text:
About GFH
Sharia Supervisory Board
Treasury & Proprietary Investments
Shares & Disclosures
Regulatory Documents & Further Disclosures
Zakah Guide
© 2021 GFH Financial Group. All rights reserved
GFH Reports Financial Results for First Half 2020
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Manama – 17 August 2020 – GFH Financial Group B.S.C ("GFH" or "the Group") (Bahrain Bourse: GFH) today announced its financial results for the first six months of the year ended 30 June 2020 ("the period").
Net profit attributable to shareholders was US$15.05 million for the six month period compared with US$49.13 million in the first half of 2019, a decrease of 69.4%. This was attributed to the COVID-19 pandemic and slower markets, which impacted the Group's investment banking, commercial banking, real estate and treasury business lines. Earnings per share for the period was US cents 0.45 compared to US cents 1.45 for the first six months of 2019. Consolidated net profits for the Group were US$20.39 million for the six month period compared to US$48.43 million in the first half of 2019, a decrease of 57.9%.
Total equity attributable to shareholders was US$0.85 billion at 30 June 2020 from US$1.00 billion at year-end 2019, a decrease of 15.0% due to the financial impact of COVID-19 resulting in modification losses, commercial banking restructuring activities and recognition of fair value losses and foreign currency translation differences at the Group level. Total assets of the Group were US$6.13 billion at 30 June 2020 compared with US$5.95 billion at 31 December 2019, an increase of 3.1%.
Total Income for the first six months of the year was US$146.53 million versus US$163.55 million for the 2019 period, a decrease of 10.4%. Given challenging market conditions, these results nevertheless reflect the resilient nature of the diverse business model of the Group where every business line has contributed to the operating income. Total Expenses for the period were US$126.14 million versus US$114.65 million for the first six months of 2019, an increase of 10.0%, due to costs associated with the issuance of the Group's Sukuks during the first half of the year.
Commenting on the results, Mr. Jassim Alseddiqi, Chairman of GFH, said, "For the first six months of 2020, the Group is pleased, despite the current challenges and the impact of COVID-19 on our business and global markets, to make steady progress. While net profit for the first six months of the year was impacted by the current conditions, the Group's achievements and underlying strong financial health and operational performance was reflected in ongoing investor and market confidence. For the period, the Group successfully placed more than US$120 million in investments with clients, issued Sukuk to regional and international investors and had its ratings reaffirmed by Fitch. Building on our strong momentum and liquidity, the remainder of 2020 will see the Group focus on continued value creation through further growth and diversification of our operations and investment portfolios."
Mr. Hisham Alrayes, CEO of GFH, added, "We are pleased to continue to report strong revenue generation especially from our core investment banking activities and from the strategic and resilient portfolio of investments we have built. Despite the daunting challenges presented by COVID-19, we have seen our investments continue to perform well. With the issuance of Sukuks in the first half of the year, our liquidity position has been even further enhanced and our ability to continue to effectively implement our strategy reinforced. That said, during this period, we have worked to restructure our commercial banking business via a Tier 1 Sukuk issuance and realigned their activities to result in strong positive contribution to the Group. While market conditions remain challenging, we see opportunities to undertake promising investments and to restructure businesses to deliver strong returns and value to our investors and shareholders. This includes continuing our focus on investing in defensive sectors such as education, healthcare, technology and other income generating assets. We remain grateful for the support we have received from the Central Bank of Bahrain and our investors in these extraordinary times and look forward to steady progress throughout the remainder of the year and beyond."
The Group's financial results in full can be found at https://www.bahrainbourse.com/. Shares of GFH are traded under the ticker "GFH" on the Bahrain Bourse, Boursa Kuwait and Dubai Financial Market.
About GFH Financial Group B.S.C.
GFH is one of the most recognised financial groups in the Gulf region. Its businesses include Asset Management, Wealth Management, Commercial Banking and Real Estate Development. The Group's operations are focused in the GCC, North Africa and India. GFH is listed in Bahrain Bourse, Boursa Kuwait and Dubai Financial Market. For more information, please visit www.gfh.com
Nawal Al-Naji
Manager- Corporate Communications Department
Email: nalnaji@gfh.com
Website: www.gfh.com
Licensed as an Islamic wholesale bank by the Central Bank of Bahrain.
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text:
Drone Images
The Essays
Parker Emily
Beckett William
Mediterranean Odyssey - Barcelona to Venice
Setting Sail...
Greenville, SC - Unity Park
The Victoria Crowned Pigeon at The Columbus Zoo
Puerto Morales
Puerto Morelos is a Mexican port town on the Caribbean coast of the Yucatán Peninsula. It's between the Riviera Maya resorts of Cancun and Playa del Carmen, but retains some of its original fishing village character.
Nuevo Vallarta and Puerto Vallarta
Nuevo Vallarta is a planned resort on Mexico's Pacific coast, just north of Puerto Vallarta. It sits on Banderas Bay, and is known for its golf courses, marina and long, sandy Nuevo Vallarta beach.
Puerto Vallarta is a resort town on Mexico's Pacific coast, in Jalisco state. El Malecón is a beachside promenade with contemporary sculptures, as well as bars, lounges and nightclubs.
Greek Odyssey - Discovering ancient empires as we explore the Aegean Sea on our voyage to important sites of antiquity.
Day One – Athens, the birthplace of democracy.
The legacy of ancient Greece looms large atop the hill of Acropolis. As you pass through the Propylaeum, the grand entrance delivers you to the plateau of the rock. Here, the Parthenon reigns over the Nike and Erechtheum temples.
The Acropolis Museum is a thematic museum, the collections of which include the findings of only one archaeological site,
the Athenian Acropolis.
Day Two - Volos
The gateway to Meteora and its spectacular monasteries perched upon rocky pillars.
In the 14th century, Greek Orthodox monks came to believe that the tops of these rock formations rising
precipitously from the surrounding plains would be the perfect location in which to attain a sense of peaceful isolation, and to reconnect with the divine.
Over time, 24 monasteries were built on the hilltops, producing a surreal sight that combines a spectacular, natural landscape with human ingenuity.
© 2021 WJ Klosterman. Powered by Blogger.

redpajama_set_name: RedPajamaCommonCrawl


Mina has a Bachelor's degree in Business Administration and a Graduate Diploma in Early Childhood Development from the University of the Philippines (U.P.). Plus, she has a Graduate Diploma in Arts Psychology at Massey University. She joined our centre in 2017.
"I believe providing children equity in care and education is achieved through knowing, understanding, respecting and valuing children's uniqueness and talents. I also believe in being passionate about looking at the hearts of children and loving them unconditionally. When children feel your love for them, their growth, development and learning are enhanced.
Mina has teaching experience working for an International School in the Philippines, as a Kindergarten and Preparatory level (New Entrant) teacher and as an ECE curriculum designer. Here in New Zealand, she's worked as an ECE relieving teacher for several different centres.


tiff-necked is a common term in the Old Testament, meaning stubborn, headstrong, and unwilling to learn. It sounds quaint to us, but the idiom make good sense 3000 years ago, among an agricultural people, when oxen were used to plow fields. An ox harnessed to a plow is steered, as today, with reins. If the ox decides he does not want to turn when the reins are pulled, he stiffens his powerful neck against them. The modern idiom, "headstrong," has a similar origin.
It meant death for most of the Hebrews merely to set foot on Mount Sinai, when God was in residence. Seventy eldersaNote that in the Gospel of Luke, Jesus sent out seventy disciples to spread his word. Luke 10:1–24. were allowed part-way up the mountain, and Joshua went nearly to the top; but only Moses has been allowed in God's presence, and even Moses cannot look upon the face of God. (More on this in the next verses.) The most righteous of people cannot be fully in God's presence, for all men sin and fall short of the glory of God; and too see God while one is tainted by sin means death.


