Miracle-dz/UniFAR-FTUs huggingface dataset:
Massive MIMO for Next Generation Wireless Systems

Massive multiple-input multiple-output (MIMO) is an emerging technology that scales up MIMO by possibly orders of magnitude compared to the current state of the art. In this article, we follow up on our earlier exposition [1], with a focus on the developments in the last three years; most particularly, energy efficiency, exploitation of excess degrees of freedom, time-division duplex (TDD) calibration, techniques to combat pilot contamination, and entirely new channel measurements. With massive MIMO, we think of systems that use antenna arrays with a few hundred antennas simultaneously serving many tens of terminals in the same time-frequency resource. The basic premise behind massive MIMO is to reap all the benefits of conventional MIMO, but on a much greater scale. Overall, massive MIMO is an enabler for the development of future broadband (fixed and mobile) networks, which will be energy-efficient, secure, and robust, and will use the spectrum efficiently. As such, it is an enabler for the future digital society infrastructure that will connect the Internet of people and Internet of Things with clouds and other network infrastructure. Many different configurations and deployment scenarios for the actual antenna arrays used by a massive MIMO system can be envisioned (Fig. 1). Each antenna unit would be small and active, preferably fed via an optical or electric digital bus. Massive MIMO relies on spatial multiplexing, which in turn relies on the base station having good enough channel knowledge, on both the uplink and the downlink. On the uplink, this is easy to accomplish by having the terminals send pilots, based on which the base station estimates the channel responses to each of the terminals. The downlink is more difficult. In conventional MIMO systems such as the Long Term Evolution (LTE) standard, the base station sends out pilot waveforms, based on which the terminals estimate the channel responses, quantize the thus obtained estimates, and feed them back to the base station. This will not be feasible in massive MIMO systems, at least not when operating in a high-mobility environment, for two reasons. First, optimal downlink pilots should be mutually orthogonal between the antennas. This means that the amount of time-frequency resources needed for downlink pilots scales with the number of antennas, so a massive MIMO system would require up to 100 times more such resources than a conventional system. Second, ABSTRACT
Efficient and Extensible Algorithms for Multi Query Optimization

Complex queries are becoming commonplace, with the growing use of decision support systems. These complex queries often have a lot of common sub-expressions, either within a single query, or across multiple such queries run as a batch. Multi-query optimization aims at exploiting common sub-expressions to reduce evaluation cost. Multi-query optimization has hither-to been viewed as impractical, since earlier algorithms were exhaustive, and explore a doubly exponential search space. In this paper we demonstrate that multi-query optimization using heuristics is practical, and provides significant benefits. We propose three cost-based heuristic algorithms: Volcano-SH and Volcano-RU, which are based on simple modifications to the Volcano search strategy, and a greedy heuristic. Our greedy heuristic incorporates novel optimizations that improve efficiency greatly. Our algorithms are designed to be easily added to existing optimizers. We present a performance study comparing the algorithms, using workloads consisting of queries from the TPC-D benchmark. The study shows that our algorithms provide significant benefits over traditional optimization, at a very acceptable overhead in optimization time.
Primera evidencia de dinosaurios ornitópodos en la base de la Formación Huincul (Cenomaniense Superior-Turoniense, Cuenca Neuquina, Argentina)

Se describe por primera vez la presencia de dinosaurios ornitopodos en la base de la Formacion Huincul (Cenomaniense-Turoniense) de Agrio del Medio (Neuquen, Argentina). Se trata de una falange ungueal del pie bien conservada y de pequeno tamano. Presenta una morfologia general en forma de garra, similar a la de los ornitopodos basales que se han encontrado en el Cretacico Superior de Sudamerica, lo que le diferencia de las formas mas derivadas de iguanodontios con ungueal en forma de casco. Este descubrimiento es una nueva evidencia que los pequenos ornitopodos basales se encontraban en la Cuenca Neuquina, al menos, desde el transito Cretacico Inferior-Superior.
On the relation between soil moisture dynamics and the geographical distribution of Culicoides imicola

Culicoides imicola is the main biological vector in the transmission of the Bluetongue virus in the Mediterranean basin. Its geographical range is constrained by environmental characteristics, and soil moisture has been proposed as an important constraint because of its effect on C. imicola breeding habitat. The few field studies on breeding habitat of C. imicola indicate the favourability of (semi‐)moist habitat that is not flooded. Data on C. imicola in Calabria revealed the presence of C. imicola at the eastern Ionian coastal area and the absence at the greater part of the western Tyrrhenian coastal area. A spatially distributed soil moisture model was applied to obtain daily soil moisture estimates for Calabria and to assess the relationship between soil moisture and the particular C. imicola distribution at the study region. The temporal soil moisture pattern was not significantly different between locations of contrasting C. imicola occurrence. The monthly average volumetric soil moisture content, however, was found to differ substantially. Soil moisture conditions of the topsoil at locations of C. imicola presence were consistently wetter. Particularly in August, at the onset of the season of high C. imicola abundance, soil moisture was significantly higher at the presence sites and sustained the larval stages of the C. imicola life cycle. This study provides evidence that soil moisture imposes an important constraint on the geographical distribution of C. imicola. Soil moisture may therefore be considered as a key environmental variable for the delineation of suitable C. imicola habitat. Copyright © 2013 John Wiley & Sons, Ltd.
Oligocene-Miocene ramp system (Asmari Formation) in the NW of the Zagros basin, Iran: Microfacies, paleoenvironment and depositional sequence

