EURECOM

Well adapted to the loosely coupled nature of distributed interaction in large-scale applications, the publish/subscribe communication paradigm has recently received increasing attention. With systems based on the publish/subscribe interaction scheme, subscribers register their interest in an event, or a pattern of events, and are subsequently asynchronously notified of events generated by publishers. Many variants of the paradigm have recently been proposed, each variant being specifically adapted to some given application or network model. This paper factors out the common denominator underlying these variants: full decoupling of the communicating entities in time, space, and synchronization. We use these three decoupling dimensions to better identify commonalities and divergences with traditional interaction paradigms. The many variations on the theme of publish/subscribe are classified and synthesized. In particular, their respective benefits and shortcomings are discussed both in terms of interfaces and implementations.

A Gaussian broadcast channel (GBC) with r single-antenna receivers and t antennas at the transmitter is considered. Both transmitter and receivers have perfect knowledge of the channel. Despite its apparent simplicity, this model is, in general, a nondegraded broadcast channel (BC), for which the capacity region is not fully known. For the two-user case, we find a special case of Marton's (1979) region that achieves optimal sum-rate (throughput). In brief, the transmitter decomposes the channel into two interference channels, where interference is caused by the other user signal. Users are successively encoded, such that encoding of the second user is based on the noncausal knowledge of the interference caused by the first user. The crosstalk parameters are optimized such that the overall throughput is maximum and, surprisingly, this is shown to be optimal over all possible strategies (not only with respect to Marton's achievable region). For the case of r>2 users, we find a somewhat simpler choice of Marton's region based on ordering and successively encoding the users. For each user i in the given ordering, the interference caused by users j>i is eliminated by zero forcing at the transmitter, while interference caused by users j<i is taken into account by coding for noncausally known interference. Under certain mild conditions, this scheme is found to be throughput-wise asymptotically optimal for both high and low signal-to-noise ratio (SNR). We conclude by providing some numerical results for the ergodic throughput of the simplified zero-forcing scheme in independent Rayleigh fading.

We consider a power control scheme for maximizing the information capacity of the uplink in single-cell multiuser communications with frequency-flat fading, under the assumption that the users attenuations are measured perfectly. Its main characteristics are that only one user transmits over the entire bandwidth at any particular time instant and that the users are allocated more power when their channels are good, and less when they are bad. Moreover, these features are independent of the statistics of the fading. Numerical results are presented for the case of single-path Rayleigh fading. We show that an increase in capacity over a perfectly-power controlled (Gaussian) channel can be achieved, especially if the number of users is large. By examining the bit error-rate with antipodal signalling, we show the inherent diversity in multiuser communications over fading channels.

This paper presents an overview of the theory and currently known techniques for multi-cell MIMO (multiple input multiple output) cooperation in wireless networks. In dense networks where interference emerges as the key capacity-limiting factor, multi-cell cooperation can dramatically improve the system performance. Remarkably, such techniques literally exploit inter-cell interference by allowing the user data to be jointly processed by several interfering base stations, thus mimicking the benefits of a large virtual MIMO array. Multi-cell MIMO cooperation concepts are examined from different perspectives, including an examination of the fundamental information-theoretic limits, a review of the coding and signal processing algorithmic developments, and, going beyond that, consideration of very practical issues related to scalability and system-level integration. A few promising and quite fundamental research avenues are also suggested.

It is now well known that employing channel adaptive signaling in wireless communication systems can yield large improvements in almost any performance metric. Unfortunately, many kinds of channel adaptive techniques have been deemed impractical in the past because of the problem of obtaining channel knowledge at the transmitter. The transmitter in many systems (such as those using frequency division duplexing) can not leverage techniques such as training to obtain channel state information. Over the last few years, research has repeatedly shown that allowing the receiver to send a small number of information bits about the channel conditions to the transmitter can allow near optimal channel adaptation. These practical systems, which are commonly referred to as limited or finite-rate feedback systems, supply benefits nearly identical to unrealizable perfect transmitter channel knowledge systems when they are judiciously designed. In this tutorial, we provide a broad look at the field of limited feedback wireless communications. We review work in systems using various combinations of single antenna, multiple antenna, narrowband, broadband, single-user, and multiuser technology. We also provide a synopsis of the role of limited feedback in the standardization of next generation wireless systems.