Using images of textbook pages to symbolize the unbearable pressure of studying that has plagued children of today, the artist displays the child as hidden behind the pages, almost as if he is drowned in them. Camouflage here, then implies the disappearance of a child in his personality, dreams and desires- as a whole, being hidden away under the cascading pressures set on them by today's unbending world.
I'm interested in this Camouflage #2 by Y Indra Wahyu N. Please advise availability and price. Thank you.


text:
package com.wepay.model.data;

import org.json.JSONException;
import org.json.JSONObject;

public class PMInlineCreditCardData {
    public String ccNumber;
    public Integer expirationMonth;
    public Integer expirationYear;
    public String userName;
    public AddressData address;
    public String cvv;
    public String email;
    public String referenceID;
    public Boolean autoUpdate;
    public String callbackUri;
    public String virtualTerminal;
    public Boolean paymentRequestFlag;
    public Boolean autoCapture;

    public JSONObject toJSON() throws JSONException{
        JSONObject o = new JSONObject();
        o.put("cc_number", ccNumber);
        o.put("expiration_month", expirationMonth);
        o.put("expiration_year", expirationYear);
        o.put("user_name", userName);
        o.put("address", AddressData.buildUnifiedAddress(address));
        o.put("cvv", cvv);
        o.put("email", email);
        o.put("reference_id", referenceID);
        o.put("auto_update", autoUpdate);
        o.put("callback_uri", callbackUri);
        o.put("virtual_terminal", virtualTerminal);
        o.put("payment_request_flag", paymentRequestFlag);
        o.put("auto_capture", autoCapture);

        return o;
    }
}

redpajama_set_name: RedPajamaGithub


text:
Marine Monuments Don't Hurt, But Help, the Fishing Industry, Scientists Say
Image: A humpback whale swimming off the New England coast. Conservationists say the establishment of marine monuments not only protect endangered marine life, but can also benefit the fishing industry in the long run.
Last month, during a roundtable with Maine fishermen, U.S. President Donald Trump signed a proclamation to open the Northeast Canyons and Seamounts Marine National Monument to the fishing industry. Some fishermen claimed that restricting commercial fishing in the monument off the coast of New England endangered their livelihoods.
A key sentence in the proclamation declares:
"Whereas, I find that removing the restrictions on commercial fishing set forth in Proclamation 9496 to allow for well-regulated commercial fishing use is in the public interest and that the objects in the monument can be, and are currently, protected pursuant to carefully tailored regulation and management under existing Federal law:"
That statement would claim that opening the almost 5,000 square miles of Atlantic Ocean to commercial fishing would benefit the fishing industry and, if managed properly, would ensure that the area's landscapes, fragile ecosystems, and rare and endangered species remain protected.
These claims are, in fact, false, some scientists say: Opening this monument to commercial fishing hurts fishermen — and the effective way to manage the marine monument is to halt commercial fishing.
"Opening a monument to fishing removes everything the monument is supposed to be. It leaves the monument in name," said Dr. Enric Sala, National Geographic explorer-in-residence and founder of the National Geographic Pristine Seas program, who has helped create marine monuments. Additionally, he noted the presence of a marine reserve benefits fishermen as part of a balanced ecological and economic system.
Map of the Northeast Canyons and Seamounts Marine National Monument (via NOAA)
Incorrect use of the Antiquities Act leads to lawsuit
Northeast Canyons and Seamounts lies about 130 miles off the east coast of the United States. The Barack Obama administration established it in 2016 as the first marine national monument in the country's Atlantic waters.
Conservationists, including Maine-based marine scientist Zack Klyver and groups like the Natural Resources Defense Council and Center for Biological Diversity, are suing the Trump administration for misusing the 1906 American Antiquities Act. The Act gives the president the ability to establish monuments. Under the Constitution, though, only Congress has the ability to manage federal lands and roll back restrictions.
"A significant change to the monument or its protections — such as allowing commercial fishing — must be done by Congress, not by the president," Brad Sewell, senior director of oceans for the Natural Resources Defense Council, said in a statement. "The Antiquities Act gives the president power to protect special areas for future generations, not the opposite power, to abolish those protections. Fishing poses a range of threats, such as harm to deep-sea corals from heavy fishing gear, and entanglement of bycatch and marine mammals."
Correcting the misconception that marine monuments undermine the fishing industry
A conversation with Dr. Sala brought out a few important inaccuracies from the proclamation that require clarifying.
The monument doesn't hurt fishermen. Dr. Sala emphasized that most fishing happens along the coastline — that includes the work done by Maine fishermen present at the roundtable. Fishing activity within that Atlantic area was modest before the monument designation, Dr. Sala said. The red crab and lobster fisheries that were using those waters were given until 2023 to move out of the area.
Highly protected areas improve fish catch. From larger yields to larger fish to greater diversity, protection benefits fishermen. A study conducted by Dr. Sala and Dr. Sylvaine Giakoumi of the University of Queensland found that fully protected marine areas have, on average, 670 percent greater fish biomass than nearby unprotected areas. Other research has shown that organism size increases by 25 percent and species richness by over 20 percent in marine reserves. These types of improvements have benefited fishermen near Marine National Monuments like the Hawaiian Papahanaumokuakea.
Around 99.9 percent of continental U.S. waters are open to the fishing industry. Only 0.1 percent of U.S. continental ocean waters are highly protected, Dr. Sala said. Research has shown that we need 30 percent of ocean environments to be highly protected by 2030 to improve climate resilience. This isn't simply a vain exercise in preserving wildlife and beauty and withholding from commercial interests: Safeguarding the strength of these areas is vital for the health of the fishing sector as well.
It takes very little to disrupt fragile ecosystems. Even though minimal fishing was happening before the monument was established, even one net or trap dropping to the depths can do significant damage, Dr. Sala said. These ecosystems support leatherback and loggerhead turtles, 54 species of deep sea corals (some of which are thousands of years old) and canyons that attract the endangered sperm whale.
Fishermen are hurting, but not because of the monument
Of course, the fishermen at the president's roundtable expressed economic woes and the fear that the monument sets a precedent for closing more of the ocean off to fishing interests.
Dr. Sala yet again emphasized that protecting the ocean is not a threat to fishing jobs. By contrast, the Trump administration has taken actions that do hurt the industry, he insisted. There has been a decrease in demand for seafood, which relates to collapsed supply chains. The trade war with China has led to increased tariffs that hurt the Maine lobster industry, for example.
Even the administration's climate change policies — including pulling out of the Paris Climate Agreement and lifting environmental regulations for coal power plants — endanger fishing. Maine lobster are already moving north to colder waters.
Marine reserves are part of a healthy fishing sector. In addition to the challenges coming from federal measures, experts say New England waters are currently mismanaged and overfished. The Northeast Canyons and Seamounts Marine National Monument is part of the solution to restoring regional fish stocks and gaining much-needed resilience. A broader, more long-term view is necessary to see how the marine economic system actually works. With this holistic view, it is clear that the fishing sector doesn't need deregulation — it needs increased marine protection.
Image credit: Todd Cravens/Unsplash
Roya Sabri
Roya is a writer and graphic designer based in Kailua Kona, HI. She writes about the circular economy, advancements in CSR, the environment and equity. Find her on LinkedIn.
Read more stories by Roya Sabri


