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PURI TOURISM | JAGANNATH TEMPLE | PLACES TO VISIT & TOUR PACKAGES
Beach | Pilgrimage
Puri Overview
Top 14 Places to Visit in Puri
15 Best Puri Tour Packages
Best Time to Visit Puri
How to Reach Puri
Driving Directions to Puri
About Puri
#1 of 3 Places to visit in Orissa | #24 of 100 Places to visit in India
Among Top
Ideal Trip Duration: 1-2 Days
Base Station: Puri
Nearest City to Puri: Puri, Bhubaneswar (58 Kms)
Best Time to Visit Puri: October to March
Peak Season: November to February
State: Orissa | District: Puri
Puri Weather: Summer: Maximum - 45°C and Minimum - 23°C
Winter: Maximum - 32°C and Minimum - 12°C
At a distance of 36 km from Konark, 58 km from Bhubaneswar, 82 km from Cuttack, 390 km from Rourkela, 407 km from Jamshedpur, 434 km from Vizag, 495 km from Kolkata, and 522 km from Ranchi, Puri is an ancient temple town situated along the coast of Bay of Bengal in Odisha. Puri is one of the places of original holy Char Dham Yatra of Hinduism and also one of the top places to experience Odisha Tourism.
Earlier called as ShriKhetra, Purushottama Khetra, and Jagannath Puri, Puri is home to the world famous Sri Jagannath Temple. Often referred as the spiritual capital of Odisha, Puri forms the Golden Triangle of Orissa along with Konark and Bhubaneswar. Puri became an important center of pilgrimage with the arrival of Gangas in the 12th century and emerged as one of the centers of Vaishnavism. Anantavarman Chodaganga, one of the powerful rulers of Puri, established the Purushottama Temple and later came to be known as the Jagannath Temple in the 15th century.
According to history, Puri and the Jagannath Temple were invaded 18 times by Hindu and Muslim rulers from 4th century AD till the early 19th century. Puri was declared as the capital of Odisha under the British till 1816 CE. In 1828 CE the whole territory was separated into three regions, Katak, Balasore, and Jagannath Puri. In 1936 CE Odisha was divided from the state of Bihar and Puri was formed in 1948 CE.
Dedicated Lord Vishnu, Lord Jagannath Temple is one of the prime places to visit in Puri and is a sacred place for the Hindus. Besides Jaganath Mandir, Puri has so many tourist attractions like Puri Beach, Gundicha Temple, Lokanath Temple, Alarnath Temple, Bedi Hanuman Temple, Pipili, Raghurajpur and Chilika Lake & Sanctuary.
Biju Patnaik Airport, Bhubaneswar is the nearest airport which is about 59 km from Puri. It has direct flights from Bangalore, Mumbai, New Delhi, Chennai, Kolkata and Hyderabad. Puri Junction is well connected by trains with Patna, Rourkela, Mumbai, Bikaner, Howrah, Cuttack, Tirupati, Guwahati, Okha, Bangalore, Haridwar, New Delhi, Sambalpur, Jodhpur, Ahmedabad, Chennai, Ajmer, and Shirdi. Puri is well connected by bus with Bhubaneswar, Kolkata, Rourkela, Cuttack and Vizag. Bhubaneswar is the nearest major bus station to Puri.
Puri is famous for its annual festival of Chariots, the 'Ratha Yatra', which is held during the month of June /July and has been attracting pilgrims and tourists for centuries. It is the sacred journey of Lord Jagannath with brother Balabhadra & sister Subhadra from the main Jagannath Temple to Gundicha Mandir for nine days.
The best time to visit Puri is from November to February. Being a coastal town, Puri has extreme weather conditions in summer while in monsoons the city witnesses medium to heavy rainfall. Winter climate is pleasant and is suitable for sightseeing.
Internet Availability: Average
STD Code: 06752
Languages Spoken: Oriya, Bengali, Hindi & English
Major Festivals: Puri Rath Yatra
Notes/Tips:
Jagannath Temple will be closed for 15 days in the month of June/July every year after 'Snana Yatra'.
Nearest Airport: Bhubaneswar - Biju Patnaik Airport (59 Kms)
Direct Flights to Bhubaneswar
Nearest Train Station: Puri Junction Railway Station (0 Kms)
Direct Trains to Puri
Nearest Bus Station: Puri Bus Station (1.5 Kms)
Direct Buses to Puri
Distance Chart
Distance Chart & Driving Directions to Puri
How to Reach Puri - Full Details
Sri Jagannath Temple
#1 of 14 Places to Visit in Puri
At a distance of 2.5 km from Puri Junction, Jagannath Temple is a sacred temple situated in the heart of Puri town in Orissa. It is the most revered pilgrimage site for Hindus as it is included in the pious Char Dham Yatra with Badrinath, Dwarka and Rameshwaram. This world famous temple is one of prime places to visit in Puri.
The temple is dedicated to Lord Jagannath, a form of Lord Vishnu and is one of the major Hindu Temples in India. The present temple was built by Anantavarman Chodaganga (1077 - 1150 CE) of Eastern Ganga dynasty, replacing an earlier structure which probably dated to the 10th century CE. Later, King Ananga Bhima Deva II (1170-1198) did much to continue the work of Chodaganga Deva, building the walls around the temple and many of the other shrines on the temple grounds.
The temple structure is full of excellent carvings and is a fine specimen of Kalinga style of architecture. Spread over an area of 4, 00,000 square feet with 20 feet high fortified wall, .....
Puri Beach
At a distance of 2 km from Jagannath Temple and 2.5 km from Puri Junction, Puri Beach is a pristine sea beach located along the coast of Bay of Bengal in Puri, Odisha. It is considered as one of the cleanest beaches in India and also one of the best Puri Tourist Places.
Known for its fine white sands and crystal clear waters, Puri Beach is quite famous for its peaceful and calm surroundings. The beach has continued to be a sacred venue and is visited by an endless number of pilgrims for a purification dip who come to Puri to pay their homage to Lord Jagannath. The long white sandy beach draws large numbers of foreigners and Indians. Visitors can take a stroll, browse pearls and seashell souvenirs sold by local vendors.
The beach is also famous for the sand sculptures made by internationally known sand artist Sudarshan Patnaik. The subjects are generally inspired by sculptures covering the temple walls, characters and episodes from the mythology and also the contemporary events. .....
Gundicha Temple
At a distance of 1.5 km from Puri Junction and 3 km from Sri Jagannath Temple, Gundicha Temple is one of the most renowned Hindu temples in Puri. Gundicha Temple is a common place of the festivities during the annual Ratha Yatra and is also one of the most famous places to visit in Puri.
The Gundicha Ghar was built by the Gundicha Maharani, the queen of King Indradyumna the founder of the 1st Jagannath Temple. The temple is located right next to the Jagannath Temple and is popularly referred to as His aunt's house because when the Rath Yatra festival takes place, Lord Jagannath is shifted to the Gundicha Temple for seven days. It is also known as Garden House of Lord Jagannath as it is located in the middle of a beautiful garden.
Gundicha Temple has some interesting legends related to it. According to one legend, Gundicha, the queen of the King Indradyumna, was a great devotee of Lord Jagannatha. Because of her deep devotion, Lord Jagannath promises her to come to her house .....
Narendra Tank / Narendra Pokhari
At a distance of 1 km from Jagannath Temple and 2.5 km from Puri Junction, Narendra Tank or Narendra Pokhari is a holy tank situated at Mauza Dandimala Sahi in Puri. This is one of the largest tanks in Orissa and also one of the must visit places in Puri.
The tank was built in 15th century AD by Narendra Dev, who shouldered the responsibility of the kingdom with Purushottama. The tank is spread over a vast area, which features an island in the middle, housing a small temple called Chandana Mandapa which is connected to the southern bank with a bridge. This tank is considered holy and has a lot of small and big temples surrounding it. The water level of the tank is 10 feet below the present road level. The tank covers 16 ghats which provide an opportunity for the pilgrims to have a holy bath. The main ghat has a flight of steps made up of Khondalite.
Also known as Chandana Pushkarini, this is the holy tank in which the famed Chandana Yatra of Lord Jagannath celebrated annually. .....
Balghai Beach
At a distance of 8 km for Puri, Balghai Beach is a pristine beach along the cost of Bay of Bengal on Puri - Konark Marine Drive Road in Odisha. Situated at the mouth of Nuanai River, Balghai Beach is one of the most notable beaches in Odisha and is relatively undiscovered.
This white sandy beach is ringed by casuarina trees, Alaska and Eucalyptus trees. The beach is completely standalone out of the crowd that attracts lots of tourists who are craving for some relaxing quite time. The magnificent view of Sunrise and Sunset from the beach amidst dazzling golden hues is an unforgettable experience for everyone who comes here from far off places.
Olive Ridley Turtles visit the beach during December and January to lay eggs and many migratory birds can be seen during winter months. Here boat riding, water skiing and parasailing facilities are available for tourists. Visitors can also visit the Balihirana Deer Sanctuary where one can spot the endangered Balihirana antelope and Indian .....
Sudarshan Craft Museum
At a distance of 2 km from Puri Junction, Sudarshan Craft Museum is located at station road in Puri. It is one of the best places to visit in Puri for art lovers.
The Sudarshan Craft Museum was started in the year 1977 by Shri Sudarhsan Sahoo. The museum exhibits great collection of artistic works done by Sudarshan Sahoo like carvings in wood, stone and fiber glass, handicraft items and also fine paintings. Within the premises of museum, one can witness a Buddhist temple built in a Japanese style, a library and a workshop.
This museum is a must visit place for art lovers as it gives a very enriching and knowledgeable experience to visitors. The items kept on display are rare works of art and are the gems of tradition and culture of the nation. The museum has metal figurines, stone carvings too for sale. The museum provides a platform for budding artists to showcase their talent to the world.
Timings: 8 AM - 12 PM & 2 PM - 8 PM, Closed on Saturdays & Sundays
Entry .....
Alarnatha Temple
At a distance of 22 km from Puri, Alarnatha Temple is an ancient temple located in Brahmagiri near Puri. Dedicated to Lord Vishnu, this is one of the famous temples of Lord Vishnu in Odisha and also one of the must visit places in Puri.
The present temple of Lord Alarnatha is said to be two hundred years older than the Jagannatha Temple in Puri. The temple is not a big tourist destination but a famous religious place for all Krishna devotees. Legend says that Lord Brahma came and worshipped Lord Vishnu here on top of a hill. According to history, the rulers of Alwar in Rajasthan had established the temple here. The temple was later known as Alwarnath or Alarnath.
The temple is associated with the visit of Chaitanya Mahaprabhu to Orissa. Chaitanya Mahaprabhu during his stay in Puri used to see Lord Jagannath daily. During Anavasara period when Lord and his siblings were taken to the secret chamber for 15 days, he was unable to see his dear Lord. So as per the legend lord directed .....
Lokanath Temple
At a distance of 2 km from Sri Jagannath Temple and 4.5 km from Puri Junction, Lokanath Temple is an ancient temple located in the town of Puri. Dedicated to Lord Shiva, it is one of the most important Shiva shrines of Odisha and also one of the popular places to visit in Puri.
The presiding deity of this 11th century temple is Lord Lokanath in the form of lingam. According to history, the lingam in this temple was established by Lord Rama. While Lord Rama on his way to Sri Lanka for searching Sita reached Puri and sat with a vow to see Shiva here. At that time, there was a village named Sabarapalli nearby presented him a Lau or Lauka (Pumpkin) looking like a Shiva linga. Lord Rama installed that as a replica of Siva linga at that place and prayed Shiva to fulfill his desire. Thus the temple is called Laukanatha and later named as Lokanath Temple.
The unique feature of the temple is the Shiva linga is always submerged with water which substantiates the legend that Goddess Ganga .....
Markandeshwara Temple
At a distance of 1 km from Sri Jagannath Temple and 3 km from Puri Junction, Markandeshwara Temple is one of the famous Hindu temples in Puri town of Odisha. The temple is situated on Markandeshwar Street adjacent to Markandeshwar tank to the north of the Jagannath Temple. This is one of the Pancha Tirthas of Puri and also one of the fifty two sacred Shiva places in India.
The temple is dedicated to Lord Shiva and is believed to be from Ganga dynasty dating back to 12th century. According to the mythology, sage Markandeya was saved by Lord Shiva from a great danger. Sage Markandeya erected a Shiva temple out of dedication. Also known as Markandeya Shiva Temple and Markandeshwara Mahadeva Temple, the temple is connected with the rituals of Chandan Yatra, Balabhadra Janma, Ashram Bije, Sital Sasthi, Kaliyadalan etc. of the Jagannath Temple.
Markandeya tank is an open structure and it enclosed within a stone wall made of laterite blocks. The tank is presently used for rituals .....
Bedi Hanuman Temple
#10 of 14 Places to Visit in Puri
At a distance of 1.5 km from Puri Junction and 3 km from Jagannath Temple, Bedi Hanuman Temple is a small sea side temple situated to the west of Chakra Narayan Temple in Puri. Known as Daria Mahavir Temple, it is one of the famous temples in Puri.
The temple is dedicated Lord Hanuman. On local language, 'Daria' Means Sea and Mahavir is another name of Lord Hanuman. The temple has an interesting story behind the name 'Bedi Hanuman'. It is believed that Daria Mahavir protects Puri against the fury of the sea. But when the Jagannath Temple was built, Varuna, the God of the Sea, able to came to the Jagannath temple to pay his respects.
In the meantime sea water entered the city, causing considerable damage to the temple. The devotees prayed to Lord Jagannath who asked Hanuman to explain how the sea entered the city in his presence. Lord Hanuman told that he was not present at that time and was left for Ayodhya without informing him. On hearing about Lord Hanuman's unscheduled .....
View All 14 Places to Visit in Puri
Most Asked Questions on Puri
What is the best season to visit Puri?
Being a coastal town, Puri has extreme weather conditions in summer while in monsoons the city witnesses medium to heavy rainfall. Winter from November to February is the best time to visit Puri when the climate is pleasant and is suitable for sightseeing.
How many days are required to visit Puri?
Puri usually requires 1-2 days to visit the most important attractions. There are several wonderful places to visit around Puri like Bhubaneswar, Konark and Cuttack which usually take additional few days.
What is the budget required to visit Puri?
Budget of Puri trip depends on the trip duration and category of hotel you choose for your stay along with the kind of vehicle you prefer. Puri tour packages offered by Trawell.in start from as low as INR 5000 per person for a 2D/1N trip and from INR 6800 per person for 3D/2N vacation.
What are some must visit places & things to do while visiting Puri?
Ensure to cover most important places to visit in Puri like Sri Jagannath Temple, Puri Beach, Gundicha Temple, Narendra Tank, Balghai Beach, Sudarshan Craft Museum, Lokanath Temple, Alarnatha Temple, etc.
When is the Rath Yatra at puri?
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When information practices are understood to be shaped by social context, privilege and marginalization alternately affect not only access to, but also use of information resources. In the context of information, privilege, and community, politics of marginalization drive stigmatized groups to develop collective norms for locating, sharing, and hiding information. In this paper, we investigate the information practices of a subcultural community whose activities are both stigmatized and of uncertain legal status: the extreme body modification community. We use the construct of information poverty to analyze the experiences of 18 people who had obtained, were interested in obtaining, or had performed extreme body modification procedures. With a holistic understanding of how members of this community use information, we complicate information poverty by working through concepts of stigma and community norms. Our research contributes to human information behavior scholarship on marginalized groups and to Internet studies research on how communities negotiate collective norms of information sharing online.
This is the peer reviewed version of the following article: Lingel, J., & boyd, d. (2013). "Keep it secret, keep it safe." Information practices, information norms and stigma. Journal for the American Society of Information Science and Technology, 64(5): 981-991. It has been published in final form at https://doi.org/10.1002/asi.22800. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving http://olabout.wiley.com/WileyCDA/Section/id-820227.html#terms.
At the time of publication, author Jessa Lingel was affiliated with Rutgers University. Currently, she is a faculty member at the Annenberg School of Communication at the University of Pennsylvania.