Maximum-likelihood (ML) decoding algorithms for Gaussian multiple-input multiple-output (MIMO) linear channels are considered. Linearity over the field of real numbers facilitates the design of ML decoders using number-theoretic tools for searching the closest lattice point. These decoders are collectively referred to as sphere decoders in the literature. In this paper, a fresh look at this class of decoding algorithms is taken. In particular, two novel algorithms are developed. The first algorithm is inspired by the Pohst enumeration strategy and is shown to offer a significant reduction in complexity compared to the Viterbo-Boutros sphere decoder. The connection between the proposed algorithm and the stack sequential decoding algorithm is then established. This connection is utilized to construct the second algorithm which can also be viewed as an application of the Schnorr-Euchner strategy to ML decoding. Aided with a detailed study of preprocessing algorithms, a variant of the second algorithm is developed and shown to offer significant reductions in the computational complexity compared to all previously proposed sphere decoders with a near-ML detection performance. This claim is supported by intuitive arguments and simulation results in many relevant scenarios.

This paper addresses the problem of channel estimation in multi-cell interference-limited cellular networks. We consider systems employing multiple antennas and are interested in both the finite and large-scale antenna number regimes (so-called "massive MIMO"). Such systems deal with the multi-cell interference by way of per-cell beamforming applied at each base station. Channel estimation in such networks, which is known to be hampered by the pilot contamination effect, constitutes a major bottleneck for overall performance. We present a novel approach which tackles this problem by enabling a low-rate coordination between cells during the channel estimation phase itself. The coordination makes use of the additional second-order statistical information about the user channels, which are shown to offer a powerful way of discriminating across interfering users with even strongly correlated pilot sequences. Importantly, we demonstrate analytically that in the large-number-of-antennas regime, the pilot contamination effect is made to vanish completely under certain conditions on the channel covariance. Gains over the conventional channel estimation framework are confirmed by our simulations for even small antenna array sizes.

