End-to-end binaural sound localisation from the raw waveform

A novel end-to-end binaural sound localisation approach is proposed which estimates the azimuth of a sound source directly from the waveform. Instead of employing hand-crafted features commonly employed for binaural sound localisation, such as the interaural time and level difference, our end-to-end system approach uses a convolutional neural network (CNN) to extract specific features from the waveform that are suitable for localisation. Two systems are proposed which differ in the initial frequency analysis stage. The first system is auditory-inspired and makes use of a gammatone filtering layer, while the second system is fully data-driven and exploits a trainable convolutional layer to perform frequency analysis. In both systems, a set of dedicated convolutional kernels are then employed to search for specific localisation cues, which are coupled with a localisation stage using fully connected layers. Localisation experiments using binaural simulation in both anechoic and reverberant environments show that the proposed systems outperform a state-of-the-art deep neural network system. Furthermore, our investigation of the frequency analysis stage in the second system suggests that the CNN is able to exploit different frequency bands for localisation according to the characteristics of the reverberant environment

Detection of activity and position of speakers by using deep neural networks and acoustic data augmentation

The task of Speaker LOCalization (SLOC) has been the focus of numerous works in the research field, where SLOC is performed on pure speech data, requiring the presence of an Oracle Voice Activity Detection (VAD) algorithm. Nevertheless, this perfect working condition is not satisfied in a real world scenario, where employed VADs do commit errors. This work addresses this issue with an extensive analysis focusing on the relationship between several data-driven VAD and SLOC models, finally proposing a reliable framework for VAD and SLOC. The effectiveness of the approach here discussed is assessed against a multi-room scenario, which is close to a real-world environment. Furthermore, up to the authors’ best knowledge, only one contribution proposes a unique framework for VAD and SLOC acting in this addressed scenario; however, this solution does not rely on data-driven approaches. This work comes as an extension of the authors’ previous research addressing the VAD and SLOC tasks, by proposing numerous advancements to the original neural network architectures. In details, four different models based on convolutional neural networks (CNNs) are here tested, in order to easily highlight the advantages of the introduced novelties. In addition, two different CNN models go under study for SLOC. Furthermore, training of data-driven models is here improved through a specific data augmentation technique. During this procedure, the room impulse responses (RIRs) of two virtual rooms are generated from the knowledge of the room size, reverberation time and microphones and sources placement. Finally, the only other framework for simultaneous detection and localization in a multi-room scenario is here taken into account to fairly compare the proposed method. As result, the proposed method is more accurate than the baseline framework, and remarkable improvements are specially observed when the data augmentation techniques are applied for both the VAD and SLOC tasks

The NF-κB pharmacopeia: novel strategies to subdue an intractable target

NF-κB transcription factors are major drivers of tumor initiation and progression. NF-κB signaling is constitutively activated by genetic alterations or environmental signals in many human cancers, where it contributes to almost all hallmarks of malignancy, including sustained proliferation, cell death resistance, tumor-promoting inflammation, metabolic reprogramming, tissue invasion, angiogenesis, and metastasis. As such, the NF-κB pathway is an attractive therapeutic target in a broad range of human cancers, as well as in numerous non-malignant diseases. Currently, however, there is no clinically useful NF-κB inhibitor to treat oncological patients, owing to the preclusive, on-target toxicities of systemic NF-κB blockade. In this review, we discuss the principal and most promising strategies being developed to circumvent the inherent limitations of conventional IκB kinase (IKK)/NF-κB-targeting drugs, focusing on new molecules that target upstream regulators or downstream effectors of oncogenic NF-κB signaling, as well as agents targeting individual NF-κB subunits

The structure and regulation of the Irish equine industries: Links to considerations of equine welfare

The equine industries in Ireland are vibrant and growing. They are broadly classified into two sectors: Thoroughbred racing, and sports and leisure. This paper describes these sectors in terms of governance, education and training in equine welfare, and available data concerning horse numbers, identification, traceability and disposal. Animal welfare, and specifically equine welfare, has received increasing attention internationally. There is general acceptance of concepts such as animal needs and persons' responsibilities toward animals in their care, as expressed in the 'Five Freedoms'. As yet, little has been published on standards of equine welfare pertaining to Ireland, or on measures to address welfare issues here. This paper highlights the central role of horse identification and legal registration of ownership to safeguard the health and welfare of horses