The most immediate threats to Sri Lankan national security in 1988 were internal rather than external. The Tamil insurgency was the most severe of these, involving a changing number of heavily armed terrorist groups that carried out attacks on military and civilian targets throughout the island and, for most of 1986, actually controlled the Jaffna Peninsula (see fig. 1). A second source of instability came from leftist nationalist Sinhalese groups opposed to Tamil autonomy. The chief among these, the JVP, launched a short-lived insurrection in 1971 that came close to toppling the government of Sirimavo R. D. Bandaranaike. After a period of open participation in the political system, the JVP resumed its violent antigovernment activities in the 1980s, and expanded its following considerably at the time of the Indo-Sri Lankan Accord of July 1987. The government also faced a growing problem of civil violence that seriously threatened the democratic process. This unrest stemmed not only from the continuing ethnic conflict but also from a general economic malaise that increasingly prevented young men from playing productive roles in society (see Labor , ch. 3). The problem of a restless, unemployed youth, although separate from the ethnic difficulties, was instrumental in providing a fertile recruiting ground for extremists in search of a following.
Throughout the 1980s, external threats to the nation's security were long term rather than immediate and tended to involve the rivalry between regional and world superpowers for influence over the Indian Ocean. The port of Trincomalee, one of the best natural harbors in the world, has long been attractive to foreign nations interested in Indian Ocean bases. India has expressed a determination to prevent either the United States or the Soviet Union from establishing a naval presence there, and the Indo-Sri Lankan Accord helped confirm the Indian claim of regional leadership.


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Beketinci – Bentež
Settlement, town, county: Beketinci, Bentež, Osijek-Baranja County
Site type: stratified settlements
Period: prehistory – Lasinja culture, Early Middle Ages, Late Middle Ages
Type of excavation: salvage
Institution: Institute of Archaeology in Zagreb
Excavation manager: Dr Kornelija Minichreiter (2007 and 2008)
The Institute of Archaeology in Zagreb carried out extensive salvage archaeological excavations of the prehistoric, early- and late mediaeval site at Bentež near the village of Beketinci, AN 18, on the route of the international motorway Budapest – Ploče, section Osijek – Đakovo. In the course of the works, which lasted between Autumn 2007 and summer 2008, a total area of 60.300 m2 was investigated.
The Bentež plot lies in the lowest part of the alluvial plain south of Osijek, intersected by the Karašica, Vučica and Vuka watercourses. The oldest part of the site belongs to the Lasinja culture from the Eneolithic period around 6000 before present, i.e. from the end of the 5th and the beginning of the 4th millennium BC, the Middle Eneolithic period. A part of an extensive settlement was discovered, whose western—working—part consisted of numerous clay-extraction pits and two large kilns for firing clay vessels and objects. In the eastern, residential part of the settlement, three large pit-houses (20 x 30 m) with wells on their edges were discovered, as well as the foundations of five above-ground houses of rectangular plan. The largest above-ground house was 30 m long and 12 m wide, while the smaller ones measured 8 x 5 m and 10 x 5 m and consisted of two rooms. Pottery types included pots and small pots, bowls, pans and ceramic spoons. Besides pottery, there were also lithic finds – fragments of stone tools, shaft-hole axes. The archaeological material belongs to the late phase of the Lasinja culture, dated between 3960 and 3360 BC.
The mediaeval part of the settlement existed in two separate periods, in the Early and Late Middle Ages. At the northern periphery of the investigated part of the prehistoric settlement two pits with early mediaeval finds were discovered. The pits yielded pots with simple rims and combed waveline decoration, and fragments of a deep biconical vessel – bucket, dated to the 9th-11th century.
The excavated portion of the late mediaeval settlement stands out by the fact that for the first time in Slavonia as many as 35 houses were identified, on an area of 20.000 m2. The structures arranged in three concentric circles surrounding a small square, opened towards the west (like a horseshoe) are the evidence of the first urban planning for rural settlements in Slavonia in the period between the 14th and 16th centuries. This date was corroborated by the radiocarbon dates of the structures in the settlement. Among the 35 excavated houses there were 11 semi-sunken ones and 24 big above-ground houses of a rectangular plan, covering an interior area between 10 and 90 m2. Several of the above-ground houses had one or two cellars, and each had at least one bread oven in the courtyard. The semi-sunken houses—leather workshops—had a fireplace inside, while two blacksmithies had large forges in their interiors. The only above-ground house of timber-frame construction was most likely a mill (big millstones). The settlement yielded big pottery kilns, various accessory structures (a stable), storage pits – granaries and five wells. Settlement planning of this type was widespread in Central Europe. The settlement, composed of a number of rural households (houses with a fenced courtyard and accessory features and bread ovens inside) and an empty space—a square—in the centre, probably served a commercial function, because historical sources mention Kisújlak (the identified name of the settlement) as a trading and customs settlement on the border of the Baranja and Vukovo Counties. The rich household assemblage of diverse tableware, a wealth of crafts and farming tools, pieces of equestrian equipment and the find of a lead bull of Pope Gregory XI (1370-1378) corroborate the synchronicity of Kisújlak (Bentež near Beketinci) not only with settlements and fortified towns in Croatia, but also its connections with a broader European circle in the period between the 14th and 16th centuries.
Minichreiter K. 2009, Arhitektura eneolitičkog naselja lasinjska kulture na lokalitetu Bentež u Beketincima, Histria antiqua sv. 18/1, 141-148.
Minichreiter, K., Marković, Z. 2009, Prapovijesno i ranosrednjovjekovno naselje Bentež kod Beketinaca, Prilozi Instituta za arheologiju u Zagrebu, vol. 26, 21-44.
Minichreiter, K., Marković, Z. 2011, Architecture of Lasinja Culture settlements in the light of new investigations in northern Croatia, Documenta Praehistorica, XXXVIII, Ljubljana, 333-344.
Minichreiter, K., Marković, Z. 2013, Beketinci, Bentež, naselja iz eneolitika, ranoga i kasnoga srednjega vijeka, monografija, Institut za arheologiju, 3, Zagreb.