v4.10.0: LayoutLM-v2, LayoutXLM, BEiT LayoutLM-v2 and LayoutXLM Four new models are released as part of the LatourLM-v2 implementation: <code>LayoutLMv2ForSequenceClassification</code>, <code>LayoutLMv2Model</code>, <code>LayoutLMv2ForTokenClassification</code> and <code>LayoutLMv2ForQuestionAnswering</code>, in PyTorch. The LayoutLMV2 model was proposed in LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding by Yang Xu, Yiheng Xu, Tengchao Lv, Lei Cui, Furu Wei, Guoxin Wang, Yijuan Lu, Dinei Florencio, Cha Zhang, Wanxiang Che, Min Zhang, Lidong Zhou. LayoutLMV2 improves LayoutLM to obtain state-of-the-art results across several document image understanding benchmarks: Add LayoutLMv2 + LayoutXLM #12604 (@NielsRogge) Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=layoutlmv2 BEiT Three new models are released as part of the BEiT implementation: <code>BeitModel</code>, <code>BeitForMaskedImageModeling</code>, and <code>BeitForImageClassification</code>, in PyTorch. The BEiT model was proposed in BEiT: BERT Pre-Training of Image Transformers by Hangbo Bao, Li Dong and Furu Wei. Inspired by BERT, BEiT is the first paper that makes self-supervised pre-training of Vision Transformers (ViTs) outperform supervised pre-training. Rather than pre-training the model to predict the class of an image (as done in the original ViT paper), BEiT models are pre-trained to predict visual tokens from the codebook of OpenAI's DALL-E model given masked patches. Add BEiT #12994 (@NielsRogge) Compatible checkpoints can be found on the Hub: https://huggingface.co/models?filter=beit Speech improvements The Wav2Vec2 and HuBERT models now have a sequence classification head available. Add Wav2Vec2 &amp; Hubert ForSequenceClassification #13153 (@anton-l) DeBERTa in TensorFlow (@kamalkraj) The DeBERTa and DeBERTa-v2 models have been converted from PyTorch to TensorFlow. Deberta tf #12972 (@kamalkraj) Deberta_v2 tf #13120 (@kamalkraj) Flax model additions EncoderDecoder, DistilBERT, and ALBERT, now have support in Flax! FlaxEncoderDecoder allowing Bert2Bert and Bert2GPT2 in Flax #13008 (@ydshieh) FlaxDistilBERT #13324 (@kamalkraj) FlaxAlBERT #13294 (@kamalkraj) TensorFlow examples A new example has been added in TensorFlow: multiple choice! Data collators have become framework agnostic and can now work for both TensorFlow and NumPy on top of PyTorch. Add TF multiple choice example #12865 (@Rocketknight1) TF/Numpy variants for all DataCollator classes #13105 (@Rocketknight1) Auto API refactor The Auto APIs have been disentangled from all the other mode modules of the Transformers library, so you can now safely import the Auto classes without importing all the models (and maybe getting errors if your setup is not compatible with one specific model). The actual model classes are only imported when needed. Disentangle auto modules from other modeling files #13023 (@sgugger) Fix AutoTokenizer when no fast tokenizer is available #13336 (@sgugger) Slight breaking change When loading some kinds of corrupted state dictionaries of models, the <code>PreTrainedModel.from_pretrained</code> method was sometimes silently ignoring weights. This has now become a real error. Fix from_pretrained with corrupted state_dict #12939 (@sgugger) General improvements and bugfixes Improving pipeline tests #12784 (@Narsil) Pin git python to &lt;3.1.19 #12858 (@patrickvonplaten) [tests] fix logging_steps requirements #12860 (@stas00) [Sequence Feature Extraction] Add truncation #12804 (@patrickvonplaten) add <code>classifier_dropout</code> to classification heads #12794 (@PhilipMay) Fix barrier for SM distributed #12853 (@sgugger) Add possibility to ignore imports in test_fecther #12801 (@sgugger) Add accelerate to examples requirements #12888 (@sgugger) Fix documentation of BigBird tokenizer #12889 (@sgugger) Better heuristic for token-classification pipeline. #12611 (@Narsil) Fix push_to_hub for TPUs #12895 (@sgugger) <code>Seq2SeqTrainer</code> set max_length and num_beams only when non None #12899 (@cchen-dialpad) [FLAX] Minor fixes in CLM example #12914 (@stefan-it) Correct validation_split_percentage argument from int (ex:5) to float (0.05) #12897 (@Elysium1436) Fix typo in the example of MobileBertForPreTraining #12919 (@buddhics) Add option to set max_len in run_ner #12929 (@sgugger) Fix QA examples for roberta tokenizer #12928 (@sgugger) Print defaults when using --help for scripts #12930 (@sgugger) Fix StoppingCriteria ABC signature #12918 (@willfrey) Add missing @classmethod decorators #12927 (@willfrey) fix distiller.py #12910 (@chutaklee) Update generation_logits_process.py #12901 (@willfrey) Update generation_logits_process.py #12900 (@willfrey) Update tokenization_auto.py #12896 (@willfrey) Fix docstring typo in tokenization_auto.py #12891 (@willfrey) [Flax] Correctly Add MT5 #12988 (@patrickvonplaten) ONNX v2 raises an Exception when using PyTorch &lt; 1.8.0 #12933 (@mfuntowicz) Moving feature-extraction pipeline to