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\section{Introduction}
Alzheimer's Disease (AD), the most common cause of Dementia~\cite{WHOdementiaFS}, is a neurodegenerative disorder characterized by loss of neurons and synapses in the cerebral cortex. Its prevalence increases with age, a study on the U.S. census reported that 3\% of people aged 65-74, 17\% of people  aged 75-84, and 32\% of people  aged 85 and older have AD~\cite{hebert2013alzheimer}. As most countries are experiencing a general increase in average lifespan, it is expected a rapidly escalation of AD cases worldwide in the next thirty years~\cite{ADReport2015}.
Pharmacological treatments may temporarily improve the symptoms of the disease, but they can not stop or reverse  its progression. For these reasons, there is an increasing need for additional, noninvasive, and cost-effective tools allowing a preliminary identification of AD in its early clinical stages.
Currently, AD is diagnosed through an analysis of patient clinical history and disability, neuropsychological tests, brain imaging and cerebrospinal fluid exams.
Although the prominent symptoms of the disease are alterations of memory and of spatial-temporal orientation,  language impairments are also an important factor confirmed by current literature~\cite{reilly2011,kempler1995}.
Some of the most well known language impairments found in AD speech include naming~\cite{reilly2011}, word-finding difficulties~\cite{kempler1994}, repetitions~\cite{croisile1996}, an overuse of indefinite and vague terms~\cite{ahmed2013}, and inappropriate use of pronouns~\cite{ripich1988}.

Over the last years, there has been an increased interest from the research community in the automatic identification of AD through the analysis of speech and language abilities.
Some studies have focused on syntactic or semantic features~\cite{hernandez2018,Fraser2016a}, some targeted plain acoustic approaches~\cite{konig2015automatic,haider2019assessment}, while other works have investigated a combination of temporal speech parameters and lexical measures~\cite{gosztolya2019,mirheidari2019}. Most of these approaches use handcrafted features and traditional classification algorithms. Very recent works investigated the use of automatically learned representations from deep neural networks
~\cite{chen2019attention,zargarbashi2019multi,warnita2018detecting,karlekar2018detecting}. Regardless of the approach used, the studies existing in the literature are difficult to analyze and compare due to the different datasets used.
In this scenario, the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge has been proposed, with the aim of providing researchers with a common, statistically balanced and acoustically enhanced dataset to test their approaches~\cite{luz2020alzheimer}.

In this work, we present  the multi-modal system proposed by the Human Language Technology Laboratory of INESC-ID for the ADReSS 2020 challenge.
Our framework is designed to solve the task of automatically distinguishing AD patients from healthy individuals.
In our previous approaches to this topic~\cite{pompili2020pragmatic,pompili2018topic} we exploited lexical, syntactic, and semantic features with measures of local, global, and topic coherence, in order to provide a more comprehensive characterization of language abilities in AD and thus a more reliable identification.
In this work, we take the challenge of using automatically learned representations instead of traditional and consolidated handcrafted features, which already proven to achieve good classification results. Inspired by recent studies, we push the limit of deep neural models to work with extreme conditions, such the ones in the health domain, in which data scarcity is ordinary. Additionally, we combine both acoustic and linguistic information to have a complete picture of patient's disabilities, in a similar way to the type of information that clinicians receive during their interactions with patients.

The rest of this work is organized as follows: Section \ref{section:SOA} introduces the relevant state on the art on the automatic identification of AD. Then, in Section \ref{section:corpus} and \ref{section:methods}, we present the dataset used in this study and a description of our methodology. Finally, classification results are reported in Section \ref{section:exp}, while conclusions are summarized in Section \ref{section:conclusions}.

\section{Related work}
\label{section:SOA}
The computational analysis of speech and language impairments in AD has gained growing attention in recent years.
Initially, existing studies explored engineered temporal and acoustic parameters of speech, linguistic features, or a combination of both.
K\"{o}nig \textit{et al.}~\cite{konig2015automatic} computed several temporal speech features on a dataset composed of 26 AD and 15 healthy subjects, while performing different tasks of isolated and continuous speech. By considering different features according to the task, the authors achieved an accuracy of 87\% in the automatic identification of AD.
Fraser \textit{et al.}~\cite{Fraser2016a} used more than 350 features to capture lexical, syntactic, grammatical, and semantic phenomena from the transcriptions of a picture description task. With a selection of 35  features, the authors achieved a classification accuracy of 81.92\% in distinguishing individuals with AD from healthy controls. Pompili \textit{et al.}~\cite{pompili2020pragmatic} exploited lexical, syntactic, semantic and pragmatic features from the descriptions of the Cookie Theft picture~\cite{Goodglass2001} attaining an accuracy of 85.5\% in the task of classifying AD patients.
Gosztolya \textit{et al.}~\cite{gosztolya2019} collected a dataset composed of 75 Hungarian speakers (25 AD, 25 MCI, and 25 healthy subjects) performing two tasks eliciting continuous speech. The set of features used considered demographic attributes, acoustic and linguistic features. Using only acoustic or linguistic information the authors achieved an accuracy of 82\% in distinguishing AD patients from healthy subjects. When the two types of features were combined, the accuracy increases to 86\%.

More recently, researchers are shifting their focus towards more complex architectures capable of overcoming the limitations of traditional approaches.
Warnita \textit{et al.}~\cite{warnita2018detecting} proposed an approach relying only on acoustic data computed from continuous speech and gated Convolutional Neural Network (GCNN). Using majority voting on speaker and the Paralinguistic Challenge (IS2010) feature set, the authors achieved an accuracy of 73.6\%.
Karlekar \textit{et al.}~\cite{karlekar2018detecting}, on the other hand, investigated linguistic impairments using CNN, LSTM-RNNs, and a combination of both. In this way, they obtained an accuracy of 91.1\% in classifying AD patients.
Chen \textit{et al.}~\cite{chen2019attention} went further, proposing a network based on attention mechanism and composed of a CNN and GRU module. In this way, the architecture should be able to analyze both local speech patterns and global macro-linguistic functions. The accuracy achieved in distinguishing AD patients was of 97.42\%.
Finally, Zargarbashi \textit{et al.}~\cite{zargarbashi2019multi} designed a multi-modal feature embedding approach based on $N$-gram, \textit{i-vectors}, and \textit{x-vectors}.
Classification accuracy results achieved with each of these models were, respectively, of 78.2\%, 75.9\%, and 75.1\%. The joint fusion of the three models reached an accuracy of 83.6\%.