redpajama_set_name: RedPajamaCommonCrawl


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redpajama_set_name: RedPajamaGithub


text:
Q: How to mask an image and make the white transparent in Swift 3? So I've written some code to mask an image
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The mask is just a diagonal line that half the screen is black and the other screen is white.
The desired effect is to have an image under this masked image so that half of the image is one image and the other half is another image.
What I'm seeing however is that when I return the CGImage my top layer image is showing through wherever my mask is black, but rather than be transparent when it is on the white, it is white.
How can I get the whites to be transparent? Thanks.

redpajama_set_name: RedPajamaStackExchange


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503 Tahoe Street, Natchitoches, LA 71457-5718.
4311 Braemar Ave., Lakeland Florida, 33813-1608 U.S.A.
Works listed Composer are available from Roger C. Vogel at the given address and email.
If there are any difficulties in contacting the publishers or obtaining works, please contact him.


text:
\section{Introduction}
Developing accurate population-wide neural encoding models that predict the evoked brain response directly from sensory stimuli has been an important goal in computational neuroscience.
Modeling neural responses to naturalistic stimuli, in particular stimuli that reflect the complexity of real-world scenes (e.g., movies), offers significant promise to aid in understanding the human brain as it functions in everyday life~\cite{pmid31257145}. Much of the recent success in predictive modeling of neural responses is driven by deep neural networks trained on tasks of behavioral relevance.
For example, features extracted from deep neural networks trained on image or auditory recognition tasks are currently the best predictors of neural responses across visual and auditory brain regions, respectively~\cite{pmid24812127, pmid26157000, Kell2018a}.
While this success is promising, the unexplained variance is still large enough to prompt novel efforts in model development for this task. One aspect that is often overlooked in existing neural encoding models in vision is visual attention.

Natural scenes are highly complex and cluttered, typically containing a myriad of objects. What we perceive upon viewing complex, naturalistic stimuli depends significantly on where we direct our attention. It is well known that multiple objects in natural scenes compete for neural resources and attentional guidance helps to resolve the ensuing competition~\cite{Serences2006SelectiveVA}. Due to the limited information processing capacity of the visual system, neural activity is biased in favor of the attended location ~\cite{Kastner2000MechanismsOV, Braun2001ThisEF}. Hence, more salient objects tend to be more strongly and robustly represented in our brains. Further, several theories have postulated that higher regions of the visual stream encode increasingly shift- and scale-invariant representations of attended objects after filtering out interference from surrounding clutter~\cite{Itti2001ComputationalMO, Poggio2016VisualCA}. These studies suggest that deployment of attention results in an information bottleneck, permitting only the most salient objects to be represented in the inferotemporal (IT) cortex, particularly the ventral visual stream which encodes object identity. These findings together indicate that visual attention mechanisms can be crucial to model neural responses of the higher visual system.

Visual attention and eye movements are tightly interlinked.
Where we direct our gaze often quite accurately signals the focus of our attention~\cite{Hoffman1995TheRO}. This form of attention, known as overt spatial attention, can be directly measured by eye-tracking. Recent work has shown that fMRI activity can be used to directly predict fixation maps or eye movement patterns under free-viewing of natural scenes, suggesting a strong link between neural representations and eye movements~\cite{OConnell2018PredictingEM}. More recent large-scale efforts in such concurrent data collection, such as the Human Connectome Project (HCP)~\cite{hcp}, that simultaneously record fMRI and eye-tracking measurements on a large population under free-viewing of movies, present a novel opportunity to probe the potential role of attention in neural encoding models of ecological stimuli.

Our contributions in this study are as follows:
\begin{itemize
\item We demonstrate that leveraging information about attended locations in an input image can be helpful in predicting the evoked neural response. Particularly, we show that attentional masking of high-level stimulus representations based on human fixation maps can dramatically improve neural response prediction accuracy for naturalistic stimuli across large parts of the cortex.
\item  We show that it is possible to use supervision from neural response prediction solely to co-train a visual attention network. This training strategy thus encourages only those salient parts of the image to dominate the prediction of the neural response.
We find that the neural encoding model with this trained attention module outperforms encoding models with no or fixed attention.
\item Interestingly, we find that despite not being explicitly trained to predict fixations, the attention network within the neural encoding model compares favorably against saliency prediction models that aim to directly predict likely human fixation locations given an input image. This suggests that neural response prediction can be a powerful supervision signal for learning where humans attend in cluttered scenes with multiple objects. This signals a novel opportunity for utilizing functional brain recordings during free-viewing to understand visual attention.

\end{itemize}

\section{Methods}
Neural encoding models comprise two major components: a representation (feature extraction) module that extracts relevant representations from raw stimuli and a response model that predicts neural activation patterns from the feature space. We propose to integrate a trainable soft-attention module on top of the representation network to learn attention schemes that guide the prediction of whole-brain neural response. Our proposed methodology is illustrated in Figure~\ref{fig:schematic}.

\begin{figure}
  \centering
  \includegraphics[width=0.9\linewidth]{images/schematic-full.png
  \caption{\textbf{Proposed method.} A trainable soft-attention module is implemented on top of a pre-trained representation network to rescale features based on their salience. The rescaled features are spatially pooled and fed into a convolutional response model to predict whole-brain neural response. We assess the value of the trained attention network by comparing it with neural encoding methods employing (i) stimulus-dependent attention maps derived from human fixations ($A_G$), (ii) stimulus-independent attention map derived from all fixations in the training set that reflects the center-weighted bias of our dataset ($A_C$) as well as a (iii) no attention model that spatially pools the features directly with no scaling.}
  \label{fig:schematic}
\end{figure}

\paragraph{Feature extraction network}
We employ the state-of-the-art ResNet-50~\cite{He2015DeepRL} architecture pre-trained for object recognition on ImageNet~\cite{Deng2009ImageNetAL} as the representation network to extract semantically rich features from raw input images. In this study, we focus on improving neural response prediction in higher-order regions of the visual pathway where receptive fields are larger and not limited to a single hemi-field. Prior evidence suggests that these regions are likely best modelled by deeper layers of object recognition networks~\cite{pmid26157000, Wen2018a}. Thus, we extract the output of the last "residual block", namely {\small \fontfamily{qbk}\selectfont res5} (after addition) before the global pooling operation to encode all images into a 2048-channel high-level feature representation image (of size $23\times32$, in our experiments), denoted as {\small \fontfamily{qbk}\selectfont F$_{rep}$}. All pre-trained weights are kept frozen during training of the neural encoding models.

\paragraph{Attention network}
The attention network operates on the 2048-channel feature representation image {\small \fontfamily{qbk}\selectfont F$_{rep}$}.
For simplicity, we employed a single convolutional layer that constructs the saliency map with a trainable {\small \fontfamily{qbk}\selectfont $5\times5$} filter {\small \fontfamily{qbk}\selectfont V$_{att}\in \mathbb{R}^{5\times 5 \times 2048 \times 1}$ } as, $S = G_{\sigma}*[V_{att}*F_{rep}]_+$. Here, $|\cdot|_+$ denotes the ReLU operation and $G_{\sigma}*$ indicates blurring using a $5\times5$ gaussian kernel with $\sigma = 1$. The attention scores for each pixel are finally computed from saliency maps by normalizing with the spatial {\small \fontfamily{qbk}\selectfont softmax} operation,
\begin{equation}
    A_l^{(i)} = \frac{\exp{S^{(i)}}}{\sum_{j=1}^n \exp{S^{(j)}}}, i\in \{1,..,n\}.
\end{equation}
Here, superscript $i$ is used to index the {\small \fontfamily{qbk}\selectfont $23 \times 32$} spatial locations in the feature map {\small \fontfamily{qbk}\selectfont F$_{rep}$}. We note that existing literature on selective visual attention suggests a hierarchical winner-take-all mechanism for saliency computation, where only the particular subset of the input image that is attended is consequently represented in higher visual systems~\cite{Braun2001ThisEF}. The {\small \fontfamily{qbk}\selectfont softmax} operation can be construed as approximating this winner-take-all mechanism.
The attention is consequently applied as element-wise scaling to {\small \fontfamily{qbk}\selectfont F$_{rep}$} to yield an attention modulated representation {\small \fontfamily{qbk}\selectfont F$_{rep}^a$} = {\small \fontfamily{qbk}\selectfont F$_{rep}$}$\odot A$.

\paragraph{Convolutional response model}
 The convolutional response model maps the spatially pooled attention modulated features \textbf{f}$_g$ = $\sum_{i=1}^n$ {\small \fontfamily{qbk}\selectfont F$_{rep}^{a(i)}$} to the neural representation space, reshapes them into coarse 3D feature maps and transforms them into an increasingly fine-grained volumetric neural activation pattern using trainable convolutions. This dramatically reduces the parameter count in comparison to linear response models with dense connections. Additionally, it captures spatial context and allows end-to-end optimization of the neural encoding model to predict high-resolution neural response, thereby alleviating the need for voxel sub-sampling or selection. The full sequence of feedforward computations in the convolutional response model are shown in the inset of Figure~\ref{fig:schematic}. The architecture of the convolutional response model is kept consistent across all CNN-based models to ensure a fair comparison.

