designed to measure mindfulness capacities: The Five Facets Mindfulness Questionnaire (FFMQ) and the Experiences Questionnaire (EQ). Ayahuasca intake led to significant increases in two facets of the FFMQ indicating a reduction in judgmental processing of experiences and in inner reactivity. It also led to a significant increase in decentering ability as measured by the EQ. These changes are classic goals of conventional mindfulness training, and the scores obtained are in the range of those observed after extensive mindfulness practice. The present findings support the claim that ayahuasca has therapeutic potential and suggest that this potential is due to an increase in mindfulness capacities.

Sex differences in the human olfactory system <=SEP=> The olfactory system (accessory) implicated in reproductive physiology and behavior in mammals is sexually dimorphic. These brain sex differences present two main characteristics: they are seen in neural circuits related to sexual behavior and sexual physiology and they take one of two opposite morphological patterns (male>female or female>male). The present work reports sex differences in the olfactory system in a large homogeneous sample of men (40) and women (51) using of voxel-based morphology. Gray matter concentration showed sexual dimorphism in several olfactory regions. Women have a higher concentration in the orbitofrontal cortex involving Brodmann's areas 10, 11 and 25 and temporomedial cortex (bilateral hippocampus and right amygdala), as well as their left basal insular cortex. In contrast, men show a higher gray matter concentration in the left entorhinal cortex (Brodmann's area 28), right ventral pallidum, dorsal left insular cortex and a region of the orbitofrontal cortex (Brodmann's area 25). This study supports the hypothesis that the mammalian olfactory system is a sexually dimorphic network and provides a theoretical framework for the morphofunctional approach to sex differences in the human brain.