Our work differs from previous studies for several reasons. First, to process the text data, we use contextual embeddings vectors as input to two different systems. One based on the training of a Global Maximum pooling and a bidirectional LSTM-RNNs architectures, and one based on the statistical computation of sentence embeddings. The latter presents the advantage of being a simple approach,  which does not require the training of deep, data-demanding architectures. Second, for the audio recordings, we use DNN speaker embeddings extracted from pre-trained models. These learned, speaker representative vectors have recently shown their potential in the discrimination of neurodegenerative disorders
~\cite{botelho2020pathological}. To the best of our knowledge, this is the first work that jointly uses automatically learned representations from neural models, instead of engineered features, for both audio signals and textual data. In fact, although existing studies have shown that linguistic impairments in AD appear to be more important than acoustic ones, traditional literature provide convincing evidence that using both source of information will definitively improve the accuracy of automatic diagnosis methods.

\section{Corpus}
\label{section:corpus}
The ADReSS dataset contains the speech recordings and corresponding annotated transcriptions of 156 subjects, 78 AD patients, and 78 healthy control matched for age and gender. Data were divided into two partitions, training and test sets composed of 108 and 48 subjects, respectively. Recorded participants were required to provide the descriptions of the Cookie Theft picture from the Boston Diagnostic Aphasia Examination~\cite{Goodglass2001}. Speech recordings were segmented using Voice Activity Detection (VAD) and later normalised~\cite{luz2020alzheimer}. The dataset  contained both full enhanced audio, and normalised audio chunks.

In our approach, we have used both the full enhanced audio and the transcriptions.
The latter were annotated with disfluencies, filled pauses, repetitions, and other more complex events.
However, to build an automated system requiring a minimal annotation effort, we removed all the annotations not corresponding to the plain textual representation of words, thus, better resembling the output that can be generated by an Automatic Speech Recognition (ASR) system.
Overall, the whole set of transcriptions contained 17127 words, of which 1009 were unique. More detailed information about the duration and size of the ADReSS dataset are reported in Table~\ref{tab:corpus}.

\begin{table}[t]
\caption{Statistical information on the ADReSS dataset}
\label{tab:corpus}
\resizebox{\columnwidth}{!}{
\begin{tabular}{lccc}
\hline
                        & \multicolumn{2}{c}{\textbf{Train}}                         & \multicolumn{1}{c}{\textbf{Test}} \\ \hline
                        & \multicolumn{1}{c}{Control} & \multicolumn{1}{c}{AD} & \multicolumn{1}{c}{--}            \\
\textbf{Audio Full}     & 00:55:46                    & 01:14:00                     & 01:06:00                          \\
\textbf{Audio chunks}   & 00:30:11                    & 00:26:31                     & 00:26:32                             \\
\textbf{\# words (unique)}     & 6097 (567)            & 5494 (552)           & 5536 (602)                  \\ \hline

\end{tabular}
}
\end{table}

\section{Proposed methods}

\label{section:methods}
As shown in Figure \ref{fig:blocks}, our multi-modal framework is based on the independent generation of acoustic and textual feature embeddings. Then, we perform an early fusion of the output of the two systems to obtain a single feature vector  containing a compact representation of both speech and language characteristics. Finally, classification is performed with an SVM classifier with linear kernel. The two systems are described in the remainder of this section.

\subsection{Acoustic system}
The acoustic system is strongly based on two models borrowed from the speaker verification field, \textit{i-vectors}~\cite{dehak2010front} and \textit{x-vectors}~\cite{snyder2017deep}. \textit{i-vectors} are statistical speaker representation vectors that have been recently used for the classification of Parkinson's Disease and for the automatic prediction of dysarthric speech metrics~\cite{hauptman2019identifying,laaridh2017automatic}. \textit{X-vectors} are discriminative deep neural network-based speaker embeddings that have outperformed \textit{i-vectors} in speaker and language recognition tasks~\cite{snyder2018x,snyder2018spoken,snyder2017deep} and have been successfully applied to AD, obstructive sleep apnea and pathological speech detection~\cite{perero2019modeling,botelho2020pathological}. Both models allow to extract a fixed sized feature vector from variable length audio signal.

Taking into consideration the small size of the ADReSS dataset, we preferred to exploit already existing pre-trained models to produce our acoustic feature embeddings, rather than  training them using in-domain challenge data.
To this end, for the \textit{x-vectors} framework we use both the SRE and the Voxceleb models.
The first was trained mainly on telephone and microphone speech using data from the Switchboard corpus, Mixer 6, and NIST SREs~\cite{snyder2018x}.
The latter was trained on augmented VoxCeleb 1 and VoxCeleb 2
datasets,
which contains speech from speakers spanning a wide range of different ethnicities, accents, professions and ages.~\cite{snyder2018x,nagrani2017voxceleb}. This dataset was used also to build the \textit{i-vectors} pre-trained model used in this work.

The inputs to the pre-trained SRE and Voxceleb models consisted of 23 and 30-dimensional MFCC vectors, extracted with Kaldi~\cite{povey2011kaldi} from the full recordings, using default values for window size and shift. Non-speech frames were removed using energy-based VAD.
For the \textit{x-vectors} model, the last layers of the pre-trained model, before the softmax output layer, can be used to compute the embeddings. In this work, we extracted a 512-dimensional \textit{x-vectors} at layer \textit{segment6} of the network.

\begin{figure}
\includegraphics[width=0.5\textwidth]{ADReSS2.png}
\caption{Summary of embedding-based approaches}
\label{fig:blocks}
\end{figure}

The \textit{i-vectors} models, is based on GMM-UBM. The universal background model (UBM) is used to capture statistics about intra-domain and inter-domain variabilities and a projection matrix is used to compute \textit{i-vectors}. We extracted a 400-dimensional \textit{i-vectors}.

\subsection{Linguistic system}
We followed two different approaches to obtain textual feature embeddings. First, we investigated the feasibility of training deep architectures with a corpus of reduced dimension like the one used in this challenge. Then, this method is compared with a less data-demanding one, based on the statistical computation of sentence embeddings using a pre-trained model.
Both strategies rely on contextual word embeddings as input, but they provide different types of learned representations as output.
In fact, to combine the information from the linguistic and the acoustic systems, the trained architectures are used only to extract linguistic features from the last layer of the models, before the final classification. In this way, we obtain a single 768-dimensional feature vector for an entire description. The sentence embedding approach, on the other hand, provide a single 768-dimensional vector for each sentence of a description. These features are then  used to classify between AD patients and healthy subjects.
For both approaches, the first step of the pipeline deals with the normalization of the data provided in the ADReSS dataset. In fact, we recall that besides the plain transcription of the descriptions these also contain additional annotations and information that were removed.
Then, we encode each word of the clean transcriptions into a 768-dimensional context embedding vector using a frozen English Bert model pre-trained with 12-layers, 768-hidden. This representation is fed to our two linguistic systems, described hereafter.

The first system is derived from the ComParE2020 Elderly Challenge baseline~\cite{schuller2020interspeech}, and was obtained by adapting the original code to deal with the classification of AD.
With this approach, three different neural models are trained on top of contextual word embeddings: (i) a Global Maximum pooling, (ii) a bidirectional LSTM-RNNs provided with an attention module, and (iii) the second model augmented with part-of-speech (POS) embeddings.
During training, the loss is evaluated on the development set.