\subsection{Baselines and upper bounds}
\paragraph{No attention}
We compared the performance of all attention-based models against a model with no attention modulation that spatially pools the feature representation as, \textbf{f}$_g$ = $\sum_{i=1}^n$ {\small \fontfamily{qbk}\selectfont F$_{rep}^{(i)}$} (denoted as `No attention').
We implemented another baseline that uses the full feature map directly (instead of spatial pooling) as a flattened input to the convolutional response model. Due to computational/memory constraints, we had to reduce the dimensionality of the fully connected layer (to 256 units instead of 1024) in the convolutional response model for this encoding method. This model is henceforth denoted as `No pooling'.

\paragraph{Center-weighted attention} To further assess the usefulness of a learned attention network, we derive a stimulus-independent attention map ({\small \fontfamily{qbk}\selectfont A$_C$}) based on averaging across all eye gaze data in the training set,
using Gaussian kernel density estimation. This essentially amounts to center-weighted attention (see Supplementary) since fixation locations on average are biased towards the center of an image~\cite{Tseng2009QuantifyingCB}. The standard deviation of the Gaussian kernel is chosen to maximize log-likelihood on the validation set and is consequently set to 20.

\paragraph{Gaze-weighted attention} We derive attention maps for every input frame from the eye gaze coordinates observed for the respective frame across different subjects. The human fixation maps are converted into attention maps {\small \fontfamily{qbk}\selectfont A$_G$} by blurring with a Gaussian kernel of same standard deviation as the center-weighted attention model. The resulting attention maps in the original input image space are subsequently resized to the spatial dimensions of {\small \fontfamily{qbk}\selectfont F$_{rep}$} and renormalized. Since these stimulus-specific attention maps are derived from actual human gaze information, they likely represent an upper bound in neural encoding performance among all attention-based models.

\paragraph{Linear models} To date, neural encoding models in all prior work employ a linear response model with appropriate regularization on the regression weights. To compare against this dominant approach, we extract global average pooled (no-attention) features as well as pooled attention modulated features for both non-trainable attention schemes (center-weighted and gaze-weighted attention) as described above, to present to the linear regressor. We apply $l_2$ regularization on the regression coefficients and adjust the optimal strength of this penalty $\lambda$ through cross-validation using 10 log-spaced values in $\{1\mathrm{e}{-5}, 1\mathrm{e}{5}\}$. In later sections, we denote the performance of the above models as `No attention (linear)', `Center-weighted attention (linear)' and `Gaze-weighted attention (linear)' respectively.

\subsection{Training procedure}
 All parameters were optimized to minimize the \textit{mean squared error} between the predicted and target fMRI response using Adam~\cite{Kingma2014AdamAM} for 25 epochs with a learning rate of 1e-4. Validation curves were monitored to ensure convergence and hyperparameters were optimized on the validation set.

\subsection{Evaluation}
\paragraph{Neural encoding}
We evaluated the performance of all encoding models on the test movie by computing the \textit{Pearson's correlation coefficient} (R) between the predicted and measured fMRI response at each voxel. Since we are only interested in the stimulus-driven response, we isolate voxels that exhibit high inter-group correlations over all training movies. Inter-group correlation (``synchrony") values were computed by splitting the population into half and computing correlations between the mean response time-course of each group (comprising 79 subjects) at every voxel. We employed a liberal threshold of 0.15 for this correlation value to consider a voxel as ``synchronous"~\cite{pmid15016991}. Finally, to summarize the prediction accuracy across the stimulus-driven cortex, we compute the mean correlation coefficient across the synchronous cortex voxels by varying the ``synchrony" thresholds.
For region level analysis, ROIs were extracted using a population-wide multi-modal parcellation of the human cerebral cortex, namely the HCP MMP parcellation~\cite{pmid27437579}.

\paragraph{Saliency prediction}
Next, we wanted to assess if the learned attention model was indeed looking at meaningful locations in input images while predicting neural responses. To address this question and put the learned attention schemes in perspective, we assessed the agreement of predicted saliency maps with human fixation maps for every frame in the test movie. Besides a qualitative evaluation, we computed quantitative metrics for comparing the predicted saliency maps against popular fixation (or saliency) prediction approaches. These include: (i) Itti-Koch~\cite{Itti2009AMO}: a biologically plausible model of saliency computation that assigns pixel-level conspicuity values based on multi-scale low-level feature maps (intensity, color, orientation) computed via center-surround like operations similar to visual receptive fields, (ii) Deepgaze-II model~\cite{Kmmerer2017UnderstandingLA}: a deep neural network based approach that extracts high-level features from a pre-trained image recognition architecture (VGG19) as input to a readout network that is subsequently trained to predict fixations using supervision from gaze data, and (iii) Intensity contrast features (ICF) model~\cite{Kmmerer2017UnderstandingLA}: a low-level saliency computation model that uses the same readout architecture as the Deepgaze-II model, but on low-level intensity and intensity contrast feature maps as opposed to high-level features. Additionally, we also report evaluation metrics for the center-weighted saliency map. We note that the Deepgaze-II and ICF models were trained with eye-tracking supervision on the MIT1003 saliency dataset~\cite{mit-saliency-benchmark}.

Developing metrics for saliency evaluation is an active area of research and several different metrics have been proposed that often exhibit discrepant behavior~\cite{Bylinskii2016WhatDD}. We report the most commonly used metrics in saliency evaluation~\cite{Bylinskii2016WhatDD}, including, (i) Similarity or histogram intersection (SIM), (ii) Pearson's correlation coefficient (CC), (iii) Normalized scanpath saliency (NSS), (iv) Area under the ROC curve (AUC) and (v) Shuffled AUC (sAUC). Following ~\cite{Kmmerer2015InformationtheoreticMC}, we used log-density predictions as saliency maps to compute all evaluation metrics.
\subsection{Dataset}
We study high-resolution 7T fMRI (TR = {\small \fontfamily{qbk}\selectfont 1s}, voxel size = {\small \fontfamily{qbk}\selectfont 1.6 \ mm isotropic}) recordings of 158 participants from the Human Connectome Project (HCP) movie-watching database while they viewed 4 audio-visual movies in separate runs~\cite{hcp, hcp2}. Each movie scan was about 15 minutes long, comprising multiple short clips from popular Hollywood movies and/or vimeo. Eye gaze locations of subjects were also recorded simultaneously at 1000Hz and resampled to 24Hz to match the video frame acquisition rate. A standard pipeline was used to preprocess all fMRI data, which includes motion and distortion correction, high-pass filtering, head motion effect regression using Friston 24-parameter model, automatic removal of artifactual timeseries identified with Independent Component Analysis (ICA) as well as nonlinear registration to the MNI template space~\cite{pmid27894889, hcp2}. Since the present study focuses on the development of population-wide predictive models, we averaged the response for each frame across subjects to obtain a single fMRI volume that represents the population average brain activation in response to that frame. After discarding rest periods as well as the first 10 seconds of every movie segment, we used about 12 minutes of audio-visual stimulation data per movie paired with the corresponding fMRI response and fixation data for analysis. We extract the last frame of every second of the video as a {\small \fontfamily{qbk}\selectfont $720 \times 1280 \times 3$} RGB input to present as stimulus to the neural encoding models. The output is the predicted response across the entire brain, represented as a volumetric image of dimensions {\small \fontfamily{qbk}\selectfont $113 \times 136 \times  113$}. We estimate a hemodynamic delay of $4$ $sec$ using regression based encoding models (see Supplementary), as the response latency that yields highest encoding performance. Thus, all proposed and baseline models are trained to use the above stimuli to predict the fMRI response 4 seconds $\textit{after}$ the corresponding stimulus presentation. We train and validate our models on three movies using a $~$9:1 train-val split and leave the fourth movie for independent testing. This yields 2000 training, 265 validation and 699 test stimulus-response pairs.

\section{Results}

\paragraph{Incorporating gaze-weighted attention significantly improves neural response prediction.}
\begin{figure}
  \centering
  \includegraphics[width=0.9\linewidth]{images/sync_all_v2.png}
  \caption{\textbf{Quantitative evaluation of all models.} (A) depicts mean correlation values across the synchronous, (i.e., stimulus-driven) cortex defined at a range of synchrony thresholds ([0.15,0.75]). Each point thus reflects the mean prediction accuracy for a model across all voxels within synchronous cortex defined by a threshold value (x-axis). (B) depicts the inter-group correlation (synchrony) values across the entire human cerebral cortex.}
  \label{fig:quant}
\end{figure}

We first examined whether attention weighted pooling helps to improve response predictions. Figure~\ref{fig:quant} shows the mean prediction accuracy across the entire synchronous cortex for all models considered in this study. We find that the `gaze-weighted attention' model significantly outperforms the `no attention' model for both linear ($\sim$ 40 \% improvement among all voxels with synchrony>0.15), as well as convolutional response model  ($\sim$ 47 \% improvement among all voxels with synchrony>0.15). The attention maps result in amplification of features of attended locations along with suppression of other irrelevant information. This re-scaling of features before pooling using fixation patterns obtained from eye-tracking data remarkably improves neural encoding performance across large areas of the cortex, suggesting that neural responses are indeed dominated by sensory signals at attended locations. Although we employed a convolutional response model primarily for computational efficiency in predicting a high-resolution (113x136x113) whole-brain neural response, we also observed a small improvement in neural encoding with this response model in comparison to a linear response model.

\begin{figure}
  \centering
  \includegraphics[width=0.9\linewidth]{images/roi_v3.png}
  \caption{\textbf{Top: ROI-level analysis} Mean correlation values across intermediate (V4), higher visual areas in the inferotemporal cortex and its neighborhood and other higher higher-level visual regions (Dorsal, MT+) as described in the HCP MMP parcellation~\cite{pmid27437579}. Error bars represent 95\% confidence intervals around mean estimates computed using bootstrap sampling. \textbf{(A)-(E) Prediction accuracy across the cortical surface for all deep CNN-based models.} Statistical significance of individual voxel predictions is computed as the p-value of the obtained sample correlation coefficient for the null hypothesis of uncorrelatedness (i.e., true correlation coefficient is zero) under the assumptions of a bivariate normal distribution. Only significantly predicted voxels (p<0.05, FDR corrected) for each method are colored on the surface. Prediction accuracy maps for encoding methods with linear response models are provided in the Supplementary.}
  \label{fig:brain_acc}
\end{figure}