new testing scheme #12843 (@Narsil) Add CpmTokenizerFast #12938 (@JetRunner) fix typo in gradient_checkpointing arg #12855 (@21jun) Log Azure ML metrics only for rank 0 #12766 (@harshithapv) Add substep end callback method #12951 (@wulu473) Add multilingual documentation support #12952 (@JetRunner) Fix division by zero in NotebookProgressPar #12953 (@sgugger) [FLAX] Minor fixes in LM example #12947 (@stefan-it) Prevent <code>Trainer.evaluate()</code> crash when using only tensorboardX #12963 (@aphedges) Fix typo in example of DPRReader #12954 (@tadejsv) Place BigBirdTokenizer in sentencepiece-only objects #12975 (@sgugger) fix typo in example/text-classification README #12974 (@fullyz) Fix template for inputs docstrings #12976 (@sgugger) fix <code>Trainer.train(resume_from_checkpoint=False)</code> is causing an exception #12981 (@PhilipMay) Cast logits from bf16 to fp32 at the end of TF_T5 #12332 (@szutenberg) Update CANINE test #12453 (@NielsRogge) pad_to_multiple_of added to DataCollatorForWholeWordMask #12999 (@Aktsvigun) [Flax] Align jax flax device name #12987 (@patrickvonplaten) [Flax] Correct flax docs #12782 (@patrickvonplaten) T5: Create position related tensors directly on device instead of CPU #12846 (@armancohan) Skip ProphetNet test #12462 (@LysandreJik) Create perplexity.rst #13004 (@sashavor) GPT-Neo ONNX export #12911 (@michaelbenayoun) Update generate method - Fix floor_divide warning #13013 (@nreimers) [Flax] Correct pt to flax conversion if from base to head #13006 (@patrickvonplaten) [Flax T5] Speed up t5 training #13012 (@patrickvonplaten) FX submodule naming fix #13016 (@michaelbenayoun) T5 with past ONNX export #13014 (@michaelbenayoun) Fix ONNX test: Put smaller ALBERT model #13028 (@LysandreJik) Tpu tie weights #13030 (@sgugger) Use min version for huggingface-hub dependency #12961 (@lewtun) tfhub.de -&gt; tfhub.dev #12565 (@abhishekkrthakur) [Flax] Refactor gpt2 &amp; bert example docs #13024 (@patrickvonplaten) Add MBART to models exportable with ONNX #13049 (@LysandreJik) Add to ONNX docs #13048 (@LysandreJik) Fix small typo in M2M100 doc #13061 (@SaulLu) Add try-except for torch_scatter #13040 (@JetRunner) docs: add HuggingArtists to community notebooks #13050 (@AlekseyKorshuk) Fix ModelOutput instantiation form dictionaries #13067 (@sgugger) Roll out the test fetcher on push tests #13055 (@sgugger) Fix fallback of test_fetcher #13071 (@sgugger) Revert to all tests whil we debug what's wrong #13072 (@sgugger) Use original key for label in DataCollatorForTokenClassification #13057 (@ibraheem-moosa) [Doctest] Setup, quicktour and task_summary #13078 (@sgugger) Add VisualBERT demo notebook #12263 (@gchhablani) Install git #13091 (@LysandreJik) Fix classifier dropout in AlbertForMultipleChoice #13087 (@ibraheem-moosa) Doctests job #13088 (@LysandreJik) Fix VisualBert Embeddings #13017 (@gchhablani) Proper import for unittest.mock.patch #13085 (@sgugger) Reactive test fecthers on scheduled test with proper git install #13097 (@sgugger) Change a parameter name in FlaxBartForConditionalGeneration.decode() #13074 (@ydshieh) [Flax/JAX] Run jitted tests at every commit #13090 (@patrickvonplaten) Rely on huggingface_hub for common tools #13100 (@sgugger) [FlaxCLIP] allow passing params to image and text feature methods #13099 (@patil-suraj) Ci last fix #13103 (@sgugger) Improve type checker performance #13094 (@bschnurr) Fix VisualBERT docs #13106 (@gchhablani) Fix CircleCI nightly tests #13113 (@sgugger) Create py.typed #12893 (@willfrey) Fix flax gpt2 hidden states #13109 (@ydshieh) Moving fill-mask pipeline to new testing scheme #12943 (@Narsil) Fix omitted lazy import for xlm-prophetnet #13052 (@minwhoo) Fix classifier dropout in bertForMultipleChoice #13129 (@mandelbrot-walker) Fix frameworks table so it's alphabetical #13118 (@osanseviero) [Feature Processing Sequence] Remove duplicated code #13051 (@patrickvonplaten) Ci continue through smi failure #13140 (@LysandreJik) Fix missing <code>seq_len</code> in <code>electra</code> model when <code>inputs_embeds</code> is used. #13128 (@sararb) Optimizes ByT5 tokenizer #13119 (@Narsil) Add splinter #12955 (@oriram) [AutoFeatureExtractor] Fix loading of local folders if config.json exists #13166 (@patrickvonplaten) Fix generation docstrings regarding input_ids=None #12823 (@jvamvas) Update namespaces inside torch.utils.data to the latest. #13167 (@qqaatw) Fix the loss calculation of ProphetNet #13132 (@StevenTang1998) Fix LUKE tests #13183 (@NielsRogge) Add min and max question length options to TapasTokenizer #12803 (@NielsRogge) SageMaker: Fix sagemaker DDP &amp; metric logs #13181 (@philschmid) correcting group beam search function output score bug #13211 (@sourabh112) Change how "additional_special_tokens" argument in the ".from_pretrained" method of the tokenizer is taken into account #13056 (@SaulLu) remove unwanted control-flow code from DeBERTa-V2 #13145 (@kamalkraj) Fix load_tf_weights alias. #13159 (@qqa