The second system provides the advantage of not requiring an additional phase of model training. Similarly to the approach followed with the acoustic system, we use automatically learned representations extracted from a pre-trained model to directly characterize linguistic deficits in AD. The contextual word embeddings obtained for each word of the clean transcriptions are now used to compute an embedding vector of fixed size for each sentence of a description. Sentence embeddings were successfully employed in tasks of humor detection and more generally sentiments analysis \cite{annamoradnejad2020colbert, le2014distributed}  and information retrieval \cite{le2014distributed}. In our approach, sentence embeddings are computed by averaging the second to twelfth hidden layers of each word.

\begin{table}[t]
\caption{Results of different acoustic approaches on the development set}
\label{tab:res_dev_ac}
\resizebox{\columnwidth}{!}{
\begin{tabular}{lcccc}
\hline
                                                & \multicolumn{1}{l}{\textbf{Accuracy}} & \multicolumn{1}{l}{\textbf{Precision}} & \multicolumn{1}{l}{\textbf{Recall}} & \multicolumn{1}{l}{\textbf{F1 Score}} \\
                                                \hline
\textbf{\textit{\textbf{x-vectors}\_Vox}   }        & 0.6818                               & 0.6834                                & 0.6919                             & 0.6812                               \\
\textbf{\textit{\textbf{x-vectors}\_SRE}}                & \textbf{0.7273}                               & \textbf{0.7273}                                & \textbf{0.7273}                             & \textbf{0.7273}                               \\
\textbf{\textit{\textbf{i-vectors}\_Vox} }          & 0.6818                               & 0.7292                                & 0.6818                             & 0.6645                               \\
\textbf{\textit{i-vectors}\_Vox\_\textit{x-vectors}\_Vox} & 0.7273                               & 0.7273                                & 0.7273                             & 0.7273                               \\
\textbf{\textit{i-vectors}\_Vox\_\textit{x-vectors}\_SRE}      & 0.7273                               & 0.7351                                & 0.7273                             & 0.7250                                \\
\hline
\end{tabular}
}
\end{table}

\section{Results and discussion}
\label{section:exp}
The ADReSS dataset contains only training and test partitions and for the latter the ground truth is not provided. Thus, in order to test our approaches, we retain the 20\% of the data from the training set and use it as development set. In this way, we are left with 86 subjects for training, 22 for development, and 48 for testing. While creating the additional partition, we kept the dataset gender balanced.

As briefly mentioned, our evaluation method relies on SVM~\cite{cortes1995svm} with linear kernel, based on a liblinear implementation. The complexity parameter \textit{C} was optimised during the development phase.
The results reported in Tables~\ref{tab:res_dev_ac} and \ref{tab:res_dev_ling} are obtained using the best complexity configuration.
Features were normalized to have zero mean and unit variance.
In the remainder of this section we first describe our results on the development set for each system independently and then for their final fusion. Finally, for the best systems, we report the results obtained on the test set.

\subsection{Results on the development set}
\subsubsection{Acoustic system}
Results using different automatically learned acoustic features embeddings are summarized in Table~\ref{tab:res_dev_ac}. Also in this case, we explored different independent models and then we do an early fusion of the best acoustic results attained.
From Table~\ref{tab:res_dev_ac} is possible to observe that the \textit{x-vectors} Voxceleb model usually achieve a lower classification accuracy. However, when we combine both \textit{i-vector}s and \textit{x-vectors} extracted from this model, the accuracy resulting from their fusion is comparable to that of \textit{x-vectors} using the SRE model, which is currently our best result on the development set.
These outcomes are slightly lower than those found in the literature review for similar works.
In fact, we recall that Warnita \textit{et al.}~\cite{warnita2018detecting} and Zargarbashi \textit{et al.}~\cite{zargarbashi2019multi} obtained an accuracy of 73.6\%,  75.9\%, and 75.1\%, using, respectively a gated CNN with the IS10 acoustic feature set and the \textit{i-vectors}/\textit{x-vectors} paradigms.
Our approach, however, is different from the ones of these authors since we are using a smaller dataset and do not rely on DNN training. Nevertheless, since we are interested in corroborating these results on the test set, we select the acoustic feature embeddings extracted from the pre-trained \textit{x-vectors} SRE model for the evaluation.

The use of pre-trained acoustic embedding extractors has been motivated by the reduced size of the ADReSS dataset, that we considered to be insufficient for data hungry deep learning approaches. To confirm this, we also trained an end-to-end LSTM model for AD classification. The architecture consisted of one dense and two LSTM layers with a softmax activation function. The network took as input chunks of 500 voiced frames using 23-dimensional MFCC with delta and delta-delta. Majority voting was performed over all the chunks from the same speaker to generate a single prediction per speaker.
This end-to-end approach performed very poorly, with an accuracy around chance result in the development set, confirming our expectations that the ADReSS dataset is not suited for training a deep learning end-to-end system.

\begin{table}[t]
\caption{Results of different linguistic approaches on the development set}

\label{tab:res_dev_ling}
\resizebox{\columnwidth}{!}{
\begin{tabular}{lcccc}
\hline
                                   & \multicolumn{1}{l}{\textbf{Accuracy}} & \multicolumn{1}{l}{\textbf{Precision}} & \multicolumn{1}{l}{\textbf{Recall}} & \multicolumn{1}{l}{\textbf{F1 Score}} \\ \hline
\textit{\textbf{Global Max Pool.}} & 0.7727                               & 0.7947                                & 0.7728                             & 0.7684                               \\
\textit{\textbf{LSTM-RNNs}}        & 0.8182                               & 0.8182                                & 0.8182                             & 0.8182                               \\
\textit{\textbf{LSTM-RNNs Pos}}    & 0.8636                               & 0.8667                                & 0.8637                             & 0.8634                               \\
\textit{\textbf{GMax/LSTM-RNNs/LSTM-RNNs-Pos} }                   & \textbf{0.9091}                               & \textbf{0.9091}                                & \textbf{0.9091}                             & \textbf{0.9091}  \\
\textit{\textbf{Sentence emb. - maj. vote}}                    & 0.7727                               &0.7947                & 0.7728                           & 0.7684
\\ \hline
\end{tabular}
}
\end{table}

\subsubsection{Linguistic system}
Results obtained with our different linguistic systems are summarized in Table~\ref{tab:res_dev_ling}. This table reports the performance for the features trained with the three neural models, their fusion, and finally for the sentence embeddings approach. For the latter, we present only results achieved using a majority voting over the entire description.
Our best classification result attained an accuracy of 90.91\% on the development set using the fusion of the linguistic features sets generated by the three neural models. Comparing this result with the one obtained by sentence embeddings, we acknowledge that neural models outperform simpler strategies even with constrained training data. This was somehow surprising and in contradiction with similar experiments performed with the acoustic system. We hypothesize that the large amount of contextual information provided by the Bert model is helpful in overcoming the limited size of the ADReSS dataset. Nevertheless, we suspect that the high accuracy attained with neural models may be too optimistic, due to the fact of having used the development set both for testing and evaluating the model's loss. Thus, in spite of their lower outcome, the sentence embeddings approach is selected as one of the systems to be evaluated on the test set. In fact, on the one hand, we think that they may represent a more reliable system, since do not require additional training. On the other hand, we also observe that they achieve higher classification scores, when compared with a similar approach based on GloVe embeddings
~\cite{mirheidari2018detecting}, thus corroborating our decision.