\paragraph{Trainable attention model outperforms models with no attention or center-weighted attention}
In addition to improving neural response prediction, the convolutional response model renders end-to-end training of encoding models on whole-brain neural data feasible by dramatically reducing the number of free parameters in comparison to linear response models. In this study, we exploited this increased parameter efficiency to co-train an attention network  on top of a pre-trained representation network (while freezing the representation network) for the goal of neural response prediction. As shown in Figure~\ref{fig:quant}, the encoding model with learned attention surpasses models with no pooling, no attention or center-weighted attention in mean prediction accuracy across the sychronous cortex as well across most ROIs involved in object processing. This suggests that even with no eye-tracking data, as is the case with majority movie-watching fMRI datasets, modelling visual attention can still be beneficial in response prediction. The improvements are most apparent in ventral stream regions such as the Fusiform Face Complex (FFC) and PIT Complex, as well as objective-selective parts of the lateral occipital complex (LO1, LO2, LO3) (Figure~\ref{fig:quant}). Studies in visual perception have shown that these lateral occipital areas respond more strongly to intact objects than scrambled objects or textures, providing strong evidence for their role in object recognition as well as object shape perception~\cite{pmid11322983, Kourtzi2000CorticalRI, Larsson2006TwoRV}. Accuracy in another group of areas within the temporo-parieto-occipital junction, which is known to be involved in visual object recognition as well as representation of facial attributes such as the intensity of facial expressions~\cite{Benedictis2014AnatomofunctionalSO}, is similarly improved with the trained attention network. In addition to these areas, we also observe some improvement in neural encoding performance in other higher order processing regions across the dorsal visual stream, motion-sensitive visual regions (MT+ complex) and their neighboring visual areas (Figure~\ref{fig:brain_acc}). We also trained the proposed and baseline models on representations of other randomly selected deep layers within the ResNet-50 architecture and observed a similar benefit of attention modulation across different layers (see Supplementary). Further, a representational similarity analysis comparing non-modulated and attention modulated representations of different layers across popular architectures showed that models that explain stimulus-dependent human fixation patterns are able to better account for the representational geometry of neural responses across intermediate and higher visual object processing areas (see Supplementary). Taken together, these findings provide further support for the utility of attention modelling in neural encoding approaches. In addition to improving accuracy, the attention model further affords interpretability by highlighting salient locations within the input image that are being employed to make response predictions.

\paragraph{Learned attention policies correspond remarkably well with human fixation maps.}
Figure~\ref{fig:saliency} depicts saliency maps predicted by the trained attention network on sampled frames from the test movie. This qualitative assessment indicates that the proposed neural encoding model learns attention policies that are consistent with human fixation maps. Since attention is learned on top of high-level features, the model learns to focus on high-level stimuli features such as the presence of faces, hands and more conspicuous objects likely to direct attention in natural scenes. A closer look at incongruent cases indicates that images where the model fails to track human fixations are often highly complex scenes, where fixations may be driven by contextual knowledge of previous movie frames (Figure~\ref{fig:saliency}, top-right) or auditory signals, e.g., who the speaker is, etc. (Figure~\ref{fig:saliency}, bottom-right).

Table~\ref{tab:saliency_eval} shows quantitative metrics that compare the quality of saliency maps computed by benchmark models trained to predict gaze on our data.
We also listed the performance of the attention network that was merely trained on fMRI data, and not eye gaze data.
We note that our attention network performs on par with popular fixation prediction models that are trained directly on the task of saliency prediction in a supervised manner (ICF and Deepgaze-II). This trend holds for almost all saliency evaluation metrics, as shown in Table~\ref{tab:saliency_eval}. This observation is particularly interesting given that the attention network is trained using supervision from neural response prediction only, without any information about gaze coordinates.

\begin{figure}
  \centering
  \includegraphics[width=0.95\linewidth]{images/saliency.png}
  \caption{\textbf{Qualitative assessment of saliency (log-density) maps.} Top row shows sampled frames from the test movie, middle row shows human fixation maps overlaid on the corresponding frame, bottom row shows saliency maps predicted by the attention network of the proposed neural encoding model.}
  \label{fig:saliency}
\end{figure}

\begin{table}
 \caption{Evaluation against saliency prediction models. Mean and standard errors for each metric are reported. Best results are bolded.}

  \centering
  \resizebox{\textwidth}{!}{
  \begin{tabular}{llllll}
    \toprule

    Model     &   SIM $\uparrow$ & CC $\uparrow$ & NSS $\uparrow$ & AUC $\uparrow$ & sAUC $\uparrow$ \\
    \midrule
    Itti-Koch & $0.318 \pm 0.002$ & $0.325 \pm 0.004$ & $1.010 \pm 0.014$ & $0.795 \pm 0.004$ & $0.537 \pm 0.006$    \\
    ICF     & $0.291 \pm 0.002$ & $0.190 \pm 0.007$ & $0.646 \pm 0.023$ & $0.665 \pm 0.006$ & $0.647\pm 0.005$      \\
    Center-weighted   & $0.327 \pm 0.002$ & $0.350 \pm 0.004$ & $1.074 \pm 0.013$ & $0.803 \pm 0.003$ & $0.496 \pm 0.006$ \\
    Deepgaze-II & $0.359 \pm 0.003$ & $\textbf{0.420} \pm 0.005$ & $\textbf{1.425} \pm 0.025$ & $\textbf{0.808} \pm 0.004$ & $\textbf{0.713} \pm 0.004$ \\

    Ours &  $\textbf{0.392} \pm 0.004$ & $0.403 \pm 0.010$ & $1.375 \pm 0.041$ & $0.754 \pm 0.006$ & $0.645 \pm 0.006$ \\
    \bottomrule
  \end{tabular}
  }
  \label{tab:saliency_eval}
\end{table}

\section{Discussion and Conclusion}
In the present study, we demonstrate that encoding models with visual attention, whether explicitly estimated from human fixation maps or modelled using a trainable soft-attention scheme, yield significant improvements in neural response prediction accuracy over non-attention based counterparts. We observe consistent improvements across most high-level visual processing regions, suggesting that unattended portions of an input image may likely have little effect on neural representations in these regions. Loosely, this aligns well with Treisman's feature integration theory~\cite{Treisman1980AFT}, which proposes that integrated object representations are only formed for attended locations. In addition to improving response prediction accuracy, inclusion of visual attention within neural encoding models promises a better understanding of spatial selection and its influence on neural representations and perceptual processing.

The saliency of a stimulus often depends on the context within which it is presented and attentional selection strategies can be modulated by task demands~\cite{Itti2001ComputationalMO}.
Thus, in movie watching, future neural encoding models should also capture the sequence of frames, rather than isolated frames, and the audio track in modeling attention.

Beyond advancing our understanding of sensory perception, neural encoding models have potential for real-world applications, most obviously for brain-machine interface. Additionally, an improved understanding of the link between sensory stimuli and evoked neural activation patterns can provide opportunities for neural population control, for e.g., by synthetically designing stimuli to elicit a specific neural activation pattern~\cite{Bashivan2018NeuralPC}.

Our study provides a first attempt in capturing visual attention within neural encoding models. We see several opportunities for extending this work. In the present framework, we employed attention as a masking strategy to filter out clutter and retain information from only the most relevant (i.e. attended) parts of an image. It would be interesting to study how and where the features of ignored stimuli (i.e. the stimuli that doesn't get past the attentional bottleneck) are encoded. Further, here, we modeled attention on top of high-level semantic features. In principle, the attention network can be implemented on top of any level within the representation network hierarchy, including lower stages and understanding where attention computations leads to best neural prediction accuracy and/or agreement with human fixation maps could be a worthwhile exploration. In the future, we aim to further explore novel ways of incorporating attention within neural encoding models.

\section*{Broader Impact}
Understanding the link between sensory stimulation and evoked neural activity in humans as revealed with encoding models, can provide foundations for developing novel therapies. Viewed in this regard, an improved understanding of information processing in the brain has tremendous potential. However, encoding models can be very sensitive to biases in the training set. Our models were trained using data from the Human Connectome Project database. While this large-scale project has made a lot of valuable data publicly available to the scientific community for studying brain structure and function, it is important to consider the representational bias in the dataset. For instance, the data we analyzed is exclusively limited to a young adult population. Such biases can possibly lead to poorer generalization of models trained with these large-scale datasets on other population groups that are inadequately represented. Once these encoding models are ripe for therapeutic applications, this dataset bias could prevent under-represented groups from deriving the benefits of a useful technology, resulting in uneven access across populations. Given these considerations, it is important to address potential representation biases in fMRI datasets and develop solutions for improving diversity and inclusion. More generally, fMRI studies involving human subjects can raise a wide range of other ethical issues as well, including data privacy issues and informed consent.

Further, one should be cautious about the deployment of attention or gaze prediction models in applications such as advertising. Given the value of eye tracking based attention in marketing spaces, public policy notices or political campaigns, it is important to be wary of a malicious use of these attention prediction methods for profit-seeking or by ill-intentioned parties seeking to further their own agendas. These applications regard attention as a commodity to be captured and the adopted technologies can be used to manipulate users in subtle ways.
An improved understanding about the link between stimuli and perceptual processing in the brain, as provided with encoding models, can also be exploited to further design or identify stimuli likely to elicit a specific emotional or cognitive response. The fact that these technologies can be deployed without the targeted individual's knowledge or consent indicates it is important to protect users from the vulnerabilities exploited by these agents.

\section*{Acknowledgements}
This work was supported by NIH grants R01LM012719 (MS), R01AG053949 (MS), R21NS10463401 (AK), R01NS10264601A1 (AK), the NSF NeuroNex grant 1707312 (MS), the NSF CAREER 1748377 grant (MS) and Anna-Maria and Stephen Kellen Foundation Junior Faculty Fellowship (AK).