The Asmari Formation deposited in the Zagros foreland basin during the Oligocene-Miocene. Four different measured sections were studied in this area in order to interpret the facies, depositional environment and sequence stratigraphy of the Asmari Formation. In this study, thirteen different microfacies types have been recognized, which can be grouped into six depositional environments: tidal flat, restricted lagoon, open lagoon, shoal, slope and basin. The Asmari Formation represents sedimentation on a carbonate ramp. Four third-order sequences are identified, on the basis of deepening and shallowing patterns in the microfacies and the distribution of the Oligocene-Miocene foraminifers. The depositional sequences 1, 2 and 3 were observed in Dehluran and Kabirkuh-Darrehshahr areas, and are synchronous with a period of either erosion or non-deposition represented by unconformities in Mamulan and Sepid Dasht areas.
Variability of circumpolar deep water transport onto the Amundsen Sea Continental shelf through a shelf break trough

[1] The presence of warm Circumpolar Deep Water (CDW) intrusions on the Amundsen continental shelf has been linked to recent thinning of the outlet glaciers draining the West Antarctic ice sheet into the Amundsen Sea. Inflow of the CDW onto the shelf is thought to occur within a series of troughs that intersect the continental shelf break. We use observations between 1994 and 2011 and a numerical model to investigate the variability of CDW transport in a trough intersecting the shelf break at 113°W. The location of the main CDW inflow into the trough varies between its eastern flank and center, while the western part of the trough is filled by a recirculation that commonly entrains cooler water originating further south on the shelf. Thermocline depth decreases between the early and late 2000s with an indication that the depth of the 1994 thermocline was similar to the later years. Mooring results show that the CDW layer cools and thins in summer and thickens and warms in winter. In addition to a deeper thermocline in summer, we observe a stronger presence of Lower CDW in the bottom of the trough. Heat flux onto the shelf is controlled by current velocities rather than CDW temperature and the majority of the heat is carried onto the shelf by background flow rather than episodic events.
Path O-RAM: An Extremely Simple Oblivious RAM Protocol

We present Path ORAM, an extremely simple Oblivious RAM protocol with a small amount of client storage. Partly due to its simplicity, Path ORAM is the most practical ORAM scheme known to date with small client storage. We formally prove that Path ORAM has a O(log N) bandwidth cost for blocks of size B = Omega(log^2 N) bits. For such block sizes, Path ORAM is asymptotically better than the best known ORAM schemes with small client storage. Due to its practicality, Path ORAM has been adopted in the design of secure processors since its proposal.
Ocean color estimation by Himawari-8/AHI

The Japanese Meteorological Agency (JMA) geostational meteorological satellite, Himawari-8, carrying Advanced Himawari Imager (AHI) has been in regular operation since July 7, 2015. Before the AHI, geostational meteorological sensors hardly observed ocean color due to only one broad band in visible wavelengths and relatively large radiometric noise. However, AHI has six spectral bands from visible to shortwave infrared wavelengths (470 nm, 510 nm, 640 nm, 856 nm, 1610 nm, and 2257 nm) in addition to ten thermal infrared bands, and their radiometric noise can be reduced by temporal average since AHI observes the full disk by every 10 minutes. This study investigates the possibility of ocean color estimation (remote sensing reflectance, Rrs, and chlorophyll-a concentration, Chla) from AHI observations. The estimation sensitivity was tested using in-situ data, a simple in-water optical model, and the spectral response of AHI bands. We found the error characteristics and limitation of the estimation by AHI as follows. Chla can be estimated by the traditional scheme, blue (470 nm) green (510 nm) ratio in Chla <3 mg/m3. The estimation in Chla > 5 mg/m3 can be improved by using the green (510 nm) red (640 nm) bands. The random noise can be reduced, by averaging original 10-minute images over an hour. Good estimates are obtained in the summer hemisphere; however, retrieved imagery becomes noisy in the mid and high latitudes (e.g., > 35°) of winter hemisphere due to a long path of the solar light.
Sentiment analysis on bangla and romanized bangla text using deep recurrent models