\subsubsection{Fusion of systems}
To provide a comprehensive evaluation of speech and language impairments in AD, the best results obtained with both the acoustic and the linguistic systems where combined together in an early fusion fashion.
We merged the \textit{x-vectors} features set obtained with the SRE model with the combination of linguistic feature sets (GMax/LSTM-RNNs/LSTM-RNNs-Pos) generated by the three neural models. Unfortunately, results on the development set using this extended set of features did not provide any further improvements with respect to using the linguistic system alone. We believe that, in this case, the predictive ability of linguistic features completely override acoustic ones.
Nevertheless, we select the combination of these two systems as our main system for the evaluation.

\subsection{Results on the test set}
Overall, we submitted three systems for the evaluation: (i) the fusion of the best results achieved by the linguistic and acoustic systems, (ii) sentence embeddings, (iii) the best acoustic system. A summary of these results is reported in Table~\ref{tab:res_test}.
In general, we found a consistent impoverishment of the performance of our methods when evaluated on the test set,
even for those systems based on features that do not required a training phase.
The first system submitted achieved the best result, with an accuracy of 81.25\%, showing that the use of deep architectures with contextual word embeddings are actually able of overcoming the limitation of a constrained dataset. The worse result is achieved by the acoustic system alone, with an average accuracy of 54.17\%. This outcome is lower than the one found in the ADReSS baseline (62.50\%)~\cite{luz2020alzheimer}, indicating that there is still room for improving our acoustic approach.
We relied on pre-trained models to overcome the lack of data, but we ended up with a similar problem. It is likely the case that an adaptation of these models to the characteristics of elderly speech would allow for better performance.

\begin{table}[t]
\caption{Results of different acoustic and linguistic approaches on the test set}
\label{tab:res_test}
\resizebox{\columnwidth}{!}{
\begin{tabular}{llcccc}
\hline
                                     & \textbf{Class} & \multicolumn{1}{l}{\textbf{Accuracy}} & \multicolumn{1}{l}{\textbf{Precision}} & \multicolumn{1}{l}{\textbf{Recall}} & \multicolumn{1}{l}{\textbf{F1 Score}} \\
                                     \hline
\textit{\textbf{Fusion of system}}   & AD             & \multirow{2}{*}{\textbf{0.8125}}                     & 0.9412                                 & 0.6667                              & 0.7805                                \\
                                     & non-AD         &                                       & 0.7419                                 & 0.9583                              & 0.8364                                \\
\textit{\textbf{Sentence embedding}} & AD             &  \multirow{2}{*}{\textbf{0.7292}}                                    & 0.8235                                 & 0.5833                              & 0.6829                                \\
                                     & non-AD         & \multicolumn{1}{l}{}                  & 0.6774                                 & 0.8750                              & 0.7636                                \\
\textit{\textbf{x-vectors\_SRE}}     & AD             & \multirow{2}{*}{\textbf{0.5417}}                                      & 0.5417                                 & 0.5417                              & 0.5417                                \\
                                     & non-AD         & \multicolumn{1}{l}{}                  & 0.5417                                 & 0.5417                              & 0.5417                                \\ \hline
\end{tabular}
}
\end{table}

\section{Conclusions}
\label{section:conclusions}
In this work we presented a multi-modal approach to the classification of AD based on automatically learned feature representations. Both for the acoustic and linguistic systems, we investigated feature embedding vectors extracted from pre-trained models, as well as the feasibility of training deep neural architectures. Using a combination of both approaches, we attained an accuracy of 90.91\% and 81.25\% on the development and test sets, respectively. Our results showed that acoustic systems, in comparison to linguistic ones, require more data in order to improve the predictive ability of neural models and  obtain fine-tuned features representations.
Nonetheless, it is worth noting that  linguistic systems used manually generated transcriptions.
In the presence of potential ASR errors --which are commonly exacerbated in the case of atypical speech, such as AD speech--,  acoustic systems may play a more relevant role. The impact of these errors could be an interesting analysis for future work, as well as the investigation of robust acoustic methods and models specially tailored to the elderly and AD speech characteristics.