\clearpage

\bibliographystyle{unsrt}

redpajama_set_name: RedPajamaArXiv


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most cherished Buddha semblance is also located at the Grand Palace. The structure truly represents the overall essence of Thailand. Tourists who desire a bit of an adventure should visit the Snake Farm and the Crocodile Farm. The Snake Farm is one of the world's largest producers of anti-venom. Sights include snake pits and feedings. This is not for faint of heart. At the Crocodile Farm visitors can feed these dangerous creatures as well as witness a man River Market in Thailand putting his head inside of a Crocodile's mouth. Sports enthusiasts may want to catch a Thai Boxing match. This sport is growing in popularity throughout Bangkok and the city has two main stadiums.
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text:
David Attlee
Crypto in the House: Execs on the march, US partisan politics and Web3
A largely proclaimatory meetup generated a positive response, with the industry appearing ready for a busy new year on the Hill.
On Dec. 8, top executives from six major crypto companies faced the United States House of Representatives' Financial Services Committee during a special hearing on digital assets. While the tone of the conversation was largely proclaimatory, the industry reacted with an optimistic buzz — it seems that crypto is bound to become a hot topic on the Hill for years to come.
The meeting that took place in Congress also garnered much attention from mainstream media. What's notable is the fact that this hearing is the first time that the industry's senior leaders (aka "crypto moguls") directly expressed the fears and hopes of the $2.2-trillion sector to U.S. legislators.
The industry representatives who were summoned to testify at the hearing included Jeremy Allaire, CEO of Circle; Sam Bankman-Fried, CEO of FTX; Chad Cascarilla, CEO of Paxos; Denelle Dixon, CEO of the Stellar Development Foundation; Brian Brooks, CEO of Bitfury; and Alesia Haas, chief financial officer of Coinbase.
this is the first positive public hearing on crypto that I can remember
congress seems to have done their homework, industry seems prepared and synchronized
still not sure what stellar is doing there
very bullish - could be the start of a trend
— Vance Spencer (@pythianism) December 8, 2021
Some of the key legislators who actively engaged with the crypto industry captains were Representative Pete Sessions, a Republican from Texas; Rep. Maxine Waters, a Democrat from California; Rep. Gregory Meeks, a Democrat from New York; Rep. Brad Sherman, a Democrat from California; Rep. Patrick McHenry, a Republican from North Carolina; Rep. Blaine Luetkemeyer, a Republican from Missouri; and Senator Sherrod Brown, a Democrat from Ohio.
So, here's how it went down on the big day.
Key arguments
Allaire supported this point with an example from his firm's operations: "Just in the past several weeks, Circle has signed on institutional customers who are using these services for small-business payments, international remittances and efficient payments for remote workers." As he optimistically stated, soon "Dollars on the internet will be as efficient and widely available as text messages and email."
Brooks took the message even closer to key political tensions of the day as he emphasized the opposition between tech behemoths such as Meta (formally Facebook) and the decentralizing impulse of crypto:
At the center of the CEOs' narrative was the humanitarian significance of digital assets and their developmental potential. Cascarilla framed crypto as a "really powerful tool for democratization of access."
The point of crypto is to have true decentralization, and the projects that succeed will be the projects that achieve that. Bitcoin succeeded because there were literally millions of participants in the node network, and so there is no CEO of Twitter to deplatform you, there's no CEO of JPMorgan to take away your credit card.
It was also Brooks who laid out the powerful promise of the blockchain-powered Web3 era.
Aside from the fiery rhetoric, the message from the industry leaders was crisp and straightforward: It's about time to bilaterally reconsider the rules of the game and put an end to the government's suspicious paternalism. The industry is still being overseen by several federal agencies, state-by-state regulation is a mess, and the Securities and Exchange Commission is trying to hold its grip, characterizing digital assets as securities.
Well #crypto community… engagement in Washington is working! I think it's clear from today's hearing that we've turned a corner. Keep building. We'll keep educating. #wagmi
— Kristin Smith (@KMSmithDC) December 8, 2021
The last point was clearly emphasized as the main problem: Coinbase's Haas proposed deeming blockchain-based tokens as digital property or a way to record ownership, which would put them outside of the SEC's jurisdiction.
Brooks didn't spare words when highlighting the dysfunctional patterns of the current situation: "What happens in the United States is you have a new crypto project, and you walk into the SEC, and you describe it in great detail, and you ask for guidance, and they say, 'We can't tell you' and 'You list it at your own peril.'"
I'm surprised that after the congress hearings we didn't get a pump! Today was a big day for crypto in the US! The hearings were super constructive and the senators were extremely positive! Incredible
— Ran NeuNer (@cryptomanran) December 8, 2021
Political divisions
The Dec. 8 hearing once again brought out a division regarding crypto-related issues that exists along party lines. Democrats focused their attention on investor protection and volatility, framing the industry as a potential threat to both uninformed investors and the global economy (environmental concerns were also mentioned.)
"Currently, cryptocurrency markets have no overarching or centralized regulatory framework, leaving investments in the digital assets space vulnerable to fraud, manipulation and abuse," as Waters, who chairs the Financial Services Committee, put it.
Related: Lines in the sand: US Congress is bringing partisan politics to crypto
Sherman, one of the industry's most consistent critics, expressed this anxiety in a quite vague, if not cryptic, form: "The powers in our society on Wall Street and in Washington have spent millions, and are trying to make billions or trillions, in the crypto world."
Republican legislators, who — following a decades-old pattern of the American electoral system — are projected to win the majority in Congress in the next midterm elections, demonstrated a pragmatic approach.
My hope is that Congress will work with the digital asset market in its infancy to help Americans prosper and progress us forward as leaders on the world stage.https://t.co/m6iKPql03Q
— Pete Sessions (@PeteSessions) December 11, 2021
In the words of McHenry, who is poised to chair the Financial Services Committee if the GOP wins back the House:
This technology is already regulated. Now, the regulations may be clunky, they may not be up to date. I ask my friends, my policymaker friends here on the Hill, this question: Do you know enough about this technology to have a serious debate?
Sessions went even further and gave an outright cheer to the industry, uttering a promise to support it: "I am tremendously impressed that from what I see, a lot of the ingenuity, a lot of entrepreneurial spirit, and lots of advice about the future, about where this can grow, is, I think, very important for us to listen to."
Industry response
Despite certain disagreements between legislators, the hearing sparked a largely positive reaction from the crypto community, with Jake Chervinsky, head of policy at the Blockchain Association, calling it "the most positive, constructive, & bipartisan public event on crypto I've seen in Congress" and other experts largely projecting similar vibes.
Today's HFSC hearing was the most positive, constructive, & bipartisan public event on crypto I've seen in Congress — ever. I mean that literally.
It's a testament to the effectiveness of industry & community engagement in DC in recent months. We've made shockingly big progress.
— Jake Chervinsky (,) (@jchervinsky) December 8, 2021
Some representatives also projected an empathic epigraph in the aftermath of the hearing. Perhaps the most eloquent reaction belongs to Meeks, who demonstrated a moderate optimism toward the industry's future:
The future of innovation in the financial markets is inevitable.
The use of digital assets can help underserved communities access additional financial tools - but we must make sure there is global coordination around regulation. pic.twitter.com/2DbvTZKMvp
— Rep. Gregory Meeks (@RepGregoryMeeks) December 8, 2021
The silence of crypto critic Sherman, normally an active Twitter user, was also notable.
The overall optimistic mood of the hearing stands in contrast to some of the recent regulatory actions taken by the U.S. government. For one, the SEC denied WisdomTree's application for a spot Bitcoin exchange-traded fund after seven months of consideration, keeping it impossible to invest in a regulated financial product providing direct exposure to the world's oldest cryptocurrency.
Surely, the hearing will not be the last turn in the crypto-government conversation, even for 2021. Already, a hearing on stablecoins took place before the Senate Banking, Housing and Urban Affairs Committee on Dec. 14.
As Representative McHenry put it, "Congress must work to fully understand and embrace these innovative new technologies, like #crypto." It looks like everyone should brace for a busy 2022 in crypto policy and regulation.
#Coinbase
#Circle
#Stellar
#Bitfury
#Bitcoin Regulation
#Paxos
#Brian Brooks
Crypto adoption in sights: Regulators and industry join to fight ransomware
Powers On... Broker disintermediation and unregulated crypto exchanges cause major concerns
Let's be clear: Blockchain technology is infrastructure


text:
Q: Linux route unexpected mtu i'm observing rather strange (at least for me) behaviour for few routes in my network. i use gre interfaces on top of ipsec tunnels. these gre interfaces have mtu 1400 on my entire network. normally i see this kind of tracepath output:
1?: [LOCALHOST]                                         pmtu 1500
 1:  10.xxx.101.1                                         13.625ms
 1:  10.xxx.101.1                                         13.178ms
 2:  10.xxx.101.1                                         13.973ms pmtu 1400
 2:  192.168.yyy.251                                      56.555ms
 3:  192.168.yyy.92                                      643.252ms
 4:  192.168.yyy.28                                      417.291ms
 5:  192.168.zzz.129                                     517.893ms reached

but for some reason i got one case when tracepath gives different result:
 1?: [LOCALHOST]                                         pmtu 1500
 1:  10.xxx.101.1                                         13.625ms
 1:  10.xxx.101.1                                         20.857ms
 2:  10.xxx.101.1                                         11.954ms pmtu 1400
 2:  192.168.yyy.251                                      46.456ms
 3:  192.168.yyy.251                                      45.563ms pmtu 1376
 3:  10.zzz.251.1                                         56.648ms
 4:  10.zzz.255.111                                       55.212ms reached

all gre interfaces on 192.168.yyy.251 have mtu 1400, all are configured identically. the 10.zzz.251.1 router doesn't send any ICMP fragm needed packets, gre interface of course with mtu 1400. 192.168.yyy.251 generates ICMP fragm needed but i have no clue why. ip route get 10.zzz.255.111 on 192.168.yyy.251 router shows:
10.zzz.255.111 from 10.xxx.101.253 tos lowdelay via 10.zzz.251.1 dev GRE_OUTPUT_INTERFACE  src 192.168.yyy.251  mark 0x2071
    cache  expires 264sec ipid 0x607b mtu 1376 iif GRE_INPUT_INTERFACE

additionally from time to time (i suspect it depends on traffic) mtu of the route changes and gets heavily lowered and for 10mins (mtu cache expiry) i can't make any new connections that need bigger packets -- but old established connections are working fine. hmmm?