Sentiment Analysis (SA) is an opinion mining study analyzing people's opinions, sentiments, evaluations and appraisals towards societal entities such as products, services, individuals, organizations, events, etc. Of late, most of the research works on SA in natural language processing (NLP) are focused on English language. However, it is noted that Bangla does not have a proper dataset that is both large and standard. As a result, recent research works with Bangla in SA have fallen short to produce results that can be both comparable to works done by others in other languages and reusable for further prospective research. In this work, a substantial textual dataset of both Bangla and Romanized Bangla texts have been provided which is first of this kind and post-processed, multiple validated, and ready for SAimplementation and experiments. Further, this dataset have been tested in Deep Recurrent model, specifically, Long Short Term Memory (LSTM), using two types of loss functions — binary cross-entropy and categorical cross-entropy, and also some experimental pre-training were conducted by using data from one validation to pre-train the other and vice versa. Lastly, the results along with analysis are presented in this research.
Aggretriever: A Simple Approach to Aggregate Textual Representation for Robust Dense Passage Retrieval

Pre-trained transformers has declared its success in many NLP tasks. One thread of work focuses on training bi-encoder models (i.e., dense retrievers) to effectively encode sentences or passages into single-vector dense vectors for efﬁcient approximate nearest neighbor (ANN) search. However, recent work has demonstrated that transformers pre-trained with mask language modeling (MLM) are not capable of effectively aggregating text information into a single dense vector due to task-mismatch between pre-training and ﬁne-tuning. Therefore, computationally expensive techniques have been adopted to train dense retrievers, such as large batch size, knowledge distillation or post pre-training. In this work, we present a simple approach to effectively aggregate textual representation from the pre-trained transformer into a dense vector. Extensive experiments show that our approach improves the robustness of the single-vector approach under both in-domain and zero-shot evaluations without any computationally expensive training techniques. Our work demonstrates that MLM pre-trained transformers can be used to effectively encode text information into a single-vector for dense retrieval. Code are available at: https://github.com/ castorini/dhr
The Neural Hawkes Process: A Neurally Self-Modulating Multivariate Point Process

Many events occur in the world. Some event types are stochastically excited or inhibited---in the sense of having their probabilities elevated or decreased---by patterns in the sequence of previous events. Discovering such patterns can help us predict which type of event will happen next and when. We model streams of discrete events in continuous time, by constructing a neurally self-modulating multivariate point process in which the intensities of multiple event types evolve according to a novel continuous-time LSTM. This generative model allows past events to influence the future in complex and realistic ways, by conditioning future event intensities on the hidden state of a recurrent neural network that has consumed the stream of past events. Our model has desirable qualitative properties. It achieves competitive likelihood and predictive accuracy on real and synthetic datasets, including under missing-data conditions.
Revisiting Random Channel Pruning for Neural Network Compression: Supplementary Material

In this supplementary material, we first explain in detail the difference between this work and the previous works. Then we provide a justification of our statement in the main paper “the performance of the channel pruned network is upper bounded by the original network”. The we show how residual blocks with skip connections are pruned in Sec. 3. Finally, more experimental results are given in Sec. 4
MAKING CONVOLUTIONAL NETWORKS SHIFT-INVARIANT AGAIN

Modern convolutional networks are not shift-invariant, despite their convolutional nature: small shifts in the input can cause drastic changes in the internal feature maps and output. In this paper, we isolate the cause – the downsampling operation in convolutional and pooling layers – and apply the appropriate signal processing fix – low-pass filtering before downsampling. This simple architectural modification boosts the shift-equivariance of the internal representations and consequently, shift-invariance of the output. Importantly, this is achieved while maintaining downstream classification performance. In addition, incorporating the inductive bias of shift-invariance largely removes the need for shift-based data augmentation. Lastly, we observe that the modification induces spatially-smoother learned convolutional kernels. Our results suggest that this classical signal processing technique has a place in modern deep networks.
Degree of Interference: A General Framework For Causal Inference Under Interference

One core assumption that is consistently adopted for valid causal inference is that of no interference between experimental units, i.e., the outcome of an experimental unit is not a function of the treatments assigned to other experimental units. This assumption can be violated in real-life experiments, which signiﬁcantly complicates the task of causal inference as one must disentangle direct treatment eﬀects from “spillover” eﬀects. Current methodologies are lacking, as they cannot handle arbitrary, unknown interference structures to permit inference on causal estimands. We present a general framework to address the limitations of existing approaches. Our framework is based on the new concept of the “degree of interference” (DoI). The DoI is a unit-level latent variable that captures the spillover eﬀects on a unit. This concept can ﬂexibly accommodate any interference structure. We also develop a Bayesian nonparametric methodology to perform inferences on the estimands under our framework. We illustrate the DoI concept and properties of our Bayesian methodology via extensive simulation studies and a real-life case study from additive manufacturing for which interference is a critical concern in achieving geometric accuracy control. Ultimately, our framework enables us to infer causal eﬀects without strong structural assumptions on interference.
Study on the Characteristics of VOCs Source Profiles of Vehicle Exhaust and Gasoline Emission