Multi-user MIMO (MU-MIMO) networks reveal the unique opportunities arising from a joint optimization of antenna combining techniques with resource allocation protocols. Furthermore, it brings robustness with respect to multipath richness, allowing for compact antenna spacing at the BS and, crucially, yielding the diversity and multiplexing gains without the need for multiple antenna user terminals. To realize these gains, however, the BS should be informed with the user's channel coefficients, which may limit practical application to TDD or low-mobility settings. To circumvent this problem and reduce feedback load, combining MU-MIMO with opportunistic scheduling seems a promising direction. The success for this type of scheduler is strongly traffic and QoS-dependent, however.

Network slicing has been identified as the backbone of the rapidly evolving 5G technology. However, as its consolidation and standardization progress, there are no literatures that comprehensively discuss its key principles, enablers, and research challenges. This paper elaborates network slicing from an end-to-end perspective detailing its historical heritage, principal concepts, enabling technologies and solutions as well as the current standardization efforts. In particular, it overviews the diverse use cases and network requirements of network slicing, the pre-slicing era, considering RAN sharing as well as the end-to-end orchestration and management, encompassing the radio access, transport network and the core network. This paper also provides details of specific slicing solutions for each part of the 5G system. Finally, this paper identifies a number of open research challenges and provides recommendations toward potential solutions.

Reinforcement learning (RL) is a promising data-driven approach for adaptive traffic signal control (ATSC) in complex urban traffic networks, and deep neural networks further enhance its learning power. However, the centralized RL is infeasible for large-scale ATSC due to the extremely high dimension of the joint action space. The multi-agent RL (MARL) overcomes the scalability issue by distributing the global control to each local RL agent, but it introduces new challenges: now, the environment becomes partially observable from the viewpoint of each local agent due to limited communication among agents. Most existing studies in MARL focus on designing efficient communication and coordination among traditional Q-learning agents. This paper presents, for the first time, a fully scalable and decentralized MARL algorithm for the state-of-the-art deep RL agent, advantage actor critic (A2C), within the context of ATSC. In particular, two methods are proposed to stabilize the learning procedure, by improving the observability and reducing the learning difficulty of each local agent. The proposed multi-agent A2C is compared against independent A2C and independent Q-learning algorithms, in both a large synthetic traffic grid and a large real-world traffic network of Monaco city, under simulated peak-hour traffic dynamics. The results demonstrate its optimality, robustness, and sample efficiency over the other state-of-the-art decentralized MARL algorithms.