\bibliographystyle{IEEEtran}

redpajama_set_name: RedPajamaArXiv


text:
Find Out: Most Recent Bitcoin Scam on Twitter What Went Wrong
Scams are everywhere, and there's no doubt that those involved in such acts will continue to advance from bad to worse. What's disheartening about them is that they cause several people, "the scammed" to suffer. Scams take various forms from personal interaction with scammers to cyberattacks like the one used for Bitcoin scam recently. On Wednesday, 15th, Mid-July will be unforgettable for numerous Bitcoin investors who, without their knowledge, were conned their hard-earned coins. A lot of questions remain unanswered, but most agree that the worse could have occurred. Let's find out how it happened and what went wrong.
Twitter Notable Users Accounts
The scam targeted some notable Twitter users with millions of followers. These included; – Jeff Bezos, Amazon's founder, Elon Musk, Bill Gates, Kanye West, and Michael Bloomberg, the New York city's former mayor. Others were Barack Obama and Joe Biden. Company accounts of Apple and Uber were also affected. The scam was also a breach of the significant-tech company's privacy and security systems.
All the followers of these prominent figures might have briefly agreed to the hackers' scheme, beginning with Elon Musk's 37 million followers. Later, other verified accounts would send the same tweet to those following them, and before it was known, the scammers had made away with over $110,000. The five-hour time frame saw at least 375 transactions carried out, and Bitcoin sent to the wallets stated in every tweet. Just imagine how the responsible hackers reaped where they didn't sow.
The events were disastrous for Twitter, whose CEO Jack Dorsey referred to that fateful day as a tough one for the company. Yes, it was since Twitter has never experienced such a security incident since its founding. It showed the vast power which administrator accounts possess over social networks like Twitter as it is linked to an inside-coordination.
What Really Happened?
No one has the correct answers to how the responsible people managed to hack the high-profile verified accounts. But, one fact remains that it happened and something had gone wrong. Twitter argues that there must have been coordination with some of the workers enabling them to take charge of the accounts and tweet on their behalf. Multiple reports show that the hackers may have paid the Twitter employees with entrée to the administrative dashboard or used a social engineering attack like a spear-phishing one to access the dashboard.
After that, they might have altered the email address to which password reset messages are sent to change the password to target accounts and gain entry. They could then post tweets from those accounts like the real account users. The trick was to get persons to send their Bitcoins to the specified crypto wallets with the assurance that whatever sent will be doubled. You can imagine being a follower of Obama and he tells you that you give $100, you get $200! You would probably agree with the idea too. All the tweets from the "imitating prominent figures" read something like, "I am giving back to the society: All Bitcoin sent to the below address will be sent back doubled! If you send $1,000, I will resend $2000. Only doing this for thirty minutes. Enjoy!" What a fraud that was! And, several people were misled and gave away their treasures to enrich thieves.
Is Twitter to Blame for The Scam?
Twitter is a large company and the favorite platform for some of the world's most influential and famous people. Can we criticize it for the loss experienced by the numerous Bitcoin holders? That depends on various factors. Naturally, Bitcoin also has its scams, and we can take this recent one as among them, but also Twitter has explanations to offer. Especially, being that it had been previously condemned in 2011 for severe lapses in data security that enabled hackers to get non-permitted administrative control. That occurrence led to both access to privat
e user information and tweets, and the capability to send fake tweets from any account- something similar to the recent incident.
According to one cybersecurity researcher, it's common for chosen employees to reset accounts and assist users locked out. What's unusual is doing so without any controls or posing as the account holder to post content. That can make Twitter face the consequences from regulators like it with the US Federal Trade Commission in 2011. Whatever it tried to do quickly to rescue the situation couldn't succeed as the administrators working on that course could be counteracted smartly by the attackers. The company stopped every verified account from tweeting for several hours while it attempted to identify the problem cause. It also blocked individuals from sharing the particular addresses of the BTC wallets under prompting. It's still trying to sort out issues and figure out what's happening.
A Major Concern Greater Than the Bitcoin Heist
Very soon, people will forget about the lost cryptos, but something is more worrying. Twitter's reputation is at stake, and several questions are coming up regarding people's data with the platform. As the worst hack in history, it took place at a crucial time as the Americans are nearing the elections. Can Twitter be a reliable stage for leading up to the vote? We're yet to see.
What of the sensitive and private information of the hacked accounts? That is a grave concern as Twitter hasn't yet identified if the attackers accessed such personal data. They must have gone through all sorts of data. Once you have access to someone's account, you can do anything on any device. These kinds of personal data need protection like that given to money. Even the company has accepted that it allowed excess employees complete access to the powerful administrative tool that aided the attack. It now has the task of gaining back the trust of the notably affected users who it relies on to attract ordinary users to the platform.
The Bitcoin scam can't be a problem linked to the crypto companies/exchanges. It's entirely Twitter's systemic failure. The over $110,000 Bitcoin value scammed, as indicated by blockchain, is a massive loss for BTC investors. Some crypto gurus like Tyler Winklevoss and his brother, who operates the Gemini exchange, argue that this was a way for Bitcoin to trend. Though trending because of scam, it has become popular as an ideal investment to millions worldwide. Bugis Credit offers recent details regarding bitcoin scams to help safeguard your investment. Be careful while trading.
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text:
WE ARE NOT PREPARED FOR EMERGENCIES
I do not claim to be an expert in this field. But just as Feyi Fawehinmi once noted, internet is the forum where people who do not know anything about a subject matter can have strong opinions about them. However, I have seen in my day-to-day activities moving from Ogun to Lagos and back and forth and from the stories I hear, how unprepared we are to adequately attend to emergencies and I wish to add my voice to the symphony of voices on this issue. For the purposes of context, emergencies are unplanned occurrences that unnecessarily affect day-to-day activities; causing deaths, loss of time and properties, etc.
Our unpreparedness is seen in how fire burns our markets unmitigatedly leading to loss of millions of naira worth of goods, how accidents on our roads lead to avoidable loss of lives, time and valuable, how collapse of properties mean deaths will occur, flooding, boats capsizing, etc. What can we do to mitigate the effect of emergencies? How can we better prepare for emergencies?
First, I think we need to consider to what extent an emergency can affect day-to-day activities. The more likely an emergency can be devastating to such activities, the more prepared we should be in reducing its likelihood and mitigating its effects. To cite an example, presently the Federal Government through its contractors, Julius Berger is repairing the Berger end of the Lagos-Ibadan Expressway. Any breakdown of vehicles especially heavy duty vehicles at the divided portions which is not quickly addressed, can extend traffic kilometers away with the effects of such traffic being felt hours after. But by the time travelers get to the point where the traffic ends, they are left wondering the cause of the traffic. They do not have an answer; the answer however is simply broken-down vehicles that were not quickly addressed. I have wondered before now, why emergency services like toll vehicles, medical services are not at this end to mitigate the effect of these emergencies. To cite another example, I and a senior colleague went to the popular Balogun market to make some purchases. He made a remark that got me thinking. He said considering how close each shop is to the other, if fire guts a part of this market, every other part is in trouble. I did not observe fire fighters during my stay in the market.
We also need to understand that emergencies happen. Regardless of how best we prepare to reduce the likelihood of emergencies, they do occur.[1] However, we can and should prepare more for it. And when we have received intelligence that an incident is likely to happen or precedent shows that it is likely to happen, responsibility suggests that we prepare for it. Minister of Works and Housing and then Governor of Lagos State, Mr Babatunde Raji Fashola once noted during the demolition of the Bank of Industry building on what his government did in preparing for possible emergencies during the demolition exercise. For instance, year-in-year-out, we receive reports of the likelihood of heavy rain leading to catastrophic floods. However, people still dump refuse in the drainage which is meant to ease movement of water. They still build houses on canals. Government's preparedness to provide adequate shelter and food for people who are displaced by these incidents is still suspect. And when the unavoidable rains occur, we cry wolf on its effects on our lives and properties. We should not since we already knew they will come; we were just unprepared for it and have to live with the burn.
I will also suggest we take insurance more seriously in this country. PwC has observed: "The industry is faced with low insurance penetration (currently 0.31%), as most Nigerians are uninsured due to lack of awareness of insurance products and services.[2]" Insurance is aimed at cushioning the effects of emergencies that are not certain to happen but which may still yet happen. For the purpose of large markets like the Balogun Market, there should not be any reason why market leaders should not organize microinsurance scheme for their members. Neither is there no reason why heavy-duty vehicles should not be adequately insured.
Preparation for emergencies is costly no doubt. It is more cost effective to ferry petroleum products through tankers than through pipelines but pipelines are safer and the effects of a mishap involving the tankers may be unimaginable.
I reckon that Nigerians need to be part of governance of their nation more than ever before. We must criticize where there is need to and must equally support governance through payment of taxes which will provide government with necessary resources. I once observed that: "For Nigeria to truly achieve the promise it has failed for so long to achieve, I reckon that Nigerians are the raw materials needed to achieve this. Our political culture must be more engaging. We cannot continue with a reactive and passive political culture and expect long lasting change." Our better engagement with the political class will mean we demand more of it.
Emergencies are part of life. Anything that can go wrong will go wrong.[3] It gladdens the heart that Lag Ferry has made it its standard to provide life jacket for every commuter on its ferry. This should reduce the fear of my learned colleague about travelling on our water ways. It is our responsibility to ensure we adequately prepare for things that can go wrong. Safety must always be our watchword.
[1] Joe Abah, Africa's Problem is Planning, Not Implementation https://www.africanliberty.org/2017/01/28/africas-problem-is-planning-not-implementation-dr-joe-abah/> accessed on 24/2/2020.
[2] PwC, Nigeria's Finance Bill Insights Series <https://www.pwc.com/ng/en/assets/pdf/finance-bill-insights.pdf assessed on 24/2/2020
[3] Joe Abah [1]
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text:
NGC 5575 (другие обозначения — NGC 5578, UGC 9184, MCG 1-37-8, ZWG 47.21, PGC 51272) — линзообразная галактика (S0) в созвездии Дева.

Этот объект занесён в новый общий каталог несколько раз, с обозначениями NGC 5575, NGC 5578.

Этот объект входит в число перечисленных в оригинальной редакции «Нового общего каталога».