A: I would say that this is caused by a GRE tunnel being routed over another GRE tunnel. Your temporary problem may be when an IPSEC tunnel goes down for a few seconds.


intrusion techniques for popular software and sells them to government agencies around the world.
to brokering them, too, as a kind of hacker middleman.
as well as other NATO countries and "NATO partners" that Bekrar declines to name.


text:
Posted on Sep 11, 2003 by Rob
"Woman arrested for making bomb threat
University of Michigan Police arrested a woman Wednesday afternoon shortly after she sent an e-mail containing a bomb threat to officials at the University of Michigan, officials said.
The woman, whose name, age and affiliation with the university are being withheld by police pending arraignment later today, will be charged with making a bomb threat, a felony, said Lt. Jesse Lewit.
Lewit said staff at the University Health Services building on Fletcher Street near Central Campus received the e-mail about 3 p.m. and evacuated the building.
Police cordoned off the building entrances but allowed the surrounding streets to remain open while officers swept the building for suspicious objects for about an hour, Lewit said. None were found and a bomb-sniffing dog indicated no explosives were in the building."
> From AANews Crime Notes
Author: Rob


text:
Biografia
Giornalista pubblicista, è iscritta all'albo della Campania in data 11 luglio 1974. Dopo aver frequentato il Liceo classico, si laurea in Lettere Moderne e comincia a lavorare per la sezione culturale de l'Espresso, dove firma varie interviste e inchieste: la più famosa e controversa è quella del 27 gennaio 1974, intitolata Le zie di Sicilia, nella quale Leonardo Sciascia denunciava presunte responsabilità delle donne nella cultura mafiosa. Diventa direttrice del mensile Cosmopolitan e successivamente cura la "terza pagina" de Il Tempo. Collabora con la Rai dal 1988, cioè da quando diviene autrice di Telefono giallo, condotto da Corrado Augias. In seguito conduce i programmi Parte Civile e I grandi processi (quest'ultimo con Sandro Curzi).

Dal 1994 conduce il programma Storie maledette, di cui è anche autrice; la trasmissione si occupa di intervistare i protagonisti delle inchieste giudiziarie italiane che hanno fatto più scalpore. Dal 2004 al 2008, oltre a Storie maledette, ha condotto Ombre sul giallo, un programma più complesso che, con servizi, interviste e ospiti in studio, ha come tema ricorrente il dubbio riguardo alla colpevolezza degli indiziati e il modo in cui sono state condotte le indagini.

Nel 2017 ha preso parte, nel ruolo di se stessa, nel film Come un gatto in tangenziale e nel 2018 è stata ospite alla LXVIII edizione del Festival di Sanremo. Nel 2021 è la co-protagonista di un documentario, La strage, e riappare, sempre nel ruolo di se stessa in Come un gatto in tangenziale - Ritorno a Coccia di Morto, sequel del film di quattro anni prima. Dal 4 novembre 2021 è su Rai 3 con due speciali intitolati Che fine ha fatto Baby Jane?, in cui torna a intervistare alcuni protagonisti di Storie maledette a distanza di anni.

Vita privata
Sposata con Massimo Leosini, ha due figlie.

Premi e riconoscimenti
 Premio Flaiano
 2002 - Programma culturale per Storie maledette
 Auronzo di Cadore
 2002 - Premio speciale
 2019 - Candidatura alla miglior giornalista per Storie maledette
 Donne per il giornalismo
 1996 - Giornalista dell'anno
 Premio Pericle d'oro
 2002 - Premio per il giornalismo
 2009 - Candidatura alla miglior conduttrice televisiva per Storie maledette
 Festival della Televisione Italiana
 2002 - Miglior giornalista
 2005 - Premio speciale
 2006 - Miglior giornalista per Ombre sul giallo
 Premio PulciNellaMente
 2010 - Miglior giornalista
 2002 - Premio Lodoletta Pini sezione Giornalismo Marcella De Palma
 2002 - Premio Saraceno d'oro per il giornalismo
 2003 - Premio Guglia di Napoli
 2004 - Premio Nazionale di Cultura nel Giornalismo "La penna d'oro"
 2007 - Premio Donna dell'anno sezione Giornalismo
 2008 - Premio Festival della Radio Televisione Italiana - Riconoscimento alla qualità nei media

Note

Voci correlate
 Ombre sul giallo
 Storie maledette

Altri progetti

Collegamenti esterni

Conduttori televisivi di Rai 3 degli anni 1980
Conduttori televisivi di Rai 3 degli anni 1990
Conduttori televisivi di Rai 3 degli anni 2000
Conduttori televisivi di Rai 3 degli anni 2010
Vincitori del Premio Flaiano di televisione e radio

redpajama_set_name: RedPajamaWikipedia


Prediction of preterm delivery in twins by cervical assessment at 23 weeks.
Ultrasound Obstet Gynecol. 2001 Jan;17(1):7-10.
Skentou C, Souka AP, To MS, Liao AW, Nicolaides KH.
To establish the relation between cervical length at 23 weeks of gestation in twin pregnancies and risk of spontaneous delivery before 33 weeks.
Cervical length was measured by transvaginal sonography at 23 (range 22-24) weeks of gestation in 464 twin pregnancies attending for routine antenatal care. In the patients who were managed expectantly the relation between cervical length and the rate of spontaneous delivery before 33 weeks was determined.
The cervical length distribution was skewed to the left and the median value was 36 mm. The rate of spontaneous delivery before 33 weeks was inversely related to cervical length at 23 weeks. It increased gradually from about 2.5% at 60 mm, to 5% at 40 mm and 12% at 25 mm, and exponentially below this length to 17% at 20 mm and 80% at 8 mm. Cervical length of 20 mm or less is found in about 8% of the population and this group contained about 40% of women delivering spontaneously before 33 weeks.
In twin pregnancies measurement of cervical length provides useful prediction of risk for spontaneous early preterm delivery.


text:
SUPPLIERSNEWSLETTER
MAKING SENSE OF LONDON PROPERTY LICENSING
SELECT BOROUGH
Mandatory HMO Licensing
Additional licensing
Selective Licensing
Licence Application Process
Choosing a letting agent
HMO Article 4 Directions
Housing Act 2004
Housing and Planning Act 2016
Housing Health and Safety Rating System
Section 21 Notice of Seeking Possession
Landlord Suppliers Directory
Bexley Council are consulting on new HMO planning restrictions
Monday, October 17, 2016	- Bexley Council
New planning restrictions for Houses in Multiple Occupation (HMOs) look set to be introduced in the London Borough of Bexley in September 2017.
The council has launched a consultation on proposals to remove permitted development rights on the conversion of single family homes into HMOs with between 3 and 6 residents. HMOs with 7 or more occupants already require planning permission.
The council say that the proposed HMO Article 4 Direction is part of their co-ordinated approach to managing HMOs. The council say they will also soon be consulting on plans for a new property licensing scheme.
The decision to introduce the Direction relates to growing concerns about an increasing number of houses in the borough being converted to HMOs.
At the moment certain works and alterations to unlisted buildings can be carried out without applying for planning permission. Development of this kind is called 'Permitted Development'. These works include the kind that could be made to a dwelling to turn it into a small HMO with up to 6 occupants.
An Article 4 Direction will mean the removal of this 'Permitted Development' right, making it easier for the Council to monitor the number of HMOs and the authority to grant or refuse permission for HMOs in the future.
A non-immediate HMO Article 4 Direction was made by the council on 16 September and they are now undertaking a public consultation exercise from 23 September to 4 November 2016. The Council will then reflect on the feedback before deciding whether to confirm the Direction.
Anyone can take part in the consultation by visiting the Council's website.
Alternatively you can email your views to SP&GTeam@bexley.gov.uk or write to the London Borough of Bexley, Strategic Planning & Growth Team, Civic Offices, 2 Watling Street, Bexleyheath, DA6 7AT. Any correspondence concerning the Article 4 consultation must be received by 5pm on 4 November 2016.
Subject to confirmation by the Council, we understand the Direction could come in to force on 24 September 2017.
Leader of the London Borough of Bexley, Cllr Teresa O'Neill OBE said:
"The introduction of an Article 4 Direction isn't something that we are considering lightly. Many other local authorities have already taken this route in their boroughs to make sure smaller HMOs meet set requirements and our reason for doing so is to protect tenants, landlords and the local community.
"We know that the majority of HMOs are legal, well run and above board, but there are some that may put health and safety at risk or may be in a totally unsuitable area without the necessary infrastructure. If introduced, the Direction will help us to control and monitor new HMOs, refusing permission where appropriate."
A copy of the Article 4 Direction and a map can be viewed during normal office hours at the Civic Offices, 2 Watling Street, Bexleyheath, DA6 7AT, at Bexley libraries, or by visiting the Council's website.
London Property Licensing has published further information about HMO Article 4 Directions that can be viewed here. Information about property licensing and HMO planning restrictions in Bexley is also available at www.londonpropertylicensing.co.uk/bexley.
< view all news
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©2019 london property licensing | website design: bumblebee


A PlayStation Vita exclusive strategy RPG, Trillion: God of Destruction is coming to North America and Europe in spring 2016 through retail and digital stores, Idea Factory International announced.
On the brink of death, Zeabolos heard a mysterious voice from deep within. "It is too early yet for the lineage of the Great Overlord to fall…" Zeabolos was revived, his shambled body patched together again by a young necromancer named Faust. Zeabolos gained the power of the Soul Grimoire, and decided henceforth to fight.
A TRILLION HP! – This big bad boss has 1,000,000,000,000 HP! How you defeat him is up to your individual strategy and training!
6 Deadly Sins to Love! – In between battles, you'll get some quality alone time with each of your six Overlord Candidates leading your army. Can you have a love story even in war?
Prepare for Battle! – While Trillion sleeps, it's training time! Hit the training grounds and discover randomly-generated treasure chests or strengthen weapons at the Blacksmith's!
Replay, Replay, Replay! – Stats carry over with each New Game+ plus and there are multiple endings to unlock! With over 10 endings, there's even more reason to stand up and keep fighting!
Trillion: God of Destruction will include Japanese and English voiceovers, as well as English subtitles. You'll also be able to play it on PlayStation TV.
Looking ahead to PlayStation Experience in December, Idea Factory International confirmed they'll be bringing Trillion: God of Destruction, Megadimension Neptunia VII, and an unannounced title (TBA before PSX) to the event.


text:
Réaumont () is a commune in the Isère department in southeastern France.