The volatile organic compounds source profiles of motor vehicle exhaus t, gasoline vapor and liquid gasoline were determined by averaging the ratios of individual VOC species concentration to the total concentrion of all VOC specie s measured. The gasoline exhaust and diesel exhaust display similar distributions . The alkanes and alkenes below C4 are the most abundant compounds in liquefied petroleum gas exhaust. The compounds below C5 and above C6 are the majo r content in gasoline vapor and liquid gasoline respectively.
Ready for Rain? A View from SPEC Research on the Future of Cloud Metrics

George Kousiouris School of Electrical and Computer Engineering National Technical University of Athens Athens, Greece gkousiou@mail.ntua. gr Athanasia Evangelinou School of Electrical and Computer Engineering National Technical University of Athens Athens, Greece aevang@mail.ntua. gr Alexandru Iosup Faculty of Engineering, Mathematics and Computer Science Delft University of Technology Delft, Netherlands A.Iosup@tudelft.nl
PlaStIL: Plastic and Stable Exemplar-Free Class-Incremental Learning

Plasticity and stability are needed in class-incremental learning in order to learn from new data while preserving past knowledge. Due to catastrophic forgetting, finding a compromise between these two properties is particularly challenging when no memory buffer is available. Mainstream methods need to store two deep models since they integrate new classes using fine-tuning with knowledge distillation from the previous incremental state. We propose a method which has similar number of parameters but distributes them differently in order to find a better balance between plasticity and stability. Following an approach already deployed by transfer-based incremental methods, we freeze the feature extractor after the initial state. Classes in the oldest incremental states are trained with this frozen extractor to ensure stability. Recent classes are predicted using partially fine-tuned models in order to introduce plasticity. Our proposed plasticity layer can be incorporated to any transfer-based method designed for exemplar-free incremental learning, and we apply it to two such methods. Evaluation is done with three large-scale datasets. Results show that performance gains are obtained in all tested configurations compared to existing methods.
Estimating Sample Size for Usability Testing

One strategy used to discover user interface requirements is the conduct of usability testing. However, when conducting such testing one of the unknowns is how much testing should be done? Since conducting extensive testing is costly, reducing the number of tests can contribute greatly to successful resource management of a project. Even though a significant number of models have been proposed to estimate sample size in usability testing, there is still not consensus on the optimal sample size. Several studies claim that 3 to 5 users suffice to uncover 80% of problems in a software interface, however is this enough? This study uses data collected from the user testing of a web application in an attempt to verify these rules of thumb, collectively known as the “magic number five”. Analysis of the empirical data shows that the 5-user assumption significantly underestimates the number of users required to achieve reasonable levels of problem discovery.
Predicting Parameters in Deep Learning

The recent success of large and deep neural network models has motivated the training of even larger and deeper networks with millions of parameters. Training these models usually requires parallel training methods where communicating large number of parameters becomes one of the main bottlenecks. We show that many deep learning models are over-parameterized and their learned features can be predicted given only a small fraction of their parameters. We then propose a method which exploits this fact during the training to reduce the number of parameters that need to be learned. Our method is orthogonal to the choice of network architecture and can be applied in a wide variety of neural network architectures and application areas. We evaluate this technique using various experiments in image and speech recognition and show that we can only learn a fraction of the parameters (up to 10% in some cases) and predict the rest without a significant loss in the predictive accuracy of the model.
Faster least squares approximation

Least squares approximation is a technique to find an approximate solution to a system of linear equations that has no exact solution. In a typical setting, one lets n be the number of constraints and d be the number of variables, with n d. Then, existing exact methods find a solution vector in O(nd2) time. We present two randomized algorithms that provide accurate relative-error approximations to the optimal value and the solution vector of a least squares approximation problem more rapidly than existing exact algorithms. Both of our algorithms preprocess the data with the Randomized Hadamard transform. One then uniformly randomly samples constraints and solves the smaller problem on those constraints, and the other performs a sparse random projection and solves the smaller problem on those projected coordinates. In both cases, solving the smaller problem provides relative-error approximations, and, if n is sufficiently larger than d, the approximate solution can be computed in O(nd ln d) time. Mathematics Subject Classification (2000) 65F99 P. Drineas (B) Department of Computer Science, Rensselaer Polytechnic Institute, Troy, NY, USA e-mail: drinep@cs.rpi. edu M. W. Mahoney Department of Mathematics, Stanford University, Stanford, CA, USA e-mail: mmahoney@cs.stanford. edu S. Muthukrishnan Google, Inc., New York, NY, USA e-mail: muthu@google.com T. Sarlós Yahoo! Research, Sunnyvale, CA, USA e-mail: stamas@yahoo-inc.com