Recently, unmanned aerial vehicles (UAVs), or drones, have attracted a lot of attention, since they represent a new potential market. Along with the maturity of the technology and relevant regulations, a worldwide deployment of these UAVs is expected. Thanks to the high mobility of drones, they can be used to provide a lot of applications, such as service delivery, pollution mitigation, farming, and in the rescue operations. Due to its ubiquitous usability, the UAV will play an important role in the Internet of Things (IoT) vision, and it may become the main key enabler of this vision. While these UAVs would be deployed for specific objectives (e.g., service delivery), they can be, at the same time, used to offer new IoT value-added services when they are equipped with suitable and remotely controllable machine type communications (MTCs) devices (i.e., sensors, cameras, and actuators). However, deploying UAVs for the envisioned purposes cannot be done before overcoming the relevant challenging issues. These challenges comprise not only technical issues, such as physical collision, but also regulation issues as this nascent technology could be associated with problems like breaking the privacy of people or even use it for illegal operations like drug smuggling. Providing the communication to UAVs is another challenging issue facing the deployment of this technology. In this paper, a comprehensive survey on the UAVs and the related issues will be introduced. In addition, our envisioned UAV-based architecture for the delivery of UAV-based value-added IoT services from the sky will be introduced, and the relevant key challenges and requirements will be presented.

The automatic supervision of IT systems is a current challenge at Orange. Given the size and complexity reached by its IT operations, the number of sensors needed to obtain measurements over time, used to infer normal and abnormal behaviors, has increased dramatically making traditional expert-based supervision methods slow or prone to errors. In this paper, we propose a fast and stable method called UnSupervised Anomaly Detection for multivariate time series (USAD) based on adversely trained autoencoders. Its autoencoder architecture makes it capable of learning in an unsupervised way. The use of adversarial training and its architecture allows it to isolate anomalies while providing fast training. We study the properties of our methods through experiments on five public datasets, thus demonstrating its robustness, training speed and high anomaly detection performance. Through a feasibility study using Orange's proprietary data we have been able to validate Orange's requirements on scalability, stability, robustness, training speed and high performance.

Anti-virus vendors are confronted with a multitude of potentially malicious samples today. Receiving thousands of new samples every day is not uncommon. The signatures that detect confirmed malicious threats are mainly still created manually, so it is important to discriminate between samples that pose a new unknown threat and those that are mere variants of known malware. This survey article provides an overview of techniques based on dynamic analysis that are used to analyze potentially malicious samples. It also covers analysis programs that leverage these It also covers analysis programs that employ these techniques to assist human analysts in assessing, in a timely and appropriate manner, whether a given sample deserves closer manual inspection due to its unknown malicious behavior.

Vehicular Ad-hoc Networks (VANETs) have been recently attracting an increasing attention from both research and industry communities. One of the challenges posed by the study of VANETs is the definition of a vehicular mobility model providing an accurate and realistic vehicular mobility description at both macroscopic and microscopic levels. Another challenge is to be able to dynamically alter this vehicular mobility as a consequence of the vehicular communication protocols. Many mobility models have been developed by the community in order to solve these two issues. However, due to the large number of available models claiming to be adapted to vehicular traffic, and also due to their different and somehow incomparable features, understanding their true characteristics, their degree of realism with respect to vehicular mobility, and real capabilities is a hard task. In this survey, we first introduce a framework that proposes a guideline for the generation of vehicular mobility models. Then, we illustrate the different approaches chosen by the community for the development of vehicular mobility models and their interactions with network simulators. Finally, we propose an overview and taxonomy of a large range of mobility models available for vehicular ad hoc networks. The objective is to provide readers with a guideline to easily understand and objectively compare the different models, and eventually identify the one required for their needs.

