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The present article is concerned with the ethnopolitical dimensions of the environmental problem within the Russian nationalistic movement in the USSR. As distinct from Western Europe, there has never been a "pure" ecological movement in the Soviet Union, and until recently the environmental issue has been raised mainly by national movements as a part of the national question.

The 'nationality question' was long central to Soviet thought and policy, and the failure to provide a convincing answer played a major role in the break-up of the Soviet Union into ethnically or nationally defined states. Zisserman-Brodsky explores various explanations of nationalism and its resurgence through a close and unprecedented examination of dissident writings of diverse ethnic groups in the former Soviet Union, thereby bridging macro-theory with micro-politics. Dissident ethnic networks were a crucial independent institution in the Soviet Union, and a basis of civil society. Voicing the discontent and resentment of the periphery at the policies of the centre or metropole, the dissident writings, known as samizdat highlighted anger at deprivations imposed in the political, cultural, social and economic spheres. Ethnic dissident writings drew on values both internal to the Soviet system and international as sources of legitimation; they met a divided reaction among Russians, with some privileging the unity of the Soviet Union and others sympathetic to the rhetoric of national rights. This focus on national, rather than individual, rights helps explain developments since the fall of the Soviet Union, including the prevalence of authoritarian governments in newly independent states of the former Soviet Union.

In December 2001, over 150 citizens of Russia, Israel, the U.S., Ukraine and some other countries gathered in Moscow in their capacity as former activists of the non-official Jewish movement in the USSR to celebrate the 25th anniversary of an event that had never taken place—an unofficial Moscow Symposium on Jewish Culture. The symposium, which had been forbidden by the KGB, acquired an important, but symbolic, meaning (as vivid evidence of the suppression of Jewish culture in the USSR) for the very fact of its non-performance. Celebrating this (non-)event 25 years later, members of the Jewish movement who had been active for some time in the period from the late 1960s to the late 1980s talked about their struggle against the Soviet regime, emphasizing the victory they had gained together with other dissenting groups. They called for the "political support of Israel, the United States, and Russia in their fight against international terrorism," spoke on behalf of repeal of the Jackson–Vannick amendment, voted for the establishment of a transnational association of Russian (or Russian-speaking or former Soviet) Jews, and even discussed the "historical mission of Russian Jewry." The issue of Jewish culture was virtually omitted from the discussion.

Investigates the role of environmentalism in the Russian nationalist movement. It is asserted that the emergence of ecological concerns in the writings of the derevenshchiki (rural writers) of the 1960s should not be interpreted as the initial politicization of environmentalism in the USSR. Rather, an analysis of V. Osipov's (1973) delineation of an environmentally ruined Moscow & A. Solzhenitsyn's (1973) ecological demands are identified as the beginning of a period in which ethnicity was connected to environmental concerns, thus resulting in the politicization of environmentalism in the USSR. A review of relevant literature from the late 1980s indicates that changing political, social, & economic conditions prompted nationalists' adoption of a more realistic approach to environmental problems. The Russian nationalist movement's suspicion toward Zionist & Freemason organizations & ecologically friendly platform are discussed. It is concluded that the Russian nationalist movement's approach to environmental problems superseded Marxism as the state's scientific doctrine. J. W. Parker

"In modern or transitional societies, politicized ethnicity has become the crucial principle of political legitimization and delegitimization of systems, states, regimes and governments." While accepting Rothschild's formulation in general, most political analysts did not expect it to be realized in the USSR so soon in its variant of "delegitimization."

International audience ; We consider the design of a single vector representation for an image that embeds and aggregates a set of local patch descriptors such as SIFT. More specifically we aim to construct a dense representation, like the Fisher Vector or VLAD, though of small or intermediate size. We make two contributions, both aimed at regularizing the individual contributions of the local descriptors in the final representation. The first is a novel embedding method that avoids the dependency on absolute distances by encoding directions. The second contribution is a ''democratization" strategy that further limits the interaction of unrelated descriptors in the aggregation stage. These methods are complementary and give a substantial performance boost over the state of the art in image search with short or mid-size vectors, as demonstrated by our experiments on standard public image retrieval benchmarks.

International audience ; We consider the design of a single vector representation for an image that embeds and aggregates a set of local patch descriptors such as SIFT. More specifically we aim to construct a dense representation, like the Fisher Vector or VLAD, though of small or intermediate size. We make two contributions, both aimed at regularizing the individual contributions of the local descriptors in the final representation. The first is a novel embedding method that avoids the dependency on absolute distances by encoding directions. The second contribution is a ''democratization" strategy that further limits the interaction of unrelated descriptors in the aggregation stage. These methods are complementary and give a substantial performance boost over the state of the art in image search with short or mid-size vectors, as demonstrated by our experiments on standard public image retrieval benchmarks.

International audience ; We consider the design of a single vector representation for an image that embeds and aggregates a set of local patch descriptors such as SIFT. More specifically we aim to construct a dense representation, like the Fisher Vector or VLAD, though of small or intermediate size. We make two contributions, both aimed at regularizing the individual contributions of the local descriptors in the final representation. The first is a novel embedding method that avoids the dependency on absolute distances by encoding directions. The second contribution is a ''democratization" strategy that further limits the interaction of unrelated descriptors in the aggregation stage. These methods are complementary and give a substantial performance boost over the state of the art in image search with short or mid-size vectors, as demonstrated by our experiments on standard public image retrieval benchmarks.

We consider the design of an image representation that embeds and aggregates a set of local descriptors into a single vector. Popular representations of this kind include the bag-of-visual-words, the Fisher vector and the VLAD. When two such image representations are compared with the dot-product, the image-to-image similarity can be interpreted as a match kernel. In match kernels, one has to deal with interference, i.e., with the fact that even if two descriptors are unrelated, their matching score may contribute to the overall similarity. We formalise this problem and propose two related solutions, both aimed at equalising the individual contributions of the local descriptors in the final representation. These methods modify the aggregation stage by including a set of per-descriptor weights. They differ by the objective function that is optimised to compute those weights. The first is a "democratisation" strategy that aims at equalising the relative importance of each descriptor in the set comparison metric. The second one involves equalising the match of a single descriptor to the aggregated vector. These concurrent methods give a substantial performance boost over the state of the art in image search with short or mid-size vectors, as demonstrated by our experiments on standard public image retrieval benchmarks.

In this paper, we introduce a new large-scale face dataset named VGGFace2. The dataset contains 3.31 million images of 9131 subjects, with an average of 362.6 images for each subject. Images are downloaded from Google Image Search and have large variations in pose, age, illumination, ethnicity and profession (e.g. actors, athletes, politicians). The dataset was collected with three goals in mind: (i) to have both a large number of identities and also a large number of images for each identity; (ii) to cover a large range of pose, age and ethnicity; and (iii) to minimise the label noise. We describe how the dataset was collected, in particular the automated and manual filtering stages to ensure a high accuracy for the images of each identity. To assess face recognition performance using the new dataset, we train ResNet-50 (with and without Squeeze-and-Excitation blocks) Convolutional Neural Networks on VGGFace2, on MS-Celeb-1M, and on their union, and show that training on VGGFace2 leads to improved recognition performance over pose and age. Finally, using the models trained on these datasets, we demonstrate state-of-the-art performance on the IJB-A and IJB-B face recognition benchmarks, exceeding the previous state-of-the-art by a large margin. The dataset and models are publicly available.