Detection of activity and position of speakers by using deep neural networks and acoustic data augmentation

The task of Speaker LOCalization (SLOC)has been the focus of numerous works in the research field, where SLOC is performed on pure speech data, requiring the presence of an Oracle Voice Activity Detection (VAD)algorithm. Nevertheless, this perfect working condition is not satisfied in a real world scenario, where employed VADs do commit errors. This work addresses this issue with an extensive analysis focusing on the relationship between several data-driven VAD and SLOC models, finally proposing a reliable framework for VAD and SLOC. The effectiveness of the approach here discussed is assessed against a multi-room scenario, which is close to a real-world environment. Furthermore, up to the authors’ best knowledge, only one contribution proposes a unique framework for VAD and SLOC acting in this addressed scenario; however, this solution does not rely on data-driven approaches. This work comes as an extension of the authors’ previous research addressing the VAD and SLOC tasks, by proposing numerous advancements to the original neural network architectures. In details, four different models based on convolutional neural networks (CNNs)are here tested, in order to easily highlight the advantages of the introduced novelties. In addition, two different CNN models go under study for SLOC. Furthermore, training of data-driven models is here improved through a specific data augmentation technique. During this procedure, the room impulse responses (RIRs)of two virtual rooms are generated from the knowledge of the room size, reverberation time and microphones and sources placement. Finally, the only other framework for simultaneous detection and localization in a multi-room scenario is here taken into account to fairly compare the proposed method. As result, the proposed method is more accurate than the baseline framework, and remarkable improvements are specially observed when the data augmentation techniques are applied for both the VAD and SLOC tasks

DESIGN OF EARTH-REINFORCED EMBANKMENTS UNDER CYCLIC/DYNAMIC LOADING

In this paper, the mechanical response of both lightweight and soil-reinforced embankments subject to seismic loads is analyzed; in particular, the problem of designing these types of structure is tackled both numerically and theoretically. To clarify the main factors influencing the response of the structure, in particular the accumulation of irreversible strains in the foundation soil stratum, the theory of macro-elements is employed. With reference to lightweight embankments, a comparison between the numerical predictions obtained from standard approaches, based both on the pseudo-static limit equilibrium method and on the Newmark method, and those derived from finite difference numerical analyses is discussed. As far as reinforced embankments are concerned, the numerical results of a large series of finite numerical analyses are illustrated and critically discussed. Finally, in order to stress the complexity of the problems to be accounted for when this type of geotechnical structure is realized, a case history concerning the design of an earth structure for debris flow protection in a seismic area is presented and critically illustrated

Il ruolo del ginecologo di Pronto Soccorso nei casi di violenza sessuale. Proposta di protocollo

The aim of this report is to provide a medical form to use in case of sexual assault, prepared taking into consideration the information lacking at present in most medical reports on the subject. This call for the necessity to conform the medical approach to the same protocol which may also be used from the forensic medicine point of view. More precise information should be obtained about the assault and the injuries inflicted, and also an immediate psychological support should be given to the victim. This information will then serve as a guide to the medical staff

AFLP (TM) markers for DNA fingerprinting in cattle

The work reports on use of the recently described amplified fragment length polymorphism (AFLP) technology for DNA fingerprinting in cattle. The AFLP technology produces molecular markers through the high-stringency polymerase chain reaction (PCR)-amplification of restriction fragments taht are lgated to synthetic adapters and amplified using primers, complementary to the adapters, which carry selective nucleotides at their 3' ends. While, for plants, the double digestion of genomic DNA with EcoRI and MseI is suggested, in mammals the enzyme combination EcoRI/TaqI produces clearer and more polymorphic AFLP patterns. In a sample of 47 italian Holstein genotypes, 16 EcoRI/TaqI orimer combinations identified 248 polymorphic bands ina species known for its low level od restriction polymorphism. In spite of the low information content carried by each AFLP polymorphism (average polymorphism information content = 0.31), the number of fragments revealed by each primer combination increased significantly the level of genetic information gained in each experiment. AFLP patternsare reproducible in independent experiments and polymorphic fragments segregate in cattle families according to Mendelian rules