In next-generation wireless communication systems, packet-oriented data transmission will be implemented in addition to standard mobile telephony. We take an information-theoretic view of some simple protocols for reliable packet communication based on "hybrid-ARQ," over a slotted multiple-access Gaussian channel with fading and study their throughput (total bit per second per hertz) and average delay under idealized but fairly general assumptions. As an application of the renewal-reward theorem, we obtain closed-form throughput formulas. Then, we consider asymptotic behaviors with respect to various system parameters. The throughput of automatic retransmission request (ARQ) protocols is compared to that of code division multiple access (CDMA) with conventional decoding. Interestingly, the ARQ systems are not interference-limited even if no multiuser detection or joint decoding is used, as opposed to conventional CDMA.

Speaker diarization is the task of determining “who spoke when?” in an audio or video recording that contains an unknown amount of speech and also an unknown number of speakers. Initially, it was proposed as a research topic related to automatic speech recognition, where speaker diarization serves as an upstream processing step. Over recent years, however, speaker diarization has become an important key technology for many tasks, such as navigation, retrieval, or higher level inference on audio data. Accordingly, many important improvements in accuracy and robustness have been reported in journals and conferences in the area. The application domains, from broadcast news, to lectures and meetings, vary greatly and pose different problems, such as having access to multiple microphones and multimodal information or overlapping speech. The most recent review of existing technology dates back to 2006 and focuses on the broadcast news domain. In this paper, we review the current state-of-the-art, focusing on research developed since 2006 that relates predominantly to speaker diarization for conference meetings. Finally, we present an analysis of speaker diarization performance as reported through the NIST Rich Transcription evaluations on meeting data and identify important areas for future research.

ASVspoof, now in its third edition, is a series of community-led challenges\nwhich promote the development of countermeasures to protect automatic speaker\nverification (ASV) from the threat of spoofing. Advances in the 2019 edition\ninclude: (i) a consideration of both logical access (LA) and physical access\n(PA) scenarios and the three major forms of spoofing attack, namely synthetic,\nconverted and replayed speech; (ii) spoofing attacks generated with\nstate-of-the-art neural acoustic and waveform models; (iii) an improved,\ncontrolled simulation of replay attacks; (iv) use of the tandem detection cost\nfunction (t-DCF) that reflects the impact of both spoofing and countermeasures\nupon ASV reliability. Even if ASV remains the core focus, in retaining the\nequal error rate (EER) as a secondary metric, ASYspoof also embraces the\ngrowing importance of fake audio detection. ASVspoof 2019 attracted the\nparticipation of 63 research teams, with more than half of these reporting\nsystems that improve upon the performance of two baseline spoofing\ncountermeasures. This paper describes the 2019 database, protocols and\nchallenge results. It also outlines major findings which demonstrate the real\nprogress made in protecting against the threat of spoofing and fake audio.\n

In this paper, we study the sum rate performance of zero-forcing (ZF) and regularized ZF (RZF) precoding in large MISO broadcast systems under the assumptions of imperfect channel state information at the transmitter and per-user channel transmit correlation. Our analysis assumes that the number of transmit antennas M and the number of single-antenna users K are large while their ratio remains bounded. We derive deterministic approximations of the empirical signal-to-interference plus noise ratio (SINR) at the receivers, which are tight as M, K → ∞. In the course of this derivation, the per-user channel correlation model requires the development of a novel deterministic equivalent of the empirical Stieltjes transform of large dimensional random matrices with generalized variance profile. The deterministic SINR approximations enable us to solve various practical optimization problems. Under sum rate maximization, we derive 1) for RZF the optimal regularization parameter; 2) for ZF the optimal number of users; 3) for ZF and RZF the optimal power allocation scheme; and 4) the optimal amount of feedback in large FDD/TDD multiuser systems. Numerical simulations suggest that the deterministic approximations are accurate even for small M, K.

We introduce a traffic model for circuit-switched all optical networks which we then use to calculate the blocking probability along a path for networks with and without wavelength changers. We investigate the effects of path length, switch size, and interference length (the expected number of hops shared by two sessions which share at least one hop) on blocking probability and the ability of wavelength changers to improve performance. Our model correctly predicts unobvious qualitative behaviour demonstrated in simulations by other authors.