Примечания

5575
Дева (созвездие)

redpajama_set_name: RedPajamaWikipedia


text:
Todrick Hall reveals tWitch boss Stephen was under a lot of pressure before his suicide over Ellen DeGeneres' 'Toxic Workplace' controversy
Stephen 'tWitch' Boss' Cause of Death Revealed: Ellen DeGeneres Heartbroken
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Todrick Hall, the friend of Stephen tWitch Boss, revealed the Ellen DeGeneres Show DJ, who died by suicide last month, was under pressure due to the show's toxic workplace controversy. So does this have anything to do with his death?
DeGeneres has been accused of allowing several former employees to cultivate a culture of racism, fear and intimidation on set. The revelation prompted massive backlash and criticism online, with some targeting Boss for endorsing the talk show host.
Hall told Page Six, people were questioning his pal's decision to continue supporting the stand-up comedian despite their actions, which put him under a lot of pressure. As such, he believed the hostile reactions online contributed to the dancer's state of mind in the months leading up to his death.
He admitted that while he didn't know what was going on in Boss's life that made him support DeGeneres, he understood. He knew that a person could only endure a certain amount of online hate and abuse that lasted for years.
Hall added that Boss was someone who was always supportive, describing him as friendly and down to earth. He even recalled how he would usually come to his house and shoot TikTok videos together.
For context, Boss died from a self-inflicted gunshot wound and was found in an LA hotel room on December 13. He was 40 years old.
He left a suicide note in which he alluded to old challenges in the past that led to his passing. From circles of law enforcement agencies found out TMZthat the So You Think You Can Dance judge put his photo in airplane mode to make sure no one could find him.
It wasn't clear what he was referring to in his suicide note, however, as coroners said it appeared to be alluding to a vague problem. The father of three had taken an Uber from home to the Oak Tree Inn Motel, less than a mile from his home.
Unable to reach him, his wife Allison Holker, who is also a dancer and met Boss on So You Think You Can Dance, rushed to an LAPD precinct to raise an alarm about his unusual behavior. From there, a motel employee found his lifeless body.
The Los Angeles County coroner confirmed the cause of death as suicide by gunshot to the head. As there were no signs of foul play, the case was officially closed.


text:
1900 – The Reichstag approved a second law that would allow the expansion of the German navy.
1901 – Cuba agreed to become an American protectorate by accepting the Platt Amendment.
1912 – Lillian Russel retired from the stage and was married for the fourth time.
1918 – The first airplane bombing raid by an American unit occurred on World War I's Western Front in France.
1921 – U.S. President Warren Harding urged every young man to attend military training camp.
1923 – Harry Houdini, while suspended upside down 40 feet above the ground, escaped from a straitjacket.
1926 – Brazil quit the League of Nations in protest over plans to admit Germany.
1935 – U.S. Senator Huey Long of Louisiana made the longest speech on Senate record. The speech took 15 1/2 hours and was filled with 150,000 words.
1935 – The Chaco War was ended with a truce. Bolivia and Paraguay had been fighting since 1932.
1937 – The Soviet Union executed eight army leaders under Joseph Stalin.
1939 – The National Baseball Hall of Fame and Museum was dedicated in Cooperstown, New York.
1941 – In London, the Inter-Allied Declaration was signed. It was the first step towards the establishment of the United Nations.
1944 – Chinese Communist leader Mao Tse-tung announced that he would support Nationalist leader Chiang Kai-shek in the war against Japan.
1948 – Ben Hogan won his first U.S. Open golf classic.
1963 – "Cleopatra" starring Elizabeth Taylor, Rex Harrison, and Richard Burton premiered at the Rivoli Theatre in New York City.
1963 – Civil rights leader Medgar Evers was fatally shot in front of his home in Jackson, MS.
1967 – State laws which prohibited interracial marriages were ruled unconstitutional by the U.S. Supreme Court.
1971 – Tricia Nixon and Edward F. Cox were married in the White House Rose Garden.
1975 – Indian Prime Minister Indira Gandhi was found guilty of corrupt election practices in 1971.
1979 – Bryan Allen flew the Gossamer Albatross, man powered, across the English Channel.
1981 – Major league baseball players began a 49-day strike. The issue was free-agent compensation.
1981 – "Raiders of the Lost Ark" opened in the U.S.
1982 – 75,000 people rallied against nuclear weapons in New York City's Central Park. Jackson Browne, James Taylor, Bruce Springsteen, and Linda Ronstadt were in attendance.
1985 – Wayne "The Great One" Gretzky was named the winner of the NHL's Hart Trophy. The award is given to the league Most Valuable Player.
1985 – The U.S. House of Representatives approved $27 million in aid to the Nicaraguan Contras.
1986 – South Africa declared a national state of emergency. Virtually unlimited power was given to security forces and restrictions were put on news coverage of the unrest.
1987 – U.S. President Reagan publicly challenged Mikhail Gorbachev to tear down the Berlin Wall.
1990 – The parliament of the Russian Federation formally declared its sovereignty.
1991 – Russians went to the election polls and elected Boris N. Yeltsin as the president of their republic.
1991 – The Chicago Bulls won their first NBA championship. The Bulls beat the Los Angeles Lakers four games to one.
1992 – In a letter to the U.S. Senate, Russian Boris Yeltsin stated that in the early 1950s the Soviet Union had shot down nine U.S. planes and held 12 American survivors.
1996 – In Philadelphia, a panel of federal judges blocked a law against indecency on the internet. The panel said that the 1996 Communications Decency Act would infringe upon the free speech rights of adults.
1997 – Interleague play began in baseball, ending a 126-year tradition of separating the major leagues until the World Series.
1997 – The U.S. Treasury Department unveiled a new $50 bill meant to be more counterfeit-resistant.
1998 – Compaq Computer paid $9 billion for Digital Equipment Corp. in the largest high-tech acquisition.
1999 – NATO peacekeeping forces entered the province of Kosovo in Yugoslavia.
2003 – In Arkansas, Terry Wallis spoke for the first time in nearly 19 years. Wallis had been in a coma since July 13, 1984, after being injured in a car accident.
2009 – In the U.S., The switch from analog TV transmission to digital was completed.
1981 – Raitis Grafs, Latvian basketball player
1981 – Paul Hasleby, Australian footballer
1981 – Adriana Lima, Brazilian model, and actress
1982 – Ben Blackwell, American drummer
1982 – Diem Brown, German-American journalist, and activist (d. 2014)
1982 – Jason David, American football player
1982 – James Tomlinson, English cricketer
1983 – Bryan Habana, South African rugby player
1983 – Alexander Pipa, German rugby player
1983 – Christine Sinclair, Canadian soccer player
1984 – James Kwalia, Kenyan-Qatari runner
1984 – Bruno Soriano, Spanish footballer
1985 – Blake Ross, American computer programmer, co-created Mozilla Firefox
1985 – Sam Thaiday, Australian rugby league player
1985 – Kendra Wilkinson, American model, actress, and author
1985 – Chris Young, American singer-songwriter and guitarist
1986 – Salim Mehajer, Australian politician
1986 – Harry Taylor, Australian footballer
1987 – Seyi Ajirotutu, American football player
1987 – Antonio Barragán, Spanish footballer
1988 – Arturs Berzins, Latvian basketball player
1988 – Eren Derdiyok, Swiss footballer
1988 – Mauricio Isla, Chilean footballer
1988 – Dave Melillo, American singer-songwriter and guitarist
1988 – Dakota Morton, Canadian actor, and radio host
1989 – Emma Eliasson, Swedish ice hockey player
1989 – Ibrahim Jeilan, Ethiopian runner
1990 – Jrue Holiday, American basketball player
1990 – Kevin López, Spanish runner
1990 – David Worrall, English footballer
1991 – Avisail García, Venezuelan baseball player
1992 – Philippe Coutinho, Brazilian footballer
1992 – Laura Jones, English gymnast
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