Weakening and Shifting of the Saharan Shallow Meridional Circulation During Wet Years of the West African Monsoon

The correlation between increased Sahel rainfall and reduced Saharan surface pressure is well established in observations and global climate models, and has been used to imply that increased Sahel rainfall is caused by a stronger shallow meridional circulation (SMC) over the Sahara. This study uses two atmospheric reanalyses to examine interannual variability of Sahel rainfall and the Saharan SMC, which consists of northward near-surface flow across the Sahel into the Sahara and southward flow near 700 hPa out of the Sahara. During wet Sahel years, the Saharan SMC shifts poleward, producing a drop in low-level geopotential and surface pressure over the Sahara. Statistically removing the effect of the poleward shift from the low-level geopotential eliminates significant correlations between this geopotential and Sahel precipitation. As the Saharan SMC shifts poleward, its mid-tropospheric divergent outflow decreases, indicating a weakening of its overturning mass flux. The poleward shift and weakening of the Saharan SMC during wet Sahel years is reproduced in an idealized model of West Africa; a wide range of imposed sea surface temperature and land surface albedo perturbations in this model produce a much larger range of SMC variations that nevertheless have similar quantitative associations with Sahel rainfall as in the reanalyses. These results disprove the idea that enhanced Sahel rainfall is caused by strengthening of the Saharan SMC. Instead, these results are consistent with the hypothesis that the a stronger SMC inhibits Sahel rainfall, perhaps by advecting mid-tropospheric warm and dry air into the precipitation maximum.Comment: Submitted to Journal of Climat

Evaluating the Representational Hub of Language and Vision Models

The multimodal models used in the emerging field at the intersection of computational linguistics and computer vision implement the bottom-up processing of the `Hub and Spoke' architecture proposed in cognitive science to represent how the brain processes and combines multi-sensory inputs. In particular, the Hub is implemented as a neural network encoder. We investigate the effect on this encoder of various vision-and-language tasks proposed in the literature: visual question answering, visual reference resolution, and visually grounded dialogue. To measure the quality of the representations learned by the encoder, we use two kinds of analyses. First, we evaluate the encoder pre-trained on the different vision-and-language tasks on an existing diagnostic task designed to assess multimodal semantic understanding. Second, we carry out a battery of analyses aimed at studying how the encoder merges and exploits the two modalities.Comment: Accepted to IWCS 201

CREATING A COMMON SECURED TUNNEL FOR NETWORK FUNCTIONS

Techniques are described herein for efficient tunnel management for secured communication between Network Functions (NFs) across different Public Land Mobile Networks (PLMNs) through Security Edge Protection Proxies (SEPPs). A common secured tunnel is created for all the NFs to interact between visited SEPPs (vSEPP) and home SEPPs (hSEPPs). There is a choice to select different authentication methods between different vSEPP-hSEPP pairs

DEFINING PROCEDURES AND AN INTERFACE BETWEEN A LOCAL NSACF AND A GLOBAL NSACF FOR EFFICIENT ADMISSION CONTROL

Presented herein are techniques that provide for defining an interface between one or more local network slice admission control functions (NSACFs) and a global or primary NSACF. Various techniques presented herein can help to report local events to a global NSACF and may also be useful for restoration procedures when a local NSACF restarts. Thus, techniques proposed herein can facilitate improved slice admission and control procedures over existing standards-based solutions

Automatic Main Road Extraction from High Resolution Satellite Imagery

Road information is essential for automatic GIS (geographical information system) data acquisition, transportation and urban planning. Automatic road (network) detection from high resolution satellite imagery will hold great potential for significant reduction of database development/updating cost and turnaround time. From so called low level feature detection to high level context supported grouping, so many algorithms and methodologies have been presented for this purpose. There is not any practical system that can fully automatically extract road network from space imagery for the purpose of automatic mapping. This paper presents the methodology of automatic main road detection from high resolution satellite IKONOS imagery. The strategies include multiresolution or image pyramid method, Gaussian blurring and the line finder using 1-dimemsional template correlation filter, line segment grouping and multi-layer result integration. Multi-layer or multi-resolution method for road extraction is a very effective strategy to save processing time and improve robustness. To realize the strategy, the original IKONOS image is compressed into different corresponding image resolution so that an image pyramid is generated; after that the line finder of 1-dimemsional template correlation filter after Gaussian blurring filtering is applied to detect the road centerline. Extracted centerline segments belong to or do not belong to roads. There are two ways to identify the attributes of the segments, the one is using segment grouping to form longer line segments and assign a possibility to the segment depending on the length and other geometric and photometric attribute of the segment, for example the longer segment means bigger possibility of being road. Perceptual-grouping based method is used for road segment linking by a possibility model that takes multi-information into account; here the clues existing in the gaps are considered. Another way to identify the segments is feature detection back-to-higher resolution layer from the image pyramid

Numerical modeling of time-lapse seismic data from fractured reservoirs including fluid flow and geochemical processes