Population

See also
Communes of the Isère department

References

External links
 Official website of Réaumont

Communes of Isère
Isère communes articles needing translation from French Wikipedia


text:
Creativity: A New Approach with Stan Lai
Lecture | January 24 | 12-1:30 p.m. | Berkeley Art Museum and Pacific Film Archive, Osher Theater
Speaker/Performer: Stan Lai
Sponsor: Arts + Design
Join us for the opening lecture of the spring 2019 A+D Thursdays Lecture Series, which is shaped around three key words: Creativity, Migration and Transformation. Stan Lai, co-curator of the series, is one of the most acclaimed playwrights/directors in Asia, known not only for creating some of the most memorable works for the contemporary Chinese stage, but also for creating bold new genres and staging innovations. His theories on creativity, heretofore only published in Chinese, are considered groundbreaking in the field. He is artistic director of Performance Workshop in Taiwan and festival artistic director of China's Wuzhen Theatre. Lai's works, which include over 30 original plays, two feature films, and four operas, include his epic 2000 play A Dream Like a Dream and 1986's Secret Love in Peach Blossom Land. Born in the U.S. and based in Taiwan, Lai received a doctorate degree in Dramatic Art from UC Berkeley. He was a professor and founding dean of the College of Theatre at Taipei National University of the Arts and has also been a visiting professor and artist-in-residence at both UC Berkeley and Stanford University.
Thursdays at 12:00pm, at the BAMPFA Theater.
Free and open to the public.
A+D Thursdays is a public lecture series embedded inside our Creative Gateway undergraduate course. The series exposes students and the public to a range of creative forms across the visual arts, performance, literature, film, and design. It introduces students and the community to our campus's major museums, presenters, and academic departments, as well as to select Bay Area arts organizations and regional partners.
Target audience: All Audiences
Open to audience: All Audiences
Event contact: 510-664-4125
stan lai


Home India Whatsapp Forward Gone wrong.. Threw Lyching..??
In this video you found the problem by whatsapp. Whatsapp is the biggest plateform of our country. Everyone using whtsapp in our country. Whtsapp is the best source for communicate people and share much things.
But in wrong way people misguide by fake massage and propaganda.
We all have to aware from fake whatsapp massage. This type massage make mob Lynching and people gets killed. We all have to stop whatsapp forward other wise we will become killer. Like digital killer.
In this video I m talking about some lynching by whatsapp massages which gone through the death. This type of work very harmful for our country and society. Any simple man/woman can be lynched by people. Hope you understand this. I'm talking about solution too how together we can stop these type massages and make people safe by our awareness.
There are 250 million user in India of whatsapp such a huge population and we are top 1 country in use of whatsapp. In year 2020 we will reach 450 million population. So this is very difficult to stop fake news in this level.
All we can do is awareness. So in this putting my work threw this video. Hope you like this and share this with everyone.
Previous articleYe Kaisi Deshbhakti Harrasment Cases on Kashmiri's..
Let's change the way of thinking..!!


text:
Power projects: Nigerian govt incurs $3bn fresh debt post COVID-19
on: June 16, 2020 In: Power
*Dr. Zainab Shamsuna Ahmed
OpeOluwani Akintayo
Lagos — As Nigeria's number one revenue earner; crude oil faces low demand as a result of the COVID-19 pandemic, the country is about to be thrown into another round of debts which may linger after the Buhari administration, and gets passed from one generation to the other, especially on the poor masses who will be made to bear the brunt of repayment through unrealistic estimated billing post COVID-19.
This is true because despite spending trillions on power projects that could not be accounted for, the federal government has again, filed for fresh loans worth about $3 billion from the World Bank, African Development Bank, AFDB, and China EXIM Bank, SweetcrdueReports can authoritatively report.
According to the documented budget by the Ministry of Finance Budget & National Planning, obtained by this newspaper, the loans tied around a post COVID-19 recovery plan for the power sector amounted to $2,966 trillion, approximately $3 billion.
The budgeted loan was signed by the Minister of Finance, Budget & National Planning herself, Dr. Zainab Shamsuna Ahmed on June 10, 2020, and will be expended on six power projects. Nigeria Electricity Transmission and Access Project will cost USD$486 million and is being sought from the World Bank. As a matter of fact, it has already been approved.
Another project tagged New Electrification Project (off-grid) will cost Nigeria USD$550 million. A Sum of $350million will be borrowed from the WB, while $200 million will come from AFDB. It is still undergoing approval processes.
The third project on the list is tagged 'North Core Dorsale Nord Interconnector Project', to cost Nigeria USD$26 million. The loan will be obtained from the World Bank Regional. It is currently undergoing approval processes.
Abuja Power Feeding Scheme which will cost the country USD$170 million would be financed by the AFDB. The loan status- still undergoing approval processes.
The budget named 'Power Sector Recovery Plan & Impact of COVID-19 Pandemic' also parades another project named 'Power Sector Recovery Program for Results'- whatever that means- will be financed by WB. Although still undergoing approval, would cost Nigeria USD$750 million.
Relief as electricity workers formally sign CBA with PHED
Last on the list; Zungeru 700megawatts hydro-power project is the most expensive- USD$984,323,013.08. Already approved to be funded by China EXIM Bank.
SweetcrudeReports noticed that apart from the 700megawatts that the Ministry said would be generated from the last project- Zungeru hydropower, other firstly mentioned five projects did not carry any specific megawatts Nigerians should expect.
Nigeria currently has a population of over 200 million, and experts say the country would need well over 30, 000 megawatts to reach sufficiency. 30, 000MW is a far cry from the trifling 2000MW currently being generated, although the country has capacity to generate 20, 000MW.
A 2019 investigative report by Premium Times had chronicled how the Nigerian government, between 1999 and 2010, spent over N4.7 trillion on power without many changes.
Eight years later, Jonathan and Buhari administrations also invested another N1.164 trillion into the sector through capital releases, yet homes and factories in the country wallow in constant low power supply, as stable, uninterrupted power supply remains a mirage to Nigerians.
Experts, as well as other stakeholders, have blamed this on poor investments, capacity, and regulatory issues among others.
While Nigeria can conveniently generate up to 20,000 MW, transmission has the capacity for about 8,000MW and distribution struggles between 2,000 and 4,000MW.
Nigeria generates its power from four main sources – hydro, gas, coal and natural gas.
However, for almost 20 years, huge and unverified amount has been invested into the sector. Specifically, The International Centre for Investigative Resources, ICIR reported that N1.164 trillion has been released to the power sector as capital budgets from 2011 to 2018-this figure was obtained from the Office of the Accountant General of the Federation.
Former President Olusegun Obasanjo was alleged to have spent N3.52 trillion during his tenure, though this figure has been contested. Also, late President Umar Yar'adua was said to have spent N1.183 trillion.
A fresh $3 billion loan by the Buhari administration amounts to N 1,158,660 trillion upon conversion with current exchange rate of $386.22 as of June 16.
Shearwater Energy picks NuScale Power's SMT for SMR wind-hybrid energy project
PHEDC carpets labour over staff optimisation, terminal benefits
Total and Iberdrola in joint bid for giant Danish wind farm


text:
Nicol Schattenstein (auch Nicolaus, Nikolaus oder Nikol; * 16. August 1877 in Ponjemon bei Kowno, Litauen; † 1954 in den USA) war ein russischer Gesellschaftsmaler, der in Wien und New York lebte und arbeitete.

Leben

Schattenstein stammte aus Ponjemon bei Kowno im heutigen Litauen. 1893 erhielt er Privatunterricht in Vilnius, ging dann nach Wien, wo er von 1893 bis zum Sommersemester 1902 an der Akademie der Bildenden Künste bei August Eisenmenger und Kasimierz Pochwalski studierte. Danach machte er sich vor allem als Porträtmaler einen Namen, wobei er stets im Künstlerhaus und an vielen Ausstellungen teilnahm. Im Künstlerhaus war er auch "Delegierter in das Komitee der Künstlervorsorge". Er schuf in Wien z. B. Porträts von Raoul Auernheimer, dessen Frau Irene und von Conrad von Hötzendorf.

1906 beteiligte er sich an der Großen Kunstausstellung in Berlin und erhielt den Michael Beer-Preis des Senats der Akademie der Bildenden Künste Berlin. Bereits 1903/04 hatte er das Michael Beer-Stipendium bekommen, woraufhin er sich länger in Rom aufhielt, es stand ihm ein Atelier in der Künstlerkolonie Villa Strohl-Fern zur Verfügung. 1912 stellte er in den internationalen Sälen im Zentralpavillon der Biennale von Venedig aus.

Zu Beginn des Ersten Weltkriegs wurde er in das Kriegspressequartier einberufen; nach dem Krieg wurde er für das Gemälde "Friedensverhandlung von Brest-Litowsk" beauftragt, wo er auch Leo Trotzki porträtierte. Noch 1917 wurde er vom Kaiser für "vorzügliche künstlerische Leistungen" im Kriegspressequartier ausgezeichnet. Nach Kriegsende ging er nach New York und ließ sich dort dauerhaft nieder. Aufgrund zahlreicher Bildnisse gesellschaftlich hochstehender Personen wie der Familie Vanderbilt sowie von Stars wie der Bühnen- und Filmschauspielerin Helen Hayes, feierte er in Amerika große Erfolge als Porträtmaler.

Obwohl er ein breites motivisches Spektrum beherrschte und auch Genreporträts und -bilder mit ungewöhnlichen Titeln wie z. B. Habemus Papam oder römischer Frühling schuf, galt er in Österreich als konventioneller Salonmaler. Während ihm in amerikanischen Ausstellungsbesprechungen hohes Lob zuteilwurde, waren einheimische Rezensionen tendenziell verhalten, z. B. 1904 anlässlich der Künstlerhausausstellung: "Die Motive sind gewählt, das Streben nach Vereinfachung sichtlich. Ein Gestalter ist Schattenstein noch nicht." In Wien wurde er zudem von Karl Kraus negativ konnotiert – in einer Widmung für Oskar Kokoschka wählte dieser 1916 Nikolaus Schattenstein als negativen Antipoden: "[…] Wie anders Schattenstein. Der malt am Kleide." Er galt als der von Kaiser Franz Joseph gelobte, von Kraus verspottete russisch-amerikanische Maler. In den 1930er Jahren wurde auch Schattensteins jüdische Herkunft betont.

Weblinks
Werke von Nicol Schattenstein auf Artnet

Einzelnachweise

Maler (Wien)
Maler (Vereinigte Staaten)
Russe
Geboren 1877
Gestorben 1954
Mann