Fractured reservoirs, especially in low permeable carbonate rocks, are important target for hydrocarbon exploration and production because fractures can control fluid flow inside the reservoir. Hence, quantitative knowledge of fracture attributes is important for optimal hydrocarbon production. However, in some cases fractures can cause leakage of injected CO2 during enhanced oil recovery (EOR) or CO2 sequestration. Furthermore, CO2 can geochemically interact with reservoir fluids and host rock. Hence, time-lapse monitoring of the progress of CO2 in fractured reservoirs is also very important. In order to address these challenges, I have developed an integrated approach for studying fluid flow and seismic wave propagation in fractured media using Discrete Fracture Network (DFN) models. My seismic simulation study suggests that CO2 saturated reservoir shows approximately ten times more attenuation than brine saturated reservoir. Similarly, large P-wave velocity variation in CO2 saturated reservoir and amplitude variation with offset (AVO) results for our example model predicts that CO2 is easier to detect than brine in the fractured reservoirs. The effects of geochemical processes on seismics are simulated by time-lapse modeling for t = 1000 years. My modeling study suggests that intra-aqueous reactions are more significant during injection of CO2 for t = 6 years, while slower mineral reactions dominate after pressure equilibrium is achieved that is from t = 6 to 1000 years. Overall both types of geochemical reactions cause change in reflection coefficient of 2 to 5%, which may be difficult to detect in some cases. However, the significant change in the seismic properties at the boundary of the CO2 front can be used to detect the flow path of CO2 inside the reservoirs. Finally, a method for generating stochastic fracture models was extended and improved to more realistic field model for seismic and fluid modeling. My detail analysis suggests that fractures generated by isotropic stress field favor orthogonal sets of fractures in most subsurface rocks that can be converted to seismic model, similar to DFN study. The quality and validity of the models is assessed by comparisons to DFN models, including calculations of fractal dimension measures that can help to characterize fractured reservoirs

Learning to merge - language and vision: A deep evaluation of the encoder, the role of the two modalities, the role of the training task.

Most human language understanding is grounded in perception. There is thus growing interest in combining information from language and vision. Multiple models based on Neural Networks have been proposed to merge language and vision information. All the models share a common backbone consisting of an encoder which learns to merge the two types of representation to perform a specific task. While some models have seemed extremely successful on those tasks, it remains unclear how the reported results should be interpreted and what those models are actually learning. Our contribution is three-fold. We have proposed (a) a new model of Visually Grounded Dialogue; (b) a diagnostic dataset to evaluate the encoder ability to merge visual and language input; (c) a method to evaluate the quality of the multimodal representation computed by the encoder as general purposed representations. We have proposed and analyzed a cognitive plausible architecture in which dialogue system modules are connected through a common \emph{grounded dialogue state encoder}. Our in-depth analysis of the dialogues shows the importance of going beyond task-success in the evaluation of Visual Dialogues: the dialogues themselves should play a crucial role in such evaluation. We have proposed a diagnostic dataset, \emph{FOIL} which consists of images associated with incorrect captions that the model has to detect and correct. Finally, we have used FOIL to evaluate the quality of the multimodal representation produced by an encoder trained on different multimodal tasks. We have shown how the training task used effects the stability of the representation, their transferability and the model confidence

5G CORE REDUNDANCY FROM EVOLVED PACKET CORE

Presented herein is a technique to provide Fifth Generation (5G) core (5GC) redundancy from a Fourth Generation (4G) Evolved Packet Core (EPC). In particular, for a 4G-5G interworking scenario, if a Session Management Function (SMF) set is implemented, the technique presented herein provides that an initial combined SMF and control plane Packet Data Network (PDN) Gateway (PGW-C) [referred to herein as SMF+PGW-C] can provide to a Serving Gateway (SGW), at session creation time, address information of all the SMF+PGW-Cs belonging to the same SMF set. The SGW can use the address information to facilitate failover to an alternate SMF+PGW-C belonging to the SMF set, in case the initial SMF+PGW-C does not respond to the SGW

SYSTEM AND METHOD FOR DETECTING INFINITE SIGNALING LOOP IN HIERARCHICAL NRF DEPLOYMENTS

For larger deployments, where multiple level of Network Function (NF) Repository Functions (NRFs) are deployed, the slightest misconfiguration could lead to an indefinite query loop between NRFs. The indefinite loop in the NRF query would mean repeated timeouts and/or failure of a service discovery request that a consumer had initiated to find a suitable producer. This could lead to signaling failure between a set of consumers and producers, which in turn can lead to Quality of Service (QoS) and Service Level Agreement (SLA) violations. A new Hypertext Transfer Protocol Version 2 (HTTP/2) header, Network Function (NF)-route-record, is proposed, which should be added by an intermediate NRF (relay or proxy) before forwarding any request. The NF-route-record should contain the identity of the peer from which the request was received. The receiving NRF checks the NF-route-record before further forwarding or serving the request. If the NF-route-record matches its own identity, then the receiving NRF would detect the loop. The same can be used in the reverse direction when sending the response from a server (producer) to a client (consumer)

CONSUMER NF AUTHENTICATION AND SERVICE AUTHORIZATION USING AN SCP FOR AN ESBA BASED 5G CORE NETWORK

For a Third Generation Partnership Project (3GPP) Fifth Generation (5G) core (5GC) enhanced Service Based Architecture (eSBA) network, 3GPP Specifications identify a number of communication models. For example, the 3GPP specification 23.501 defines four communication models, encompassing direct and indirect modes, between a Consumer Network Function (NF) and a Producer NF. Within the indirect modes, a Service Communication Proxy (SCP) performs a range of important activities including among other things load balancing and load distribution of signaling traffic between for example different NF instances of a NF Set or different NF Services of a NF Service set. As a network scales and as traffic complexity grows, a range of challenges may arise encompassing excess or redundant signaling traffic, token invalidation, etc. Techniques are presented herein that address these challenges through the incorporation of an Open Authorization (OAuth) service into a SCP, yielding a number of improvements including, for example, a reduction in the overall number of signaling messages; significant optimization of the signaling between Consumer, SCP and NRF; simplification of the Auth call flow in scenarios where an initial producer instance is changed or multiple SCPs are deployed in a system; among others